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.
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Automated menu planning for pregnancy based on nutrition and budget using population-based optimization method
Nutritional fulfilment during pregnancy depends on the budget. Meanwhile, nutrition is needed during pregnancy to keep the mother and fetus healthy. Therefore, this study aims to assist maternal nutrition planning by using population-based optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), duck swarm algorithm (DSA), and whale optimization (WO) according to their nutritional needs at minimum cost. Additionally, this study compares the method performance to find the best method. There are 55 foods obtained from previous studies divided into five groups: staple food (SF), vegetables (VG), plant-source food (PS), animal-source food (AS), and complementary (CP). The model evaluation results show that GA's performance differed significantly from other models because it obtained the highest fitness by 439.73 and more variation in fitness results. Three models other than GA have no significant difference, but DSA performance obtained a superior fitness of 367.18. Furthermore, optimization methods must be combined with other artificial intelligence methods to develop innovative technology to support maternal nutrition and prevent stunting.
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Optimizing nonlinear autoregressive with exogenous inputs network architecture for agarwood oil quality assessment
Agarwood oil is highly valued in perfumes, incense, and traditional medicine. However, the lack of standardized grading methods poses challenges for consistent quality assessment. This study proposes a data-driven classification approach using the nonlinear autoregressive with exogenous inputs (NARX) model, implemented in MATLAB R2020a with the Levenberg-Marquardt (LM) algorithm. The dataset, sourced from the Universiti Malaysia Pahang Al-Sultan Abdullah under the Bio Aromatic Research Centre of Excellence (BARCE) and Forest Research Institute Malaysia (FRIM), comprises chemical compound data used for model training and validation. To optimize model performance, the number of hidden neurons is systematically adjusted. Model evaluation uses performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), epochs, accuracy, and model validation. Results show that the NARX model effectively classifies agarwood oil into four quality grades which is high, medium-high, medium-low, and low. The best performance is achieved with three hidden neurons, offering a balance between accuracy and computational efficiency. This work demonstrates the potential of automated, standardized agarwood oil quality grading. Future research should explore alternative training algorithms and larger datasets to further enhance model robustness and generalizability.
Artificial intelligence applications in agriculture: a systematic review of literature
Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.
Object detection for indoor mobile robot: deep learning approaches review
Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Efficiency search: application of nature-inspired algorithms in artificial intelligence forecasting models
This study reviews how nature-inspired optimization algorithms (NIOAs) have been applied to artificial intelligence-based demand forecasting, using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and clustering analysis to examine 36 selected articles. The findings reveal that NIOAs, particularly genetic algorithms and swarm intelligence methods, including their hybrids, have been frequently applied to long short-term memory (LSTM) and other backpropagation neural network models (BPNN). A key insight is the differentiated application of NIOAs depending on network depth: In shallow networks, they have been effectively used to optimize trainable parameters, whereas in deep networks, their role has focused primarily on hyperparameter optimization due to the prohibitive dimensionality of trainable weights. In all studies, NIOA-optimized models consistently outperform conventional baselines based on backpropagation. However, persistent challenges such as excessive execution times and slow convergence have led to the development of more efficient hybrid strategies and adaptive mechanisms for automated exploration-exploitation control. By mapping explored and unexplored pathways, summarizing key outcomes and techniques, and identifying promising methodologies, this review offers a practical foundation to guide future experiments and implementations involving NIOA-based optimization strategies in neural network models. As a conceptual contribution, it also proposes an innovative use of multispace optimization to address one of the most critical challenges identified: the optimization of trainable parameters in deep neural networks.
Temporal context of lightweight network model for detecting boats approaching the tsunami early warning system
The tsunami early warning system (TEWS) is a device that detects potential tsunamis. However, a boat that approaches TEWS is a source of communication disturbance. A convolutional neural network (CNN), as part of intelligent computer vision, is one solution for detecting boats and providing a warning to move away from the TEWS area. Water segmentation and refinement-temporal (WaSR-T), as the current advanced CNN network, exhibits impressive performance in detecting object obstacles in the marine domain, although it requires a powerful computational device. In the paper, we propose a modification of WaSR-T, replacing the most computationally intensive stages with a lightweight version called lightweight WaSR-T. On the proposed lightweight WaSR-T, the previous encoder of WaSR-T was replaced with MobileNetV3, and some feature layer maps were reduced as input to the decoder. For training and validating the lightweight WaSR-T, the image dataset representing the open sea and our extended dataset from Indonesia's ocean region were used. Based on the quantitative results and evaluation of the computational load, the sensitivity to detect a boat for WaSR-T and lightweight WaSR-T is 95.71% and 90.00%, respectively. The lightweight WaSR-T required less memory at 32.57%, resulting in a 0.0761% reduction in total processing time compared to the original WaSR-T. Therefore, our proposed lightweight WaSR-T is promising for use as the central part of an intelligent maritime computer vision system in TEWS.
Enhanced solar panels fault detection approach using lightweight YOLO
Artificial intelligence (AI)-driven fault detection improves the reliability of solar energy by reducing the chances of system failures. However, existing single-stage object detection methods excel in accuracy but demand high computational resources, preventing seamless integration into embedded systems. This paper introduces a lightweight approach using YOLOv5, which incorporates a multi-backbone design, specifically tailored for accurate fault detection in solar cells. It evaluates YOLOv5 and TinyYOLOv5. The findings emphasize the effectiveness of YOLOv5l with Ghost backbone, particularly notable for its precision rates of 96% for faulty and 93% for non-faulty instances. Additionally, it showcases commendable mean average precision (mAP) scores, achieving 78% at an intersection over union (IoU) threshold of 0.5 and 72% at an IoU of 0.95. Additionally, YOLOv5_Ghost emerges as the optimal selection, showcasing competitive precision, processing speed of 42.1 giga floating point operations per second (GFLOPS), and remarkable efficiency with 2.4 million parameters. This evaluation underscores the effectiveness of YOLOv5 models, thereby leading to advanced solar energy technology significantly.
A hybrid steganography scheme with reduced difference expansion and pixel-value ordering
Steganography embeds secret messages into public media while ensuring the stego content remains visually indistinguishable from the original. The primary challenge lies in maximizing embedding capacity and image quality without introducing noticeable distortions. This research proposes a novel reversible data hiding (RDH) scheme that integrates reduced difference expansion (RDE) with four directional pixel-value ordering (PVO) schemes, horizontal, vertical, diagonal-right, and diagonal-left, to enhance embedding efficiency and visual fidelity. Unlike existing RDH methods that apply RDE with fixed or limited PVO directions, the proposed scheme dynamically selects the optimal PVO orientation based on pixel pair characteristics, effectively improving local prediction accuracy and reducing embedding-induced distortion. Previous studies have largely overlooked this relationship between pixel pair selection and embedding performance. Experimental evaluation on medical images with secret data sizes ranging from 5 kb to 100 kb demonstrates significant gains over recent PVO-based methods. The proposed method increases the average embedding capacity from 0.8315 to 0.9781 bit per pixel (bpp) (a 17.6% improvement) and raises the average peak signal-to-noise ratio (PSNR) from 49.44 to 53.40 dB, reducing distortion by approximately 3.96 dB.
Pre-trained convolutional neural network-based algorithms: application for recognizing the age category
Cybercrime is a major issue in the current digital era, with one of its branches-cyber pornography-notably affecting Indonesia. Various efforts have been made to suppress or prevent this problem. One alternative solution involves using technological advances to recognize age ranges based on facial recognition. This age range recognition can be implemented to prevent users from accessing content that is not appropriate for their age. An optimal age-range recognition system is essential for this purpose. However, limited research has focused on this domain. Therefore, our research aimed to develop the best possible system. The proposed method applies a trained convolutional neural network (CNN) as a feature extractor to the artificial neural network (ANN) and k-nearest neighbor (K-NN) methods for age recognition based on facial images. By incorporating computational learning techniques, the system's performance is significantly enhanced, leveraging advanced algorithms to improve accuracy. The test results show that the performance of the pre-trained CNN-based ANN model is superior. This is indicated by the model's accuracy and F1-score, which were 11% and 0.11 higher, than the pre-trained CNN-based K-NN model. The error rate of the pre-trained CNN-based ANN model was also reduced by 0.11.
FaceSynth: text-to-face generation using CLIP and its variants with generative adversarial networks
In recent years, there have been massive developments in the field of generative AI, especially in generative adversarial networks (GANs). GANs generate original images that haven't been seen during training and have had several advancements like StyleGAN, StyleGAN2, and StyleGAN2-adaptive discriminator augmentation (ADA). Contrastive language-image pre-training (CLIP), by OpenAI, is a visual linguistic model that has been trained to associate texts with images. Recently, new CLIP variants were developed, such as metadata-curated language-image pre-training (MetaCLIP), released by Facebook and trained on a larger dataset, and Multilinigual-CLIP, which adapts CLIP to multiple languages. We compare CLIP and its variants in text-to-face synthesis with a custom StyleGAN2-ADA model and a pre-trained StyleGAN2 model. Our training-free algorithm starts with an initial image latent code that is iteratively manipulated to match a given text description. It achieves this by minimizing the distance between the text and image embedding in the multi-modal embedding space of the CLIP models. An examination of CLIP and its variants showed that MetaCLIP outperformed its competitors in LPIPS similarity and closeness of the synthesized image to the actual prompt. CLIP produced the most realistic images with the best FID score and multilingual-CLIP presented a choice of input text language and generated decent images.
