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
A bibliometric analysis of feature selection techniques: trends, innovations, and future directions
Feature selection techniques have become increasingly important in addressing the challenges of high dimensionality in machine learning and other artificial intelligence domains. In this study, we present a comprehensive bibliometric analysis of research on feature selection techniques over the past decade, focusing on mapping the intellectual structure, identifying emerging trends, and highlighting productive collaborations in the field. Using merged data from Scopus and Web of Science databases, we collected and analyzed 2,079 relevant documents published between 2014 and 2024, applying citation analysis, co-authorship networks, and keyword co-occurrence mapping. Our findings reveal that feature selection methodologies, including supervised, unsupervised, and hybrid approaches across filter, wrapper, and embedded techniques, have been widely applied across various domains. The authors who have most contributed to the development of these methods are primarily affiliated with institutions in China, India, and the USA. The insights provided by this analysis offer researchers and practitioners a valuable foundation for guiding future research directions in feature selection.
Copyright (c) 2025 Oumaima Semmar, Wissal El Habti, Donalson Wilson, Abdellah Azmani http://creativecommons.org/licenses/by-sa/4.0
A review of driver distraction detection while driving based on convolutional neural networks
Driver distraction represents a major cause of traffic accidents, posing a serious threat to human life. In this review, we present the latest research findings of driver distraction detection based on convolutional neural networks (CNNs). In general, the analysis of driver behavior while driving is represented by either detecting driver drowsiness or attention diversion from driving by other activities, all of which fall under the definition of driver distraction. Facial features are often the basis for detecting driver drowsiness. In most papers, it is typically done by eye blinking, yawning, and head movement. As for the driver attention diversion, it is through the position of the hand and face. It involves many activities, text messages, making phone calls, adjusting the radio, consuming beverages, reaching for objects behind the driver, applying makeup, interacting with passengers, and other similar distractions. However, suggesting new methodologies in driver distraction detection and choosing appropriate CNN-based techniques is a big challenge given the wide variety experiments and studies in this field. Therefore, previous papers should be revisited to produce new methods by taking advantage of the techniques used. As a result, this paper reviews research approaches and reveals the effectiveness of CNN in detecting driver distraction. Finally, the article lists techniques that can be used as benchmarks in this context.
Copyright (c) 2025 Ghady Alhamad, Mohamad-Bassam Kurdy http://creativecommons.org/licenses/by-sa/4.0
A review on long short-term memory combination development
Long short-term memory (LSTM) has continued to develop since it was proposed in 1997. LSTM has optimized solutions to various problems. The LSTM cell, architecture, and memory model have been reviewed. A review of LSTM implementation has been carried out in various problem domains. There are combinations of LSTM with other methods to optimize solutions. However, there is no review on the development of LSTM combination (LC). This research reviews the development of the LC model on nine research questions, namely: development framework, data, preprocessing, learning process, tasks, optimization and evaluation, domain problems, trends, and challenges. The results show that the LC model is increasingly widespread in solving problems. The LC model has completed 26 types of tasks. Prediction, detection, forecasting, classification, and recognition are the most frequently performed tasks. LC model development trends show that LSTM is increasingly collaborative with other methods on a wider scope. The challenges identified include research areas, data, model developments, the area of implementation, performance, and efficiency.
Copyright (c) 2025 Ahmad Riyadi, Nur Rokhman, Lukman Heryawan http://creativecommons.org/licenses/by-sa/4.0
The contribution of artificial intelligence in people with autism: a systematic literature review
Autism is a disorder that poses significant challenges in various areas such as health, education, social interaction, and how the world perceives them. The implementation of artificial intelligence in daily life and different fields offers an innovative approach to addressing these challenges, facilitating early detection, support in learning, and social interaction for individuals with this condition. The systematic literature review focuses on studying 50 out of 144 articles obtained from various databases such as EBSCO Host, IEEE Xplore, ScienceDirect, Scopus, ProQuest, and Web of Science. These articles were systematically organized using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, providing information about machine learning as the most utilized discipline, the types of infrastructure it relies on, and the countries that are at the forefront of this topic. This review will serve as a reference for stakeholders regarding the advancements and contributions of artificial intelligence for individuals with autism.
Copyright (c) 2025 Anderson Moza-Villalobos, Michael Cabanillas-Carbonell http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence for individuals with disabilities in higher education institutions: a systematic review
With the growing integration of artificial intelligence (AI) in education, its potential to support students with disabilities in higher education remains significant but underexplored. This systematic review synthesizes existing literature on AI's effectiveness, barriers, and implications for inclusive education. Using the sample, phenomenon of interest, design, evaluation, and research type (SPIDER) framework, studies published between 2013 and 2024 were identified through a systematic search in databases such as PubMed, Scopus, Embase, Cochrane Library, and Google Scholar. Eighteen studies met the inclusion criteria, focusing on higher education settings and students with disabilities. The findings emphasize AI's role in enhancing accessibility, personalizing learning experiences, and fostering inclusiveness. However, persistent challenges include technological barriers, ethical concerns, and insufficient training. While AI holds transformative potential to support students with disabilities in higher education, addressing infrastructure gaps and ethical and training deficiencies is crucial for sustainable implementation and equitable learning environments.
Copyright (c) 2025 Finita Glory Roy, Friggita Johnson http://creativecommons.org/licenses/by-sa/4.0
Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach
Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.
Copyright (c) 2025 Halima Drissi Touzani, Sanaa Faquir, Ali Yahyaouy http://creativecommons.org/licenses/by-sa/4.0
AI-driven creativity in software development using services information
In competitive markets, organizations that do not innovate risk becoming outdated. At it is core, innovation depends on creativity, with creative outcomes typically characterized by novelty, usefulness, and surprisingness. In software development, creative solutions are often generated through brainstorming sessions. However, brainstorming is constrained by the knowledge of the participant and facilitator. In this paper, we present an artificial intelligence (AI)-based method to generate creative solutions in software by leveraging service information. The presented method includes two phases, where the first phase involves constructing creativity resources through text clustering (TF-IDF, K-medoid) and capability extraction (dependency parsing), and the second phase employs semantic similarity along with structured creativity techniques (exploration, transformation, and combination) to generate creative solutions in software. Besides, experimental results showed that the AI-based method achieved comparable creativity scores to traditional brainstorming with more limited time, demonstrating fast and strong performance in generating novel and useful solutions, although participants perceived some results as less surprising due to overlap with brainstorming outcomes.
Copyright (c) 2025 Faisal Fahmi, Feng-Jian Wang, Hema Subramaniam http://creativecommons.org/licenses/by-sa/4.0
Enhancing academic conferences with AI: defining the role of the human AI editor
Academic conferences serve as key platforms for knowledge exchange, yet they face challenges in managing large volumes of content efficiently while maintaining academic rigor. To address these challenges, this study introduces and evaluates the "AI editor": a novel human expert role who, using tools like ChatGPT, supervises, refines, and structures artificial intelligence (AI)-generated content in real time. Through a mixed-methods approach, we examine the role of AI in enhancing content creation and engagement. This approach included the experimental deployment of the AI editor in three sustainability-focused European academic conferences (in Spain and UK) and formative workshops with 127 university students from the same countries. While AI-assisted tools improve efficiency, concerns persist regarding traceability, reliability, and ethical oversight. Our findings indicate that AI by itself cannot guarantee scholarly integrity; continuous human oversight is indispensable. The AI editor ensures coherence, quality control, and compliance with academic standards, addressing a critical gap in AI adoption within research environments. This study contributes to the discourse on responsible AI use in academia by proposing a structured framework for its integration into conferences, balancing automation with human oversight. Moreover, it highlights the growing need for digital intelligence that enables researchers to interact ethically and effectively with AI and other digital technologies, fostering responsible and informed academic innovation.
Copyright (c) 2025 Esteban Galan-Cubillo, Emilio Saez-Soro http://creativecommons.org/licenses/by-sa/4.0
Explainable artificial intelligence with anchors method for breast cancer treatment recommendation
In the search of precision medicine for breast cancer, the integration of artificial intelligence (AI) offers unprecedented opportunities to improve diagnosis, prognosis, and treatment strategies. This paper discovers the prospective of explainable artificial intelligence (XAI) to demystify the black-box landscape of AI, fostering both transparency and trust. We introduce an XAI-based approach, anchored by the anchors explanation method, to provide interpretable predictions for breast cancer treatment. Our results demonstrate that while anchors improve the interpretability of model predictions, the precision and coverage of these explanations vary, highlighting the challenges of achieving high-fidelity explanations in complex clinical scenarios. Our findings underscore the importance of balancing the trade-off between model complexity and explainability. They advocate for the iterative development of AI systems with iterative feedback loops from clinicians to align the model's logic with clinical reasoning. We propose a framework for the clinical deployment of XAI in breast cancer. Ultimately, XAI, equipped with techniques like Anchors, holds the promise of enhancing precision medicine by making AI-assisted decisions more transparent and trustworthy, empowering clinicians and enabling patients to engage in informed discussions about their treatment options. However, anchors lag in the accuracy of rules and remains a challenge to the AI developers.
Copyright (c) 2025 Reena Lokare, Mansing Rathod, Jyoti Sunil More http://creativecommons.org/licenses/by-sa/4.0
Inexpensive human audiometric system using Raspberry Pi and artificial intelligence
The most common and widespread disease in Iraq is hearing impairment for children and newborns. Also, in cities, people are exposed to high levels of noise, loud sounds at work, like factories, and machinery noise. In this paper, a system was designed and implemented to measure the level of hearing in the human ear, in order to reduce the cost of these devices. This system uses Raspberry Pi 3 microcontrollers, which are considered cheap and have high capabilities in open-source programming. Their abundant availability will lead to the provision of these systems in homes, health centers, and hospitals. In this proposed algorithm, two sine waves are generated by the microcontroller with different frequencies. It is transmitted by the MP3 audio transmission cable through the analog-to-digital (ADC) port. These audio signals are generated at a frequency of (0.5 to 12 kHz), these frequencies are the ones that humans can hear, and they can be represented by pulse width modulator (PWM) technology (x=255 samples). Convolutional neural network (CNN) is trained on the dataset acquired through deep learning algorithms.
Copyright (c) 2025 Abdulrafa Hussain Maray, Muataz Akram Hassan, Taha Hussein Marai Al-Hassan http://creativecommons.org/licenses/by-sa/4.0
Automated legal content management system for multi-country integration
This paper presents an automated legal content management system (CMS) designed for multi-country integration, addressing the complex challenges of legal document migration across more than 180 countries while ensuring regulatory compliance and accessibility standards. The system implements a hierarchical four-level architecture, migrating more than 2,740 legal documents with zero data loss incidents through fault-tolerant processing pipelines. The automated portable document format (PDF) migration component demonstrates exceptional efficiency, processing documents 36 times faster compared to manual approaches, while article migration achieves 230 times faster processing speeds. The integrated artificial intelligence (AI)-powered accessibility enhancement system generates contextually appropriate alt text descriptions, allowing organizations to process 10,000 images annually with savings of $14,990. The complete country migration process, covering both PDF and article processing, executes in 30 seconds compared to 56 minutes for manual processing, representing a 112-fold improvement in performance. System scalability demonstrates linear performance characteristics up to more than 5,000 documents with consistent processing metrics while maintaining compliance across diverse regulatory frameworks. These quantitative improvements establish a new paradigm for automated legal content management, providing a scalable foundation for global enterprises managing multi jurisdictional legal documentation requirements.
