IAES International Journal of Artificial Intelligence (IJ-AI)
IAES International Journal of Artificial Intelligence (IJ-AI), ISSN/e-ISSN 2089-4872/2252-8938 publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence (AI) and machine learning (ML) areas and their applications in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithms, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; supervised learning; unsupervised learning; deep learning; big data and AI approaches; reinforcement learning; and learning with generative adversarial networks; etc. This journal is indexed in Scopus and all published papers since 2018 issues were included in scopus.com. This journal has been included in SCImago Journal Rank (SJR) since 2020 (or since SJR 2019). The CiteScore for the IAES International Journal of Artificial Intelligence is 3.9 (Q2 CiteScore Best Quartile), with an SNIP of 0.800 and an SJR of 0.365. The journal is published by Institute of Advanced Engineering and Science (IAES). Submit your paper now through Online submission ONLY. Kindly please download the IJ-AI template in MS Word or LaTeX. Submission for publication in this journal presumes that the paper is not being considered for publication elsewhere and is not under review by any other publication. A submitted paper should be an original work, and should not have been presented or published previously.All papers undergo a thorough evaluation process while following all ethical guidelines to maintain the highest publishing standards. The IJ-AI Team strictly review suitability, originality of manuscripts for publication. Publication integrity is one of the principle responsibilities we take. Our Editors maintain transparency of research, assess manuscript for scientific value, ensure that content is free from any manifestations of discrimination, and evaluate information objectively without any conflict of interests. Papers are published in the bimonthly journal (Feb, Apr, Jun, Aug, Oct, and Dec). If you have any problems submitting your papers, please contact us at email: ijai@iaesjournal.com, cc: editor@iaesjournal.com.
Institute of Advanced Engineering and Science
An exploratory review on conceptualizing generative artificial intelligence literacy
Generative artificial intelligence (AI) has rapidly evolved, demanding new forms of literacy that go beyond traditional AI concepts. However, current definitions of generative AI literacy often overlook its unique challenges, including prompt engineering, critical evaluation of AI-generated outputs, and complex ethical considerations. This study addresses these gaps through an exploratory review of 20 peer-reviewed articles. These articles were identified using systematic searches across major academic databases and selected based on predefined inclusion criteria. The analysis reveals conceptual limitations in existing frameworks, particularly their lack of structure and their generalization of AI literacy. To overcome these issues, we propose a new competency framework adapted from Bloom’s taxonomy. The framework integrates three essential dimensions: technical proficiency, ethical responsibility, and societal awareness. It is organized into five progressive cognitive stages: understand, apply, analyze, evaluate, and create. This framework clarifies the distinct demands of generative AI literacy and can be implemented to guide curriculum design, professional training, and the development of generative AI literacy across sectors.
Copyright (c) 2026 Mohammed Afandi Zainal, Mohd Effendi Ewan Mohd Matore, Siti Mistima Maat http://creativecommons.org/licenses/by-sa/4.0
Venture capital and risks in growth stages of artificial intelligence tech start-ups
Venture capital (VC) investment is important for the growth and innovation of artificial intelligence (AI)-driven tech start-ups, which are often characterized by high uncertainty and rapid technological change. While existing literature has explored general risk factors in AI start-ups, however, limited understanding of how these risks vary across different stages of start up development. This study addresses this gap through a systematic literature review (SLR) of 29 studies published between 2019-2024, sourced from IEEE Xplore, Web of Science (WoS), Scopus, and ProQuest databases. Guiding investment lifecycle, risks management and ISO 31000 principles, the study identified key risks variations including market, operational, financial, technological, performance, regulatory and exit risks faced by AI tech start-ups during the seed and early, growth and maturity stages. Findings indicate that early-stage start-ups are more affected by funding, market entry, and feasibility risks, while at growth stage face issues with scaling and resource management, maturity stage with regulatory and exit related risks become more significant. A taxonomy matrix is developed to categorize these risks in a stage-specific and AI-relevant context. The review contributes to the literature by offering a structured understanding of how VC related risks evolve across start-ups stages and highlights the need for further empirical research to validate these findings and guide better investment decisions.
Copyright (c) 2026 Sara Aziz, Noorlizawati Abd Rahim http://creativecommons.org/licenses/by-sa/4.0
Structured data collection and deep learning for retinal OCT image-to-text translation: a comprehensive framework
This paper presents a comprehensive framework for structured data collection and deep learning (DL)-based translation of retinal optical coherence tomography (OCT) images into diagnostic text. The suggested approach guarantees high-quality OCT data for model training through the use of sophisticated image processing methods like edge detection, noise suppression, and contrast improvement. The study utilizes 84,484 retinal images from the OCT dataset available on Kaggle. The research utilizes various preprocessing techniques, such as median and Gaussian filtering, along with data augmentation strategies like translation, rotation, and scaling, to mitigate class imbalances and improve model performance. The system automatically identifies and categorizes retinal diseases such as drusen, diabetic macular edema (DME), and choroidal neovascularization (CNV) by integrating feature extraction and selection with DL techniques. The research highlights the importance of effective data handling and model scalability to address the increasing need for automated diagnostic tools in ophthalmology. This framework aims to support ophthalmologists in managing the increasing incidence of diabetic retinopathy (DR) and other retinal conditions by enhancing the efficiency of retinal image analysis, thereby improving patient results through early detection and treatment.
Copyright (c) 2026 Uday Mande, Shafi Pathan, Pankaj Chandre , Sharvari Mande http://creativecommons.org/licenses/by-sa/4.0
Assessing student perspectives on ChatGPT in higher education: a quantitative analysis
The rapid advancement of artificial intelligence (AI) has transformed higher education, with ChatGPT increasingly used as an academic support tool. This study examines university students’ perceptions of ChatGPT in Indonesian higher education through a quantitative survey involving 56 undergraduate, master’s, and doctoral students at Universitas Negeri Medan. The survey assessed perceived ease of use, quality of responses, learning support, and ethical concerns related to ChatGPT usage. The results indicate that most students perceive ChatGPT as easy to use and helpful for understanding academic materials and improving learning efficiency. However, concerns regarding academic integrity, overreliance, and potential reductions in problem-solving skills were also identified. Significant differences in perceptions emerged across academic levels, with undergraduate students expressing higher enthusiasm, while postgraduate and doctoral students demonstrated greater caution toward ethical and pedagogical implications. These findings highlight both the opportunities and challenges of integrating generative AI into higher education. This study provides the first quantitative empirical evidence on ChatGPT perceptions in Indonesian higher education and underscores the importance of embedding AI literacy, ethical guidelines, and critical thinking strategies into university curricula to ensure responsible and effective AI adoption.
Copyright (c) 2026 Muhammad Amin, Bima Mustaqim, Wegig Pratama, Abdul Muin Sibuea http://creativecommons.org/licenses/by-sa/4.0
The role of prompt engineering in enhancing LLMs: a systematic review of applications and ethical implications
Large language models (LLMs) have transformed natural language processing (NLP), demonstrating exceptional proficiency in tasks such as text generation, translation, and summarization. However, LLMs are prone to generating biased, inaccurate, or contextually irrelevant outputs, posing significant risks in high-stakes domains such as healthcare, legal reasoning, and engineering. This paper systematically investigates the role of prompt engineering as a solution to these challenges. By strategically designing inputs, prompt engineering enhances LLM performance, yielding more accurate, contextually relevant, and ethically aligned outputs. Advanced techniques, including chain-of-thought (CoT) prompting and retrieval augmented generation (RAG), are examined for their ability to improve reasoning capabilities, reduce errors, and mitigate bias. CoT prompting facilitates structured, stepwise reasoning, while RAG incorporates real-time data, ensuring output accuracy in rapidly evolving fields. In addition, we present a novel comparative perspective on these techniques, highlighting their distinct strengths and limitations across specialized applications such as healthcare diagnostics and scientific data extraction. The findings demonstrate that sophisticated prompt engineering significantly elevates the reliability and precision of LLM outputs, while addressing critical ethical concerns such as data privacy, bias, and hallucination. These insights underscore the necessity of advanced prompt design in optimizing LLMs for high-impact applications, ensuring both performance and ethical integrity.
Copyright (c) 2026 Izzul Fatawi, Muhammad Roil Bilad, Muhammad Asy’ari http://creativecommons.org/licenses/by-sa/4.0
Applications of artificial intelligence in analyzing Aquilaria essential oils: a review of current machine learning techniques
This study explores the application of machine learning (ML) techniques in the classification of agarwood oil, focusing on the use of various algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), random forest (RF), and artificial neural networks (ANN). Since 2013, ML has played a pivotal role in analyzing agarwood oil, particularly by leveraging data from a variety of chemical compounds found in the Aquilaria genus. Through a systematic review and bibliometric analysis using the SCOPUS database, this study compiles and highlights recent works that have successfully employed ML techniques for the quality assessment of agarwood oil. These studies utilize chemical data, such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR), for the classification and detection of different oil grades. The review reveals a broad range of ML applications, demonstrating their growing importance in the field of essential oil analysis. By systematically presenting the findings from recent research, this work emphasizes the potential for further exploration of ML in the standardization and improvement of agarwood oil classification techniques.
Copyright (c) 2026 Noor Aida Syakira Ahmad Sabri, Nur Athirah Syafiqah Noramli, Muhammad Ikhsan Roslan, Nurlaila Ismail, Zakiah Mohd Yusoff, Ali Abd Almisreb, Mohd Nasir Taib http://creativecommons.org/licenses/by-sa/4.0
Intelligent self-organizing microservice composition using hybrid learning for neonatal ward
This research presents an innovative self-organizing microservice composition model specifically tailored for dynamic and time-sensitive healthcare environments such as Neonatal Intensive Care Units(NICU). A hybrid machine learning classifier detects neonatal conditions and assigns treatment plans based on real-time vitals. The composition process is guided by a deep learning agent that combines unsupervised and reinforcement learning to develop intelligent bonding strategies. Microservices act as autonomous agents, supporting decentralised service choreography within the self-organizing framework. The bonding strategies of direct bonding and shared bonding are implemented for single conditions and coexisting conditions, respectively. The simulation results are based on actual NICU data, demonstrating the ability of the model to dynamically compose services while ensuring optimal resource utilisation. The model demonstrates an adaptive and dynamic composition through emergence and continuous learning for changing clinical conditions, and demonstrates emergent behaviour through reinforcement learning. The model’s predictive capabilities enable anticipatory service loading, providing context-aware treatment in critical healthcare scenarios. This self-organizing architecture model offers a scalable and robust solution for autonomous, decentralised service choreography in critical healthcare environments.
Copyright (c) 2026 Sharon Poornima, Ashok Immanuel V http://creativecommons.org/licenses/by-sa/4.0
An intelligent and explainable IoT-Edge-Cloud architecture for real-time water quality monitoring
Continuous and reliable monitoring of water quality is critical for early detection of environmental deterioration, yet conventional monitoring approaches are often slow and lack timely data availability. This study proposes an intelligent and explainable internet of things (IoT)–Edge–Cloud architecture to monitor water quality in real time, using IoT sensing, edge based artificial intelligence (Edge AI), cloud-stream processing, and explainable artificial intelligence (XAI). The system calculates the water quality index (WQI) directly at the edge and predicts its evolution using a stacking ensemble model trained on physicochemical measurements taken from the Moulouya River Basin in Morocco. An explainability module based on Shapley additive explanations (SHAP) values gives a clearer image of the contribution of various parameters to WQI predictions, providing transparency of the features, which builds trust in the model’s output. The proposed architecture was implemented as an end-to-end prototype and validated using a simulation-based experimental that mimicked realistic sensor dynamics and connectivity interruptions. The experimental results show strong predictive performance (R² =0.945), stable system operations, and reliable interpretability highlighting the potential of the proposed approach for scalable, intelligent, and transparent environmental monitoring.
Copyright (c) 2026 Sara Bouziane, Badraddine Aghoutane, Aniss Moumen, Anas El Ouali, Ali Essahlaoui, Abdellah El Hmaidi http://creativecommons.org/licenses/by-sa/4.0
Generative artificial intelligence as powered writing tools in academic writing
Generative Artificial Intelligence (GAI) as a writing tool is rampantly developing and attracting attention in academic writing. This study aimed to analyze the use of GAI as an AI-powered writing tool in academic writing among college students. By using a mixed method design with criterion purposive sampling, the researchers gathered the data from eighty students through a survey and selected individuals from all year levels underwent interviews. Descriptive statistics and thematic analysis were used to analyze their perceptions and integration of GAI tools. The result reveals mainly high levels of perception: knowledge perception, “High”; frequency and extent of use, “Average”; impact on academic writing, “High”; and integration with human writers, “High”. The study further identified that the students integrate GAI writing tools to improve writing quality, efficiency, and productivity. On the other hand, their disadvantages include over-reliance on GAI tools and inaccuracy issues. The findings suggest that GAI tools integration improves academic writing, but negatively impacts the students’ character. This study stresses the importance of moderation in using GAI writing tools and recommends looking further into the different ways of effective integration.