Automatic essay scoring: leveraging Jaccard coefficient and Cosine similarity with n-gram variation in vector space model approach
Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models (VSM) employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.
Students’ perceptions and effect of ChatGPT on research proposal quality across gender in Indonesia
The use of ChatGPT in improving students' academic writing abilities has been extensively studied, but how students perceive the use of ChatGPT affecting the quality of thesis proposals remains unclear becomes the novelty of this research. To address this gap, this study examines final semester students (N=55) utilizing ChatGPT in preparing final assignments across universities in East Indonesia. Employing a mixed-methods research design, this study collected data through surveys and short essays. Descriptive statistics, bivariate correlations, independent t-tests, linear regression, and thematic analysis were used to analyze the data. Findings indicate that i) perceptions of ChatGPT positively correlate with the quality of students' proposals, ii) perceptions of ChatGPT use predict the quality of research proposals, iii) gender does not influence perceptions of ChatGPT use or the quality of students' proposals, and iv) ChatGPT has a positive impact as research reference model, source of ideas, framework reference, translation tool, and paraphraser, but it also has limitations, particularly in providing accurate responses and posing a risk of reducing critical thinking abilities. ChatGPT has proven effective in helping students prepare research proposals, developing ideas, and research frameworks. However, institutions must provide appropriate guidance to prevent the decrease of critical thinking abilities.
Stock market liquidity: hybrid deep learning approaches for prediction
Predicting stock market liquidity especially in emerging or frontier financial markets, such as the Casablanca stock exchange (CSE), presents significant challenges given the relative narrowness and volatility of these markets. In this paper, we conduct a comprehensive study to address the predictions accuracy gaps between five main deep learning models: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and two hybrid architectures, CNN-LSTM and CNN-BiLSTM. The proposed methodology focused on training and testing these models on historical data from the CSE, with precision on capturing both spatial and temporal market dynamics. The models were fine-tuned using key hyperparameters and validated on 20% of the dataset to ensure reliable results. The evaluation of performance was conducted using error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The study demonstrates that the hybrid CNN-biLSTM model consistently outperformed all standalone and other hybrid models in predictive accuracy. This underscores the considerable promise of hybrid deep learning architectures for addressing the unique challenges of predicting stock market liquidity in volatile and emerging financial markets.
Comparative analysis of gender classification methods using convolutional neural networks
Gender classification has become an important application in the fields of system automation and artificial intelligence, having important implications across various fields. The main challenge in this classification task is the variation in illumination that affects the quality of facial images. This study presents a method for identifying genders with Convolutional Neural Networks (CNNs). To address this issue, various preprocessing methods are applied, including Self Quotient Image (SQI), Histogram Equalization, Locally Tuned Inverse Sine Nonlinear (LTISN), Gamma Intensity Correction (GIC), and Difference of Gaussian (DoG), to stabilize the effects of illumination variations before the images are processed by the CNN. The CNN architecture used consists of 5 convolutional blocks and 2 fully connected blocks, which have proven effective in image recognition. The results of the study show that the model trained with the DoG method achieved an accuracy of 91.07%, making it the best preprocessing technique compared to other methods such as SQI and HE, which achieved accuracies of 90.39% and 88.76%, respectively. These findings demonstrate that the application of SQI in CNN can improve the accuracy of gender classification on facial images, providing better performance than previous methods. These findings are expected to serve as a foundation for further developments in facial image classification and its applications in various fields.
A competitive learning approach to enhancing teacher effectiveness and student outcomes
Machine learning has found extensive application and improvement in the field of education. Nevertheless, there remains a lack of research studies focusing on unsupervised learning within this domain. To address this gap, our study aims to investigate the relationship between teacher attributes and student achievement in Morocco while identifying regions requiring attention and intervention, using a novel clustering approach based on unsupervised competitive learning, specifically the 'Centroid neural network', to cluster Moroccan teachers based on their qualities and qualifications. Teacher qualities and qualifications are operationalized as initial teaching qualifications, completion of training programs, and employment status. To achieve our objective, we utilize the program for international student assessment (PISA) dataset, which provides comprehensive responses from individual students, including information on parental backgrounds, socio-economic positions, and school conditions. Additionally, we incorporate data from the teacher questionnaire, which encompasses background information, initial education, professional development, teaching practice, and teacher beliefs and attitudes. Consistent with previous research, our findings suggest that teachers' qualities and qualifications significantly influence student performance. Furthermore, our clustering approach identifies regions where there is a pronounced prevalence of attributes negatively impacting student achievement. Urging academicians to incorporate resilience-building measures into the design of policies in these regions to improve students' educational outcomes.
Inverse-Mel scale spectrograms for high-frequency feature extraction and audio anomaly detection in industrial machines
Unlike humans, the energies in industrial machine sounds (IMS) vary across a wide range of frequencies. Mel scales, which are developed for the perception of human audio, fail to capture the complete information present in IMS. To improve performance, we propose using an inverse-Mel scale, along with the concatenation and combination of Mel and inverse-Mel scale based spectrograms, as feature vectors for audio anomaly detection (AAD) in industrial machines. Adaptation in the Librosa Python package and the DCASE 2022 Challenge Task 2 baseline system is pursued for the construction of inverse-Mel scale spectrograms. Experiments are conducted using the malfunctioning industrial machine investigation and inspection for domain generalization (MIMII DG) datasets. Systems based on the inverse-Mel scale achieve a maximum improvement of up to 37% in the bearing machine and an average improvement of up to 9% in the area under the curve (AUC) score across all machines in the MIMII DG datasets. The proposed features also enhance DG, overcoming the effects of environmental and operational domain shifts caused by variations in recording setup, load, background noise, and operational patterns. Challenge official evaluator assessed the proposed system against the evaluation datasets, ranking it three positions higher than the baseline system.
Transforming campus mobility: the DigiSticker system in digital parking solutions
University digital parking systems have several benefits and solve many problems with traditional parking. Universities without a digital parking system face restricted parking, traffic congestion, inefficient space utilization, security issues, limited decision-making data, and diminished sustainability initiatives. This study paper discusses the benefits of digital parking systems and the drawbacks of traditional methods. By using technology to streamline university parking administration, the DigiSticker system offers an innovative solution. The DigiSticker system improves parking efficiency, convenience, and security for students, professors, and staff by delivering real-time parking information, assistance, and automated payments. This system has a user-friendly website and mobile app, fast registration, gate access management, security, user experience enhancement, and sustainability. Universities can improve student and staff parking experiences while improving parking management efficiency, security, and sustainability by meeting these requirements.
Optimizing diabetes prediction: unveiling patient subgroups through clustering
Diabetes is a significant global health concern, leading to numerous deaths annually and affecting many individuals who remain undiagnosed. As its prevalence rises, the importance of early detection becomes increasingly vital. The rising diabetes epidemic demands data-driven strategies to catch health problems sooner and identify them clearly. This study utilizes the Pima Indians diabetes dataset (PIDD) to compare three powerful clustering schemes such as k-means, fuzzy C-means, and hierarchical. Uncontrolled diabetes, arising from the body's struggle to manage blood sugar due to insulin deficiency, can lead to devastating complications. Early detection and intervention are the cornerstones of effective management and improved patient outcomes. This study breaks new ground by meticulously evaluating the performance of each clustering algorithm using advanced metrics like silhouette score and adjusted Rand index. The goal is to identify the method that generates the most accurate and well-defined clusters for diabetes-related attributes. This, in turn, has the potential to revolutionize diabetes diagnosis, enabling earlier interventions and ultimately leading to better disease management and patient care. By providing a comprehensive comparison of these clustering techniques, this research offers a significant contribution to the fight against diabetes.
Fuzzy risk assessment system for indoor air quality and respiratory disease prevention
This study addresses the evaluation of indoor air quality, with a focus on mitigating respiratory diseases and sick building syndrome (SBS). Recognizing that different pollutants exhibit variable behavior depending on environmental factors and human activity, the objective was to develop a fuzzy logic-based classification system that integrates environmental variables such as temperature, relative humidity, and pollutant concentrations‒particulate matter (PM10, PM2.5), carbon dioxide (CO₂), and total volatile organic compound (TVOC)‒into a unified model. The method involved defining risk levels as low, moderate, high, and very high, and implementing 54 fuzzy rules to dynamically and accurately categorize these risks, based on measurements taken between 2022 and 2024 in the states of Morelos and Puebla under various relative humidity and temperature scenarios. The analysis of the results demonstrated robust system performance, with an overall accuracy of 94.08%, but also revealed challenges in distinguishing between adjacent risk classes. This research contributes to a better understanding of the complex impacts of air quality on health and reinforces efforts to mitigate respiratory problems and SBS in densely populated indoor environments.
Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic
Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.