Copyright (c) 2025 Hardik Pawar, Nidhi Prakash, Smriti Srivastava, Sneha, Shaik Mohideen Syedabdulkader, Pratiba D., Sandhya S. http://creativecommons.org/licenses/by-sa/4.0
Classification algorithm with artificial intelligence for the diagnostic process of obstructive sleep apnea
Obstructive sleep apnea (OSA) is a disease that affects millions of people worldwide, and a large proportion of them remain undiagnosed due to the high cost of polysomnography (PSG) tests. For this reason, it is crucial to develop affordable diagnostic tools to facilitate early detection of this condition. This study aims to analyze how an artificial intelligence (AI) based classification algorithm impacts the diagnostic process of OSA in Lima, Peru. The algorithm was developed following the Kanban methodology, which guaranteed an efficient and transparent follow-up during the development cycle, which is key in the medical context where software quality and traceability are fundamental. A decision tree (DT) was used for diagnosis and classification, employing a training dataset provided by the National Sleep Research Resource (NSRR), from which six relevant attributes were selected for analysis. The research results indicated that, although the improvement in clinical diagnostic accuracy was minimal at 10.81%, positive results were obtained in other aspects: diagnostic time was significantly reduced by 28.17%, and the number of tests required decreased by 24.07%.
Copyright (c) 2025 Jehil Ventura-Tecco, Jesús Fajardo-Avalos, Michael Cabanillas-Carbonell http://creativecommons.org/licenses/by-sa/4.0
Computer vision syndrome prevention: detection of expression and eye distance with monitor screens
Computer vision syndrome (CVS) is a vision-related complaint caused by computer usage. CVS can be analyzed through facial expressions detected by a camera. Expression detection is categorized into two groups: safe and dangerous. The safe category comprises happy, neutral, disgusted, sad, angry, and surprised, while the dangerous category includes sad and fearful emotions. This division is based on the similarity of CVS symptoms to facial emotion characteristics. Additionally, an additional feature is implemented to detect the distance between the screen and the user's eyes using the FaceMeshModule to prevent the user's eyes from getting too close to the screen. Both detections will provide warning notifications when a dangerous category expression is detected ≥70% every minute, and when the distance between the screen and the eyes is ≤40 cm. Notifications in this program use the Tkinter library as a graphical user interface (GUI) message box. In this research, facial expressions are detected using the CascadeClassifier for face detection and the extreme inception (Xception) as the facial expression classifier. The results of expression detection achieved an accuracy of 94%, an F1-score of 94%, precision of 95%, and recall of 94%.
Copyright (c) 2025 Aufaclav Zatu Kusuma Frisky, Elang Arkanaufa Azrien, Raden Sumiharto, Sri Hartati http://creativecommons.org/licenses/by-sa/4.0
Combining convolutional operators in unsupervised networks for kidney abnormalities
Deep learning plays a pivotal role in advancing the diagnosis of renal dysfunction, achieving performance levels comparable to those of medical experts. However, disease domain variations and model differences can impact learning quality. To address renal dysfunction, we propose dual stream convolutional (DSC) and dual-input convolutional (DIC) for unsupervised learning. The proposed network is designed to process multi scale data and employs parallel data aggregation to enhance learning capabilities, improving the reliability of the experimental results. DSC achieved training losses of 0.0069, 0.0056, 0.0042, and 0.0048 for normal, cyst, stone, and tumor datasets, respectively, while DIC achieved losses of 0.0066, 0.0063, 0.0044, and 0.0058 for the same categories. The experimental results demonstrate that our proposed models outperform state of-the-art approaches, making them well-suited for broad application in clinical research studies.
Copyright (c) 2025 Aekkarat Suksukont, Anuruk Prommakhot, Jakkree Srinonchat http://creativecommons.org/licenses/by-sa/4.0
Humans’ psychological traits classification from their spending categories using artificial intelligence algorithms
The analysis of human behavior data generated by digital technologies has gained increasing attention in recent years. Spending categories form a significant part of this digital footprint. In this study, we investigate the degree to which human expenditure records can be used to infer psychological traits from transaction data. A broad feature space was constructed, consisting of overall spending behavior, category-related spending behavior, and customer category profiles. These features were examined to identify their correlations with the Big Five personality traits. A dataset containing over 1,200 users’ transaction histories over three months was obtained from Kaggle. Personality trait labels were derived using a percentile-based classification method. Multiple AI algorithms: decision tree (DT), random forest (RF), logistic regression (LR), and support vector machine (SVM) were employed, along with a convolutional neural network (CNN) to classify personality traits. The CNN model, incorporating multi-dimensional convolutional layers and the full feature space, achieved a high accuracy of 99.03%. The outcomes of the experiment indicate the efficiency of combining behavioral features and AI models in psychological trait classification. The study also highlights ethical considerations, including privacy risks and misuse of inferred personality details.
Copyright (c) 2025 Arpitha Chikkamagaluru Narasimhe Gowda, Sunitha Madasi Ramachandra http://creativecommons.org/licenses/by-sa/4.0
EmoVibe: AI-driven multimodal emotion analysis for mental health via social media dashboards
Monitoring mental health via social media often utilizes unimodal approaches, such as sentiment analysis on text or single-staged image categorization, or executes early feature fusion. However, in real-world contexts where emotions are conveyed via text, emojis, and images, unimodal approach leads to obscured decision-making pathways and overall diminished performance. To overcome these limitations, we propose EmoVibe, a hybrid multimodal AI framework for emotive analysis. EmoVibe uses attention-based late fusion strategy, where text embeddings are generated from bidirectional encoder representations from transformers (BERT) and visual features are extracted by vision transformer. Subsequently, emoticon vectors linked to avatars are processed independently. Later, these independent data features are integrated at higher levels, enhancing interpretability and performance. In contrast to early fusion methods and integrated multimodal large language models (LLMs) like CLIP, Flamingo, GPT-4V, MentaLLaMA, and domain-adapted models like EmoBERTa, EmoVibe preserves modality-specific contexts without premature fusion. This architecture saves processing cost, allowing for clearer, unambiguous rationalization and explanations. EmoVibe outperforms unimodal baselines and early fusion models, obtaining 89.7% accuracy on GoEmotions, FER, and AffectNet, compared to BERT's 87.4% and ResNet-50's 84.2%, respectively. Furthermore, a customizable, real time, privacy-aware dashboard is created, supporting physicians and end users. This technology enables scalable and proactive intervention options and fosters user self-awareness of mental health.
Copyright (c) 2025 Deepali Vora, Aryan Sharma, Mudit Garg, Steve Francis http://creativecommons.org/licenses/by-sa/4.0
A comparative study of large language models with chain-of thought prompting for automated program repair
Automatic code repair is an important task in software development to reduce bugs efficiently. This research focuses on developing and evaluating a chain-of-thought (CoT) prompting approach to improve the ability of large language models (LLMs) in automated program repair (APR) tasks. CoT prompting is a technique that guides LLM to generate step-by-step explanations before providing the final answer, so it is expected to improve the accuracy and quality of code repair. This research uses the QuixBugs dataset to evaluate the performance of several LLM models, including DeepSeek-V3 and GPT-4o, with two prompting methods, namely standard and CoT prompting. The evaluation is based on the average number of plausible patches generated as well as the estimated token usage cost. The results show that CoT prompting improves performance in most models compared with the standard. DeepSeek-V3 recorded the highest performance with an average of 36.6 plausible patches and the lowest cost of $0.006. GPT-4o also showed competitive results with an average of 35.8 plausible patches and a cost of $0.226. These results confirm that CoT prompting is an effective technique to improve LLM reasoning ability in APR tasks.
Copyright (c) 2025 Eko Darwiyanto, Rizky Akbar Gusnaen, Rio Nurtantyana http://creativecommons.org/licenses/by-sa/4.0
Optimizing robotic motion in dynamic manufacturing environments
The field of robotics has been a trending technology over the years due to its ability to revolutionize industries. This study highlights the role of optimized robotic motion in enhancing productivity in dynamic manufacturing environments using MATLAB simulations. By modeling the arrival of manufactured parts in batches via a conveyor system governed by a negative exponential distribution in a Poisson process, MATLAB is employed to design optimal robotic trajectories for pick-and-place operations. The research carefully analyzes parameters such as arrival rates and cycle times to manage the stochastic nature of part delivery. The result reveals a significant improvement in operational efficiency, with throughput increasing by up to 20% due to real-time optimization of robotic motion. The non-linear relationship between throughput and arrival rates highlights the system’s complexity, with optimal conditions observed at specific arrival rates, such as 0.16 s for peak efficiency. MATLAB’s Polynomial Trajectory Planning tool generates smooth, continuous paths, ensuring that robotic operations dynamically adapt to changing conditions. This foundation supports future innovations in robotic system integration and automated production lines, offering a significant step forward in the application of advanced simulation tools an advanced manufacturing environment.
Copyright (c) 2025 Ganiyat Abiodun Salawu, Glen Bright http://creativecommons.org/licenses/by-sa/4.0
Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models
Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.
Copyright (c) 2025 Erda Guslinar Perdana, Arya Adhi Nugraha http://creativecommons.org/licenses/by-sa/4.0
Transformer and text augmentation for tourism aspect-based sentiment analysis
The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Copyright (c) 2025 Samuel Situmeang, Sarah Rosdiana Tambunan, Jevania, Mastawila Febryanti Simanjuntak, Sandro Sinaga http://creativecommons.org/licenses/by-sa/4.0
Low-speed scalar control of induction motor by fuzzy logic
Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.
Copyright (c) 2025 Alfonso Alejandro Sevilla-Hidalgo, Rossy Uscamaita-Quispetupa, Julio Cesar Herrera-Levano, Limberg Walter Utrilla Mego, Roger Jesus Coaquira-Castillo http://creativecommons.org/licenses/by-sa/4.0
Image segmentation using fuzzy clustering for industrial applications
This paper presents a fuzzy logic clustering algorithm oriented to image segmentation and the procedure designed to evaluate its performance by varying two parameters: the number of clusters (c) and the diffusivity parameter (m), which leads to the conclusion that an adjusted number of clusters is sufficient to recognize main elements of the image, but a more detailed reconstruction requires a higher number of clusters. Also, the diffusivity parameter influences the smoothness of the boundaries between clusters, low values generate a segmentation with more abrupt transitions and sharper contours, high values smooth the segmentation, its excessive increase may cause the elements to merge, losing details. In general, the balance between these two parameters is key to obtaining an effective segmentation. Three validation scenarios were used, the first two allowed to establish the most appropriate parameters for segmentation, regulating the clusters to a maximum of 4 and keeping the diffusivity level at 2.0, the third scenario validated the algorithm with real images of industrial cleaning products, all with noise, establishing the computational cost and processing times for images of 350×350 and 2000×3000 pixels resolution. In conclusion, applications of the algorithm are foreseen in automatic quality control and inventory control of finished products and raw materials, thanks to its high efficiency and low response time, even in scenarios involving noisy and large images.