Copyright (c) 2026 Exequiel B. Gonzaga, Nasrah A. Manguda, Rodelina B. Tado, Ivy F. Amante, Rovy M. Banguis, Shem A. Cedeño, Joveth Jay D. Montaña, Jai Rondo S. Apilar http://creativecommons.org/licenses/by-sa/4.0
Automated bacteria and fungi classification using convolutional neural network on embedded system
In this study, we created and applied novel concepts for hardware-based image identification and categorization. For artificial intelligence (AI) and image recognition applications, this includes putting algorithms for recognizing colors, textures, and shapes into practice. Our contribution uses an embedded device with a camera and a microcomputer (Raspberry-Pi4 type) to replace the optical assessment of Petri dishes. Our object recognition system processes images efficiently by using a state-of-the-art kernel function and a new neighborhood architecture. Using the well-known convolutional neural network (CNN) architecture, YOLOv8, as a pre-trained model, we evaluated the proposed CNN-based method for object recognition in a number of demanding scenarios. Several Petri plates, uncontrolled settings, and different backgrounds and illumination were used to evaluate the technology. Our dynamic mode integrates a CNN network with an attention mask to highlight the traits of bacteria and fungi, ensuring robust recognition. We implemented our algorithm on a Raspberry Pi 400, connected to a CMOS 3.0 camera sensor and a human-machine interface (HMI) for instant display of results.
Copyright (c) 2026 Tarik Bouganssa, Maryem Ait Moulay, Samar Aarabi, Abdelali Lasfar, Abdellatif El Afia http://creativecommons.org/licenses/by-sa/4.0
Multi-dimensional performance-optimized array design framework for efficient mmWave energy harvesting
The proliferation of next-generation wireless networks and autonomous devices has intensified the need for efficient and compact energy harvesting solutions at millimeter-wave (mmWave) frequencies. This paper presents a multi-dimensional performance-optimized array design framework for mmWave energy harvesting (MAPLE-H), which enables the systematic development of advanced antenna arrays that fulfill the simultaneous demands of wide operational bandwidth, high efficiency, polarization diversity, and miniaturization. The proposed framework integrates simulation-driven array modeling with integrated analog–digital beamforming and adaptive entity partitioning, accommodating real-world energy harvesting array non-idealities. Furthermore, an energy–information optimization factor is introduced to dynamically balance the trade-off between energy harvesting and data communication performance. Intelligent energy–information resource optimization algorithms jointly tune design parameters to maximize harvested power and signal integrity across diverse deployment scenarios. Comprehensive simulation results and comparative benchmarking demonstrate that the proposed framework consistently outperforms state-of-the-art designs in terms of gain, bandwidth, robustness, and flexibility, positioning it as an enabling technology for future energy autonomous wireless systems.
Copyright (c) 2026 Shalini Mirle Gajendra, Naveen Kalenahalli Bhoganna http://creativecommons.org/licenses/by-sa/4.0
Gradient-based stochastic depth with convolutional neural network for coconut tree leaf disease classification
The coconut palm (Cocos nucifera) is vital plantation crop, valued for their different uses, ranging from their fruit to its trunk. In recent times, it has been observed that many coconut trees are affected by diseases that reduce production and weaken the strength of the coconut. The classification of coconut leaf diseases is challenging because of intra-class and inter-class variability. This research introduces the gradient-based stochastic depth (GSD) with convolutional neural network (CNN) technique to coconut leaf disease classification to overcome these challenges. The GSD technique is incorporated into every layer of the CNN, where it calculates the probability using gradient magnitudes and skips layers that contribute minimally to the classification. The images are segmented using the GrabCut segmentation algorithm, which isolates the leaf from the background using graph-based segmentation, helping to differentiate between various disease classes. The GSD with CNN algorithm obtains an accuracy of 96.42%, precision of 96.15%, recall of 95.87%, and F1-score of 95.93%, while comparing with existing algorithms.
Copyright (c) 2026 Kavitha Magadi Gopalakrishna, Raviprakash Madenur Lingaraju, Ananda Babu Jayachandra http://creativecommons.org/licenses/by-sa/4.0
Recognition system based on artificial vision using OpenCV for discarding and detecting ceramics with defects
Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.
Copyright (c) 2026 Fernando Alvarado, Ricardo Yauri http://creativecommons.org/licenses/by-sa/4.0
Double-hop of reconfigurable intelligent surfaces-aided for wireless optical link under log-normal fading channels
In optical wireless communication (OWC), the reconfigurable intelligent surfaces (RIS) are used to manipulate optical signals by controlling the phase shifts or amplitude of reflected beams, which helps improve signal quality. RIS units can be tailored to increase the strength and reliability of the communication link, especially in challenging fading conditions. The double-hop scenario involves two RIS-assisted segments, such as transmitter to RIS-1 and RIS-1 to RIS-2 or a receiver. Each hop encounters log-normal fading, which impacts the overall link performance. Log-normal fading models the irradiance fluctuation caused by turbulence, which is significant in free-space optical (FSO) systems, this fading model assumes that the received optical signal’s amplitude varies with a log-normal distribution, making it more suited for weak to moderate turbulence. Numerical results are obtained under different of link distance, subcarrier quadrature amplitude modulation (QAM) is displayed quantitatively illustrate the average symbol error rate in the absence of RIS and with double-hop of RIS.
Copyright (c) 2026 Duong Huu Ai, Van Loi Nguyen, Khanh Ty Luong http://creativecommons.org/licenses/by-sa/4.0
Knowledge graph-based enhanced virtual network embedding for 6G cloud datacenter deployment
Virtual network embedding (VNE) is the effective mapping of virtual networks onto shared physical substrate networks while boosting resource utilization and ensuring quality of service (QoS). VNE is a real challenge in network virtualization, especially in the perspective of 6G-enabled datacenters, where the demand for ultra-low latency, heavy connectivity, and dynamic resource allocation is vital. The proposed solution enables the ability to infer indirect paths for the resources prediction task on the knowledge graph (KG) by making implicit meaningful relations among the entities that compose the resource network. The simulation results indicated the inference mechanism significantly improves efficiency and adaptability. This leads to overall performance gains in terms of runtime stability, resource utilization, and energy savings in dynamic 6G scenarios. The experimental results showed that the proposed solution provided a 24.9% reduction in energy consumption for small-sized virtual network requests (VNRs), while maintaining 24.8% and 23.9% for medium and large VNRs, respectively, while it significantly decreased the delay time compared to the resulted delay using the baseline models such as asynchronous advantage actor-critic (A3C) + graph convolutional network (GCN). The results also confirmed that the integration of the inference engine algorithm with the embedding process results in remarkable reduction in the execution time while preserving embedding accuracy.
Copyright (c) 2026 Shourok Abdelrahim, Samy Ghoniemy, Mohamed Aborizka http://creativecommons.org/licenses/by-sa/4.0
IoT-enabled smart nutrition scale using fuzzy logic for dietary assessment and recommendation
Childhood malnutrition, particularly stunting, remains a major public health challenge that requires preventive and technology-supported nutritional interventions. This study presents an IoT-enabled smart nutrition scale integrated with fuzzy logic to support real-time dietary assessment and personalized recommendation. The system combines IoT-based sensing, mobile and web applications, and a fuzzy inference engine that evaluates child profiles and food composition data to generate nutritional adequacy scores and tailored dietary guidance. Experimental validation demonstrates high measurement accuracy of the sensing system, achieving a strong linear correlation (R² ≈0.9995). Comparison with expert nutritionist assessments shows strong agreement, supported by low error values (mean absolute error (MAE) =2.96; root mean square error (RMSE) =3.41), and Bland–Altman analysis. Usability evaluation involving community health workers and caregivers yields an excellent system usability scale (SUS) score, indicating strong acceptance for practical deployment. By integrating IoT sensing with fuzzy reasoning, the proposed system shifts nutritional monitoring from retrospective assessment toward proactive dietary intervention. This work highlights the potential of intelligent nutrition technologies to enhance decision-making in community-based stunting prevention programs and provides a scalable foundation for preventive digital health applications.
Copyright (c) 2026 Wahyu Wijaya Widiyanto, Edy Susanto, Sri Suparti http://creativecommons.org/licenses/by-sa/4.0
Adaptive control of ball and beam system using SNA-PID combined with recurrent fuzzy neural network identifier
The ball and beam system is a nonlinear and inherently unstable single input, multiple-output (SIMO) system, which poses significant challenges for control design. Intelligent control algorithms are often applied to autonomously control complex systems when there are changes in parameters or the control environment. Therefore, in this paper, we research and develop two methods: proportional integral derivative (PID) and single neuron adaptive (SNA)-PID-recurrent fuzzy neural network identifier (RFNNI) to control the ball and beam system. Simulation results on MATLAB/Simulink show that the SNA-PID-RFNNI controller provides a more stable output signal than the traditional PID controller, with minimal overshoot and a settling time of about 15 seconds. Next, we will conduct real-time experiments on the object using the proposed algorithm through the MEGA2560 control board with an ultrasonic positioning mechanism.
Copyright (c) 2026 Minh-Thanh Le, Chi-Ngon Nguyen http://creativecommons.org/licenses/by-sa/4.0
Deep neural network classification in chatbot system family health counseling services
Mental health problems affect many aspects of life, including physical well being, work productivity, social functioning, and suicide risk. In Indonesia, access to professional mental health services remains very limited: only a small proportion of people with depression receive treatment and the number of mental health professionals per population is far below international recommendations, creating an urgent service gap. This study proposes an artificial intelligence–based chatbot to support family mental health counseling services in Indonesia. The chatbot uses a deep neural network (DNN) to classify user questions into counseling intent categories and to provide appropriate responses. Psychologists compiled and verified a dataset of Indonesian counseling questions and responses, which was then pre processed using standard text processing techniques and encoded with a bag of words (BoW) representation. A fully connected DNN with one input layer, two hidden layers of eight neurons each, and a SoftMax output layer was trained using the Adam optimizer (learning rate 0.01) on 80% of the data and evaluated on the remaining 20%. The best configuration achieved a training accuracy of 96%, with test results of 93% accuracy, 92% precision, 93% recall, and 92% F1-score. These findings indicate that proposed DNN based chatbot can accurately classify counseling intents and generate contextually appropriate responses, suggesting its potential as complementary tool to support initial family mental health counseling in Indonesia.
Copyright (c) 2026 Andi Riansyah, Sam Farisa Chaerul Haviana, Ratna Supradewi, Muhammad Ainul Wahib http://creativecommons.org/licenses/by-sa/4.0
An efficient approach for cyber-attack detection by using machine learning and deep learning algorithms
Cybercrime gained traction in the late 20th century. The capabilities of cyber-attackers have improved dramatically. One of the biggest challenges facing cybersecurity developers is safeguarding consumers' security and privacy. Interest in using AI approaches in cybersecurity has grown significantly because of the incredible proficiency these techniques have demonstrated across all domains. Even while machine learning algorithms are very effective at identifying malicious activity, there are still certain issues that lower performance accuracy. This paper has the novelty of deploying the Artificial Bee Colony (ABC) meta-heuristic algorithm with the K-Nearest Neighbors (KNN) classifier to detect cyber-attacks. It proposes a variant approach called KNN+Bee that detects attacks efficiently, achieving 99.86% overall accuracy. The NSL-KDD dataset of cyberattacks has been leveraged in the training and testing phases. The proposed approach has been contrasted with the most popular machine learning. According to experimental findings, the suggested model delves deeper into the identification of cyberattacks. It achieves unprecedented performance, outperforming other models in terms of precision, Recall, F-score and MCC. Furthermore, popular deep learning models have been implemented and examined on the same dataset. Results prove that GRU is the most accurate, reaching 99.71%.
Copyright (c) 2026 Yasir Hussein Shakir, Mahmoud Mohamed Abdelhamied, Eshaq Aziz Awadh AL Mandhari, Ali Alkhazraji, Naglaa M. Reda http://creativecommons.org/licenses/by-sa/4.0
Technical analysis model for stock prediction using a grammatical evolution algorithm
Stocks are a popular investment instrument but carry high risks, where investors may incur losses when stocks are bought at high prices and sold at lower prices. Technical analysis is used to study past stock price behavior to predict future prices. In this study, grammatical evolution (GE) is applied as an evolutionary computing technique to discover optimal functions or programs that represent historical stock price data. This study develops GE based prediction models by utilizing objective functions and search spaces defined through grammar. The model integrates technical indicators based on complex statistical models such as autoregressive integrated moving average (ARIMA), prophet, exponential smoothing, and Fibonacci retracements. Furthermore, this study employs GE to generate ensemble weights randomly, ensuring each model contributes equitably to the final prediction formula. Experiments were conducted using multiple stock datasets, including SMAR, S&P 500, the Johannesburg Stock Exchange (JSE), the New York Stock Exchange (NYSE), and Adani Enterprises (ADANIENT), to evaluate the model’s adaptability and generalization capability. The results demonstrate that the proposed GE model effectively captures complex market patterns and produces more reliable stock price predictions compared to deep learning-based approaches. Although GE requires greater computational time, the findings suggest that GE provides a flexible and effective framework for constructing hybrid stock price forecasting models in dynamic market environments.