Comparative analysis of convolutional neural network architectures for poultry meat classification
The increasing demand for standardized food quality assurance, particularly in regions like Morocco, emphasizes the need for accurate classification of poultry meat. This study evaluates and compares ten convolutional neural network (CNN) architectures—VGG19, VGG16, ResNet50, GoogleNet, MobileNetV1, MobileNetV2, DenseNet, NasNet, EfficientNet, and AlexNet—for classifying commonly consumed poultry meat types in Moroccan markets, including chicken, turkey, fayoumi, and farmer’s chicken. A labeled image dataset was used to train and test each model, with performance assessed using metrics such as accuracy, precision, recall, training time, and computational complexity. Additionally, the study investigates how dataset size influences model performance, addressing challenges like limited data availability and scalability. The results highlight DenseNet as the top-performing architecture, achieving 98% classification accuracy while also demonstrating superior computational efficiency. These findings are valuable for improving food quality control, offering data-driven support for stakeholders in poultry production, distribution, and regulatory bodies. By identifying optimal deep learning models for poultry meat classification, the study contributes to enhancing food authentication and safety in Morocco and similar regions. It also encourages the integration of AI-driven systems in food inspection processes, providing scalable, accurate, and efficient solutions for ensuring standardized quality in the poultry supply chain.
Optimized convolution neural network with ant colony algorithm for accurate plant disease detection
In India, agriculture is the primary source of income for half the people. Even in situations of fast population growth, agriculture supplies nourishment for all people. To provide food for the entire population, it is advised to detect plant diseases at an early stage. Plant leaf diseases are recognized using images of the affected leaves. Deep learning (DL) research seems to offer several opportunities for increased accuracy. Ant colony optimization with convolution-neural-network (ACO-CNN), a new deep learning technique for identifying and categorizing diseases, is presented in this article. Ant colony optimization (ACO) was used to examine the efficacy of disease diagnostics in plant leaves. The convolution neural network (CNN) classifier is used to remove texture, color, and leaf arrangement geometry from the input images. The ACO-CNN model outperformed the support vector machine (SVM) and CNN models in terms of precision, recall, and accuracy. CNN's rate is 81.6% as compared to SVM's 80% accuracy level. In the “ACO-CNN” approach, the F1-score, recall, and precision have higher rates as compared to other models, and the “F1-score” has the highest rate compared with other models since the ACO-CNN model has an accuracy rate of 91.00%.
Comparison of HSV-color and ANN-HSV-color segmentation for detecting soybean adulteration
Soybeans are an important food crop, but their quality is often compromised by contamination with other materials, a process known as adulteration. Conventional methods for detecting adulteration are slow; therefore, there is a need for rapid and non-invasive alternatives. This study aimed to assess the capability of hue-saturation-value (HSV) color segmentation and its combination with artificial neural networks (ANN) to identify adulteration in soybean samples. This research employed image processing and machine learning to segment soybeans mixed with adulterants at concentrations of 5%, 10%, 15%, 20%, and 25%. The HSV method successfully distinguished soybeans and other materials, but some challenges were observed in shadow regions and areas with similar colors. The HSV-ANN model with six hidden layers performed well with a calibration accuracy of R² value of 0.97 and root-mean-square error (RMSE) of 2.16%, which provided more detailed segmentation, although it still had some problems in shadow regions and undetected corn embryo parts. The validation results indicated that the HSV model had an R² value of 0.98 and RMSE of 4.48%, while the HSV-ANN model had an R² value of 0.96 and RMSE of 1.3%. Both models were capable of predicting the levels of adulteration, and the HSV-ANN model proved to be more accurate. It is concluded that both methods are efficient; however, there is a need for more work on modeling and sampling to increase the segmentation precision and decrease the biases, especially in the shadow and overlapped color.
Backpropagation neural networks for solving gas flow equations in porous media
This study proposes a backpropagation neural network (BPNN) as an alternative solver for nonlinear equations in gas flow simulation through porous media. Conventional solvers like the Newton-Raphson (N-R) method are accurate but may become inefficient for large-scale or heterogeneous systems. We develop a feedforward BPNN architecture with adaptive learning rates to solve discretized residual equations from the one-dimensional gas flow model. The methodology includes finite difference discretization and mapping the nonlinear algebraic system into a four-layer neural network. The BPNN solver is validated against the Newton method across various grid sizes and heterogeneous permeability-porosity distributions. Results show that BPNN achieves high accuracy, with maximum absolute errors (MAE) of only 0.241 psi in the homogeneous model and 0.0418 psi in the heterogeneous model. While the BPNN requires more iterations and longer computation time, especially for finer grids, it exhibits the ability to learn pressure patterns and improve efficiency over time. This approach demonstrates that BPNN can serve as a viable nonlinear solver in reservoir simulation, offering flexibility in handling nonlinearities while maintaining accuracy.
MobileChiliNet: convolutional neural network for chili leaves classification
Chili pepper (Capsicum annuum) is an important crop in many countries, including Indonesia, which plays an important role in local economy and food production. To meet the high demand, effective agricultural management, especially the diagnosis and treatment of plant diseases, is essential. This study aims to improve the accuracy of chili leaf disease classification while reducing the computational cost so that it can be applied to low-cost smart farming systems. Through the development of the MobileChiliNet architecture, which is the result of pruning and fine-tuning of MobileNetV2, this model achieves the best accuracy, better than other CNNs such as ResNet50 and VGG16. Testing with various optimizers and learning rate schedulers shows that AdamW with PolynomialDecay provides the best performance by increasing the validation accuracy to 96.48%. This approach successfully reduces the computational complexity while maintaining high accuracy, so that it can be implemented in smart farming systems at a lower cost.
Data-driven clustering and prediction of high school graduation rates in Indonesia (2015-2023) using machine learning
This study aims to analyze the graduation rate of senior high school education in 34 Indonesian provinces during the period 2015-2023 and identify patterns of educational disparities between regions. To achieve the objectives, this study applies a neural network to predict education completion patterns based on historical data, then the prediction results are analyzed using K-means clustering technique utilizing the elbow method to select the ideal number of clusters. The clustering results show three categories of provinces based on education completion rates: high, medium, and low. The provinces with high completion rates, generally, supported with good education infrastructure and effective policies, while the medium category faces challenges in resource distribution, but still potentially improve. In contrast, the low category suffers from limited access, geographical constraints, and socio-economic disparities. This research contributes to education policy-making by offering a machine learning-based approach to understanding education disparities between regions. The new insight offered by this study lies in the integration of neural network and K-means clustering in mapping education completion rates to support strategies for improving access and quality of education in Indonesia.
An algorithm for training neural networks with L1 regularization
This paper presents a new algorithm for building neural network models that automatically selects the most important features and parameters while improving prediction accuracy. Traditional neural networks often use all available input parameters, leading to complex models that are slow to train and prone to overfitting. The proposed algorithm addresses this challenge by automatically identifying and retaining only the most significant parameters during training, resulting in simpler, faster, and more accurate models. We demonstrate the practical benefits of the proposed algorithm through two real-world applications: stock market forecasting using the Wilshire index and business profitability prediction based on company financial data. The results show significant improvements over conventional methods: models use fewer parameters–creating simpler, more interpretable solutions–achieve better prediction accuracy, and require less training time. These advantages make the algorithm particularly valuable for business applications where model simplicity, speed, and accuracy are crucial. The method is especially beneficial for organizations with limited computational resources or that require fast model deployment. By automatically selecting the most relevant features, it reduces the need for manual feature engineering and helps practitioners build more efficient predictive models without requiring deep technical expertise in neural network optimization.
Copyright (c) 2025 Ekaterina Gribanova, Roman Gerasimov http://creativecommons.org/licenses/by-sa/4.0
Arithmetic artificial bee colony optimization algorithm with flexible manipulator system
The artificial bee colony (ABC) algorithm, a well-known swarm intelligence-based metaheuristic inspired by the food foraging behavior of honeybees, has been widely applied to solve complex optimization problems. Despite its effectiveness, the standard ABC algorithm suffers from drawbacks such as slow convergence rates, limited balance between exploration and exploitation, and a tendency to get stuck in local optima, thereby hindering its overall performance. This study introduces an enhanced variant of the ABC algorithm, integrating the exploration strategy of the arithmetic optimization algorithm (AOA) to overcome these limitations. The enhanced algorithm is thoroughly tested on a set of benchmark functions as well as a flexible manipulator system model. Comprehensive statistical analyses are employed to evaluate and compare the performance of the improved algorithm against the original ABC. The results demonstrate that the enhanced ABC algorithm delivers superior performance in both benchmark scenarios and the flexible manipulator application.