Copyright (c) 2025 Robinson Jiménez-Moreno, Laura María Vargas Duanca, Anny Astrid Espitia-Cubillos http://creativecommons.org/licenses/by-sa/4.0
Greywater treatment system based on fuzzy logic control
Greywater from households and public facilities represents a major source of untreated wastewater, carrying high microbial loads and variable chemical composition that threaten environmental and public health. Conventional treatment systems often lack adaptive control mechanisms capable of handling the dynamic fluctuations of greywater quality. This study presents the design and validation of an intelligent greywater treatment system that integrates real–time sensing with a Sugeno fuzzy logic controller to regulate pump and solenoid valve operation. The system continuously monitors pH, total dissolved solids (TDS), dissolved oxygen (DO), and ammonia (NH3), and dynamically adjusts treatment cycles based on sensor feedback. Experimental deployment demonstrated significant improvements in effluent quality, with pH reduced from 9.04 to 8.08, TDS from 611.04 ppm to 393.96 ppm, and NH₃ from 0.52 ppm to 0.19 ppm, while DO increase from 2.52 mg/L to 6.07 mg/L. These results confirm that fuzzy logic–based control enhances system responsiveness and ensures effluent compliance under variable influent conditions. The proposed framework provides a scalable, cost-effective solution for decentralized wastewater management, advancing the development of intelligent treatment technologies for sustainable urban water systems.
Copyright (c) 2025 I Putu Eka Widya Pratama, Muhammad Rasyid Ridha, Anis Mahmuda Chafsah, Akhmad Ibnu Hija, Siti Nur Azella Zaine http://creativecommons.org/licenses/by-sa/4.0
Deep learning approaches for Braille detection and classification: comparative analysis
This study proposes a hybrid approach to Braille translation leveraging the strengths of both YOLO for object detection and multitude of classification models such as ResNet, and ResNet for accurate Braille character classification from images. Upon comparing numerous models on various performance metrics, ResNet and DenseNet outperformed other models, exhibiting high accuracy (0.9487 and 0.9647 respectively) and F1-scores (0.9481 and 0.9666) due to their deep, densely connected architectures adept at capturing intricate Braille patterns. CNNs with pooling showed balanced results, while MobileNetV2's lightweight design limited complex classification. ResNeXt's multi-path learning achieved respectable performance but lagged behind ResNet and DenseNet. In the future the results from our study could be further explored on contracted Braille recognition, be adapted to various Braille codes, and optimized for mobile devices, for real time Braille detection and translation on smartphones.
Copyright (c) 2025 Surekha Janrao, Tavion Fernandes, Ojas Golatkar, Swaraj Dusane http://creativecommons.org/licenses/by-sa/4.0
Enhancing communication and interaction in the movie industry based SparkMLlib's recommendation system
In the ever-evolving landscape of streaming platforms, recommendation systems contribute significantly to enhancing the user experience. This article examines the significance of these systems in suggesting movies, analyzing their impact on user satisfaction and platform performance. Utilizing SparkMLlib, a powerful tool for large-scale data processing, we explore various recommendation techniques, including collaborative filtering and content-based filtering. We highlight the dimension of digital communication to further enhance the accuracy of recommendations and foster greater user engagement. Our study also addresses the challenges and future opportunities related to recommendation systems, emphasizing the need for transparency and ethical algorithms. This research highlights the potential for recommendation systems to revolutionize the digital entertainment landscape and shape the future of the movie industry.
Copyright (c) 2025 Said Chakouk, Abdelkerim Zitouni, Nazif Tchagafo, Ahiod Belaid http://creativecommons.org/licenses/by-sa/4.0
Prediction of flood-affected areas based on geographic information system data using machine learning
Flood disasters have become more frequent and severe due to climate variability, posing significant threats to human lives, agriculture, and infrastructure. Effective disaster management and mitigation require accurate identification of flood-prone areas. This study develops an intelligent flood prediction system by integrating machine learning algorithms with geographic information systems (GIS) data to enhance flood risk assessment. The proposed system utilizes two machine learning models, including random forest (RF) and support vector machine (SVM), to predict flood-susceptible areas. The models are trained on historical flood data and GIS-derived features, including elevation, slope, topographic wetness index (TWI), aspect, and curvature. The dataset undergoes preprocessing, including normalization and feature selection, before being divided into training, validation, and test sets. The models are then trained and evaluated based on their predictive performance. Evaluation metrics, particularly the area under the curve (AUC), demonstrate that RF outperforms SVM in predicting flood-prone areas. RF achieves an accuracy of 82%, while SVM records a lower accuracy of 68%. The superior performance of RF is attributed to its ability to handle complex, nonlinear relationships in flood prediction. These results highlight the effectiveness of machine learning algorithms in flood susceptibility modeling and support the integration of data-driven techniques into flood and disaster risk reduction management strategies.
Copyright (c) 2025 Amrul Faruq, Lailis Syafaah, Muhammad Irfan, Shahrum Shah Abdullah, Shamsul Faisal Mohd Hussein, Fitri Yakub http://creativecommons.org/licenses/by-sa/4.0
Fine-tuning multilingual transformers for Hinglish sentiment analysis: a comparative evaluation with BiLSTM
Growing trend of code-mixing in languages, in the form of Hinglish, greatly tests the skills of conventional sentiment analysis tools. The research contributes a fine-tuned multilingual transformer model built exclusively for classifying sentiment of Hinglish customer reviews. Drawing from pre trained BERT-base-multilingual-case architecture, the model gets transformed with the process of fine-tuning the same on synthetically prepared and balanced dataset simulating positive, negative, and neutral sentiments. Sophisticated methods like focal loss for addressing the class imbalance and mixed precision training for maximization of computational effectiveness are embedded within the training process. Experimental results suggest that the proposed method significantly captures the fine-grained linguistic patterns of code-mixed text, improving sentiment classification accuracy. The results show promising potential for enhancing customer feedback analysis in e-commerce, social media monitoring, and customer support use cases, where it is crucial to comprehend the sentiment behind code-mixed reviews.
Copyright (c) 2025 Jyoti S. Verma, Jaimin N. Undavia http://creativecommons.org/licenses/by-sa/4.0
A comprehensive impression on identifying plant diseases using machine learning and deep learning methodologies
Maintaining healthy plants is essential for long-term agricultural production because agriculture is the backbone of many economies. Agricultural productivity is greatly endangered by plant diseases, which result in huge economic losses. Identifying plant diseases using traditional approaches can be quite laborious, time-consuming, and knowledge-intensive. Automated, precise, and quick diagnosis of plant diseases has been made possible by recent developments in artificial intelligence, mainly in deep learning, and machine learning. This study gives a thorough analysis of how machine learning and deep learning are currently being used to detect plant diseases. Methodologies, datasets, evaluation measures, and the inherent difficulties of the area are all examined. In order to better understand these technologies in practical agricultural contexts, this review will try to shed light on their advantages and disadvantages.
Copyright (c) 2025 Ravikanth Motupalli, John T Mesia Dhas, Swapna Neerumalla, Janjhyam Venkata Naga Ramesh, Butti Gouthami, Pavan Kumar Ande http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based feature selection for lung adenocarcinoma classification and biomarker discovery
Lung adenocarcinoma, a leading cause of cancer-related mortality, underscores the need for reliable diagnostic tools. This study proposes a robust multi-stage feature selection and classification framework for biomarker discovery, using the cancer genome atlas lung adenocarcinoma (TCGA-LUAD) as the primary dataset and GSE19188 for independent validation. The framework combines differential expression analysis (Wilcoxon rank-sum test), joint mutual information maximization (JMIM), and sparse autoencoder-based refinement to identify a compact and predictive set of five genes. These genes are involved in key lung cancer pathways, including epidermal growth factor receptor (EGFR) signaling, cell cycle regulation, and immune response, and include biomarkers such as surfactant protein A2 (SFTPA2), napsin an aspartic peptidase (NAPSA), and T-box transcription factor 4 (TBX4). The hybrid deep learning classifier achieved high accuracy (98.4%) and area under the receiver operating characteristic curve (AUC-ROC) (0.996) on TCGA-LUAD, with strong generalization on GSE19188 (accuracy: 96.7%, AUC-ROC: 0.993%). Overall, the framework offers an interpretable and effective solution for LUAD classification and biomarker identification.
Copyright (c) 2025 Sara Haddou Bouazza, Jihad Haddou Bouazza http://creativecommons.org/licenses/by-sa/4.0
Enhancing learning outcomes in smart education: a supervised machine learning predictive analytics model for course completion
Predictive analytics have become increasingly capable of delivering actionable and accessible feedback to enhance teacher performance to enhance student outcomes in higher education. This study introduces a supervised machine learning predictive model designed to forecast the duration required to complete a course in a video learning environment using a dataset of 8,665 statements from 490 students from National Higher School of Art and Design at Hassan II University in Casablanca over six academic years (2019-24). This paper analyzes decision trees (DT), random forest (RF), support vector machines (SVM), gradient boosting (GB), and linear regression (LR) techniques. The CMI-5 standard and JSON format are used to automatically transfer learning activity data from the learning management system (LMS) to the learning record store (LRS). The results indicate that DT, RF, and GB achieved 100 percent predictor accuracy.
Copyright (c) 2025 Abdellah Bakhouyi, Amine Dehbi, Lahcen Amhaimar, Yassine Tazouti, Younes Nadir, Abderrahim Khalidi http://creativecommons.org/licenses/by-sa/4.0
Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning
Ensuring food security and meeting the economic needs of farmers and nations depend heavily on detecting and preventing crop yield losses. Early detection of tar spot caused by Phyllachora maydis is crucial to implementing efficient mitigation actions in the earliest stages of infestation. Currently, visual methods are used for detection, which require extensive training and experience from the operator. However, remote sensing techniques can be used to detect tar spot infestation through the selection of wavelengths present in the maize plant spectral signature. This research proposes using machine learning techniques and logistic regression to determine the first stage of tar spot infestation. The results show that the logistic regression model is the most suitable for detecting this first stage, and the K-Nearest Neighbors Classification and Random Forest Classification algorithms generate the best classification results. This approach can significantly reduce costs in terms of time, labor, and subjective analysis.
Copyright (c) 2025 Claudia Nohemy Montoya-Estrada, Oscar Cardona-Morales, Oscar López-Naranjo, Freddy Eliseo Hernandez-Jorge, Yeison Alberto Garcés-Gómez http://creativecommons.org/licenses/by-sa/4.0
A two-step intelligent framework for gene expression-based cancer diagnosis
DNA microarray technology has advanced cancer diagnosis by enabling large-scale gene expression analysis, yet challenges remain in selecting relevant genes and achieving accurate classification. This study introduces two novel methods: the three-stage gene selection (3SGS) method and the statistics classifier (SC). By eliminating redundant, noisy, and less informative genes, the 3SGS method effectively lowers the dimensionality of gene expression data, while the SC classifier uses statistical measures of gene expression to classify samples with high accuracy and speed. Evaluated on leukemia, prostate cancer, and colon cancer datasets, the 3SGS method effectively identified minimal yet informative gene subsets, achieving 100% accuracy for leukemia, 99.3% for prostate cancer, and 97% for colon cancer. The SC classifier consistently outperformed traditional models in both accuracy and computational efficiency, completing predictions in under 2 seconds per dataset. Compared to conventional classifiers, it requires no parameter tuning and performs reliably even with small gene sets. While promising, future work should address multiclass classification and clinical validation to broaden the framework’s applicability. Together, these methods offer a precise and rapid cancer classification framework, supporting early diagnosis and personalized treatment strategies across diverse cancer types.