Copyright (c) 2026 Aditya Kusuma Setyanegara, Imas Sukaesih Sitanggang, Mushthofa http://creativecommons.org/licenses/by-sa/4.0
Genetic algorithm-based chicken manure weight prediction system development
This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.
Copyright (c) 2026 Rida Hudaya, Septriandi Wirayoga, Moechammad Sarosa, Muhammad Yusuf, Armanda Dwi Prayugo http://creativecommons.org/licenses/by-sa/4.0
Genetic algorithm for generalized time-window assignment problem
This paper presents a hybrid genetic algorithm (GA) for the generalized time-window assignment problem (GTWAP), a complex artificial intelligence (AI) scheduling challenge that involves assigning agents to resources under strict temporal and capacity constraints. Our method integrates a problem specific heuristics and a repair mechanism to generate feasible and high quality solutions. We provide a mathematical formulation for GTWAP and introduce a new public benchmark set, using CPLEX to obtain exact solutions. Computational experiments demonstrate that our GA is highly competitive with CPLEX, often matching its performance. This effectiveness makes our method a practical and scalable AI-driven tool for complex scheduling in domains like logistics and healthcare.
Copyright (c) 2026 Ali Kansou, Bilal Kanso, Houssein Wehbe, Haydar Bazzi, Ali Mcheik http://creativecommons.org/licenses/by-sa/4.0
Energy-efficient and secure WSN clustering for IoT using particle swarm optimization and advanced encryption standard
Wireless sensor networks (WSNs) are made up of distributed sensor nodes that work together under energy and communication constraints. They support diverse internet of things (IoT) applications such as smart agriculture and environmental monitoring. This paper proposes a technique to optimize the WSN framework for secure and energy-efficient data transmission. To improve cluster formation and network energy consumption, the suggested model combines k-means clustering with particle swarm optimization (PSO). Inter-cluster data is encrypted by the cluster head (CH) using the advanced encryption standard (AES)-128. To protect data and save energy, the low-energy adaptive clustering hierarchy (LEACH) protocol uses a number of techniques. Energy efficiency, model accuracy, likelihood of privacy breaches, and network longevity are examples of performance metrics. The system is tested by Python simulations on the Intel Berkeley Research Lab (IBRL) real-world dataset, which includes 54 sensor nodes measuring temperature and humidity. The results demonstrate significant energy savings and a model accuracy of 96.50%, thereby reducing privacy breaches and extending network lifetime. The framework offers scalability, effective privacy monitoring, and adaptability to changing topologies.
Copyright (c) 2026 S. Swapna Kumar, Kalli Satyanarayan Reddy http://creativecommons.org/licenses/by-sa/4.0
Energy-efficient virtual machine allocation using directional and boundary-aware bobcat optimization
Cloud computing (CC) has gained significant traction due to its ability to deliver services in a scalable and adaptable manner, catering to diverse user requirements. However, in virtualization technology, one of the primary challenges is managing the energy consumption required to maintain service quality, as it directly impacts the operational expenses of data centers. To address this challenge, this research proposes a directional movement and boundary-aware strategy-based bobcat optimization algorithm (DMBABOA) for energy-efficient virtual machine (VM) allocation aimed at minimizing energy consumption in cloud environments. The directional search and boundary-aware correction enhance convergence and ensure feasible resource distribution. This ensures effective utilization of resources, improved virtualization management, and substantial energy savings. The experimental findings establish that the proposed DMBABOA optimizer reaches a minimum execution time of 134.48 s when the number of VMs is equal to 1,200 with 200 users, compared to existing methods such as the metaheuristic VM allocation approach to power efficiency of sustainable cloud environment (MV-PESC).
Copyright (c) 2026 Nida Kousar Gouse, Gopala Krishnan Chandrasekaran http://creativecommons.org/licenses/by-sa/4.0
Heart disease detection and classification using grid search with random forest
Cardiovascular disease (CVD) is basically stated as heart disease, is a significant impact of mortality rate in worldwide. Diagnosing heart disease is challenging because of the complexity of patient data, which establishes multiple categories of the disease and also irrelevant features, making it difficult to achieve classification accuracy. This research proposed a grid search with random forest (GS-RF) approach, which effectively identifies heart disease and significantly enhances classification accuracy by fine tuning the random forest (RF) approach. It optimizes key hyperparameters like number of trees and greater number of features, improving model performance. The chaotic maps-based dwarf mongoose optimization (CMDMO) is used for feature selection, which efficiently selects the relevant feature and prevents the algorithm from getting trapped in local minima. The classification using grid search’s effectiveness ensures that resources are spent on finding the best model rather than performing random, less efficient tuning. The proposed GS-RF model demonstrates high classification performance, achieving 99.43% accuracy on Cleveland dataset, while also attaining 99.10% accuracy on Statlog dataset, thereby confirming its robustness and effectiveness across different datasets. The proposed approach is evaluated in comparison with existing classification techniques, such as support vector machine (SVM), to demonstrate its greater effectiveness with respect to accuracy.
Copyright (c) 2026 Ramakrishna Reddy Badveli, Nijaguna Gollara Siddappa, Sundeep Kumar Kanipakapatnam http://creativecommons.org/licenses/by-sa/4.0
Hybrid kernel support vector machine with cuckoo search optimization for malaria detection from blood smear images
Microscopic image-based malaria detection still struggles to capture complex features due to variations in lighting and color. The support vector machine (SVM) method is often used in medical image detection, but its performance depends heavily on the selection of optimal kernel and hyperparameters (C and gamma). Conventional approaches, with single kernels and manual tuning, have limitations in capturing both spatial information and color distribution simultaneously. Therefore, this research proposes hybrid kernel support vector machine-cuckoo search algorithm (HKSVM-CSA) method that combines the radial basis function (RBF) kernel and histogram intersection for SVM, along with hyperparameter optimization using the CSA. The dataset used is malaria cell images, which contains parasitized and uninfected images of blood cells. The proposed method comprises five main steps: dataset preparation, feature extraction, HKSVM, hyperparameter optimization, and model evaluation. Experiments demonstrate that the proposed model achieves 94% accuracy, 93% sensitivity, 94% specificity, and area under the curve (AUC) of 0.98, which is significantly better than standard SVM, SVM-genetic algorithm (GA), and k-nearest neighbors (KNN). These results show that combining kernel and CSA significantly improves detection accuracy. This approach is promising for image-based automatic systems for infectious disease diagnosis.
Copyright (c) 2026 Sri Huning Anwariningsih, Indrarini Dyah Irawati http://creativecommons.org/licenses/by-sa/4.0
Attribute optimization to improve breast cancer prediction using machine learning techniques
Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment.
Copyright (c) 2026 Raghavendra Srinivasaiah, Santosh Kumar Jankatti, Niranjana Shravanabelagola Jinachandra, Manjunath Ramanna Lamani, Bellam Vijaya Lakshmi, Rishita Bhelwa http://creativecommons.org/licenses/by-sa/4.0
Identification of chemical markers for species differentiation in Aquilaria essential oils using self-organizing maps
This study analyzes the chemical diversity of essential oils from four Aquilaria species, A. beccariana, A. malaccensis, A. crassna, and A. subintegra, which are important sources of agarwood used in perfumery and traditional medicine. Despite their economic and ecological value, the chemical profiles of these species remain insufficiently characterized, hindering accurate species differentiation and resource management. This research aims to identify distinctive chemical patterns to improve species classification. Self-organizing maps (SOMs) were employed to analyze complex chemical composition data and to identify significant compounds responsible for species separation. The analysis revealed several compounds with strong discriminatory power and species-specific distribution patterns, with compounds C, D, and E identified as the most significant markers. These findings demonstrate substantial biochemical diversity among Aquilaria species and confirm the effectiveness of SOM for essential oil profiling. The results support improved species identification and have important implications for ecological conservation, sustainable agarwood management, and pharmacological development.
Copyright (c) 2026 Nur Athirah Syafiqah Noramli, Muhammad Ikhsan Roslan, Noor Aida Syakira Ahmad Sabri, Nurlaila Ismail, Zakiah Mohd Yusoff, Mohd Nasir Taib http://creativecommons.org/licenses/by-sa/4.0
NN-SVM: a hybrid neural network–support vector machine framework for accurate pneumonia detection from chest X-rays
We present neural network (NN)–support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making.
Copyright (c) 2026 Santosh Kumar Jankatti, Raghavendra Srinivasaiah, Mohammad Shahina Parveen, Harish H. Kenchannavar, Danthuluri Sudha, Srihari Sharma Karigiri Narah, Mahadev Shivaraj http://creativecommons.org/licenses/by-sa/4.0
Machine learning-based solar power prediction for major Indian metro cities
The growing reliance on renewable energy has intensified the need for accurate solar power forecasting to support efficient grid operation and energy planning. However, reliable prediction remains challenging due to the strong dependence of solar power output on dynamic meteorological conditions. This study proposes a data-driven machine learning (ML) framework for high-precision solar power prediction across several major Indian metro cities. Using hourly weather and power generation data for the year 2023, a random forest regressor was developed to model complex non linear relationships between environmental variables and solar energy output. The proposed model achieved exceptional predictive performance, with an R² score of 0.9999 and a mean absolute error (MAE) of 0.15 kW, significantly outperforming conventional regression approaches. Feature contribution analysis revealed solar radiation as the dominant factor influencing power generation, while cloud cover and elevated temperatures exhibited negative effects. The key contribution of this work lies in demonstrating the robustness and generalizability of ensemble learning for urban-scale solar forecasting under diverse climatic conditions. The findings provide actionable insights for policymakers, grid operators, and energy planners to optimize solar integration and resource management.
Copyright (c) 2026 Komal Kumar Napa, Rajkumar Govindarajan, J. Senthil Murugan, Billa Manindhar http://creativecommons.org/licenses/by-sa/4.0
Blockchain-enabled framework using diversity mutation with siberian tiger optimization for offloading in fog computing
Fog computing has developed as a promising framework to support latency sensitive internet of things (IoT) applications for mobile devices operating in dynamic environments. During the offloading process, malicious activities interrupt the existing methods, which increases the execution time. Therefore, this research proposes a diversity mutation with siberian tiger optimization (DM-STO) for computation offloading in blockchain based fog computing. The blockchain is used to secure offload and attain quality of service (QoS) mobile users with less energy consumption and execution time. The DM-STO can balance workloads among local devices and fog servers. The diversity mutation operation improves the exploration ability to dynamic network conditions, leading to efficient computational offloading in fog computing. The execution time, service cost and energy consumption are evaluated to calculate the performance of the proposed DM-STO with varying numbers of IoT requests such as 50, 100, 200, and 300. For 50 IoT requests with a fixed fog server of 10, the DM-STO achieves an execution time of 18 s, a service cost of 10$ and energy consumption of 5 mJ compared to the BAT algorithm.
Copyright (c) 2026 Srikanta Murthy Rajini, Reginald Shilpa http://creativecommons.org/licenses/by-sa/4.0
Automated classification of apple bruises from hyperspectral images: an approach for fruit quality assessment
Apple bruise detection plays a crucial role in post-harvest quality control; however, conventional manual inspection remains labor-intensive, subjective, and unsuitable for large-scale industrial deployment. This study proposes an automated classification framework for identifying bruised regions in apples using hyperspectral imaging combined with deep learning and adaptive optimization techniques. The proposed model integrates a long short-term memory (LSTM) network optimized using an adaptive sand cat swarm optimization (ASCSO) algorithm, along with a ResNet-50 feature extraction backbone. The adaptive behavior embedded within ASCSO dynamically adjusts the optimization parameters to enhance convergence and prevent premature stagnation during LSTM hyperparameter tuning. Hyperspectral images were processed to extract relevant spectral–spatial features, which were subsequently fed into the optimized classifier. Experimental evaluations demonstrate that the proposed hybrid model significantly outperforms conventional and baseline deep learning approaches, achieving a classification accuracy of 98.0% while maintaining robustness across varying bruise patterns and intensity levels. The results highlight the effectiveness of combining hyperspectral imaging with adaptive deep learning optimization for high-precision fruit quality assessment. This research contributes a reliable, scalable solution for automated bruise detection and quality grading in the fruit supply chain, offering strong potential to reduce post-harvest losses and improve operational efficiency in the agro-food industry.
Copyright (c) 2026 Peddireddy Venkateswara Reddy, Alaguchamy Parivazhagan http://creativecommons.org/licenses/by-sa/4.0
Accurate stroke area classification using extreme gradient boosting with multi-feature extraction
Stroke, one of the most common neurological disorders leading to long-term disability and mortality, requires accurate detection of affected brain regions for timely treatment planning. However, conventional deep learning models face challenges in achieving precise segmentation and robust classification due to noisy inputs, weak feature representation, and poor generalization. To address these gaps, this study introduces a hybrid framework that integrates the ConvNeXt architecture for stroke region segmentation with XGBoost based classification, strengthened through three complementary feature extraction methods: local binary patterns (LBP), adaptive threshold directional binary gradient matrix (AT-DBGM), and wavelet packet transform (WPT). These methods capture textural, directional, and multi resolution features, which are concatenated into a stacked vector and classified using XGBoost. Preprocessing steps, including normalization and resizing, ensure improved input consistency. Experimental evaluations on benchmark stroke imaging datasets show that the proposed framework achieves 98.56% Dice similarity coefficient (DSC), 12.96 mm Hausdorff distance (HD), 99.12% accuracy, 98.69% sensitivity, 99.06% specificity, 98.98% precision, and 98.85% F1-score.