Copyright (c) 2025 Mohd Ruzaini Hashim, Ahmad Fitri Mazlan, Mohammad Osman Tokhi http://creativecommons.org/licenses/by-sa/4.0
Comparison among search algorithms for hyperparameter of support vector machine optimization
Support vector machine (SVM) is widely used in machine learning for classification and regression tasks, but its performance is highly dependent on hyperparameter tuning. Therefore, fine-tuning these parameters is key to improving accuracy and generality. Recently, many researchers have focused only on applying different algorithms to optimize these parameters. There is a shortage of studies that compare the performance of these methods. Hence, research is needed to compare the performance of these algorithms for the hyperparameters of the SVM optimization problem. This paper compares five optimization algorithms for tuning SVM hyperparameters: grid search (GS), random search (RS), Bayesian optimization (BO), genetic algorithm (GA), and the novel chemical reaction optimization (CRO) algorithm. Experimental results on benchmark datasets such as iris, digits, wine, breast cancer Wisconsin, and credit card fraud demonstrate that CRO consistently outperforms other methods in terms of classification scoring metrics and computational time. It achieves improvements in accuracy, precision, recall, and F1-score of up to 1% on balanced datasets and up to 10% on highly imbalanced datasets such as credit card fraud. It also reduces computation time by up to 50% compared to GS, BO, and RS. These findings suggest that CRO is a promising approach for hyperparameter optimization (HPO) of SVM models.
Copyright (c) 2025 Nguyen Ba Nghien, Cuong Nguyen Cong, Nhung Nguyen Thi, Vuong Quoc Dung http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition
Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.
Copyright (c) 2025 Areen Arabiat, Muneera Altayeb, Sanaa Salama http://creativecommons.org/licenses/by-sa/4.0
A comprehensive artificial intelligence framework for reducing patient rehospitalizations
The role of artificial intelligence (AI) in the healthcare sector is increasing daily. Readmissions of patients have become a significant challenge for the medical sector, adding unnecessary burden. Governments and public sectors are continuously working on the hospital readmissions reduction program (HRRP). In this research work, an AI framework has been developed to reduce patient readmissions. The accuracy of the framework has been increased by continuous refinement in feature engineering, incorporating several complex datasets. The framework analyses the different algorithms like bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and XGBoost for prediction. This framework has shown a 92% accuracy rate during testing, showing a 37% reduction in 40-day rehospitalization rates. This reduces the overburden on hospital systems by avoiding unnecessary readmissions of patients. The system’s real-time development, scalability, management of things in an ethical manner, and long-term viability will remain as future scope.
Copyright (c) 2025 Ganesh Khekare, Midhunchakkaravarthy Janarthanan http://creativecommons.org/licenses/by-sa/4.0
Early goat disease detection using temperature models: k-nearest neighbor, decision tree, naive Bayes, and random forest
This study aims to aid livestock activities by enabling early detection of diseases in goats through body temperature measurement. Early detection is crucial to prevent disease spread and improve livestock welfare. Using the knowledge discovery in databases (KDD) methodology, the study involves collecting, processing, and analyzing goat body temperature data. Four algorithms—k-nearest neighbor (KNN), decision tree, naive Bayes, and random forest—were used to develop disease detection models. The decision tree algorithm was found to be the most accurate, achieving 100% accuracy. This demonstrates its effectiveness in detecting diseases based on body temperature. Implementing this model is expected to significantly benefit farmers by helping maintain the health and productivity of their livestock.
Copyright (c) 2025 Fareza Ananda Putra, Wella http://creativecommons.org/licenses/by-sa/4.0
Recommendation system for football player recruitment using k-nearest neighbor
In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.
Copyright (c) 2025 Maukar, Rodiah http://creativecommons.org/licenses/by-sa/4.0
Optimizing battery life: a TinyML approach to lithium-ion battery health monitoring
Electrical vehicles (EVs) are crucial nowadays due to their reduction in greenhouse gas emissions, decreasing dependence on remnant fuels, and improving air quality. For EVs, the battery is the heart that determines range, performance, and efficiency. Also, it directly impacts the cost and overall vehicle life span. Lithium-ion (Li-ion) batteries are pivotal in powering modern portable electronics and electric vehicles due to their high energy density and durability. Issues with current batteries include slow charging, short cycles, and low energy density. Most of the problems with current batteries are resolved by Li-ion batteries, which also helps explain why EV usage is increasing globally. However, to guarantee maximum performance and safety, estimating the remaining useful life and health state of these batteries remains a major difficulty. To improve battery lifetime of the battery and to overcome the problems of delayed charging, this study introduces a tiny machine learning (TinyML) method. An innovative machine learning approach is put forth that allows for effective learning on devices with limited resources, which enables real-time monitoring of the health status of the Li-ion batteries.
Padmanabhan Puthiyaveedu Krishnankutty Nair
Copyright (c) 2025 Kamaraj Lalitha Nisha, Vasanth Pradeep, Padmanabhan Puthiyaveedu Krishnankutty Nair, Sreelakshmi Pillai, Manikandan Arunachalam, Rakesh Thoppaen Suresh Babu http://creativecommons.org/licenses/by-sa/4.0
Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning
This study presents a novel sensor data fusion framework designed to improve accuracy and energy efficiency in internet of things (IoT)-driven wireless sensor networks (WSNs). The proposed approach combines machine learning techniques with the Kalman filter, addressing the limitations of traditional methods, such as high computational overhead and limited precision. By utilizing machine learning algorithms for pattern recognition and the Kalman filter for precise state estimation, the framework optimizes data processing while minimizing energy consumption. MATLAB-based simulations validate the model’s effectiveness, demonstrating a significant improvement in key performance metrics, including F1-score, recall, and precision, with an overall accuracy of 98.36%. The results highlight the framework’s ability to enhance fault tolerance, accelerate convergence rates, extend network lifespan, and optimize energy utilization, making it highly suitable for real-time data fusion applications in complex sensor environments. Furthermore, the proposed hybrid model is scalable and adaptable, allowing it to be implemented across various fields, including environmental surveillance, industrial automation, and healthcare monitoring. With integration of intelligent data processing techniques, this research contributes to the development of sustainable and efficient IoT-based monitoring systems capable of handling dynamic and resource-constrained environments.
Copyright (c) 2025 Naganna Shankar Sollapure, Poornima Govindaswamy http://creativecommons.org/licenses/by-sa/4.0
Optimized ensemble framework for predicting hydroponic stock and sales using machine learning
The increasing global demand for food necessitates the adoption of sustainable agricultural practices. Hydroponic farming, while efficient in resource utilization, faces challenges in accurately predicting stock levels and sales due to dynamic, ever-changing factors. This research presents an optimized ensemble framework for forecasting hydroponic stock levels and sales by integrating linear regression (LR), random forest (RF), and XGBoost, further enhanced through an evolutionary algorithm (EA). The proposed framework is evaluated using root mean square error (RMSE) and mean absolute error (MAE), demonstrating significant accuracy improvements over individual models. The ensemble model achieves an RMSE reduction of 43.82% for stock prediction and 55.3% for sales forecasting compared to the best-performing individual model. Additionally, local interpretable model-agnostic explanations (LIME) are employed to offer stakeholders clear insights into decision-making processes, such as identifying "number of harvested crops" and "sales data" as key drivers of prediction outcomes. This framework supports sustainable development goals (SDGs) 9.3, 12.3, and 12.C by promoting resource efficiency, reducing food waste, and improving small-scale farmer market access. Future research will explore real-time data integration for dynamic adaptation and further model enhancements.
Copyright (c) 2025 Viktor Handrianus Pranatawijaya, Ressa Priskila, Putu Bagus Adidyana Anugrah Putra, Nova Noor Kamala Sari, Efrans Christian, Septian Geges, Novera Kristianti http://creativecommons.org/licenses/by-sa/4.0
Accuracy of long short-term memory model in predicting YoY inflation of cities in Indonesia
Our research evaluates the effectiveness of the long short-term memory (LSTM) model in forecasting annual year-on-year (YoY) inflation across 82 cities in Indonesia based on time series data from BPS economic reports for 2014-2024. This study tests the accuracy of the model in reconstructing past inflation patterns, then evaluates the capabilities and limitations of the model in various urban area contexts with the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination(R2) metrics. The findings show that LSTM performs well in metropolitan areas such as Jakarta, Bandung, and Surabaya with R2values >0.8 and the lowest MAPE of 10.91% in Jakarta. However, in small cities with higher economic volatility such as Tanjung Pandan, the model shows significant prediction errors (R²<0.50 and MAPE up to 283.11%). Moderate performance (0.50≤ R²≤0.80) was found in cities such as Palembang, Semarang, and Makassar, reflecting the model's adaptive ability to moderate inflation patterns. These results emphasize the important role of structured economic data in improving the reliability of predictions, so that the policy implications of this study include the use of the LSTM model as an early warning system by fiscal and monetary authorities, as well as the need for a data-based inflation control strategy to strengthen regional and national economic resilience in supporting sustainable development towards Indonesia Emas 2045.
Copyright (c) 2025 Harfely Leipary, Adi Setiawan http://creativecommons.org/licenses/by-sa/4.0
Advanced risk assessment using machine learning and sentiment analysis on log data
Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.