Copyright (c) 2025 Sara Haddou Bouazza, Jihad Haddou bouazza http://creativecommons.org/licenses/by-sa/4.0
Advancements in latent fingerprint recognition: a comprehensive review of techniques and applications
The identification of individuals has been in greater demand, whether it’s for criminal investigation, law enforcement, or the basic attendance marking system. Fingerprints are one of the most reliable and dependable methods for biometric identification systems; as such, they are crafted in the womb. Latent fingerprints refer to inadvertent impressions that are left behind at crime scenes and are of utmost importance in the field of forensic investigation and verification of the authenticity of an individual. However, because these impressions are unintentional, the quality of the prints uplifted is often poorer. To enhance the overall accuracy of fingerprint recognition, it is required to develop approaches that enhance the accuracy and reliability of existing techniques. Therefore, this paper provides a detailed analysis of the existing techniques for the reconstruction, enhancement, and matching of latent fingerprints.
Copyright (c) 2025 Nandita Manchanda, Sanjay Singla, Gopal Rathinam http://creativecommons.org/licenses/by-sa/4.0
Enhancing credit card fraud detection with synthetic minority over-sampling technique-integrated extreme learning machine
Many works in cybersecurity detection suffer from low accuracy rates, particularly in real-world applications, where imbalanced datasets and evolving fraud strategies pose significant hurdles. This study introduces an optimized extreme learning machine (ELM) algorithm to address these challenges by dynamically adjusting hidden nodes ranging from 10 to 100 with an increment step of 10 and integrating two activation functions. The proposed method utilizes the synthetic minority over-sampling technique (SMOTE) to handle class imbalance effectively and incorporates a comprehensive evaluation using descriptive statistics, visualization, and significance testing. The proposed ELM-SMOTE method achieves the highest results including an accuracy of 99.710%, recall of 85.811%, specificity of 99.743%, and G-mean of 92.068%. These outcomes reflect the robustness and adaptability of the proposed ELM algorithm in detecting fraudulent transactions. This study emphasizes the importance of a holistic performance analysis, addressing gaps in existing methods and providing a scalable framework for real-world fraud detection applications.
Copyright (c) 2025 Iman Kadhim Ajlan, Mohammed Ibrahim Mahdi, Hayder Murad, Fahad Taha AL-Dhief, Nurhizam Safie, Yasir Hussein Shakir, Ali Hashim Abbas http://creativecommons.org/licenses/by-sa/4.0
Hybrid N-gram-based framework for payload distributed denial of service detection and classification
There are three primary approaches to DDoS detection: anomaly-based, pattern-based, and heuristic-based. The heuristic-based method integrates both anomaly- and pattern-based techniques. However, existing DDoS detection systems face challenges in performing HTTP payload-level analysis, mainly due to high false positive rates and insufficient granularity in current datasets. To address this, the study introduces a novel heuristic approach based on a hybrid N-Gram model. This hybrid combines two components: CSDPayload+N-Gram and CSPayload+N-Gram. CSDPayload represents the gap (measured via Chi-Square Distance) between a given payload and normal traffic payloads, while CSPayload reflects the similarity (measured via Cosine Similarity) between them. These metrics form a new feature set evaluated using three datasets: CIC2019, MIB2016, and H2N-Payload. The methodology begins with packet extraction and conversion of TCP/IP traffic—specifically HTTP traffic—into hexadecimal payloads. N-Gram analysis (from 1-Gram to 6-Gram) is then applied to these payloads. For each N-Gram, frequency counts are computed, followed by calculations of Chi-Square Distance (CSD), Cosine Similarity (CS), and Pearson’s Chi-Square test to classify payloads as either benign or malicious. Subsequently, feature selection is performed using weight correlation, and the resulting features are fed into three machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network. Experimental results demonstrate high detection accuracy, particularly in the 4-Gram feature category: Neural Network achieves 99.65%, KNN 95.14%, and SVM 99.73% accuracy on average.
Copyright (c) 2025 Andi Maslan, Cik Feresa Mohd Foozy, Kamaruddin Malik Bin Mohamad, Abdul Hamid, Dedy Fitriawan, Joni Hasugian http://creativecommons.org/licenses/by-sa/4.0
Enhancing waste management through municipal solid waste classification: a convolutional neural network approach
The escalation of population, economic expansion, and industrialization has resulted in an increase in waste production. This has made waste management more challenging and has resulted in environmental deterioration, negatively impacting the quality of life. Recycling, reducing, and reusing are viable methods to eradicate the escalating waste issue, requiring the appropriate classification of municipal solid waste. This study focuses on comparing six advanced waste classification systems that employ a pre-trained convolutional neural network (CNN) designed to recognize twelve distinct categories of municipal waste. It has been determined that DarkNet53 is the most effective classifier among these six models. To assess the effectiveness of each waste classifier, the confusion matrix, precision, recall, F1 score, the area under the receiver operating characteristic curve, and the loss function are examined. It has been found that DarkNet53 has an F1 score of 98.7% and validation accuracy of 99%, respectively. The suggested approach will be useful in promoting garbage recovery and reuse in the direction of a circular and sustainable economy.
Copyright (c) 2025 Md. Tarequzzaman, Mojahidul Alom Akash, Zakir Hossain, Md. Sabbir Reza, Shajjadul Haque http://creativecommons.org/licenses/by-sa/4.0
Enhancing software fault prediction through data balancing techniques and machine learning
Software fault prediction is essential for ensuring the reliability and quality of software systems by identifying potential defects early in the development lifecycle. However, the presence of imbalanced datasets poses a significant challenge to the effectiveness of fault prediction models. In this paper, we investigate the impact of different data balancing techniques, including generative adversarial networks (GANs), synthetic minority over-sampling technique (SMOTE), and NearMiss, on machine learning (ML) model performance for software fault prediction. Through a comparative analysis across multiple datasets commonly used in software engineering research, we evaluate the efficacy of these techniques in addressing class imbalance and improving predictive accuracy. Our findings provide insights into the most effective approaches for handling imbalanced data in software fault prediction tasks, thereby advancing the state-of-the-art in software engineering research and practice. An extensive experimentation is performed and analyzed in this study here that includes 8 datasets, 4 data balancing techniques, and 4 ML techniques in order to demonstrate the efficacy of various models in software fault prediction.
Copyright (c) 2025 Akshat Raj, Durva Mahadeo Chavan, Priyal Agarwal, Jestin Gigi, Madhuri Rao, Vinayak Musale, Akshita Chanchlani, Murtaza Shabbirbhai Dholkawala, Kulamala Vinod Kumar http://creativecommons.org/licenses/by-sa/4.0
Multi-phase feature selection for detection of epithelial ovarian cancer using ensemble machine learning techniques
Epithelial ovarian carcinoma is one of the most prevalent causes of death. Timely ovarian cancer diagnosis is significant for bettering patient outcomes and rates of survival. For prognostic and diagnostic evaluation of malignancies, AI-based machine learning algorithms are used. This novel technique is undoubtedly an effective tool that may aid in selecting the best course of action. The collection of data comprising 150 patients contained an extensive selection of clinical characteristics and markers of tumors. The recursive feature elimination (RFE) and correlation coefficient feature selection techniques were assimilated to pick the features for the machine learning model, such as age, CA-125, tumor laterality, size, tumor type, grade of tumor, and International Federation of Gynecology and Obstetrics (FIGO) stage. The study’s findings indicate that the base model accuracy was around 96%, sensitivity 93%, and specificity 100%. Using ensemble classification, accuracy was around 96%, sensitivity 98%, and specificity 94% for the RFE technique. By obtaining a deeper understanding of their decision-making process, explainable artificial intelligence makes sophisticated machine learning methods easier to explain. Before beginning treatment, this research offers crucial data for the diagnosis and prognosis assessment of individuals with epithelial ovarian cancer (EOC).
Copyright (c) 2025 Suma Palani Subramanya, Suma Kuncha Venkatapathiah http://creativecommons.org/licenses/by-sa/4.0
Machine and deep learning classifiers for binary and multi-class network intrusion detection systems
The rapid proliferation of the internet and advancements in communication technologies have significantly improved networking and increased data vol ume. This phenomenon has subsequently caused a multitude of novel attacks, thereby presenting significant challenges for network security in the intrusion detection system (IDS). Moreover, the ongoing threat from authorized entities who try to carry out various types of attacks on the network is a concern that must be handled seriously. IDS are used to provide network availability, confidentiality, and integrity by employing machine learning (ML) and deep learning (DL) algorithms. This research aimed to study the impacts of the binary and multi-attack instances label by establishing IDS that leverages hybrid algorithms, including artificial neural networks (ANN), random forest (RF), and logistic model trees (LMTs). The paper addresses challenges such as data pre processing, feature selection, and managing imbalanced datasets by applying synthetic minority oversampling technique (SMOTE) and Pearson’s correlation methodologies. The IDS was tested using network security laboratory knowledge discovery datasets (NSL-KDD) and catalonia independence corpus intrusion detection system (CIC-IDS-2017) datasets, achieving an average F1-score of 96% for binary classification on NSL-KDD and 85% for binary classification on CIC-IDS-2017, while for multi-classification, the proposed model achieved an average F1-score of 82% and 96% for NSL-KDD and CIC-IDS-2017 successively.
Copyright (c) 2025 Ahmad Aloqaily, Emad Eddien Abdallah, Esraa Abu Elsoud, Yazan Hamdan, Khaled Jallad http://creativecommons.org/licenses/by-sa/4.0
Integrating machine learning and deep learning with landscape metrics for urban heat island prediction
Elevated temperatures in urban areas relative to surrounding rural areas, known as the urban heat island (UHI) effect, constitute a pressing challenge to urban sustainability, public health, and energy efficiency. With a comprehensive global dataset from NASA's Socioeconomic Data and Applications Center (SEDAC) that encompasses land surface temperature (LST) and different urban characteristics, this study investigates the UHI phenomenon. The UHI intensity was predicted using advanced machine learning models, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and long short-term memory (LSTM) with attention mechanism. The LSTM with attention achieved top R2:0.9998 (day) and 0.9992 (night). Key landscape metrics include urban area size, population, and location. We analyzed spatial temporal UHI patterns to identify local factors like geometry and vegetation. These findings are critical for urban planners and policy makers to identify targeted mitigation options, including green space expansion, the use of low thermal mass, and urban climate resilience strategies. These results advance predictive modeling, supporting resilient, and sustainable cities.
Copyright (c) 2025 Siddharth Pal, Kavita Jhajharia http://creativecommons.org/licenses/by-sa/4.0
Spam social media profile detection using hybrid positive unlabelled learning
Online social networks (OSNs) are a communication medium of social interaction for people, where social activities, entertainment, business oriented activities, and information are exchanged. It creates an environment with worldwide connectivity where groups of individuals may discuss their interests and activities on social media platforms. Billions of people routinely interact with social content, opinion sharing, recommendations, networking, scouting, social campaigns, alerting on OSNs. The increase in popularity of OSNs creates new challenges and perspectives to the researchers of social networks, which is of interest in various fields. One of the most popular networking platforms for microblogging is X (formerly Twitter). Millions of spam accounts have inundated the X network, which could damage normal users' security and privacy. Hence, the research in this filed has become essential for enhancing real users' protection and identifying spam profiles. In this manuscript, we propose hybrid approach based on semi-supervised learning to detect the spam profiles. The proposed work is based on the positive and unlabeled (PU) learning algorithm, which learns from an unlabeled dataset and a small number of positive instances. Simulation results demonstrate that our approach outperformed existing PU learning approach by 17.39% and 17.51% improvement respectively in spam detection rate on X and Instagram datasets.