Copyright (c) 2026 Kavikondala Praveen Kumar Rao, Maha Lakshmi Bondla, Bommaraju Srinivasa Rao, Ambidi Naveena, K. V. Balaramakrishna, Srinivasarao Goda http://creativecommons.org/licenses/by-sa/4.0
The effects of data imbalance on fraud detection model accuracy
Machine learning (ML) model performance is often assessed by accuracy, but the quality and balance of data also play crucial roles. Imbalanced datasets, where the minority class has fewer samples than the majority class, can lead to biased predictions favoring the majority class. This study addresses the issue of class imbalance through resampling techniques, including random undersampling (RUS) and random oversampling (ROS), specifically applied to a fraud detection dataset. We classify the resampled datasets using random forest (RF) and gradient boosting (GB) models. Our findings indicate that the RF model, when combined with ROS, achieves an accuracy of 97.4%, surpassing the 96.1% accuracy of the GB model with RUS. This approach demonstrates the importance of addressing class imbalance to improve prediction accuracy in ML.
Copyright (c) 2026 Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin http://creativecommons.org/licenses/by-sa/4.0
Real-time detection of rider fatigue: a comparative study of black-box and glass-box artificial intelligence approaches
Rider fatigue poses a critical safety challenge in two-wheeled vehicle operation due to limited physical protection, increased balance demands, and prolonged exposure to environmental stressors. Effective real-time fatigue detection is essential to mitigate accident risks, particularly in high-traffic regions such as Indonesia. This study presents a comparative analysis of black-box and glass-box artificial intelligence (AI) models for real-time detection of rider fatigue, evaluated through a human factor’s lens emphasizing interpretability, intrusiveness, and cognitive compatibility. Multimodal data comprising physiological signals, behavioral indicators, and environmental context were collected using wearable sensors and rider telemetry to train and assess the models. Experimental results reveal that black-box models, including convolutional neural network (CNN) + long short-term memory (LSTM), random forest (RF), and support vector machine (SVM), achieve superior predictive accuracy (94.3%, 91.5%, and 88.2%, respectively) but lack inherent transparency. Conversely, glass-box models such as decision tree (DT) and logistic regression (LR) offer greater interpretability, a critical factor in safety-sensitive applications, though with reduced accuracy (approximately 83–85%). These findings underscore the trade-off between predictive performance and explainability, highlighting the need to tailor model choice to specific operational requirements. This research advances the design of intelligent, human-centered rider support systems that balance accuracy, transparency, and user trust, fostering safer two-wheeled transportation.
Copyright (c) 2026 Cynthia Hayat, Iwan Aang Soenandi, Budi Harsono http://creativecommons.org/licenses/by-sa/4.0
Novel convolution neural network model for dysgraphia affected handwriting classification
It is estimated that 10% of the population in the world suffers from learning disabilities like dyslexia, dysgraphia, and dyscalculia. Learning disabilities are neurological disorders in which children struggle with reading, writing and mathematical skills. Dysgraphia disorder impacts on writing abilities of students and thus may be a hurdle in their learning and evaluation of subject matter. Hence early detection/prediction of learning disability (LD) in school going children will greatly help in providing necessary accommodations so as to ease their future learning curve. In recent years researchers have used several deep learning algorithms that produce automated and trained models which can be useful in the handwriting classification. To properly capture the distinct handwriting inconsistencies linked to dysgraphia, this study contains experiments that determine how various convolution neural network (CNN) model layers contribute to performance. To address it, this research focused on the improved novel model based on CNN and targeted dysgraphia English handwriting classification with 98% accuracy with 102,691 trainable parameters. The model is trained on both normal and dysgraphia-affected handwriting, increasing its accuracy in identifying individual differences.
Copyright (c) 2026 Nisha Vanjari, Prasanna J. Shete http://creativecommons.org/licenses/by-sa/4.0
Drone-assisted deep learning weed detection for sustainable agriculture and environmental resilience
Effective weed detection plays a crucial role in sustainable agriculture, boosting crop productivity and supporting environmental conservation. This study compares three deep learning models—YOLOv5, YOLO-NAS, and mask region-based convolutional neural network (Mask R-CNN)-against traditional methods in terms of accuracy, processing speed, and adaptability in tropical agricultural conditions, with Merauke, Indonesia, as the case study. The results show that YOLO-NAS delivers the highest accuracy at 96% with a processing time of 25 ms per image, making it suitable for high precision applications. YOLOv5 balances strong accuracy (94%) with faster processing at 12 ms per image, establishing it as the most effective for real time scenarios. Mask R-CNN also achieves 94% accuracy and provides advanced segmentation capabilities, but its slower processing speed of 31 ms limits large-scale implementation. Traditional methods perform poorly in comparison, with only 85% accuracy and processing time above 50 ms per image. These findings highlight the transformative potential of artificial intelligence (AI)-based weed detection for precision agriculture, particularly in tropical regions like Merauke. Adoption of models such as YOLOv5 reduces manual labor dependence while advancing efficient, eco-friendly weed management. Future research should expand datasets and explore newer models like YOLOv8, YOLO-NAS, vision transformers (ViTs), and hybrid approaches.
Copyright (c) 2026 Agustan Latif, Handaru Jati, Herman Dwi Surjono, Mani Yusuf http://creativecommons.org/licenses/by-sa/4.0
TMA-Net: a transformer-based multi-modal attention network for abnormal behavior detection
Abnormal behavior detection in crowded environments remains challenging due to complex motion patterns, occlusions, and domain variability. This paper presents transformer-based multi-modal attention network (TMA-Net), a unified framework that integrates red, green, and blue (RGB), optical flow (OF), and heat map (HM) modalities through a dual-stage attention fusion mechanism. The system employs you only look once version 11 (YOLOv11) for human localization and vision transformer (ViT)-B/16 for feature encoding, followed by intra-modal self-attention and cross-modal fusion to capture fine-grained spatial–temporal and motion energy dependencies. Extensive experiments on six public benchmarks as UMN, Crowd-11, UBNormal, ShanghaiTech, CUHK Avenue, UCSD Ped2, and EPUAbN dataset, demonstrate that TMA-Net achieves up to 97.5% area under the curve (AUC) and 96–100% accuracy, outperforming previous other state-of-the-art approaches. These results highlight the framework’s strong generalization and robustness across both single- and cross-dataset evaluations, underscoring its potential for reliable deployment in real intelligent surveillance systems.
Copyright (c) 2026 Huong-Giang Doan, Ngoc-Trung Nguyen http://creativecommons.org/licenses/by-sa/4.0
Exponential long short-term memory with Levy flight optimization for lung nodule classification
Lung cancer, which commonly appears as lung nodules is a deadly type of cancer that develops in a lung. Early detection of lung cancer is critical and challenging task due to presence of overlapping structures, which make it challenging to differentiate the benign and malignant regions. This research proposes long short-term memory (LSTM) with exponential linear unit (ELU) method for the classification of different classes of lung nodules. The hyperparameters of the LSTM network are optimized using the developed dynamic Levy flight – Archimedes optimization algorithm (DLF-AOA), which effectively identifies the optimal parameters for classification. The ResNet-18 method is used for the extraction of high-level features to differentiate various classes of lung nodules. Furthermore, Bayesian active contour (BAC) is employed for the segmentation of images as containing cancerous and non-cancerous regions of lung nodules. The LSTM with ELU method achieves 98.56% accuracy, 97.54% sensitivity, 98.22% specificity, 96.93% precision, 96.33% F1-score, and 1.44 error rate in IQ-OTH/NCCD lung cancer dataset.
Copyright (c) 2026 Kaliba Gowthami, Kamalakannan Jayaseelan http://creativecommons.org/licenses/by-sa/4.0
Explainable social media disaster image classification using a lightweight attention-based deep learning approach
In recent years, the rapid dissemination of social media content during natural and man-made disasters has created a need for automated and accurate disaster image classification systems. This paper proposes lightweight explainable attention-based disaster network (LEAD-Net), a deep learning (DL) model designed for classifying disaster-related images with high accuracy and interpretability. The system integrates an EfficientNet-B0 backbone enhanced with squeeze-and-excitation (SE) attention modules and a lightweight neural architecture search (NAS-lite) strategy for tuning the classifier head and training hyperparameters. The model was evaluated on two benchmark datasets comprehensive disaster dataset (CDD) and damage multimodal dataset (DMD) achieving 96% and 87% accuracy, respectively, outperforming several established convolutional neural network (CNN) baselines. To ensure transparency, gradient-weighted class activation mapping (Grad-CAM) was employed to generate visual explanations of the model’s decisions, confirming its focus on semantically relevant image regions.
Copyright (c) 2026 Rashmi Kangokar Taranath, Geeta Chidanandappa Mara http://creativecommons.org/licenses/by-sa/4.0
Scaler enhanced deformable attention with graph neural network for video compression
Video compression is widely used to reduce bandwidth and storage requirements when storing and transmitting videos, most existing neural video compression approaches adopt the predictive residue-coding framework, which is suboptimal for removing redundancy across frames. Additionally, minimizing only the pixel-wise differences between the raw and decompressed frames is ineffective in improving the perceptual quality of the videos, blocking artifacts degrade the visual quality, especially near edges and texture areas. Hence, to solve these problems, this research proposes a scaler enhanced deformable attention graph neural network (SEDA-GNN) to utilized for reduce inter-frame redundancy by employing a deformable attention mechanism that efficiently captures motion and structural changes, thereby minimizing redundancy. Modelling complex temporal dynamics with graph neural networks (GNNs) captures dependencies between frames, thereby facilitating highly efficient video encoding, then constrained directional enhancement filter (CDEF) effectively reduces blocking artifacts while preserving sharp edges through directional and constrained filtering, thereby improving visual quality in compressed video. The SEDA-GNN approach achieved a bjontegaard delta bit rate (BD-BR) reduction of 2.372% on the joint collaborative team on video coding (JCT-VC) database and 3.230% of BD-BR on the ultra video group (UVG) dataset, demonstrating significant performance when compared to invertible neural networks (INNs).
Copyright (c) 2026 Revathi Kasinathaperumal, Hosanna Princye Periapandi http://creativecommons.org/licenses/by-sa/4.0
RBC_Frame_Net: a hybrid deep learning framework for detection of red blood cells in malaria diagnostic smear
Malaria continues to pose a major global health threat, especially in areas where timely and accurate diagnosis is essential for effective treatment. Conventional diagnostic techniques, such as manually examining Giemsa stained blood smears, are often time-intensive, laborious, and susceptible to human error. To overcome these challenges, this study presents red blood cell frame network (RBC_Frame_Net), a novel deep-learning framework that combines convolutional neural networks (CNNs) with transformer based architectures, augmented by attention mechanisms, for the automated identification of RBCs in malaria smear images. The framework leverages the convolutional block attention modules (CBAM)-UNet model for segmentation, enhancing both spatial and channel features through CBAM and integrates the detection transformer (DETR) to accurately detect and classify RBCs within the diagnostic images. The model achieved outstanding performance with a segmentation intersection over union (IoU) of 0.97, a Dice coefficient of 0.98, and near-perfect detection results (precision: 0.999, recall: 0.998, and mean average precision (mAP): 0.995). When compared to leading models such as YOLOv8, faster region-based convolutional neural network (Faster R-CNN), and EfficientDet-D3, and RBC_Frame_Net demonstrated superior accuracy and robustness. The inclusion of attention mechanisms and a hybrid architecture enhance its adaptability, making it well-suited for deployment in real-world, resource limited environments and positioning it as a valuable asset in automated malaria diagnostics.
Copyright (c) 2026 Muhammad Shameem P., Mathiarasi Balakrishnan http://creativecommons.org/licenses/by-sa/4.0
A reinforcement-guided multi-phase hybrid architecture for threat profiling and defense towards IoT handheld device
The contribution of artificial intelligence (AI) towards offering proactive security in handheld devices of internet of things (IoT) is in evolving stage. Review of literature showcases noteworthy attempts of machine learning (ML) and deep learning (DL) models; however, they are a large scope of improvement towards bridging the trade-off between security and computational-communication efficiency. This problem is addressed in this manuscript by presenting a unique and innovative solution where reinforcement learning (RL) has been hybridized with standalone ML and DL models. The model reads the permission-based data in cloud, followed by vulnerability prediction carried out by hybridization of RL and logistic regression (LR). Further, RL is integrated with deep neural network (DNN) for exploring a secure path to facilitate data transmission. The proposed model witnessed 97.9% accuracy, 67.35% of higher accuracy, 55.14% of reduced latency, and 52.54% of faster response time in contrast to baselines.