Copyright (c) 2025 Nidal Turab, Abdelrahman Abushattal, Jamal Al-Nabulsi, Hamza Abu Owida http://creativecommons.org/licenses/by-sa/4.0
User acceptance of the gender and development mobile app with a rating checklist using a modified technology acceptance model
Resource centers of gender and development (GAD) in local government use the traditional method of disseminating information about GAD awareness, such as distributing printed campaign materials and conducting gender sensitivity training (GST) on faculty and staff, students, and selected barangay communities in the Philippines. Some recipients of campaign materials are text-heavy and unappealing to read, which makes them less interested. However, faculty and students conducting research are not aware if their study is gender-responsive or if GAD is invisible. Hence, this study examines the user acceptance of the GAD app mobile application using the modified technology acceptance model (TAM) with a machine learning (ML) algorithm applied. The results of statistics and analyses from the intended users (N=100) were presented including data-driven modeling using a support vector machine (SVM) to show precise findings for the research on how this technology was used and accepted. The study’s findings show widespread acceptance among experts and users of the mobile application employing external factors like self-efficacy (SE) and specific anxiety (SA) and moderating variables such as age, sex, highest educational attainment (HEA), and knowledge in GAD implementation, which are crucial for predicting and understanding the consequences of the research made clear.
Copyright (c) 2025 Rossian V. Perea, Abigael M. Miranda http://creativecommons.org/licenses/by-sa/4.0
Voting classifier in pain points identification
A successful app understands and addresses the needs of its users. Pain points-specific difficulties and frustrations that users experience while using an application-are crucial for understanding user expectations and improving user experience. Google Play Store reviews can be a valuable source for identifying these pain points, but this raw data requires processing to be useful for developers. This study develops a model to automatically classify reviews as either containing pain points or not. We chose the voting classifier as our primary algorithm because of its proven ability to produce models with high accuracy through combining the strengths of multiple classifiers. After evaluating 5 different classifier methods, our research shows that the optimal model combines XGradient boosting, multinomial naïve Bayes, and logistic regression-with each contributing unique strengths in text classification. This combination achieves 90% accuracy and a 90% F1-Score, outperforming previous studies that used neural networks (which achieved 80% accuracy). The model successfully identifies user frustrations from app reviews, providing developers with actionable insights to improve their applications.
Copyright (c) 2025 Yusup Miftahuddin, Muhammad Alif Firdaus http://creativecommons.org/licenses/by-sa/4.0
Hybridized deep learning model with novel recommender for predicting criticality state of patient using MIMIC-IV dataset
The contribution of machine learning towards prediction of critical state of patient is the prime focus of the current study. The review of current approaches of machine learning has been witnessed with various shortcomings. Hence, the proposed study adopts medical information mart for intensive care (MIMIC-IV) dataset in order to develop a novel analytical model that can predict the criticality state of patient in their next visit. The model has been designed by hybridizing convolution neural network (CNN) and long short-term memory (LSTM) which takes the discrete input of hospital and individual patient information in each visit. The concatenated feature is then subjected to a newly introduced recommender module which offers implicit feedback by assigning a ranking score. The final predictive outcome of study offers criticality rank. The study model is benchmarked with existing machine learning approaches to find 54% of increased accuracy and 70% of reduced processing time.
Copyright (c) 2025 Sarika Khope, Deepali Kotambkar, Rama Vasantha Adiraju, Smita Suhas Battalwar http://creativecommons.org/licenses/by-sa/4.0
IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings
Several international organizations of public health or countries have predicted the rise of heat-related illness cases due to climate change, which result high environment temperature. Previous studies of heat-related illness prediction using internet of things (IoT) and machine learning (ML) are mainly focusing on early detection or prediction of heat stroke incidence. To overcome the problem of heat stress prediction in outdoor settings, especially for an individual, the objective of this study is to identify a prediction method for heat stress using IoT technology and analyze the accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetooth low energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor, and MAX30100 heart rate sensor were used to build IoT based wearable device. Besides, Python language is used for data pre-processing and data labelling after getting the sensor data from wearable device. Lastly, model training using neural network algorithms was directed in Edge Impulse. The result shows 94.6% of training accuracy with the loss of 0.27 and 90.22% of accuracy in testing set.
Copyright (c) 2025 Lim Ke Yin, Sumendra Yogarayan, Siti Fatimah Abdul Razak, Md Shohel Sayeed, Umar Ali Bukar http://creativecommons.org/licenses/by-sa/4.0
Two-steps feature selection for detection variant distributed denial of services attack in cloud environment
The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%.
Copyright (c) 2025 Kurniabudi, Eko Arip Winanto, Sharipuddin http://creativecommons.org/licenses/by-sa/4.0
Translation-based image steganography system utilizing autoencoder and CycleGAN
Traditional image steganography involves embedding secret information into a cover image, a process that requires modification of the carrier and potentially leaves detectable marks. This paper proposes a novel method of coverless image steganography based on generative models. Initially, a CycleGAN model is constructed and trained to learn the features of different image domains. Subsequently, an Autoencoder model is trained using two sets of images to achieve a precise one-to-one mapping. Once the models are trained, the autoencoder is used on both the sender and receiver sides to convert the cover image (also known as the stego image) into the secret image and vice versa. The CycleGAN model is then utilized to enhance the visual quality of the images generated by the autoencoder. Experimental results demonstrate that this method not only effectively secures secret information transmission but also improves efficiency and increases the capacity for information hiding compared to similar methods.
Copyright (c) 2025 Thakwan Akram Jawad, Jamshid Bagherzadeh Mohasefi, Mohammed Salah Reda Abdelghany http://creativecommons.org/licenses/by-sa/4.0
Optimizing nitik batik classification through comparative analysis of image augmentation
Nitik batik is one of the most intricate and culturally significant motifs in Yogyakarta's batik tradition, characterized by its complex, geometric dot-based patterns. The unique challenges of automatically classifying nitik batik motifs stem from the high variability within the class and the limited availability of training data. This study investigates how different image data augmentation techniques can enhance the performance of a random forest classifier for nitik batik motifs. Techniques such as geometric transformations (flip, rotate, and scaling), intensity transformations (cut-out, grid mask, and random erasing), non-instance level augmentation (pairing samples), and unconditional image generation (deep convolutional generative adversarial network (DCGAN)) were used to expand the dataset and improve the model's ability to generalize. The results show that specific techniques, notably flip, cut-out, and DCGAN, significantly improved classification accuracy, with flip achieving the highest accuracy improvement of 20.20%, followed by cut-out at 19.27% and DCGAN at 16.25%. Moreover, DCGAN demonstrated the lowest standard deviation (0.78%), indicating high stability and robustness in classification performance across multiple validation folds. These findings suggest that augmentation techniques effectively improve classification accuracy and enhance the model's ability to generalize from limited and complex datasets.
Copyright (c) 2025 Suprapto, Meilany Nonsi Tentua, Ahmad Rizki Maulana http://creativecommons.org/licenses/by-sa/4.0
A hybrid model for handling the imbalanced multiclass classification problem
Data in many application domains is imbalanced. In machine learning, addressing imbalanced data is crucial to prevent bias towards the dominant class label and ensure that prediction models can learn and predict the minority class proficiently. This paper proposes a hybrid imbalanced classification model (HICD) to address the multiclass imbalanced data problem. The primary idea is to combine effective methods to construct a classification model that can handle multiclass imbalanced data effectively. Four methods are employed: an oversampling method to balance the data, a decomposition method to convert the multiclass problem into a set of binary problems, ensemble classification to integrate base classifiers to improve prediction, and a boosting method to encourage the classifier to pay more attention to misclassified samples. To evaluate the proposed model, seventeen imbalanced datasets from various application domains, featuring different numbers of classes, instances, features, and imbalance ratios, are assessed. The experimental results and statistical significance tests demonstrate that the proposed hybrid model significantly outperforms the standard one-vs-one (OVO) approach and the OVO combined with oversampling technique (SMOTE), both considered state-of-the-art for addressing imbalanced multiclass datasets, in terms of F1-score.
Copyright (c) 2025 Esra'a Alshdaifat, Fairouz Hussein, Ala'a Al-shdaifat, Malak Al-Hassan, Enshirah Altarawneh http://creativecommons.org/licenses/by-sa/4.0
Improving multilayer perceptron on rainfall data using modified genetics algorithm
Rainfall prediction is essential for managing water resources, agriculture, and disaster response, particularly in regions affected by climate variability. This study introduces a modified genetic algorithm (MGA) to optimize hyperparameters of a multilayer perceptron (MLP) for rainfall forecasting. The MGA incorporates elitism to retain top-performing solutions and adaptive selection based on model accuracy. The proposed MGA–MLP model was tested on rainfall datasets from Australia and Indonesia (BMKG). Experimental results show that configurations with two hidden layers, rectified linear unit (ReLU) activation and limited-memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer, a learning rate of 0.001 and 1000 epochs consistently delivered strong performance. The model achieved accuracies of 86.02% and 79.05%, respectively. These findings indicate that MGA significantly improves MLP performance and provides a reliable, generalizable method for rainfall prediction across diverse climatic conditions.