Copyright (c) 2025 Nidhi A. Patel, Nirali Nanavati http://creativecommons.org/licenses/by-sa/4.0
A merchant analytics framework for revenue forecasting and financial stress detection using transaction data
By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.
Copyright (c) 2025 Yara Harb, Wissam Baaklini, Nadine Abbas, Seifedine Kadry http://creativecommons.org/licenses/by-sa/4.0
Solving sparsity and scalability problems for book recommendations on e-commerce
This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Copyright (c) 2025 Muhammad Ichsanudin, Bevina Desjwiandra Handari, Bambang Dwi Wijanarko, Gatot Fatwanto Hertono http://creativecommons.org/licenses/by-sa/4.0
Support vector machine performance: simulation and rice phenology application
In the case of classification, model accuracy is expected to result in correct predictions. This study aims to analyze the performance of two kinds of support vector machine (SVM) methods: the support vector machine one versus one (SVM OvO) method and the generalized multiclass support vector machine (GenSVM) method. This method will compare to the generalized linear model, namely the multinomial logistic regression (MLR) method. Simulations were conducted using SVM OvO and GenSVM methods to get an overview of the parameters affecting both methods' performance. Furthermore, the three classification methods are implemented in the case of modelling the rice phenology and tested for performance. Simulation results show that, however, the SVM OvO and GenSVM machine learning methods are sensitive to the choice of model parameters. The empirical study results show that the SVM OvO and GenSVM methods can produce satisfactory model accuracy and are comparable to the MLR method. The best rice phenology model accuracy was obtained from the SVM OvO model, where 79.20 ± 0.21 overall accuracy and 70.69 ± 0.29 kappa were obtained. This research can be continued by handling samples, especially when class members are a minority, and can also add random effects to the SVM model.
Copyright (c) 2025 Hengki Muradi, Asep Saefuddin, I Made Sumertajaya, Agus Mohamad Soleh, Dede Dirgahayu Domiri http://creativecommons.org/licenses/by-sa/4.0
Classification system of banana types and ripeness levels based on convolutional neural network
Recently, the availability of bananas in supermarkets has been relatively abundant. However, most buyers experience problems categorizing the type and level of ripeness of bananas, so the level of purchases of this fruit decreases. This study implements a system that can automatically classify bananas based on type and level of ripeness so that buyers can choose them based on their needs. In this study, the proposed system could classify the types and degrees of banana ripeness using a Convolutional Neural Network (CNN) where the system was implemented in real-time using the hardware of the Jetson Nano as a processing unit and a camera system as a sensor. The methodology adopted in this research involves implementing CNN architectures, i.e., ResNet-18 and ResNet-50, under various conditions. The training phase comprises 60 epochs, while testing considers illumination parameters from LED lights with power of 6 watts, 12 watts, and 22 watts under distances ranging from 10 to 100 cm. The results show that the system could classify the type and level of ripeness of bananas in real-time with an accuracy of 93% that is achieved using the 22-watt power for all type and ripeness levels.
Copyright (c) 2025 Lucia Jambola, Arsyad Ramadhan Darlis, Windi Malaha, Dwi Aryanta http://creativecommons.org/licenses/by-sa/4.0
Optimizing sparse ternary compression with thresholds for communication-efficient federated learning
Federated learning (FL) enables decentralized model training while preserving client data privacy, yet suffers from significant communication overhead due to frequent parameter exchanges. This study investigates how varying sparse ternary compression (STC) thresholds impact communication efficiency and model accuracy across the CIFAR-10 and MedMNIST datasets. Experiments tested thresholds ranging from 1.0 to 1.9 and batch sizes of 10, 15, and 20. Results demonstrated that selecting thresholds between 1.2 and 1.5 reduced total communication costs by approximately 10–15%, while maintaining acceptable accuracy levels. These findings suggest that careful threshold tuning can achieve substantial communication savings with minimal compromise in model performance, offering practical guidance for improving the efficiency and scalability of FL systems.
Copyright (c) 2025 Nithyaniranjana Murthy Chittaiah, Manjula Sunkadakatte Haladappa http://creativecommons.org/licenses/by-sa/4.0
Comparative evaluation of machine learning models for intrusion detection in WSNs using the IDSAI dataset
This paper provides comparative assessment of three lightweight machine learning (ML) models (logistic regression (LR), random forest (RF), and gradient boosting (GB)), which are employed to detect intrusions in wireless sensor networks (WSNs) using the IDSAI dataset. The goal is to determine the most effective and deployable classifier within the constraints of WSN resources. In order to prevent data leakage and report accuracy, precision, recall, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) with mean±SD, we implement stratified 5-fold cross validation with in fold preprocessing. The results indicate that RF provides the most optimal generalization and overall performance (accuracy 0.9994 ± 0.0001, precision 0.9995±0.0001, recall 0.9994±0.0001, F1-score 0.9994±0.0001, ROC–AUC 0.9998 ± 0.0000). RF is closely followed by GB (accuracy 0.9990±0.0001, precision 0.9995±0.0001, recall 0.9985±0.0001, F1-score 0.9990 ± 0.0001, ROC-AUC ≈ 1.0000). LR demonstrates limitations in linearly overlapping classes, as evidenced by its high precision but reduced recall (accuracy 0.9167±0.0010, precision 0.9829±0.0002, recall 0.8481±0.0018, F1-score 0.9105 ± 0.0011, ROC–AUC 0.9707 ± 0.0001). In order to evaluate deployability, we characterize the inference throughput on a modest PC: LR ∼ 6.5 × 105 samples/s, GB ∼ 2.2 × 105 samples/s, and RF ∼ 1.3 × 105 samples/s, indicating a tiered intrusion detection system (IDS) (LR at sensors, RF at cluster-heads, and GB at the gateway). We also address the potential dangers of overfitting that may arise from the cleanliness of the dataset and provide a roadmap for future validation on a more diverse set of traffic. The research establishes a baseline for lightweight IDS in actual WSNs that is deployable and reproducible.
Copyright (c) 2025 Mansour Lmkaiti, Houda Moudni, Hicham Mouncif http://creativecommons.org/licenses/by-sa/4.0
Enhancing cross-site scripting attack detection by using FastText as word embeddings and long-short term memory
Cross-site scripting (XSS) is one of the dangerous cyber-attacks and the number of attacks continues to increase. This study takes a new approach to detect attacks by utilizing FastText as word embedding, and long-short term memory (LSTM), which aims to improve the performance of deep learning. This method is proposed to capture the broader meaning and context of the data used, leading to better feature extraction and model performance. This study not only improves the detection of XSS attacks, but also highlights the potential for better text processing techniques. The results obtained showing this method achieves higher results than other methods, with an accuracy of 99.89%.
Copyright (c) 2025 Muhammad Alkhairi Mashuri, Nico Surantha http://creativecommons.org/licenses/by-sa/4.0
Classifying classical music’s therapeutic effects using deep learning: a review
Mental health issues are the leading cause of global disability, increasing the need for treatment options. While there is much research on the emotional recognition of music in general, there is a gap in studies that directly connect musical features with their therapeutic effects using deep learning. This systematic literature review explores the use of deep learning in classifying the therapeutic effects of classical music for mental health. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 15 papers were reviewed. This review synthesized studies on the role of musical elements that affect mental states. Different feature extraction methods, including mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features, are discussed for their roles in classifying these therapeutic effects. This review also looks at deep learning algorithms like convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) network, and combined models to assess their effectiveness. Common evaluation methods are also assessed to measure the performance of these models in audio classification. This review highlights the potential of combining deep learning with classical music for mental health support, and to future possibilities for applying these methods in the real world.
Copyright (c) 2025 Angelin, Samuel Ady Sanjaya, Dinar Ajeng Kristiyanti http://creativecommons.org/licenses/by-sa/4.0
Melanoma classification using ensemble deep transfer learning
Melanoma, a type of skin cancer, poses significant challenges in early detection and diagnosis. Several methods for early melanoma detection, including visual inspection and several machine learning models, face challenges with accuracy. To overcome these issues, deep learning has been widely adopted in various biomedical applications. In this work, we employ deep transfer learning methods to classify melanoma. Firstly, we collect publicly available datasets containing melanoma images, their corresponding ground truth for segmentation, and class labels. Subsequently, we perform data preprocessing, normalization, and label encoding to address issues of varied illumination, image noise, and data imbalance. Next, we conduct feature extraction utilizing the previously trained deep learning models, VGG, ResNet, InceptionResNet, and MobileNet. The characteristic vectors obtained from each model are fused to produce a comprehensive depiction among the provided pictures. In the classification stage, we employ ensemble learning using transfer learning models, including EfficientNet, Xception, and DenseNet. These models are trained on the final feature vector to classify melanoma images effectively. The effectiveness of the suggested method is verified using publicly available ISIC 2017–2020 datasets, these model reports average accuracy scores of 96.10%, 97.23%, 97.50%, 98.33%, and 98.60%, in that order.
Copyright (c) 2025 Soumya Gadag, Panduranga Rao Malode Vishwanatha, Virupaxi Balachandra Dalal http://creativecommons.org/licenses/by-sa/4.0
Catalysing precision in bone x-ray analysis for image detection and classification: the triple context attention model advancement
Accurate detection and classification of fractures in bone x-ray images are crucial for effective medical diagnosis and treatment. In this study, we propose the triple context attention model (TCAN) as a novel approach to address the challenges in this domain. TCAN offers several key contributions that significantly enhance the accuracy and efficiency of bone x-ray image recognition and classification. Firstly, TCAN introduces the coordination attention mechanism, which considers both horizontal and vertical positional data during the recognition process. Secondly, TCAN mitigates the common issue of mislabelling fractures in bone x-ray images, particularly in the you only look once (YOLO) model, due to the absence of positional data during training. Thirdly, TCAN efficiently enhances positional data by focusing on weights, and increasing feature dimension while maintaining a manageable model size. This allows for effective utilization of positional data without computational overhead. Lastly, TCAN combines the visual attention network (VAN) with its capabilities, resulting in a comprehensive system that can handle diverse image dimensions and accurately classify various types of fractures across different body regions. Overall, TCAN presents a promising advancement in medical image analysis, improving fracture detection accuracy and classification efficiency in bone x-ray images, thus aiding in more effective clinical decision-making.
Copyright (c) 2025 Tabassum N. Sultana, Nagaratna P. Hegde, Asma Parveen http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of pre-trained deep learning model on garbage classification with data augmentation approach
Waste classification is one of the interesting topics for classifications in which data can be very varied and complex. This data diversity is a challenge to develop a model that is able to classify well. The purpose of this study is to analyze the performance of the pre-trained deep learning model using a data augmentation approach. There are three pre-training models used in this study, namely residual networks 50 (ResNet50), visual geometric group with 16 layers (VGG-16), and MobileNetV2. The results showed that the MobileNetV2 model received the highest accuracy value, reaching 84.45% for data without augmentation. With data augmentation there is a decrease of 2.73%. Conversely, VGG-16 shows performance stability with an increase in accuracy with augmentation data, reaching 75.84%. While ResNet50 gets the lowest results compared to both models. The application of data augmentation techniques with the aim of increasing data variations does not always have an impact on increasing the generalization of the model.