Copyright (c) 2026 Pushpa Rajput Narayana Singh, Neelambike Siddalingaiah http://creativecommons.org/licenses/by-sa/4.0
Zoneout regularization-gated recurrent unit algorithm on NIDS with class imbalance handling
Network intrusion detection system (NIDS) is primarily utilized tool to identify malicious threats on the network. It plays an essential role in safeguarding against an increasing variety of attacks and ensures enhanced security for the network. The existing model struggled to handle the imbalance of class issues during the process of classification due to their biased nature, which reduced the performance of the algorithm. In this paper, the zoneout regularization–gated recurrent unit (ZR-GRU) algorithm is developed to detect and classify intrusions in the network. Incorporating the ZR into GRU reduces overfitting by preventing the model from becoming overly dependent on specific features. It provides good generalization by maintaining diversity in learned representation. Synthetic minority oversampling technique (SMOTE) and Near Miss methods are utilized to balance the samples in the dataset, which helps to increase the performance of a classifier in NIDS. The ZR-GRU technique attained 99.91% accuracy on UNSW-NB15, 99.92% accuracy on CIC-IDS2018, and 99.14% accuracy on CIC-DDoS2019 when comparing with a convolutional neural network bidirectional long short-term memory (CNN-BiLSTM).
Copyright (c) 2026 Institute of Advanced Engineering and Science http://creativecommons.org/licenses/by-sa/4.0
MNetNCR: MobileNet model for efficient traditional Nusantara script character recognition
Preservation of traditional Nusantara scripts is very important because these traditional scripts are part of the cultural heritage that reflects the identity and history of the nation. This research proposed MobileNet for Nusantara character recognition (MNetNCR) model based on MobileNetV3 architecture to recognize traditional Nusantara scripts with lightweight, efficient architecture, and accurate recognition. The novel and comprehensive datasets for traditional Nusantara scripts have been curated in this research, that will later be stored digitally and can be used in further research. This novel dataset includes handwritten Balinese, Batak, Javanese, Lontara, and Sundanese scripts, each with unique visual characteristics. The proposed MNetNCR model is highly effective in recognizing characters, achieving F1-scores of 0.9934 for Balinese, 0.9450 for Batak, 0.9788 for Javanese, 0.9936 for Lontara, and 0.9961 for Sundanese scripts, according to the experimental results. The MNetNCR model built in this research has been proven to be effective and efficient in recognizing traditional scripts accurately. It also supports the preservation and promotion of the nation's cultural and historical heritage.
Copyright (c) 2026 Untari Novia Wisesty, Aditya Firman Ihsan, Mahmud Dwi Sulistiyo, Donni Richasdy, Prasti Eko Yunanto, Gamma Kosala, Arfive Gandhi, Febryanti Sthevanie http://creativecommons.org/licenses/by-sa/4.0
2D-CNN-GACL-ECGNet graph attention: a robust framework for electrocardiogram-based stress detection
Early detection of cardiovascular diseases (CVDs) via electrocardiogram (ECG) classification during physiological stress is critical and remains challenging due to stress-induced morphological variability, noise from ambulatory settings, and inter-class ambiguities. Existing models, such as 1D signal-based models with convolutional neural networks (CNNs) and graph convolutional networks (GCNs), struggle to adapt to dynamic stress conditions and generate interpretable insights. In response, we propose 2D CNN and graph attention network (GAT) for optimizer. The model 2D-CNN and GACL-ECG-Net, an innovative framework integrating GATs with adaptive contrastive learning (ACL) and morpho-temporal graph construction. Key innovations include 2D-CNN denoising, 2D transformation, dynamic morpho-temporal graphs modeling ECG beats as nodes with hybrid edges (70% morphological similarity, 30% temporal proximity), and stress-adaptive contrastive loss with learnable margins on stress-conditioned labels, reducing class ambiguity by 18%. Multi-head attention mechanisms provide interpretable heatmaps aligned with cardiologist annotations (κ =0.82) and are evaluated using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, wearable stress and affect detection (WESAD) dataset for emotional stress, and stress at work, knowledge work (SWELL-KW) dataset for cognitive stress. 2D-CNN-GACL-ECG-Net achieves state-of-the-art performance with 98.7% F1-score (MIT-BIH), 94.2% (WESAD), and 92.8% (SWELL-KW), outperforming CNN-bidirectional long short-term memory (BiLSTM) and GCN baselines by 95%. The framework is computationally efficient and clinically validated for wearable health monitoring.
Copyright (c) 2026 P. Kavitha, L. Shakkeera http://creativecommons.org/licenses/by-sa/4.0
Session click sentiment behavior aware personalized recommendations system
Session-based recommendations use short-term behavior of users to provide personalized suggestions to consumers in ecommerce platform. However, cold start, considering newly joined users and sparsity issues, where not enough short-term behavior is available, and the performance of traditional session-based recommendations is significantly impacted. Deep learning (DL) like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs) have been employed to capture session-clicks and enhance product recommendation accuracy. However, the current method is significantly affected due to the gradient descent problem in meeting convergence for top-K product recommendation. Further, the current method failed to capture product sentiment for session-clicks between inter-session and intra-session clicks. In addressing the research problems, the current research work introduced a session click sentiment behavior aware (SCSBA) personalized recommendation system using novel inter and intra session (IIS)-LSTM model. Finally, the objective function to recommend top K items to users is done using optimized Bayesian personalized ranking (OBPR) algorithm. Experiment outcome shows the SCSBA model achieves much better performance than state of art model, considering standard Tmall dataset.
Padma Muthalambikashettahally Cheluvegowda
Copyright (c) 2026 Suraj Bevinahalli Suresh, Padma Muthalambikashettahally Cheluvegowda http://creativecommons.org/licenses/by-sa/4.0
A novel approach to detect tomato leaf disease using vision transformer
Tomatoes are one of the most widely consumed vegetables across the world. However, tomatoes are prone to diseases. Recognizing and classifying tomato leaf diseases is crucial task. Various deep learning (DL) methods have been developed by several researchers, but they have some complex issues like noise in images, high computational complexity, poor accuracy, and limited feature selection. The main goal of this research is to present novel DL based tomato leaf disease classification framework with neural network based gated vision transformer (G-ViT) model assisted attention mechanism. The proposed framework uses dilated convolution with bidirectional long short-term memory (Bi-DLSTM) used for efficient feature extraction to enhance the classification. An effective chaotic spider wasp optimization (CSWO) is used for feature selection. Further, novel attention based gated vision transformer (A-GVT) is used to classify tomato leaf diseases which integrates strengths of attention mechanism and G-ViT models. Further, to improve the generalizability of classification model, its parameters are tuned with black widow optimization (BWO) algorithm. The experimental findings shows that proposed framework outperformed previous studies on tomato leaf disease identification and classification models in terms of accuracy, precision, recall, F1-score, specificity, mean absolute error (MAE), and root mean square error (RMSE) with 99.7%, 98.29%, 98.22%, 98.25%, 99.19%, 0.03, and 0.25 respectively. The proposed study can pave a way for new agricultural revolution.
Copyright (c) 2026 Sanjeela Sagar, Jaswinder Singh http://creativecommons.org/licenses/by-sa/4.0
Multimodal facial expression recognition using residual mogrifier long short-term memory
Multimodal facial expression recognition aims to improve emotion analysis by integrating visual, audio, and textual cues to achieve accuracy and robustness. However, effectively recognizing facial expressions across video, text, and audio presents challenges due to inconsistencies in how emotions are expressed among these modalities. To overcome this issue, this research proposes a residual mogrifier long short-term memory (RMLSTM) model to enhance robustness in multimodal facial expression recognition. By integrating residual connections into the long short-term memory (LSTM), the model improves its ability to capture complex dependencies among various modalities, including video, text, and audio. The residual connection overcomes the vanishing gradient problem and ensures stable training with better gradient flow in deeper networks. The mogrifier mechanism refines the input features dynamically, enhancing feature interaction and alignment across modalities. The RMLSTM achieves 99.57% and 97.83% accuracy on the SAVEE and YouTube datasets, respectively, outperforming both the mel-frequency cepstral coefficients time-domain feature with iterative dilated convolutional neural network (MFCCT-1DCNN) and attention-based multi-modal popularity prediction model of short-form videos (AMPS).
Copyright (c) 2026 Mamatha Kariyappa Rajanna, Thejaswini Shankar, Rashmi Narasimhamurthy, Nandhini Vannivedu Lakshmanan, Hariprasad S. Ananthapadmanabharao http://creativecommons.org/licenses/by-sa/4.0
Image feature extraction for road surface damage classification
Road surface deterioration poses a critical risk to driving safety and comfort, necessitating timely and accurate detection to support effective maintenance. Manual inspection methods are often inefficient, underscoring the need for automated approaches based on computer vision. This study investigates the integration of feature extraction techniques histogram of oriented gradients (HOG) and local binary pattern (LBP) with convolutional neural network (CNN) architectures ResNet50 and InceptionV3 for the classification of road damage. A dataset of 1,580 images was categorized into five damage types: alligator crack, longitudinal crack, other crack, patching, and potholes. Experimental results indicate that HOG–ResNet50 achieved 79% accuracy, while LBP–InceptionV3 yielded the best performance at 97%. The contributions of this study are threefold: i) an automated framework is proposed that combines texture-based features with deep learning for road damage detection, ii) the LBP–InceptionV3 combination is shown to provide superior accuracy compared to conventional pairings, and iii) the approach offers a scalable and reliable alternative to manual inspection methods, supporting more efficient road maintenance planning.
Copyright (c) 2026 Octaviani Hutapea, Sarifuddin Madenda, Hustinawaty, Iffatul Mardhiyah http://creativecommons.org/licenses/by-sa/4.0
Fetal organ detection using feature enhancement with attention and residual block
The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Copyright (c) 2026 Nuswil Bernolian, Siti Nurmaini, Ade Iriani Sapitri, Annisa Darmawahyuni, Muhammad Naufal Rachmatullah, Bambang Tutuko, Firdaus Firdaus http://creativecommons.org/licenses/by-sa/4.0
Deep learning ensembles for lung cancer detection in thoracic CT scans leveraging generative adversarial network technology
Effective treatment of lung cancer depends on early and accurate detection, which continues to be a major cause of cancer-related fatalities globally. Conventional diagnostic techniques are useful, but their efficacy in handling large amounts of thoracic computed tomography (CT) scan data is limited by their time-consuming nature and susceptibility to human error. The research here suggests a new deep learning model that integrates generative adversarial networks (GANs) for data improvement with a sophisticated ensemble approach to classification. GANs are employed to generate realistic synthetic CT images, addressing the challenges of limited datasets. The backbone of the proposed approach is a consensus-guided adaptive blending (CGAB) ensemble model that learns to dynamically combine the predictions of three top-performing convolutional neural networks (CNNs): ResNet-152, DenseNet-169, and EfficientNet-B7. The CGAB model improves prediction accuracy through model contribution weighting based on confidence scores and inter-model consensus, while a conflict-resolving auxiliary decision model is used. The approach was tested using the lung image database consortium and the image database resource initiative (LIDC-IDRI) dataset with a detection rate of 97.35%, surpassing single model and traditional ensemble methods. The current work provides a solid and scalable approach to lung cancer detection with better generalization, increased diagnostic consistency, and applicability for clinical use.
Copyright (c) 2026 Bineesh Moozhippurath, Jayapandian Natarajan http://creativecommons.org/licenses/by-sa/4.0
Enhancing imbalanced dataset diagnosis using class-based input image composition
Deep learning models often falter when faced with small, imbalanced datasets or degraded image quality, leading to unacceptably high false prediction rates. To bridge this gap, we introduce class-based image composition. This technique reformulates training inputs by fusing multiple intra-class images into unique composite input images (CoImg). By concentrating information density and amplifying intra-class variance, CoImg forces the model to discern subtle, nuanced disease patterns that might otherwise be lost. We validated this approach using the optical coherence tomography dataset for image-based deep learning methods (OCTDL), a collection of seven imbalanced retinal disease scan categories. From this, we engineered Co-OCTDL: a perfectly balanced variant where each training sample exists as a 3×1 layout composite. To measure the impact of this new representation, we benchmarked the original dataset against its composite counterpart using a VGG16 architecture. Precision was paramount. We maintained identical hyperparameters and model structures across all experiments to ensure a rigorous, fair comparison. The results were transformative. While baseline datasets struggled, the enhanced Co OCTDL achieved a near-perfect F1-score of 0.995 and an AUC of 0.9996. The method effectively neutralized the risks of class imbalance. It didn't just improve the numbers; it refined the diagnostic reliability of the model.
Copyright (c) 2026 Azzeddine Hlali, Majid Ben Yakhlef, Soulaiman El Hazzat http://creativecommons.org/licenses/by-sa/4.0
Analysis of tuberculosis detection using deep learning technique and explainable artificial intelligence
Tuberculosis (TB) affects the health of many individuals and is still a prime worldwide health concern despite having so many advanced treatments, as it still lacks technical advancement in its treatment and diagnosis. Accuracy in identification and early detection is essential to reduce the spread and improve treatment outcomes. Traditional methods of diagnosis, such as sputum microscopy and culture, are labor-dependent and subject to human mistakes as it is done by lab technicians. Recent improvements in deep learning have demonstrated significant potential for enhancing and automating diagnostic accuracy. Our research proposes a deep learning based technique that detects TB from chest X-rays after image processing techniques like augmentation. After training on big data, our model pulls off an astonishing accuracy of 97.42% and a loss of 7.17%, outperforming traditional methods. The model uses convolutional neural network (CNN) as a base and transfer learning method, like DenseNet-121, and explainable artificial intelligence (XAI) technique, like Grad-CAM, to recognize TB related patterns effectively and with low false positives. This approach has the ability to revolutionize the diagnosis of TB and offer more dependable, scalable, and timely solutions to healthcare systems worldwide.