Copyright (c) 2025 Marji, Wayan Firdaus Mahmudi, Endang Wahyu Handamari, Edy Santoso, Maulana Muhamad Arifin http://creativecommons.org/licenses/by-sa/4.0
A novel method for examining promoters using statistical analysis and artificial intelligence learning
Accurately classifying promoters has become a significant focus in bioinformatics research. Although numerous studies have attempted to address this challenge, the performance of existing methods still leaves room for improvement this study, statistical feature analysis has been applied to the features that have been developed in our previous work. This approach extracted additional informative features from basic sequence characteristics and then used them together with the original and newly engineered features. Utilizing statistical feature analysis enhanced key patterns, which lead to an improvement in the accuracy of the promoter classification. Results demonstrated that our proposed method outperforms other models that use only basic features. The value of the area under the curve (AUC) of 0.83958 achieved when using the combined feature set confirmed the effectiveness of our approach. Furthermore, the AUC value reached 1 when these optimized features were used with naive Bayes (NB) classifier, referring to the strength of incorporating statistical analysis into feature design.
Copyright (c) 2025 Sinan Salim Mohammed Sheet, Marwa Mawfaq Mohamedsheet Al-Hatab, Maysaloon Abed Qasim http://creativecommons.org/licenses/by-sa/4.0
Detection of chronic kidney disease based on ensemble approach with optimal feature selection using machine learning
Chronic kidney disease (CKD) poses a significant health risk globally, necessitating early and accurate detection to ensure timely intervention and effective treatment. This study presents an advanced ensemble machine learning (ML) approach combined with optimal feature selection to enhance the detection of CKD. Using five baseline ML classifiers like gradient boosting (GB), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT), and utilizing grid search for hyperparameter tuning, the proposed ensemble model capitalizes on the strengths of each algorithm. Our approach was tested on a public benchmark CKD dataset from Kaggle. The experimental results demonstrate that the ensemble model consistently outperforms individual classifiers and existing methods, achieving 97.5% accuracy, precision, recall, and an F1-score of 97.4%. This superior performance underscores the ensemble model's potential as a reliable early CKD detection tool. Integrating ML into CKD diagnostics enhances accuracy. It facilitates the development of automated, scalable diagnostic tools, aiding healthcare professionals in making informed decisions and ultimately improving patient outcomes.
Copyright (c) 2025 Deepika Amol Ajalkar, Jyoti Yogesh Deshmukh, Mayura Vishal Shelke, Shalini Vaibhav Wankhade, Shwetal Kishor Patil http://creativecommons.org/licenses/by-sa/4.0
Residual edge dense enhanced module network: a deep learning approach with multi-class SVM for lung tumor stage classification
Lung cancer segmentation with positron emission tomography (PET) and computed tomography (CT) images plays a critical role to accurately detect lung cancer. Nevertheless, lung tumor segmentation in PET/CT images were extremely difficult due to the movement caused by respiration. Despite this fact, the lung tumor images shown large number of variations mostly in PET images and CT images. As PET-CT images are acquired concurrently the shape and size of lung tumor varies according to modality. To address these issues, we developed a residual edge dense enhanced module network (REDEM-NET) framework for lung tumor stage classification. The proposed REDEM-NET can process PET and CT images as inputs. In addition, the dense residual convolutional network (DRCN) collects both inputs and extracts high-dimensional features concurrently. The extracted features from both imaging modalities were fed into UNet+++ to obtain multi-level decoded features. The extracted decoded features are concurrently supplied to the pixel level learning module (PELM) and edge level learning module (E2LM) which resulting in two outputs for subsequent learning. The outputs were merged to provide a very precise lung tumor segmentation. Furthermore, segmented tumor was fed to multi-class support vector machine (MC-SVM) for lung tumor stage classification. Moreover, it was able to identify three stages and its substages namely primary tumor, region lymph node and distant metastasis.
Copyright (c) 2025 Prabakaran Jayaraman, Pandiaraj Selvaraj, Ashwini Elango http://creativecommons.org/licenses/by-sa/4.0
Enhanced deepfake detection using an ensemble of convolutional neural networks
Digital media integrity and authenticity have been seriously challenged with the rise of deepfakes. The challenge is to automatically detect this artificial intelligence (AI) generated manipulations. These manipulations or forgeries can cause harmful consequences such as spreading fake news in politics, scamming people online and invading privacy. Convolutional neural networks (CNN) models are found to be good at classification tasks, but the performance could not reach high accuracy, especially when they were tested on more challenging deepfake datasets. In this paper we present a deepfake detection system based on an ensemble of CNN architectures, ResNet50 and EfficientNet, capable of distinguishing between real and deepfake videos with high accuracy. For the experiment, we have chosen Celeb-DF version 2, as it has emerged to be one of the most challenging deepfake dataset. The ensemble model achieved an F1-score of 94.69% and an accuracy of 90.58%, outperforming the individual CNN models. This study shows that ensemble learning can increase the reliability and accuracy of deepfake detection systems on challenging datasets.
Copyright (c) 2025 Yeeshu Ralhen, Sharad Sharma http://creativecommons.org/licenses/by-sa/4.0
Laurent series intelligent multidimensional object optimization classification for crop disease detection
Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively.
Copyright (c) 2025 Anandhan Karunanithi, Ajay Shanker Singh http://creativecommons.org/licenses/by-sa/4.0
Design and analysis of reinforcement learning models for automated penetration testing
Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks.
Copyright (c) 2025 Suresh Jaganathan, Mrithula Kesavan Latha, Krithika Dharanikota http://creativecommons.org/licenses/by-sa/4.0
Facial paralysis image analysis for stroke detection using deep ensemble transfer learning and optimization
Facial paralysis (FP) weakens facial muscles, leading to asymmetric facial actions and complicating stroke diagnosis. Machine learning (ML) and deep learning (DL) systems have been explored for diagnosing FP, but the effectiveness of these methods is hindered by the limited size and diversity of available datasets. This study proposes a novel deep ensemble transfer learning method for accurate stroke diagnosis using facial paralysis imaging (DETLM-ASDFPI). The method leverages pre-trained models to reduce computation costs on edge devices. The framework includes data acquisition, preparation, and pre-processing, with image rescaling to standardize input dimensions. Feature extraction is performed using a deep capsule network (DCapsNet) to capture complex features. For stroke detection, an ensemble transfer learning model integrates three classifiers: gated recurrent unit (GRU), deep convolutional neural network (DCNN), and stacked sparse auto-encoder (SSAE). The hippopotamus optimization algorithm (HOA) is applied to fine-tune model parameters. The method was validated using two benchmark datasets, Massachusetts eye and ear infirmary (MEEI) and YouTube facial palsy (YFP), achieving an accuracy of 97.06%, outperforming recent approaches. This research demonstrates the effectiveness of the DETLM-ASDFPI method in accurately diagnosing strokes from FP images while addressing challenges related to dataset limitations and computational efficiency.
Copyright (c) 2025 Kiruthiga Subramaniyan, Chinnasamy Anbuananth, Dhilip Kumar Venkatesan http://creativecommons.org/licenses/by-sa/4.0
An improved real time detection transformer method for retail product detection
The main problem in retail product detection is intra-class variation, as some products have similar but distinct characteristics. The primary goal of this study is to address the problem of object detection on intra-class variation in retail environments. As a result, a new approach for object detection of retail products was developed by modifying the Real Time Detection Transformer (RT-DETR) model. To manage intra-class variation more successfully, the RT-DETR model is updated by modifying its architecture. There are two convolutions in the Contextual Cross-Feature Module (CCFM) fusion block section, which is adjusted by adding one convolution layer to each CCFM fusion block. A customized dataset was meticulously constructed to reflect the wide range of products frequently seen in retail outlets. For the constructed datasets, tests were run using the mean Average Precision (mAP) metric, which had a mAP0.5 of 99.5% and a mAP0.5:0.95 of 88.2%. The updated model is superior compared to original model. The difference in mAP0.5:0.95 was 2.5%, while precision increased by 1.3% and recall increased by 0.1%. Although the mAP0.5 results stay unchanged, the gains in the other metrics suggest that the RT-DETR model modifications can improve object detection skills, particularly when dealing with intra-class variation in retail merchandise.
Copyright (c) 2025 Andi Wahyu Maulana, Suryo Adhi Wibowo http://creativecommons.org/licenses/by-sa/4.0
Lung sound classification using YAMNet, neural network, and augmentation
Globally, lung disease occupies a significant position as one of the main contributors to mortality rates. The characteristics of human respiratory sound signals can show a wide spectrum, ranging from normal patterns to indications of lung abnormalities. The proposed lung sound classification system is based on YAMNet as a pre-trained neural network model for medical audio recognition, which is then refined using artificial neural networks (ANN). This study presents the integration of multiple datasets and advanced pre-processing approaches. A total of 1,363 lung sound recordings from Kaggle, ICBHI, and Mendeley. This reflects the variety of clinical conditions, and differences in recording devices are combined. In order to increase the diversity of lung sound signal input, the pre-processing process is carried out through several stages, including adjusting the sampling frequency to 4 kHz, segmenting for 6 seconds, signal filtering with wavelet, min–max normalization, and data augmentation using window warping, jittering, cropping, and padding. A fold cross-validation scheme is employed to comprehensively evaluate the model's effectiveness. The evaluation results indicate that the model achieves an accuracy of 93.64%, a precision of 93.60%, a recall of 93.64%, and an F1-score of 93.52%, collectively reflecting outstanding classification performance. This work may incorporate deep learning technology into clinical practice, ultimately improving diagnosis accuracy and efficiency in the hospital setting.