Copyright (c) 2025 I Komang Arya Ganda Wiguna, I Gusti Made Ngurah Desnanjaya, I Kadek Budi Sandika http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based evaluation for distributed denial of service attacks detection
Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Copyright (c) 2025 Neethu S., H. V. Ravish Aradhya, Viswavardhan Reddy Karna http://creativecommons.org/licenses/by-sa/4.0
Dynamic service-aware network selection framework for multi objective optimization in 5G-advanced heterogeneous wireless networks
The increasing complexity of heterogeneous wireless networks (HWNs) and the diverse requirements of mobility patterns and service classes necessitate advanced solutions for network selection and resource optimization. Existing models often fall short in addressing dynamic mobility scenarios and service differentiation, leading to inefficiencies in resource allocation, suboptimal throughput, and increased latency. To overcome these limitations, this study proposes a dynamic service-aware network selector (DSANS) framework for 5G-advanced environments. The framework integrates an adaptive deep decision network (ADDN) for multi-objective optimization, addressing critical quality of service (QoS) metrics such as throughput, delay, and energy efficiency while enhancing quality of experience (QoE) for applications like enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and internet of things (IoT). The DSANS framework dynamically adapts to mobility patterns and varying network conditions, ensuring efficient resource estimation and optimal network selection. Simulation results highlight its superiority, achieving up to 25% improvement in throughput and a 15% reduction in latency compared to state-of-the-art algorithms. These findings validate DSANS as a robust solution for mitigating the limitations of existing models, optimizing network performance, and meeting the stringent demands of next-generation HWNs.
Copyright (c) 2025 Bhavana Srinivas, Nadig Vijayendra Uma Reddy http://creativecommons.org/licenses/by-sa/4.0
Recognition of Indonesian sign language using deep learning: convolutional neural network-based approach
This study focuses on developing an automatic Indonesian sign language (SIBI) recognition system using a convolutional neural network (CNN). Sign language is essential for communication among deaf and hard-of hearing individuals, and automatic recognition helps improve accessibility and inclusivity. CNNs are chosen for their ability to learn image features automatically, eliminating manual extraction and improving classification accuracy. The SIBI dataset used contains 5,280 images of 26 letters, divided into training and validation sets. In early training, the model achieved low accuracy (3.63% training, 3.33% validation), but after five epochs, it significantly improved to 97.58% for training and 100% for validation.
Copyright (c) 2025 Olivia Kembuan, Haryanto, Mochamad Bruri Triyono http://creativecommons.org/licenses/by-sa/4.0
Classification of regional language dialects using convolutional neural network and multilayer perceptron
Regional languages are vital for communication and preserving cultural identity, safeguarding local heritage. However, globalization and modernization endanger their existence as they are increasingly replaced by national or global languages. Despite progress in dialect recognition research, particularly for certain languages, further studies are needed to improve model performance and address less-represented dialects, including those in Indonesia. This study enhances a custom-built dataset for dialect recognition through the application of data augmentation techniques, specifically adding noise, time stretching, and pitch shifting. Using Mel-frequency cepstral coefficients (MFCC) for feature extraction, it evaluates the performance of convolutional neural network (CNN) and multilayer perceptron (MLP) in classifying six Indonesian dialects. Results indicate that CNN outperformed, achieving 97.92% accuracy, 97.90% recall, 97.97% precision, 97.92% F1-score, and a kappa score of 97.49% with combined augmentation techniques, setting a foundation for further research.
Copyright (c) 2025 Fahmi B. Marasabessy, Dwiza Riana, Muji Ernawati http://creativecommons.org/licenses/by-sa/4.0
Metaheuristic optimization for sarcasm detection in social media with embedding and padding techniques
Sarcasm is a sophisticated mode of expression that allows speakers to express their opinions subtly. Stakeholders provide unstructured messages with extended phrases, making it difficult for computers and people to understand. This research aims to develop a sarcasm detection method to identify words in phrases as sarcastic or non-sarcastic from text, utilizing natural language processing appliances. The first step is pre-processing, when the padding and embedding are performed. Zero padding and end padding are used for the padding. At the same time, different embedding techniques, such as word2vec, Glove, and BERT, are used. Following pre-processing, the features are extracted from the pre-processed data, including "information gain, chi-square, mutual information, and symmetrical uncertainty-based features." Then, a hybrid optimization technique known as clan-updated grey wolf optimization (CU-GWO) is used for optimized features and weight selection. An ensemble technique was applied to extract optimal features. The classifiers in the proposed suggested ensemble technique with deep convolution neural network (DCNN). DCNN offers fine weight tuning and detection results.The performance analysis and its impact on the proposed model for sarcasm detection are classified with good accuracy into sarcastic and non-sarcastic categories. The results are also compared with against those of the GloVe and BERT techniques.
Copyright (c) 2025 Geeta Sahu, Manoj Hudnurkar http://creativecommons.org/licenses/by-sa/4.0
Kannada handwritten numeral recognition through deep learning and optimized hyperparameter tuning
The classification of handwritten numerals is a vital and challenging task in developing automated systems, including postal address sorting and license plate recognition. The present study elucidates a new methodology for recognizing Kannada handwritten numerals using deep learning ResNet and VGG architecture with transfer learning. The challenge in Kannada handwritten recognition is complicated structural hierarchy and large vocabulary. The major problem in deep neural networks is vanishing gradient, which can lead to degradation in character recognition, and was addressed using our new methodology using ResNet architecture. We apply the proposed ResNet method in various real-world applications and compare it with convolutional neural networks (CNN) architecture, VGG. The experiment was implemented with the Google Colab software version on a self-created dataset, with handwritten Kannada numerals fed as the input to the recognition process. Our proposed method achieved a high accuracy of 99.20% on training samples and a generalization accuracy of 97.5% on test samples, indicating our method's effectiveness in recognizing handwritten Kannada numerals.
Copyright (c) 2025 Ujwala B. S., Pramod Kumar S., H R Mahadevaswamy, Sumathi K. http://creativecommons.org/licenses/by-sa/4.0
Intelligent route optimization for internet of vehicles using federated learning: promoting green and sustainable IoT networks
As the internet of vehicles (IoV) continues to evolve, optimizing vehicle routing becomes increasingly important for enhancing traffic efficiency and minimizing environmental impact. This paper introduces an intelligent vehicle route optimization protocol leveraging federated learning (FL) to achieve green and sustainable IoV systems. By distributing the learning process across multiple edge devices, the proposed protocol minimizes the need for centralized data processing, reducing network congestion, and preserving user privacy. The system optimizes vehicle routes based on real time traffic conditions, fuel efficiency, and carbon emissions, and promoting greener transportation practices. Simulations conducted in a dynamic IoV environment demonstrate significant improvements in route efficiency, fuel consumption, and carbon emissions. The results underscore the potential of FL in transforming IoV routing by balancing performance and sustainability, making it a promising solution for the future of connected transportation.
Copyright (c) 2025 Desidi Narsimha Reddy, Swathi Buragadda, Janjhyam Venkata Naga Ramesh, Garapati Satyanarayana Murthy, Nallathambi Srija, Sarihaddu Kavitha http://creativecommons.org/licenses/by-sa/4.0
Deep learning-aided polar-low density parity check decoding for enhanced telemedicine ECG transmission reliability
Telemedicine has emerged as a crucial solution for remote patient monitoring and diagnosis, yet ensuring the reliable transmission of medical data, particularly electrocardiogram (ECG) signals, remains a significant challenge. This work proposes a novel approach that integrates deep learning with a polar-low density parity check (LDPC) decoder to enhance the accuracy, robustness, and efficiency of ECG signal transmission within telemedicine systems. The study aims to evaluate the effectiveness of this integration in improving error correction and decoding performance, validate its efficacy under diverse signal to noise ratios (SNRs) and code rates, and assess its potential impact on remote healthcare delivery. Experimental results confirm that the deep learning-empowered polar-LDPC decoder achieves superior error correction and decoding efficiency compared to conventional methods, ensuring higher fidelity in ECG reconstruction. This advancement presents a promising pathway toward more reliable, precise, and efficient telemedicine systems, thereby enabling improved patient care, especially in remote and underserved regions. The proposed method also opens opportunities for integrating intelligent decision-support tools. Such integration could further enhance real-time diagnostics and broaden telemedicine’s scope.
Copyright (c) 2025 Sushma Nagesh, Santhosh Kumar Kenkere Basavaraju, Dakshayani Mandikeri Ramaiah, Triveni Chitralingappa Lingappa, Indira Bahaddur, Venkateswara Rao Kolli http://creativecommons.org/licenses/by-sa/4.0
Designing a squeeze-and-excitation-capsule BiLSTM transformer for plant leaf disease recognition
Deep learning (DL) is critical in plant disease recognition and classification with precision like those of expert human evaluators. However, development of effective systems is often disrupted due to the complexity and variability of disease pathogenesis. To address these challenges, this research applies to a hybrid DL architecture that integrates spatial encoding, sequential modelling, and attention for visual recognition. This proposed model can incorporate squeeze-and-excitation (SE) with residual blocks, capsule network (CapsNet), bidirectional long short-term memory (BiLSTM), and transformer network (TransNet)-based attention to realize spatial relationships and long-range dependencies for improving recognition accuracy. The proposed model is assessed on the corn leaf disease dataset (CLDD) and rice leaf diseases dataset (RLDD), and its performance is compared to leading-edge models. CLDD and RLDD achieved 99.88 and 99.10% training accuracy respectively. The area under the curve (AUC) reached almost ceiling recognition on CLDD, with 99.73, 99.96, 99.96, and 99.98% for blight (BL), common rust (CR), gray leaf spot (GL), and healthy (HE) result. RLDD results were also high, with 94.98, 93.70, 97.66, 84.57, 99.58, and 98.85% for bacterial leaf blight (BLB), brown spot (BS), HE, leaf blast (LB), leaf scald (LS), and narrow brown spot (NBS), respectively. The results of these tests show the remarkable promise and performance of the proposed model in plant disease recognition applications.
Copyright (c) 2025 Aekkarat Suksukont, Ekachai Naowanich http://creativecommons.org/licenses/by-sa/4.0
Unified BERT-LSTM framework enhances machine learning in fraud detection, financial sentiment, and biomedical classification
The current paper proposes a hybrid framework based on the bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) networks for classification tasks in three diverse domains: credit card fraud detection (CCFD), financial news sentiment analysis (FNSA), and biomedical paper abstract classification (BPAC). The model leverages the strengths of BERT regarding the learning of contextual embeddings and those of LSTM in capturing sequential dependencies, thus setting the new state-of-the-art performance in each of the three domains. In the CCFD use case, the model was able to achieve an accuracy of 99.11%, considerably outperforming all the competing systems in fraud transaction detection. The BERT-LSTM model achieved a performance of 96.74% for FNSA, improving significantly in sentiment analysis. Finally, the use case of BPAC was robust, with 88.42% accuracy, which clearly classified biomedical abstract sections correctly. It is evident from the findings that this framework generalizes to a wide range of tasks and hence is an adaptable but strong tool in combating challenges of cross-domain classification.