Copyright (c) 2026 Shashikiran Srinivas, Kavita Avinash Patil, Kushalatha Monappa Rama, Sudha Venkateshlu, Jayanthi Muthuswamy, Srinivas Babu Narayanappa http://creativecommons.org/licenses/by-sa/4.0
Breast cancer detection using residual DenseNets in deep learning
Breast cancer, the leading cause of cancer-related deaths among women globally, requires a prompt and precise diagnosis in order to increase survival rates via therapy. There is a possibility of bias and inconsistency in the results of traditional diagnostic procedures like mammography, ultrasound, and histological testing since they rely on the expertise of radiologists and pathologists. There are exciting new opportunities for breast cancer diagnostics to be enhanced by artificial intelligence (AI) and deep learning. The purpose of this research is to examine the feasibility of using convolutional neural networks (CNNs) and residual dense networks (ResDenseNets) used for breast cancer automated detection in medical images. Because of their superior capacity to learn hierarchical features from raw image data, CNNs are ideal for medical image interpretation. By including residual connections, which allow for the training of considerably deeper models, ResDenseNets—an extension of CNNs—mitigate the problem of vanishing gradient in deep networks. ResDenseNet and CNNs considerably enhance the accuracy of breast cancer diagnosis in comparison to conventional approaches, according to the findings. Notably, ResDenseNets outperform other types of networks because they are able to learn intricate and nuanced properties directly from the data.
Copyright (c) 2026 Naganandini Gururajarao, Vishwanath R. Hulipalled http://creativecommons.org/licenses/by-sa/4.0
Unified voting-based ensemble learning for rice leaf disease detection using improved pretrained models
As a staple food for a large portion of the global population, rice is particularly susceptible to leaf diseases that adversely affect its yield and overall quality. This study utilizes four pretrained convolutional neural network (CNN) models to construct a unified voting-based ensemble approach for rice leaf disease classification. The models include VGG16, DenseNet121, InceptionV3, and Xception. The dataset used in this study was collected from Kaggle and further enriched with images obtained from Google sources. It comprises a total of 4,000 images categorized into six classes: bacterial leaf blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaves. It was split into training (327 images/class), validation (140 images/class), and testing (200 images/class). Images were normalized to [0,1] and augmented through rotation, flipping, shifting, shear, zoom, brightness, and channel adjustments to improve generalization. Individually, the fine-tuned models achieved accuracies of 91.3% (VGG16), 95.6% (DenseNet121), 92.1% (InceptionV3), and 89.8% (Xception). The ensemble leveraged majority voting (93.6%), weighted voting (96.5%), and soft voting (97%), yielding an absolute gain of 1.4% over the best individual model and 4.8% over the average of all models. To our knowledge, this is the first ensemble combining these four architectures with unified voting for identifying diseases in rice leaves, delivering a scalable and computationally efficient solution suitable in advance diagnosis and timely execution in agricultural settings with limited resources.
Copyright (c) 2026 Govindarajan Subburaman, Mary Vennila Selvadurai http://creativecommons.org/licenses/by-sa/4.0
Efficient text detection and recognition in natural scene images using novel blended ensemble deep learning
Text detection and recognition in natural scene images is a critical task in computer vision, with applications ranging from document analysis to autonomous navigation. This work presents a robust and efficient pipeline that integrates YOLOv8 for text detection and EasyOCR for recognition, enhanced by an adaptive preprocessing mechanism between the two stages. The YOLOv8 model is trained on a custom dataset with polygonal annotations converted into YOLO format ensures precise bounding box formations around the text regions. An adaptive preprocessing module dynamically optimizes the detected regions adjusting resolution, noise reduction, and orientation before passing them to EasyOCR, significantly improving robustness. The lightweight yet powerful EasyOCR engine then recognizes text across diverse fonts, styles, and orientations. Evaluated on the benchmark Total-Text dataset, the proposed method demonstrates superior performance in detection accuracy, recognition precision, and computational efficiency. Additionally, this work provides a detailed analysis of training metrics, to validate the model’s robustness. The proposed system is scalable and can be integrated into real-time applications such as license plate recognition, document digitization, and assistive technologies for the visually impaired.
Copyright (c) 2026 Rajeswari Reddy Patil, Aradhana Dammergidda http://creativecommons.org/licenses/by-sa/4.0
Climate change and pollinator dynamics: integrating social media insights and ecological data for conservation strategies
Pollination is an essential ecosystem service intricately linked to biodiversity, ecosystem health, and agricultural systems. The need to understand the effect of climate change on pollination processes has never been greater, given that a significant portion of global crop production is dependent on biotic pollination. This survey paper examines the multifaceted challenges that climate change poses to pollination dynamics across various ecosystems. By synthesizing existing literature to highlight how alterations in temperature and precipitation patterns have led to a phenological mismatch between pollinators and plants, potentially disrupting established trophic relationships and ecosystem functions. Our review reveals that insect-pollinated plants, particularly those that bloom early in the season, exhibit a heightened sensitivity to climate-induced phenological shifts. Moreover, exploring how the altered life cycles of pollinators, struggling to synchronize with the new flowering schedules, may precipitate declines in pollination services. Our findings underscore the critical need for conservation strategies that address climate adaptation for pollinators, focusing on enhancing landscape connectivity and heterogeneity. By bridging diverse studies ranging from the application of social media data in ecological research to advanced predictive models for pollination services, the main aim is to foster a deeper understanding of the consequences of climate change on pollination.
Copyright (c) 2026 Pooja Hadimane, Ashoka Kukkuvada, Gangamma Hediyalad, Govardhan Hegde Kota, Rajeshwari Kisan, Shivanand Patil, Arjun Myala, Basavaraja Anekonda Subhash http://creativecommons.org/licenses/by-sa/4.0
Enhanced VGG-19 model for rice plant disease detection and classification
Rice is the main staple food and rice farming plays a crucial role in the agriculture sector of Myanmar. It is also an essential pillar in generating foreign income. However, rice diseases seriously reduced the rice production and quality. Early detection of rice diseases is one of the effective ways to reduce the disease spreading and increase yields. Most Myanmar farmers detect rice diseases based on visual judgment and their experience, which leads to delay in taking efficient action. To overcome this challenge, we intend to propose an enhanced rice plant disease classification model that contributes as artificial intelligence (AI) in Myanmar agriculture sector. The proposed model enhances original visual geometry group 19 (VGG-19) by integrating the algorithms: mixture of Gaussians 2 (MOG2), GrabCut, and relevance estimation with linear feature (RELIEF) for classification. It was trained on 6,326 rice plant images of Kaggle and Eastern Shan State and validated using 5-fold nested cross-validation. The training and testing of proposed model are followed as 80:20. The proposed model experimental result is (98.3%) and lowest standard deviation (0.004) across seven classes than the original VGG-19, MobileNet, Efficient Net, and RestNet50 respectively. Future work will expand dataset diversity, enhance early-stage disease prediction, and support mobile diagnostics for real-world agricultural application.
Copyright (c) 2026 Aye Thida Win, Khin Mar Soe, Myint Myint Lwin http://creativecommons.org/licenses/by-sa/4.0
A deep learning-based approach for hearing loss detection
Millions of people across the world are affected by hearing loss and early detection is very important for effective intervention. The traditional hearing screening methods are effective but they often rely on specialized equipment and clinical resources, making them less accessible to common people. Hearing loss is a state that affects the ability to communicate, socially interact and overall quality of life. The advancements in recent years have aimed to enhance the accessibility and efficiency of hearing tests, mainly in remote areas. The accurate classification of hearing loss is essential for effective detection and treatment in audiology. This study presents a deep learning (DL)-based approach based on a feedforward neural network (FNN). This paper focuses on common causes like cerumen impaction, otitis media, and otosclerosis. The study tries to explore ways to improve the diagnosis of hearing loss. The goal is to develop solutions that make hearing screenings more accessible and cost-effective for populations with limited access to healthcare resources. The results show the advantages of DL models in supporting automated accurate classification of hearing loss for intelligent diagnostic systems in audiological healthcare.
Copyright (c) 2026 Deepa, Manjula Gururaj Rao http://creativecommons.org/licenses/by-sa/4.0
YOLOv8-TMS: spatiotemporal attention networks for real-time occlusion-resilient urban traffic monitoring
Traffic monitoring from roadside cameras benefits from fast object detection, yet real street scenes remain difficult because occlusions, small targets, and adverse weather conditions reduce visual reliability. This study presents YOLOv8 for traffic management system (TMS), which enhances YOLOv8 using hybrid attention refinement, temporal coherence modeling, and adaptive occlusion handling to improve stability in crowded frames. Experiments on the traffic management enhanced dataset from the Roboflow universe street view project use 5,805 training images and 279 testing images across five road-user categories. The model achieves 95.2% mAP@0.50 in sunny scenes and 90.0% mAP@0.50inrainyscenes, whilesustaining 50msinference time and30frames per second throughput with 8 GB graphics processing unit memory. The results support reliable deployment for near real-time traffic analytics under varying conditions.
Copyright (c) 2026 Vidhya Kandasamy, Antony Taurshia, Thavittupalayam M. Thiyagu, Catherine Joy RusselRaj, Jenefa Archpaul http://creativecommons.org/licenses/by-sa/4.0
Comparative deep learning study for downy mildew detection in vegetables
Several vegetable crops are affected by downy mildew, a major foliar disease resulting in notable reductions in yield. For sustainable agriculture and disease prevention, early and precise detection is crucial. To be able to detect downy mildew in five varied vegetables—bitter gourd, bottle gourd, cauliflower, cucumber (Rashid), and cucumber (Sultana)—this study evaluates three deep learning architectures: VGG19, DenseNet201, and MobileNetV2. This work focuses on imbalanced datasets collected from several sources, in opposition to prior work that depended on balanced laboratory datasets. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models shortly after they were trained using transfer learning, data augmentation, and 5-fold cross-validation. Model focus regions were assessed by using gradient-weighted class activation mapping (Grad-CAM) visualizations, and statistical reliability was assessed based on paired t-tests and Wilcoxon signed-rank tests. By achieving mean accuracies above 98% and statistically significant results (p <0.05) on cucumber datasets, DenseNet201 accomplished superior performance. Despite attaining slightly lower accuracy (89.6–100%), MobileNetV2 offered the smallest model size (12.9 MB) and minimum inference time (85 ms). The proposed approach demonstrated a transparent, generalizable, and computationally efficient deep learning pipeline for precision agriculture’s real-time downy mildew detection.
Copyright (c) 2026 Supreetha Shivaraj, Manjula Sunkadakatte Haladappa http://creativecommons.org/licenses/by-sa/4.0
ResNet based deep learning approach for chronic obstructive pulmonary disease prediction using lung sound analysis
Chronic obstructive pulmonary disease (COPD) affects around 300-400 million people worldwide representing a critical healthcare challenge that requires early detection for effective intervention. This work introduces chronic lung analysis via audio signal prediction (CLASP), a novel framework achieving 97.90% accuracy in predicting COPD automatically through respiratory audio signal analysis. This method integrates advanced signal processing and deep learning architectures, comparing long short-term memory (LSTM), convolutional neural networks (CNN), and residual networks (ResNet) models for optimal performance. The ResNet architecture exhibits superior diagnostic capability with precision of 98.72%, recall of 96.86%, and 0.9937 area under the curve (AUC), as compared to existing methods by significant margins. These results establish a new benchmark for noninvasive COPD detection, thus enabling practical deployment in clinical settings thereby dramatically improving the patient outcomes by early detection and also reduce healthcare costs.
Copyright (c) 2026 Babitha Sudhakar Ullal, Veena Kalludi Narasimhaiah, Rithul Kamesh http://creativecommons.org/licenses/by-sa/4.0
Deep learning for early detection of cardiovascular diseases via auscultation sound classification
Heart diseases are one of the most prominent causes of death globally, which requires immediate and accurate diagnosis. The auscultation methods used in conventional medical practice, where the doctor listens to the sounds produced by the body without intervention is very ineffective because of the limitations in the actual skills and perception of the doctor. The main goal of this project will be designing a mobile-based system for the early detection of cardiovascular disease (CVD) by utilizing deep learning for auscultation sound classification. The approach involves the use of deep learning structures to classify cardiac sounds into normal and abnormal patterns on its own. Wavelet transformations, time-frequency representations, and Mel frequency cepstral coefficients (MFCC) have been used in feature extraction. The ResNet152V2 model showed high classification performance with area under the receiver operating characteristic curve (AUROC) of 0.9797 and 0.9636 on two datasets. Contrary to that, data augmentation, hyperparameter optimization, attention mechanisms, as well as input-output residual connections, led to better functionality and interpretability. This research seeks to overcome the limitations of traditional stethoscope use through the incorporation of sophisticated algorithms and the availability of mobile technology that could result in early diagnosis and prevention of CVDs, especially in underprivileged areas.