Copyright (c) 2025 Jaenal Arifin, Tri Arief Sardjono, Hendra Kusuma http://creativecommons.org/licenses/by-sa/4.0
Educational data mining approach for predicting student performance and behavior using deep learning techniques
Educational Data Mining (EDM) uncovers insights from large datasets collected from various educational platforms, such as online learning systems, student information databases, and classroom tools. EDM helps educators identify hidden patterns that improve teaching strategies, personalize learning experiences, and predict student performance. Predicting student success has become a key focus of EDM, allowing institutions to implement targeted interventions and personalized support. The dataset included academic achievement grades from 1,001 students enrolled in various courses during the fall semester across multiple years, to demonstrate how proposed models provide more accurate predictions compared to traditional machine learning methods. Models such as YOLO, Fast R-CNN, Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks are used to capture complex, non-linear relationships within the data. The comparative analysis shows that these deep learning models significantly outperform traditional techniques, such as decision trees and support vector machines (SVMs). The results indicate that proposed method offers improved predictive accuracy, enabling educational institutions to identify at-risk students and deliver tailored interventions. This study highlights the potential of enhanced method to transform personalized education and enhance student success by better understanding individual learning needs and behaviors.
Copyright (c) 2025 Muniappan Ramaraj, Sabareeswaran Dhendapani, Jothish Chembath, Selvaraj Srividhya, Nainan Thangarasu, Bhaarathi Ilango http://creativecommons.org/licenses/by-sa/4.0
Enhanced classification of aromatic herbs using EfficientNet and transfer learning
Herbs have long been used for culinary and medicinal purposes, as well as in religious rituals, due to their essential oils and aromatic properties. However, distinguishing between aromatic and medicinal herbs based on visual characteristics alone can be challenging. With recent advances in computer vision, plant identification from images has seen significant growth, offering promising applications in several domains. This article aims to evaluate the classification of aromatic herbs using the EfficientNet convolutional neural network (CNN) technique with transfer learning. The methodology used is experimental research, systematically manipulating variables to observe their effects on the object of study. The researcher plays an active role in this process, rather than being a passive observer. Based on the results and the literature review, it is evident that the objective of this research was achieved, as despite the opportunities for improvement in training to achieve accuracy above 0.8, it was possible to evaluate the classification of aromatic herbs using EfficientNet CNN through the transfer learning technique.
Copyright (c) 2025 Samira Nascimento Antunes, Madalena De Oliveira Barbosa Divino, Luana Dos Santos Cordeiro, Fernanda Pereira Leite Aguiar, Marcelo Tsuguio Okano http://creativecommons.org/licenses/by-sa/4.0
Multiclass instance segmentation optimization for fetal heart image object interpretation
This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.
Copyright (c) 2025 Hadi Syaputra, Siti Nurmaini, Radiyati Umi Partan, Muhammad Taufik Roseno http://creativecommons.org/licenses/by-sa/4.0
An energy-efficient and secure framework for wireless sensor networks
In wireless sensor networks (WSNs), achieving energy efficiency, security, and minimizing route change propagation time is essential for maintaining optimal performance. This paper introduces a new approach that combines Bray Jaccard Curtis-based Calinski Harabasz k-means (BJC-CHKMeans) for clustering and Karl Pearson correlation-based egret swarm optimization algorithm (KPC-ESOA) for selecting the best cluster head (CH) and path, along with classifying long short-term memory with gated recurrent units (CLE-GRU) for detecting harmful nodes. The methodology aims to enhance energy usage, improve routing efficiency, and strengthen security by identifying malicious nodes. Additionally, it integrates a secure routing table using elbow de-swinging k-anonymity (EDS-KA) and employs digital signature algorithm-based Zeta Bernoulli Merkle tree (DSA-ZBMT) to ensure secure communication with sink nodes. The WSN-DS dataset was used for training and testing, with rigorous preprocessing, feature extraction, and selection to maintain data integrity. Experimental results revealed that the proposed BJC-CHKMeans and CLE-GRU models outperform traditional methods in power consumption, latency, and accuracy. The system achieved a power consumption of 2.1 mW for clustering and 1.9 mW for classification, while also providing near-perfect accuracy in detecting harmful nodes. These findings demonstrate that the framework significantly enhances the energy efficiency and security of WSNs, making it a highly effective solution for large, dynamic sensor networks.
Copyright (c) 2025 Maruthi Hanumanthappa Chandrappa, Poornima Govindaswamy http://creativecommons.org/licenses/by-sa/4.0
Ensemble reverse knowledge distillation: training robust model using weak models
To ensure that artificial intelligence (AI) can be aligned with humans, AI models need to be developed and supervised by humans. Unfortunately, it is possible for an AI to exceed human capabilities, which is commonly referred to as superalignment models. Thus, it raised the question of whether humans can still supervise a superalignment model, which is encapsulated in a concept called weak-to-strong generalization. To address this issue, we introduce ensemble reverse knowledge distillation (ERKD), which leverages two weaker models to supervise a more robust model. This technique is a potential solution for humans to manage a super-alignment of models. ERKD enables a more robust model to achieve optimal performance with the assistance of two weaker models. We tried to train a more robust EfficientNet model with weaker convolutional neural network (CNN) models in a supervised fashion. With this method, the EfficientNet model performed better than the model trained with the standard transfer learning (STL) method. It also performed better than a model that was supervised by a single weaker model. Finally, ERKD-trained EfficientNet models can perform better than EfficientNet models that are one or even two levels stronger.
Copyright (c) 2025 Christopher Gavra Reswara, Tjeng Wawan Cenggoro http://creativecommons.org/licenses/by-sa/4.0
BonoNet: a deep convolutional neural network for recognizing bangla compound characters
The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
Copyright (c) 2025 Kazi Rifat Ahmed, Nusrat Jahan, Adiba Masud, Nusrat Tasnim, Sazia Sharmin, Nusrat Jahan Mim, Imran Mahmud http://creativecommons.org/licenses/by-sa/4.0
Robust 3D finger knuckles biometric identification with hierarchical featureNet architecture
A novel biometric identifier known as the 3D finger knuckle pattern provides highly discriminative characteristics for the finger knuckle-based personal identification. This paper addresses the challenge of 3D finger knuckle recognition, aiming to enhance precision and overcome limitations in existing approaches. Leveraging neural network technology, it introduces a novel neural network framework for this purpose. Recent research has made significant progress in 3D finger knuckle recognition, particularly in the areas of matching schemes, feature representations, and specialized deep neural networks. Challenges such as limited training data and dataset heterogeneity are discussed. The proposed 3D hierarchical featureNet (HFN) methodology involves a multi-stage pre-processing process for 3D images, encompassing detection, cropping, smoothing, and hole-filling. Feature similarity is evaluated with nearest neighbor distance ratios, enabling precise recognition. The key contribution of this work is the introduction of a new feature vector that incorporates curvature data, improving the state-of-the-art. The methodology employs statistical distribution analysis for feature similarity and 3D geometry for accurate curvature representation. Overall, this research offers a comprehensive solution for 3D finger knuckle recognition, enhancing accuracy and efficiency through innovative pre-processing, feature extraction, and similarity evaluation methods.
Copyright (c) 2025 Divya Gangachannaiah, Mamatha Aruvanalli Shivaraj, Honganur Chandrasekharaiah Nagaraj http://creativecommons.org/licenses/by-sa/4.0
Integrating IndoBERT and balanced iterative reducing and clustering using hierarchies of BERTopic in Indonesian short text
Short text topic modeling remains challenging due to data sparsity, limited word co-occurrences, and unstable clustering results, particularly for Indonesian texts. This study proposes an improved BERTopic framework that integrates IndoBERT embeddings, best match 25 (BM25)-based topic representation, and balanced iterative reducing and clustering using hierarchies (BIRCH) clustering to address these issues. IndoBERT generates contextual embeddings adapted to Indonesian linguistic features, and BM25 weighting improves keyword relevance by considering document length and term saturation. BIRCH clustering minimizes outliers by assigning most documents to valid clusters, which enhances data utilization and topic stability. Experiments on Indonesian datasets from X (formerly Twitter), Google Reviews, and YouTube demonstrate that the proposed approach consistently achieves higher topic coherence. The proposed method yields stable topic diversity values between 0.91 and 0.94, maintains embedding density from 0.60 to 0.66, and achieves intra-topic similarity between 0.39 and 0.41 across increasing dataset sizes. The proposed framework successfully reduces outlier proportions to 1-5%, which significantly outperforms standard BERTopic and K-Means. Furthermore, the model maintains stable topic counts as the data volume grows, confirming robustness and scalability for sparse short text modeling. Overall, integrating IndoBERT, BM25, and BIRCH provides a more coherent, stable, and effective solution for Indonesian short text topic modeling.