Copyright (c) 2025 Oussama Ndama, Ismail Bensassi, Safae Ndama, El Mokhtar En-Naimi http://creativecommons.org/licenses/by-sa/4.0
A smart grid fault detection using neuro-fuzzy deep learning algorithm
This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Copyright (c) 2025 Etienne François Mouckomey, Jacques Bikai, Camille Franklin Mbey, Alexandre Teplaira Boum, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu http://creativecommons.org/licenses/by-sa/4.0
Optimizing brain tumor MRI classification using advanced preprocessing techniques and ensemble learning methods
Brain tumor classification is a critical task in medical imaging that directly impacts the accuracy of diagnosis and treatment planning. However, the complexity and variability of magnetic resonance imaging (MRI) images pose significant challenges, often resulting in reduced model reliability and generalization. This study addresses these limitations by proposing a novel ResNet+Bagging model, leveraging the strengths of residual networks and ensemble learning to enhance classification performance. Using publicly available brain tumor MRI datasets, including images labeled as benign, malignant, and normal, the study employs advanced preprocessing techniques such as normalization, data augmentation, and noise reduction to ensure high-quality inputs. The proposed model demonstrated significant improvements, achieving the highest testing accuracy of 72%, outperforming other tested models such as LeNet, standard ResNet, GoogleNet, and VGGNet. Precision (0.6010), recall (0.6000), and F1-score (0.5990) metrics further highlight its superior balance in detecting positive and negative classes. The novelty of this research lies in the application of Bagging to ResNet, which effectively mitigates overfitting and enhances predictive stability in complex medical datasets. These findings underscore the proposed model's potential as a robust solution for brain tumor classification, contributing to more accurate and reliable diagnostics.
Copyright (c) 2025 Akim Manaor Hara Pardede, Ahmad Zamsuri, Indi Nuroini, Putrama Alkhairi http://creativecommons.org/licenses/by-sa/4.0
Chest X-ray image classification using deep belief network with Al-Biruni earth radius and particle swarm optimization
Chest X-ray (CXR) is a widely employed radiological clinical assessment tool that provides a quick and effective means of classifying various diseases using CXR images. However, several researchers face challenges with CXR images due to imbalanced datasets and image quality issues. Pre-processing is performed using contrast limited adaptive histogram equalization (CLAHE) to enhance image quality and mitigate noise in the data. The synthetic minority oversampling technique (SMOTE) is applied to create synthetic samples for the minority class and handle class imbalance. The MobileNetV2 performs depth-wise separable convolution is used for feature extraction, while maintaining high efficiency for CXR images. This research proposes a deep belief network (DBN) to classify CXR, which helps capture hierarchical features and complex patterns in CXR images. The combination of particle swarm optimization (PSO) and Al-Biruni earth radius (BER) method is employed for hyperparameter tuning with enhanced DBN classification accuracy. Furthermore, BER is integrated with the PSO algorithm to balance exploration and exploitation while the fitness function is fine-tuned for optimal DBN classification performance. The proposed PSOBER-DBN achieves a high accuracy of 99.86% on the CXR14 dataset, in comparison to existing techniques such as the multi-level residual feature fusion network (MLRFNet).
Copyright (c) 2025 Rakesh Selva Raj, Pavan Kumar Madalu Palakshamurthy, Bidarakere Eswarappa Rangaswamy http://creativecommons.org/licenses/by-sa/4.0
A blended ensemble approach for accurate human activity recognition
Human activity recognition (HAR) is a novel computer vision area with applications in fashion, entertainment, healthcare, and urban planning. Previously, convolutional neural networks (CNNs) were used in HAR due to their ability to extract spatial features from images. However, CNNs are not effective in processing varying input sizes and long-range dependencies in complex human motions. This work examines another approach using vision transformers (ViT) and swin transformers (SwinT) that process images as patch sequences and perform self-attention. These models particularly excel in learning global relationships and minor motion changes in body motion and are therefore very well-suited to variegated and subtle activity detection. To further enhance recognition performance, we propose a hybrid ensemble method by combining ViT and SwinT models with different scales (small, base, and large). Experimental outcomes show that while single transformer models are competitive, the hybrid ensemble beats them across the board with the highest accuracy and balanced precision, recall, and F1-score. These findings confirm that the intended ensemble model provides a more scalable and robust solution than either single-model or CNN-based approaches, and this encourages accurate human activity recognition.
Copyright (c) 2025 Rezwana Karim, Afsana Begum, Miskatul Jannat, Abu Kowshir Bitto http://creativecommons.org/licenses/by-sa/4.0
Classification of single origin Indonesian coffee beans using convolutional neural network
This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
Copyright (c) 2025 Achmad Pratama Rifai, Wangi Pandan Sari, Haidar Rabbani, Tari Hardiani Safitri, Makbul Hajad, Edi Sutoyo, Huu-Tho Nguyen http://creativecommons.org/licenses/by-sa/4.0
Classifier model for lecturer evaluation by students using speech emotion recognition and deep learning approaches
Lecturers play a crucial role in higher education, with their teaching behavior directly impacting learning and teaching quality. Lecturer evaluation by students (LES) is a common method for assessing lecturer performance, though it often relies on subjective perceptions. As a more objective alternative, speech emotion recognition (SER) uses speech technology to analyze emotions in the speech of lecturers during classes. This study proposes using deep learning-based SER, including convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM), to evaluate teaching quality by analyzing displayed emotions. Removing silence from audio signals is crucial for enhancing feature analysis, such as energy, zero-crossing rate (ZCR), and mel-frequency cepstral coefficients (MFCC). This method removes inactive segments, emphasizing significant segments, and improving accuracy in detecting voice and emotions. Results show that the 1D CNN model with Bi-LSTM, using MFCC with 13 coefficients, energy, and ZCR, performs excellently in emotion detection, achieving a validation accuracy of over 0.851 with an accuracy gap of 0.002. This small gap indicates good generalization and reduces the risk of overfitting, making teaching evaluations more objective and valuable for improving practices.
Copyright (c) 2025 Yesy Diah Rosita, Wahyu Andi Saputra http://creativecommons.org/licenses/by-sa/4.0
Deep neural network solutions to Newell-Whitehead-Segel equations
In this work, we use the deep neural network (DNN) approach called NeuroDiffEq, and the unified finite difference exponential approach for obtaining the approximated and exact solutions of Newell-Whitehead-Segel systems that are essential for the biology of mathematics. A unified approach was used to generate several solutions for solitary waves of those systems. The approximated solutions for selected studies are explored using the NeuroDiffEq approach, which is the artificial neural networks (ANN) approach and is based upon trial approximate solution (TAS). The comparison between the obtained approximated solutions and the analytical solutions indicates that the applied method has proved an efficient as well as a highly successful approach to solving various types of the Newell-Whitehead-Segel equations.
Copyright (c) 2025 Soumaya Nouna, Ilyas Tammouch, Assia Nouna, Mohamed Mansouri http://creativecommons.org/licenses/by-sa/4.0
Impact of smoothing techniques for text classification: implementation in hidden Markov model
A hidden Markov model (HMM) is widely used for sequence modeling in various text classification tasks. This study investigates the impact of different smoothing techniques, such as Laplace, absolute discounting, and Gibbs sampling on HMM performance across three distinct domains: e-commerce products, spam filtering, and occupational data mining. Through the comparative analysis, Laplace smoothing consistently outperforms other techniques in handling zero-probability issues, demonstrating superior performance in the e-commerce and SMS spam datasets. The HMM without any smoothing technique achieved the best results for job title classification. This divergence underscores the dataset-specific nature of smoothing requirements, where the simplicity of parameter estimation proves effective in contexts characterized by a limited and repetitive vocabulary. Hence, the findings suggest that tailored smoothing strategies are crucial for optimizing HMM performance in different textual analysis applications.
Copyright (c) 2025 Norsyela Muhammad Noor Mathivanan, Roziah Mohd Janor, Shukor Abd Razak, Nor Azura Md.Ghani http://creativecommons.org/licenses/by-sa/4.0
Sentiment classification using gradient modulation and layered attention
Sentiment analysis is a technique for evaluating text to ascertain whether a statement is positive, negative, or neutral. Currently, transformer-based models capture the contextual relationships among words in a phrase and accomplish sentiment analysis in a nuanced manner via multi-head attention. This approach, with a fixed number of layers and heads, struggles to find the complex relationships between phrases and their semantic structures. To mitigate this issue, the suggested technique incorporates the graded multi head attention model (GMHA) at the base of the distilled bidirectional encoder representations from transformers (DistilBERT) model. It is employed to augment the layers and heads progressively, capturing the relationships between sentences in a sophisticated manner. By increasing the layers and heads the proposed model extracts long-term and hierarchical relationships from the sentence. Additionally, the attention sentient optimization technique is introduced, which improves model learning by giving more weight to important words in a sentence. During training, the process checks to see which words (“amazing" or "worst") get more attention and gives them more weight in the model update. This makes it easier for the model to understand important emotions. Our suggested model enhances performance in sentiment exploration, with an accuracy of 96.53%. This interpretation includes a comparison analysis with another contemporary framework.
Copyright (c) 2025 Bagiyalakshmi Natarajan, T. Veeramakali http://creativecommons.org/licenses/by-sa/4.0
Arabic text classification using machine learning and deep learning algorithms
The classification of Arabic textual content presents considerable challenges due to the language's rich morphological structure and the wide variation among its dialects. This study aims to enhance classification accuracy by leveraging ensemble learning techniques and a deep bidirectional transformer-based model, specifically the multilingual autoregressive BERT (MARBERT). To address linguistic variability, advanced preprocessing techniques were employed, including Farasa, Tashaphyne, and Assem stemming methods. The Al Khaleej dataset served as the basis for supervised learning, providing a representative sample of Arabic text. Furthermore, term frequency-inverse document frequency (TF-IDF) with bigram and trigram feature extraction was utilized to effectively capture contextual semantics. Experimental results indicate that the proposed approach, particularly with the integration of MARBERT, achieves a peak classification accuracy of 98.59%, outperforming existing models. This research underscores the efficacy of combining ensemble learning with deep transformer-based models for Arabic text classification and highlights the critical role of robust preprocessing techniques in managing linguistic complexity and improving model performance.
Copyright (c) 2025 Rawad Awad Alqahtani, Hoda A. Abdelhafez http://creativecommons.org/licenses/by-sa/4.0
Graph based semantic email classification: a novel approach for academic institutions
Electronic mail classification in educational institutes becomes the fundamental task to manage information efficiently. Due to the globalization and the technological advancement, volume of email users increasing consistently, which in turn increases the volume of digital data exponentially. This necessitates the developing automated email classification systems for the better and organized work. This paper develops a novel graph-based similarity (GBS) approach based on semantic similarity to address these challenges. The method initially selects the most relevant features based on feature weights, later it builds a graph by using Jaccard co efficient method for each category with features as nodes and correlation between the nodes as edges. Later, these graphs are used as templates for each category and classifies each new incoming email into the specific class based on the similarity among the graph templates and a new email. The GBS method was compared with the well-known benchmarked email classifiers and the findings demonstrated that the GBS method outperformed with 98.91% accuracy after fine-tuning of graph parameters and the classifier hyper parameters. Additionally, receiver operating characteristic (ROC) curve analysis was conducted, achieving a highest area under curve (AUC) score 0.989, demonstrating robust classification proficiency across all categories.