Copyright (c) 2026 Shreyas Kasture, Sudhanshu Maurya, Amit Kumar Sharma, Santhosh Chitraju Gopal Varma, Kashish Mirza, Firdous Sadaf Mohammad Ismail http://creativecommons.org/licenses/by-sa/4.0
Deep learning for mental health analysis: long short-term memory approach to text-based condition classification
The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.
Copyright (c) 2026 Dinda Lestarini, Sarifah Putri Raflesia, Purwita Sari, Ghita Atalina http://creativecommons.org/licenses/by-sa/4.0
Transformer-based Hindi image description and storytelling using enhanced attention and FastText embeddings
This work presents a novel image description generation framework that combines a Transformer-based encoder-decoder architecture with a custom squeeze-and-excitation (SE) attention block integrated into an EfficientNet feature extractor. The decoder uses FastText embeddings specifically trained for Hindi and is evaluated on the Microsoft common objects in context (MS-COCO) dataset. To improve the captioning process, the model incorporates a generative pre-trained transformer (GPT) module to generate narrative descriptions based on the initial captions and applies multiple similarity metrics to assess output quality. The proposed system significantly outperforms existing methods, achieving high bilingual evaluation understudy (BLEU) scores (BLEU-1 to BLEU-4: 83.24, 73.17, 64.56, and 58.22), a consensus-based image description evaluation (CIDEr) score of 81.41, an F1 score of 90.29, and a metric for evaluation of translation with explicit ordering (METEOR) score of 81.18, indicating strong caption accuracy. Furthermore, the model achieves low error rates, with a word error rate (WER) of 15% and a character error rate (CER) of 11%. This work highlights the challenges of applying large-scale datasets like MS-COCO to resource-limited languages and demonstrates the effectiveness of integrating FastText embeddings with transformer-based models for Hindi image captioning.
Copyright (c) 2026 Anjali Sharma, Mayank Aggarwal, Jitin Khanna http://creativecommons.org/licenses/by-sa/4.0
Hybrid machine learning for imbalanced lettuce disease classification
This study investigates a hybrid machine learning framework combining EfficientNet-B3 feature extraction with classical classifiers for lettuce disease classification under conditions of extreme class imbalance. The system utilizes EfficientNet-B3 to extract high-dimensional feature embeddings from 2,337 images, which are subsequently classified using support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN). Although the proposed SVM-based model achieves a high overall accuracy of 94.01%, experimental results reveal a substantial performance discrepancy compared to the macro F1-score of 37.94%. This critical gap indicates that while the model successfully identifies the majority classes, it fails to recognize rare disease categories with limited samples. Theoretical analysis suggests that while SVM handles high-dimensional feature spaces more effectively than RF and KNN, the deep features extracted are biased toward majority class characteristics. These findings highlight the severe limitations of accuracy-centric evaluation in agricultural diagnostics and demonstrate that deep feature extraction alone is insufficient to guarantee robust detection for minority pathologies. The study concludes that relying on aggregate accuracy can mask diagnostic failures, emphasizing the urgent need for per-class performance analysis and data-level mitigation strategies in future research.
Copyright (c) 2026 Fazlur Ihzanurahman, Wayan Firdaus Mahmudy http://creativecommons.org/licenses/by-sa/4.0
A sequential attention-enhanced deep learning framework for robust potato leaf disease diagnosis under real field conditions
Diagnosing potato leaf diseases from images collected in real-life field settings is challenging, mainly because of uneven lighting, complex backgrounds, and disease symptoms that are often subtle or visually inconsistent. In this study, a deep learning-based framework was developed to support potato leaf disease diagnosis, with particular attention given to improving generalization and interpretation. Several convolutional neural network (CNN) architectures were first examined under the same experimental conditions, and ResNeXt-50 showed the most stable overall performance. The model was then extended by applying efficient channel attention (ECA), followed by spatial attention adapted from the convolutional block attention module (CBAM). Test results indicate that this sequential attention design performs better than the baseline model as well as variants using only a single attention mechanism. Additional evaluation using 300 real-field images collected under different field conditions suggests improved robustness, while visualization results from gradient weighted class activation mapping (Grad-CAM) show clearer focus on lesion-related regions. Overall, the findings suggest that combining channel wise and spatial attention can improve both prediction reliability and interpretability, making the approach suitable for practical agricultural use.
Copyright (c) 2026 Watcharkorn Yoochomboon, Nithizethe Mhuadthongon, Piyaporn Krachodnok http://creativecommons.org/licenses/by-sa/4.0
IndoBERT for educational assessment: comparative analysis of transformer models in Indonesian question generation
This study asks whether a monolingual encoder can realistically outperform multilingual and larger transformer models for Indonesian automatic question generation (AQG) when all models share the same training budget. We compare Indonesian bidirectional encoder representations from transformers (IndoBERT), multilingual BERT (mBERT), and BERT-large using a single fine-tuning pipeline with answer highlighting, applied to an Indonesian version of TyDiQA-GoldP and a 20,000 translated subset of SQuAD 2.0. We evaluate model quality using bilingual evaluation understudy score n-gram 4 (BLEU-4), metric for evaluation of translation with explicit ordering (METEOR), and ROUGE-Lincoln (ROUGE-L). IndoBERT consistently achieves the best scores on both datasets (e.g., BLEU-4 of 19.69 on TyDiQA-GoldP and 3.79 on the SQuAD 2.0 subset) while requiring less computation than mBERT and BERT-large. Our results show that language-specific pretraining gives clear advantages for Indonesian AQG, yielding higher accuracy at lower computational cost than multilingual or larger encoders. The work closes a gap in Indonesian AQG benchmarking by providing the first head-to-head comparison of IndoBERT, mBERT, and BERT-large under a shared fine-tuning and evaluation protocol. For educational assessment, the findings offer a practical recipe for building deployable AQG systems on mid-range GPUs that generate higher quality questions without prohibitive training or inference budgets.
Copyright (c) 2026 Handaru Jati, Yuniar Indrihapsari, Pradana Setialana, Danang Wijaya, Satya Adhiyaksa Ardy, Dhista Dwi Nur Ardiansyah http://creativecommons.org/licenses/by-sa/4.0
Semantic-syntactic graph network for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that identifies sentiment polarities toward specific aspects within a sentence. While conventional models have achieved progress, they often neglect to jointly consider both semantic context and syntactic structure, limiting performance in complex linguistic scenarios. Nevertheless, most existing graph convolutional network (GCN)-based approaches have recently focused on either semantic or syntactic information individually, leading to suboptimal sentiment classification accuracy. Hence, this work aims to design an effective ABSA model that simultaneously captures both semantic relationships and syntactic dependencies for enhanced aspect-level sentiment analysis. For solving issues of GCN-based approaches, this work proposed a model called sentiment semantic syntactic network (SentSemSynNet), which constructs a unified graph by integrating semantic and syntactic features and applies graph neural networks to learn rich, aspect-specific representations. The model was evaluated on the SemEval2014 restaurant and laptop datasets. It achieved 88.25% accuracy and 82.95% macro-F-score for restaurant, and 84.52% accuracy and 80.26% macro-F-score for laptop. The model’s unique integration of both semantic and syntactic importance through a unified graph structure improved sentiment detection accuracy.
Copyright (c) 2026 Rekha Bdurga Harish, Neelambike Siddalingaiah http://creativecommons.org/licenses/by-sa/4.0
A hybrid model for enhanced aspect-based sentiment analysis using large language models
Aspect-based sentiment analysis (ABSA) is a crucial task within natural language processing (NLP), enabling fine-grained opinion mining by identifying sentiments associated with specific aspects of a product or service. While transformer-based models like bidirectional encoder representations from transformers (BERT) have improved sentiment classification, they still struggle with limited contextual adaptability, especially in customer reviews containing complex expressions. Most existing approaches rely heavily on benchmark datasets such as semantic evaluation (SemEval) and multi-aspect multi-sentiment (MAMS), which do not fully capture the diversity of real-world review scenarios. Hence, this research addresses these limitations by proposing a novel hybrid model, called as hybrid-BERT (H-BERT), that integrates span-aware BERT (SpanBERT) with bidirectional long short-term memory (BiLSTM), conditional random field (CRF), and large language models (LLMs). The objective is to enhance aspect extraction and sentiment classification performance using both annotated and synthetic data. The methodology includes preprocessing, hybrid model training, and evaluation using the SemEval 2014 dataset. Experimental results show that H-BERT achieved 90.58% accuracy and 90.56% F-score in the laptop domain and 91.21% accuracy with a 92.03% F-score in the restaurant domain. These results outperform existing models, confirming H-BERT’s robustness and effectiveness. In conclusion, H-BERT improves sentiment understanding in customer reviews.
Copyright (c) 2026 Mohammed Ziaulla, Arun Biradar http://creativecommons.org/licenses/by-sa/4.0
Revolutionising essay writing: a systematic review of Google Gemini
The emergence of generative artificial intelligence (GenAI) has significantly impacted the education sector in essay writing. This study focuses on Google Gemini as a viable alternative to ChatGPT. A systematic literature review (SLR) was conducted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) method to investigate existing research on Gemini and its application in essay writing. The review examined articles published from 2022 to August 2024. It focuses on the years, research design, population, and learning theories involved in the use of Gemini. Several stages of the PRISMA method were implemented to filter and collect relevant information, resulting in a comprehensive analysis of articles discussing Gemini’s role in essay writing across various publication platforms. The findings highlight the functions of Gemini in essay writing. It provides valuable insights for researchers and practitioners in language teaching and learning. This research aims to enhance understanding and promote the effective use of Google Gemini in education.
Copyright (c) 2026 Shirley Ling Jen, Abdul Rahim Salam, Hamidah Mat, Wong Wei Lun http://creativecommons.org/licenses/by-sa/4.0
Sentiment-aware user-item recommendation combining weighted XGBoost and optimized similarity metrics
User-item recommendation systems play a vital role in enhancing personalized digital experiences across e-commerce and social media platforms. Traditional recommendation approaches, such as collaborative filtering (CF) and content-based filtering (CBF), often suffer from challenges like data sparsity, cold-start issues, and limited contextual understanding. Sentiment-aware recommendation systems have emerged as a promising solution by incorporating emotional insights extracted from user reviews, thereby improving recommendation accuracy and personalization. This study proposes a novel sentiment-aware user-item recommendation system (SAUIRS) framework that integrates optimized term frequency inverse document frequency (O-TF-IDF), parameterized bidirectional encoder representations from transformers (P-BERT), weighted extreme gradient boosting (WXGBoost), and an optimized similarity metrics model. The optimized TF-IDF enhances feature selection, reducing dimensionality while preserving relevant textual information. P-BERT, a fine-tuned BERT model, improves sentiment classification accuracy by leveraging deep contextual embeddings. WXGBoost further refined sentiment predictions, addressing class imbalance and enhancing model robustness. The extracted sentiment information is incorporated into an optimized similarity metrics model to improve recommendation precision by aligning user preferences with sentiment-driven insights. Extensive experiments conducted on Amazon benchmark datasets demonstrate the superior performance in terms of accuracy, root mean square error (RMSE), and mean absolute error (MAE) of the proposed framework compared to state-of-the-art recommendation models.
Copyright (c) 2026 Snehal Bhogan, Vijay S. Rajpurohit, Sanjeev S. Sannakki http://creativecommons.org/licenses/by-sa/4.0
Research themes and trends in the field of blockchain engineering: a topic modelling analysis
This study employed topic modeling to identify key research themes in blockchain engineering and examined how these themes have evolved over time. The dataset of collected abstracts from 3,665 relevant papers of Web of Science (WoS) core collection for the period from 2019 to 2024 was analyzed with latent Dirichlet allocation (LDA) approach. Based on the results of the topic development trends analysis, the topics collectively highlight the evolving landscape of technologies such as blockchain, smart contracts, the internet of things (IoT), and edge computing, focusing on their integration and impact across sectors like finance, healthcare, supply chain management, and energy systems. It offers valuable insights and implications for research related to blockchain engineering. Latent semantic indexing (LSI) provided further understanding by highlighting strong connections between specific topics, such as energy trading, supply chains, and medical applications. A comparison of LDA and LSI topics revealed overlapping themes, which supports the reliability of the topic structure identified by LDA.
Copyright (c) 2026 Dinara Zhaisanova, Madina Mansurova http://creativecommons.org/licenses/by-sa/4.0
A comparative study of Arabic morphological analyzers
The field of Arabic natural language processing (NLP) has witnessed significant advancements, driven by the development of various morphological analyzers. This paper compares several major Arabic morphological analyzers and examines their ability to handle word ambiguities, process dialects, operate efficiently, and support downstream NLP tasks. By reviewing previous studies, we identify key gaps, including the limited resources for dialects, the shortage of annotated corpora, and challenges related to system scalability. The study also highlights future directions, such as building larger and more diverse corpora, adapting neural models for dialects, and developing analyzers that are more interpretable and trustworthy. Overall, this comparative overview aims to provide a clearer understanding of the current state of Arabic morphological analyzers, synthesize existing research, and offer practical recommendations for future work in this area.