Copyright (c) 2025 Aruna Kumara B., Madan H. T., Rashmi C., Sarvamangala D. R. http://creativecommons.org/licenses/by-sa/4.0
Exploring the influence of soft information from economic news on exchange rate and gold price movements
Information on business conditions is an important concern for market players and regulators. Hard information relates to easily validated characteristics such as production levels and employment conditions. In contrast, soft information such as consumer and public perceptions—is subjective and difficult to verify. Although previous studies on hard and soft information mainly focus on microeconomics and banking, current developments in big data and machine learning enable broader applications in financial market analysis. This study combined VADER sentiment analysis and support vector machine (SVM) classification (accuracy=85%) to analyze economic news, followed by Granger causality and multiple linear regression to examine causal effects and predictive relationships. The findings reveal that negative news sentiment and the Indonesian Rupiah (IDR) exchange rate influence each other, while positive sentiment has no causal impact on the exchange rate. Both negative and positive sentiments affect gold prices, whereas gold price movements do not influence sentiment. Regression analysis shows that negative sentiment has a stronger effect in decreasing the IDR exchange rate than positive sentiment, with the model explaining approximately 20% of the variance. Integrating sentiment and exchange rate data enhances the predictive model for gold price forecasting and highlights the asymmetric roles of positive and negative news in financial dynamics.
Copyright (c) 2025 Rahardito Dio Prastowo, Indra Budi, Amanah Ramadiah, Aris Budi Santoso, Prabu Kresna Putra http://creativecommons.org/licenses/by-sa/4.0
A web-based learning platform to assess student performance using online session activity engagement
Predicting students' performance and engagement is crucial for academic eLearning partners in colleges and universities as well as students themselves considering post-COVID-19 pandemic and university grant commission (UGC) dual degree regulation era. An educational system's data on students’ engagement in taking courses that are a significant component of an institution of higher learning with a cogent vertical syllabus can be used to make predictions. By examining how closely a student's course-taking actions correspond with the requirements of the syllabus, one can utilize the student's conduct in the classroom and online eLearning web tool as a predictor of future achievement. This paper presents a study that uses an eLearning web-based dataset to predict students' success throughout a series of online interactive sessions. The dataset records how students engage with each other during online lab work, including how many keystrokes they make, how long they spend on each task, and how well they perform on exams overall. The current methods lacks accuracy to assess student performance and engagement with high precision. In addressing this paper introduces novel multi-label ensemble learning (MLEL) using XGBoost (XGB) and K-fold cross validation. Experiment outcome shows the proposed (MLEL-XGB) achieves much improved outcome than other existing models.
Copyright (c) 2025 Shashirekha Hanumanthappa, Chetana Prakash http://creativecommons.org/licenses/by-sa/4.0
Securing post-quantum cryptography: side-channel resilience in CRYSTALS-Kyber key encapsulation mechanism
This study evaluates side-channel vulnerabilities in hardware implementations of the cryptographic suite for Algebraic lattices (CRYSTALS)-Kyber key encapsulation mechanism (KEM) using correlation and differential power analysis (DPA) techniques. Unprotected field-programmable gate array (FPGA) implementations across all Kyber parameter sets were successfully compromised, revealing significant information leakage. Attack complexity scaled linearly with key size. Additive Boolean masking provided varying protection levels, with 4-bit masking offering a 100× security increase at notable performance cost. Performance characterization showed increased slice utilization and reduced maximum frequency for higher-order masking. A novel hybrid countermeasure combining higher-order masking with controlled time randomization enhanced protection against machine learning-based attacks. Comprehensive power trace analysis using 12-bit precision at 500 MS/s sampling rates was conducted. Statistical evaluation utilized Pearson's correlation and Welch's t-tests with a 0.8 threshold for key recovery. Real world validation in IoT, financial, and satellite scenarios highlighted practical post-quantum cryptography (PQC) deployment challenges. The study provides concrete design guidance for efficiently securing hardware Kyber implementations against side-channel attacks.
Copyright (c) 2025 Shreyas Kasture, Sudhanshu Maurya, Alakshendra Pratap Singh, Amit Shukla, Arnav Kotiyal, Kashish Mirza http://creativecommons.org/licenses/by-sa/4.0
Evaluation of midwifery educated mobile applications for labor guidance and a roadmap for future developers
The objective of the study was to review the midwifery guided mobile apps for labor advice, assessing features, functions, and content relevance. In February to March 2024, midwifery labor-guided applications were reviewed in mobile platforms such as the Google Play Store and Apple iTunes Store. We used multimodal evaluation tools, such as the mobile app rating scale (MARS), specific statements, and IQVIA ratings, to assess the quality of these applications. The study evaluated midwifery-guided applications, resulting in an average objective quality score of 3.96±0.96 out of 5. 'Safe delivery' scored the highest rating of 4.94, followed by 'Pregnancy mentor' (4.89), 'Hypno-birthing' (4.61), 'Obstetrics 6th edition' (4.68), and 'MSD manual guide to obstetrics' (4.56). Functionality received the highest score (4.16±0.865), followed by information (3.99±0.97), engagement (3.88±1.07), and aesthetics (3.82±0.28) areas. Subjective quality score was 3.6±1.18 out of 5 for an overall MARS score of 3.76±1.02. Most applications received favorable reviews, indicating good quality, and it is recommended that future app developers design applications that include comprehensive information on labor management.
Copyright (c) 2025 Seeta Devi, Swapnil Vitthal Rahane, Lily Podder, Sangeetha X., Kumari Dimple http://creativecommons.org/licenses/by-sa/4.0
Improved copy-move forgery detection through multilevel clustering
Copy move forgery detection (CMFD) based on keypoints remains a widely used technique; however, it often struggles to effectively identify small and smoothly tampered regions within images. This paper introduces a CMFD method that enhances detection accuracy by integrating a double-matching process with advanced region localization techniques. Delaunay triangles formed by accelerated KAZE (AKAZE) and scale-invariant feature transform (SIFT) features are matched in the double-matching process to identify suspicious regions. To ensure sufficient keypoint pairs, the set of matching triangles is iteratively expanded to include neighboring triangles, covering the entire tampered area. Subsequently, a second matching with a looser threshold is performed on the vertices. In the region localization process, the multilevel density-based spatial clustering of applications with noise (DBSCAN) effectively handles scenarios involving multiple copied regions with varying sizes. Using the standard MICC-F600 and COVERAGE datasets, experiments demonstrate that the proposed CMFD method is robust and achieves better performance than state-of-the-art baselines.
Copyright (c) 2025 Doaa Gamal Abdelazem, Hala H. Zayed, Ahmed Taha http://creativecommons.org/licenses/by-sa/4.0
Hybrid AI framework for anomaly detection and root cause analysis in multi-agent systems
Anomaly detection and root cause analysis (RCA) are critical for securing intelligent systems against evolving threats. Traditional models often suffer from high false alarms, weak adaptability to streaming contexts, and limited interpretability. This work proposes a hybrid artificial intelligence (AI) framework that integrates machine learning (ML) with prior knowledge, semantic rules, and bio-inspired modeling. The approach strengthens detection of diverse attacks, including DoS/DDoS, Probe, U2R, and R2L, while reducing human intervention. Experiments on the NSL-KDD dataset demonstrate that our method decreases spurious alerts by up to 90%, improves accuracy by 2–4%, and reduces false positives/negatives by about 4%. Beyond statistical gains, the framework ensures robustness in real-time environments, offering interpretable and scalable anomaly detection for heterogeneous systems. These results highlight the potential of hybrid symbolic–subsymbolic AI to enhance reliability in next-generation security infrastructures.
Copyright (c) 2025 Tahri Rachid, Ouammou Abdellah, Lasbahani Abdellatif, Abdessamad Jarrar, Balouki Youssef http://creativecommons.org/licenses/by-sa/4.0
Parametric optimization of microchannel heat exchanger using socio-inspired algorithms
Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.
Copyright (c) 2025 Vikas Gulia, Aniket Nargundkar http://creativecommons.org/licenses/by-sa/4.0
Large language models for pattern recognition in text data
Large language models (LLMs) are widely deployed in settings where both reliability and efficiency matter. We present a calibrated, seed‑robust empirical comparison of an encoder fine‑tuned model (bidirectional encoder representations from transformers (BERT)‑base) and a decoder in‑context model (generative pre-trained transformer (GPT)‑2 small) across Stanford question answering dataset v2.0 (SQuAD v2.0) and general language understanding evaluation (GLUE)-multi-genre natural language inference (MNLI), Stanford sentiment treebank 2 (SST‑2). Beyond accuracy, we assess reliability (expected calibration error with reliability diagrams and confidence–coverage analysis) and efficiency (latency, memory, throughput) under matched conditions and three fixed seeds. BERT‑base yields higher accuracy and lower calibration error, while GPT‑2 narrows gaps under few‑shot prompting but remains more sensitive to prompt design and context length. Efficiency benchmarks show that decoder‑only prompting incurs near‑linear latency/memory growth with k‑shot exemplars, whereas fine‑tuned encoders maintain stable per‑example cost. These findings offer practical guidance on when to prefer fine‑tuning versus prompting and demonstrate that reliability must be evaluated alongside accuracy for risk‑aware deployment.
Copyright (c) 2025 Aknur Kossayakova, Kurmashev Ildar, Luigi La Spada, Nida Zeeshan, Makhabbat Bakyt, Moldamurat Khuralay, Omirzak Abdirashev http://creativecommons.org/licenses/by-sa/4.0
The use of geographic information systems to measure the financial performance of micro enterprises
This study examines the application of geographic information systems (GIS) to measure and visualize the financial performance of micro enterprises in remote areas of East Kalimantan, Indonesia. Micro enterprises are crucial to local economies but often face barriers such as limited capital access, inadequate infrastructure, and insufficient business training. Using a mixed-method approach, the research combined surveys of 200 micro business owners, secondary economic data, and GIS-based spatial analysis. The results indicate clear spatial disparities: enterprises located closer to financial institutions and training programs achieved 25–30% higher profitability and stronger operational resilience. GIS mapping effectively identified performance clusters and underserved zones, providing actionable insights for targeted policy interventions. Key factors influencing financial outcomes include access to capital, training opportunities, and infrastructure quality. This study demonstrates the value of GIS as a decision-support tool for policymakers in designing spatially informed financial assistance, infrastructure planning, and mobile training deployment. The findings contribute to socio-economic planning discourse and propose a replicable GIS-based framework for strengthening microenterprise resilience in underdeveloped regions.
Copyright (c) 2025 Elfreda Aplonia Lau, Sri Endayani, Umi Kulsum, Andrew Stefano, Abdul Rokhim, Purbawati http://creativecommons.org/licenses/by-sa/4.0
Enhanced object tracking with artificial bee colony, motion modeling, and deep learning
As a fundamental aspect of computer vision, visual object tracking supports a wide array of applications, notably in transport infrastructure and advanced industrial automation. Although correlation filter-based trackers demonstrate robust performance, they face persistent limitations including scale changes, object occlusion, boundary artifacts, and complex background interference. To address these issues, we have introduced an approach that combines artificial bee colony (ABC) optimization, deep neural architectures, and Kalman filtering techniques. Our methodology begins with reliability assessment of the tracking pipeline, proceeding to compute target confidence measures at the predicted position, followed by an adaptive update mechanism. The proposed system leverages ABC optimization for dynamic scale adaptation while employing Kalman filtering to model inter-frame target motion dynamics. Comprehensive evaluation across multiple benchmark datasets demonstrates our method's efficacy, precision, and resilience, achieving enhanced performance relative to existing state-of-the art approaches.
Copyright (c) 2025 Ramdane Taglout, Bilal Saoud http://creativecommons.org/licenses/by-sa/4.0