Copyright (c) 2026 OmarSaadiyeh, Alaaeddine Ramadan, Chamseddine Zaki, Mohamad Hajjar, Gilles Bernard http://creativecommons.org/licenses/by-sa/4.0
TunDC: a public benchmark dataset for sentiment analysis and language modeling in the Tunisian dialect
The development of natural language processing (NLP) applications has increasingly focused on dialectal variations of languages. The Tunisian dialect (TD), a widely spoken variant of Arabic, poses unique linguistic challenges due to its lack of standardized writing conventions and influences from multiple languages, including French, Italian, Turkish, and Berber. In this work, we introduce TunDC, a dataset of 20,044 labeled comments designed to advance NLP research on the TD. The dataset covers diverse linguistic forms (Arabic, Latin, and mixed scripts), and each comment was manually annotated for positive or negative sentiment by native speakers, achieving high inter-annotator agreement. To evaluate its effectiveness, we fine-tuned various models on TunDC. The bert-base-arabic-TunDC-mixed model achieved an accuracy of 0.84 and a macro-averaged F1-score of 0.83, demonstrating strong generalization across sentiment categories and writing systems. A stratified data-splitting strategy considering both sentiment and script type further improved accuracy by approximately 8% compared to standard splits. As a publicly available resource, TunDC contributes to the computational linguistics community, fostering advancements in language modeling and applications tailored to the TD.
Copyright (c) 2026 Ahmed Khalil Boulahia, Mourad Mars http://creativecommons.org/licenses/by-sa/4.0
Evaluating document chunking approaches for retrieval augmented generation in editorial content
Retrieval-augmented generation (RAG) systems promise grounded answers from large language models (LLMs), yet performance depends critically on how source documents are segmented before indexing. This study investigates how pre-index chunking strategies affect both retrieval accuracy and answer quality in domain-specific scenarios. We curated a corpus on software-as-a-service (SaaS) editorial content and constructed a high-quality evaluation dataset containing 2,419 question-answer (QA) pairs generated through automated prompting and quality control. We compared four chunking approaches, including fixed-size, structure-aware recursive, semantic, and LLM-based methods. Our evaluation protocol assessed retrieval through document localization, semantic similarity, and context relevance, while generation quality was evaluated using chain-of-thought (CoT) criteria driven by judgments from LLMs. Results demonstrate that recursive chunking consistently outperforms other approaches across all metrics. Smaller chunks improve document localization, while moderately larger chunks enhance semantic alignment and generation scores. LLM based chunking variants show competitive performance but do not exceed top recursive configurations on the dataset. These findings indicate that preserving document structure through recursive chunking is beneficial for practical RAG implementations, providing actionable guidance for chunk size selection while highlighting token-budget constraints in current long context models.
Copyright (c) 2026 Erwann Lavarec, Yu Du http://creativecommons.org/licenses/by-sa/4.0
Summarization of IndoSum dataset using enhanced TextRank with weighted word embedding
This study evaluates the effectiveness of combining the TextRank method with word embedding on the Indonesian text summarization (IndoSum) dataset. Two experimental scenarios were applied: unweighted and weighted. The unweighted scenario incorporates word embedding, such as Word2Vec, FastText, and Indonesian bidirectional encoder representations from transformers (IndoBERT), into the TextRank framework. The weighted scenario further augments the term frequency-inverse document frequency (TF-IDF) weighting to the word embedding in the initial scenario. Our results on the effectiveness of enhanced TextRank using word embedding on IndoSum data are consistent with those reported in previous work on Liputan6 data. Both scenarios can significantly improve the effectiveness of TextRank summarization. Then, the weighted scenario showed performance improvement in most summarization systems compared to the unweighted scenario, with an average performance increase of 5.55% in recall-oriented understudy for gisting evaluation (ROUGE)-1 and 9.95% in ROUGE-2. This result confirms the robustness of the enhanced TextRank with weighted word embedding on the IndoSum data. Lastly, our study also highlights the importance of using domain-specific training data to optimize summarization performance.
Copyright (c) 2026 Evi Yulianti, Piawai Said Umbara http://creativecommons.org/licenses/by-sa/4.0
Hybrid recommender for computer aided design software
Choosing the right computer-aided design (CAD) software is a complex task due to the wide variety of available options. Using user opinions and reviews may not be sufficient, which highlighting the need for a decision support system. In this paper, we develop and evaluate a hybrid recommendation program (HRP) for CAD software written in the Python programming language, combining collaborative filtering (CF) and content-based filtering (CBF) using k-nearest neighbors (KNN). CF uses user ratings to identify similar users, while CBF compares software characteristics to find similar options. In our hybrid approach, we integrate both filtering techniques with KNN to generate personalized recommendations. It will improve the relevance of software options, help users make choices (students, educators, and professionals), and encourage the adoption of tools most appropriate for every profile. We used the analytic hierarchy process (AHP) method to choose the criteria for our recommendation program. We tested the HRP on a simulated CAD dataset and found that it made recommendations much more accurately than using CF and CBF separately. Evaluation metrics like precision (0.81), recall (0.95), and F1-score (0.87) show that this hybrid approach works, making it a more reliable tool for helping people choose CAD software.
Copyright (c) 2026 Younes Zidani, Younes Zahrou, Salah Nissabouri, Moulay El Houssine Ech-Chhibat, Khalifa Mansouri http://creativecommons.org/licenses/by-sa/4.0
Unimodal and multimodal techniques for depression diagnosis: a comprehensive survey
Depression is a common and major mental health condition that affects individuals across all age groups and any backgrounds, severely reducing their physical, emotional, and cognitive functioning. It goes beyond typical mood swings and requires a timely and accurate diagnosis to prevent severe consequences such as suicidal tendencies, self-harm, and long-term mental decline. The improving performance of deep learning and machine learning techniques has significantly enhanced the speed and accuracy of depression diagnosis using both unimodal and multimodal features. This comprehensive study gives a complete overview of the unimodal and multimodal methods used to diagnose depression in its early stages. Additionally, this survey summarizes the dataset, methods, and limitations of previous work presented in the domain of depression diagnosis and serves as a suitable reference for future analysis.
Copyright (c) 2026 Swathy Jayasree, Yashawini Sridhar http://creativecommons.org/licenses/by-sa/4.0
Usability analysis of the individual creativity assessment tool using the adjusted system usability scale
Creativity is a critical element in the learning environment, which leads to innovation and research advancement in higher education. However, assessing creativity is challenging due to its diverse nature and the lack of standardized tools. The existing assessment tools often overlook the critical role of organizational culture in shaping individual creativity within academic settings. To address this gap, the individual creativity assessment tool (i-CAT) was developed based on the framework of organizational culture to assess its contribution to creativity among Malaysian academicians. This study aimed to i) assess the usability of i-CAT and ii) determine the significant effect of demographic factors on its usability assessment. A quantitative methodology, utilizing expert sampling and the system usability scale (SUS), was employed as the primary evaluation method. 20 experts with relevant professional and academic experience were selected for the validation. The results showed excellent usability, with 95% of experts rating the information system as functionally acceptable. A one way analysis of variance (ANOVA) found no significant difference in usability based on profession or education levels, but a significant difference was observed for experience levels. These findings confirm that i-CAT is a functional, user-friendly, and culturally relevant tool for creativity assessment within Malaysia’s higher education institutions.
Copyright (c) 2026 Mohamad Rahimi Mohamad Rosman, Noor Arina Md Arifin, Siti Aishah Mokhtar, Nur Ainatul Mardiah Mat Nawi, Huda Hamidon, Salliza Md Radzi http://creativecommons.org/licenses/by-sa/4.0
AI-induced fatigue among students in higher education: a latent profile analysis
The integration of artificial intelligence (AI) tools in education offers significant benefits but also introduces challenges, including AI-induced fatigue among students. This study aimed to classify students’ experiences with AI tools using latent profile analysis (LPA). A quantitative cross sectional design and referral approach were used to collect survey data from 388 college students who actively used AI tools for academic purposes from November to December 2024. The survey measured AI usage intensity, AI literacy, self-efficacy, perceived usefulness, cognitive load, technostress, sleep quality, general fatigue levels, and attitude toward AI. Descriptive results indicated moderate levels of AI usage intensity, AI literacy, perceived usefulness, cognitive load, sleep quality, and general fatigue, with technostress and attitude toward AI also at moderate levels. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and profile size adequacy, and expert review supported the retained six-profile structure. The LPA identified six interpretable user groups: competent but sleep-deprived users, overwhelmed and high-strain users, stable moderate users, strained moderate users, high intensity strained users, and low-strain selective users. The findings show differences in patterns of competence, strain, fatigue, and sleep outcomes associated with AI tool use, which supports the development of profile specific strategies to manage technostress, cognitive load, fatigue, and sleep disruption among higher education students.
Copyright (c) 2026 Dynah D. Soriano, Jordan L. Salenga, John Paul P. Miranda, Juvy C. Grume, Emerson Q. Fernando, Amado B. Martinez, Jr., Raymond A. Cabrera, Jaymark A. Yambao http://creativecommons.org/licenses/by-sa/4.0
Enhancing digital asset ownership through decentralized non fungible token applications
The rapid expansion of the digital ecosystem has introduced pressing challenges surrounding identity, authenticity, trust, and transparency. The ease with which digital content can be duplicated often undermines creators, whose works are distributed without consent or fair compensation. Blockchain technology offers a transformative solution through its decentralized, transparent, and tamper-resistant structure. Among its innovations, non-fungible tokens (NFTs) provide a mechanism to verify the authenticity and ownership of unique digital assets. This study explores the transformative potential of NFTs in strengthening digital ownership and authenticity while identifying critical challenges such as market concentration, interoperability limitations, and security vulnerabilities within public NFT platforms. Employing the extreme programming (XP) methodology, this research proposes a secure framework for NFT creation outside public marketplaces to enhance the protection of smart contracts and user accounts. The findings demonstrate that this approach grants users’ greater control, minimizes exposure to platform-level risks, and promotes trust in decentralized asset management. Overall, this study underscores NFTs’ pivotal role in reshaping digital ownership models and highlights the need for continued innovation to ensure security, transparency, and equitable value distribution in the evolving digital economy.
Copyright (c) 2026 Yusuf Kurnia, Rino, Edy, Junaedi, Aditiya Hermawan, Kevin http://creativecommons.org/licenses/by-sa/4.0
Development of rough set based machine learning approach to screen breast cancer
One of the major causes of death for women is breast cancer. A substantial number of women diagnosed with breast cancer die due to inaccuracies in diagnosis and delays in treatment. Cancer prediction must be accurate in order to improve treatment quality and patient survival rates. This study evaluates logistic regression (LR), decision tree algorithm (DTA), and adaptive boosting (AdaBoost) (AB ensemble learning algorithm) in conjunction with rough set theory (RST) to enhance breast cancer classification using the Wisconsin diagnosis breast cancer dataset (WDBC). By employing rough set approximations, including the upper and lower bounds of features, this study introduces a novel rough AdaBoost (Rough AB) algorithm to improve classification accuracy. Various performance indices are compared across algorithms. The proposed Rough AB algorithm demonstrated superior performance, particularly in prediction accuracy for both benign and malignant cases. It incorporates roughness to determine the starting node of the decision stump, offering a significant improvement in ensemble learning techniques for medical diagnostics. It gives practical implications for clinical decision-making, potentially enabling more reliable and timely breast cancer diagnoses, which can significantly impact patient outcomes. The proposed method leverages rough set approximations to refine feature selection and improve prediction accuracy. Also, it positions RST as an explainable artificial intelligence (XAI) technique, highlighting its interpretability, ethical transparency, and potential integration with deep learning for clinical deployment.
Copyright (c) 2026 Sangeetha Sivakumar, Shakeela Sathish, Debabrata Datta http://creativecommons.org/licenses/by-sa/4.0
Improvised mask faster recurrent convolutional neural network for breast cancer classification using histopathology images
Despite the prevalence of this disease, the existing method for obtaining an exact breast cancer diagnosis would need a lot of time and labor. It needs a qualified pathologist to manually process and review histopathological images to distinguish the characteristics that characterize different cancer severity levels. Building a model for automatically detecting, segmenting, and classifying breast lesions using histopathological images seems to be the goal of this work. Various deep learning methods have been used in computational pathology for the diagnosis of cancer. Improved faster recurrent convolutional neural network (IMFRCNN) is a supervised learning system with proposed for recognizing small items like mitotic and non mitotic nuclei. To protect small items from vanishing in the deep layers, this system uses expanded layers in the spine. To close image and the things gap size includes, this approach uses expanded layers. The region proposal network has been created for precise tiny object identification. Researchers examined time for training and testing time for various techniques for identifying objects. The total accuracy of benign/malignant categorization in proposed system reaches 96.5%. The proposed technique offers a thorough and non-invasive method for identifying and categorizes an area of abnormal breast tissue.
Copyright (c) 2026 Pattan M. D. Ali Khan, Xavier Arputha Rathina http://creativecommons.org/licenses/by-sa/4.0