Copyright (c) 2025 Muhammad Muhajir, Gunardi, Danardono, Dedi Rosadi http://creativecommons.org/licenses/by-sa/4.0
Learning assistance module based on a small language model
This paper presents the development of a low-cost learning assistant embedded in an NVIDIA Jetson Xavier board that uses speech and gesture recognition, together with a long language model for offline work. Using the large language model (LLM) Phi-3 Mini (3.8B) model and the Whisper (model base) model for automatic speech recognition, a learning assistant is obtained under a compact and efficient design based on extensive language model architectures that give a general answer set of a topic. Average processing times of 0.108 seconds per character, a speech transcription efficiency of 94.75%, an average accuracy of 9.5/10 and 8.5/10 in the consistency of the responses generated by the learning assistant, a full recognition of the hand raising gesture when done for at least 2 seconds, even without fully extending the fingers, were obtained. The prototype is based on the design of a graphical interface capable of responding to voice commands and generating dynamic interactions in response to the user's gesture detection, representing a significant advance towards the creation of comprehensive and accessible human-machine interface solutions.
Copyright (c) 2025 Marco Antonio Jinete, Robinson Jiménez-Moreno, Anny Astrid Espitia-Cubillos http://creativecommons.org/licenses/by-sa/4.0
Exploring social media sentiment patterns for improved cyberbullying detection
Cases of online bullying and aggressive behaviors directed at social media users have surged in recent years. These behaviors have had negative impacts on victims from a wide range of demographic groups. While efforts have been made to address persistent digital harassment, the expected outcome has been limited due to the lack of effective tools to quickly identify cyberbullying behaviors and censor them accordingly on social media platforms. This study presents a scalable and systematic method to detect and analyze offensive behavior and bullying on Twitter (now known as X). Our methodology involves extracting textual, user-related, and network-related attributes to understand the traits of individuals involved in such behaviors. This approach aims to recognize distinctive characteristics that set them apart from regular users. This study proposes a novel model by employing an integrated deep-learning model, combining the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN). This model aims to classify X comments into offensive and non-offensive categories. The proposed model’s efficiacy has been evaluated through several experiments by combining three widely recognized datasets of hate speech. The proposed model achieves an accuracy rate of approximately 98.95%, showing promising results in identifying and categorizing offensive behavior in cyberbullying.
Copyright (c) 2025 Wael M. S. Yafooz, Abdulsamad Ebrahim Yahya, Abdullah Alsaeedi, Reyadh Alluhaibi, Faisal Jamil, Mahmoud Salaheldin Elsayed http://creativecommons.org/licenses/by-sa/4.0
Multilabel classification sentiment analysis on Indonesian mobile app reviews
Mobile applications continue to evolve to satisfy the users. For that, the developers need to understand user feedback for improvements. Indonesia, one of the countries with the most mobile app users, has many textual mobile app reviews that may be processed and analyzed. Understanding the value of mobile app reviews requires understanding the value of sentiments and emotions to create more appropriate features to satisfy the users. To acquire a more accurate analysis of user reviews, it is important to detect sentiments that are closely associated with human emotion values due to the nature of multilabeled data. This research classifies the sentiments and emotions in Indonesian textual mobile app reviews, which are multilabel and multiclass in the form of 3 sentiments, namely positive, negative, and neutral, paired with 6 emotions, namely anger, sad, fear, happy, love, and neutral. We employ the Transformers architecture model, which includes two monolingual (a generic English and an Indonesian) and a multilingual pre-trained models with the results: bidirectional encoder representations from transformers (BERT) base uncased (micro avg. F1-score=0.69, precision=0.68, recall=0.70, receiver operating characteristic-area under the curve (ROC-AUC)=0.78), IndoBERT base uncased as best result (micro avg. F1-score=0.77, precision=0.78, recall=0.76, ROC-AUC=0.85), and multilingual BERT (M-BERT) base uncased (micro avg. F1-score=0.72, precision=0.73, recall=0.71, ROC-AUC=0.82).
Copyright (c) 2025 Riccosan, Karen Etania Saputra http://creativecommons.org/licenses/by-sa/4.0
Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification
Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet).
Copyright (c) 2025 Sangi Narasimhulu, Ch D V Subba Rao http://creativecommons.org/licenses/by-sa/4.0
Intent detection in AI chatbots: a comprehensive review of techniques and the role of external knowledge
Artificial intelligence (AI) chatbots have become essential across various industries, including customer service, healthcare, education, and entertainment, enabling seamless, and intelligent user interactions. A key component of chatbot functionality is intent detection, which determines the underlying purpose of user queries to provide relevant responses. Traditional intent detection methods, such as rule-based and statistical approaches, often struggle with adaptability, especially in complex, dynamic conversations. This review examines the evolution of intent detection techniques, from early methods to modern deep learning and knowledge-enriched models. It introduces the domain type-conversation turns-adaptivity-external knowledge (DCAD) classification, highlighting its significance in improving chatbot accuracy and contextual awareness. The paper categorizes existing intent detection models, analyzes their applications across various sectors, and discusses key challenges, including data integration, language ambiguity, and ethical concerns. By exploring emerging trends and future directions, this review underscores the critical role of external knowledge in enhancing chatbot performance and user experience.
Copyright (c) 2025 Jemimah Kandaraj, Rajkumar Kannan, Frederic Andres http://creativecommons.org/licenses/by-sa/4.0
Anisa: artificial intelligence companion for elderly care with empathetic conversations and health management
This study introduces Anisa, an advanced artificial intelligence (AI) companion designed to enhance elderly care by addressing the multifaceted needs and challenges of older adults. The system integrates the Llama 3.2 model, powered by Groq, to facilitate context-aware dialogues and empathetic interactions. This capability helps alleviate loneliness and provides essential companionship. Agenda.js is used for scheduling and managing reminders, ensuring timely notifications for medications and appointments. Additionally, Twilio enables emergency alerts when distress signals are detected. Anisa promotes physical activity, tracks daily routines, and generates activity reports shared with caregivers and healthcare providers. Expo CLI implements step-tracking and document-sharing features. By integrating these functionalities, Anisa improves the quality of life for seniors, eases caregiver responsibilities, and fosters a safer, more supportive environment.
Copyright (c) 2025 Shilpa Karegoudra, Pawan Hegde, Sinchana C. Poojary, Pranitha P. Shetty, Sahana M. Kotian, Saanvi Kallianpur, Veeresha R. Koti http://creativecommons.org/licenses/by-sa/4.0
AI-driven hyper-personalization and transfer learning for precision recruitment
The research study demonstrates how artificial intelligence (AI)-powered models can transform the hiring process by maximizing the match between candidates and jobs, leading to better hiring options and increased worker productivity. Our research develops highly personalized AI-powered recruitment applications. By using hyper-personalization to tailor job recommendations based on job compatibility and big five personality traits, this study leverages AI to improve job matching. Unlike traditional recruitment models that depend only on complex skill matching, hyper-personalization combines soft skills and personality dimensions to achieve a more precise candidate-job alignment. Transformer-based models, including bidirectional encoder representations from transformers (BERT), RoBERTa, and cross-lingual language model (XLM)-RoBERTa, have shown exceptional performance in natural language processing (NLP) and classification tasks; thus, we apply them. Transfer learning helps us to fine-tune these models to improve the accuracy of personality classification. Compared to conventional models, experimental data achieves up to 80% accuracy in binary classification and 72% in multi-class classification. By demonstrating job-candidate compatibility, this study emphasizes the potential of AI-driven models to transform recruitment, leading to better hiring decisions and workforce productivity. Our outcomes play a crucial role in advancing hyper-personalized AI applications in talent.
Copyright (c) 2025 Nour Alqudah, Qusai Q. Abuein, Mohammed Q. Shatnawi http://creativecommons.org/licenses/by-sa/4.0
Automated vial defect inspection using Gabor wavelets and k-means clustering
This study proposes a machine vision-based defect inspection system for pharmaceutical vials, aiming to ensure the quality and safety of medicinal fluids. The system employs a series of image processing techniques, including denoising, feature extraction using the Gabor wavelet transform, segmentation, clustering with the K-means algorithm, and precise defect identification using the Canny edge operator. Experimental results demonstrate high performance, with recall, precision, accuracy, and F1-score exceeding 98%. Additionally, the proposed method achieves area under the curve-receiver-operating characteristic curve (AUC-ROC) and AUC-precision-recall (PR) values of approximately 98%. The system's average computational time is 355 microseconds, indicating its potential for real-time defect detection. Overall, this approach offers an effective solution for identifying various cosmetic defects such as scratches, bruises, cracks, and black spots, in pharmaceutical vials without the need for vial classification training.
Copyright (c) 2025 Vishwanatha C. R., V. Asha, Channabasava, Sreekanth Rallapalli http://creativecommons.org/licenses/by-sa/4.0
An algorithm for controlling the transmission of video streams in a flying ad hoc network
This article discussing the enhancement of video surveillance in various territories through the implementation of a flying ad hoc network (FANET). The primary objective of the surveillance is for search and rescue operations. To optimize the quality of FANET video broadcasting, a decision-making algorithm for video stream management is introduced. This algorithm evaluates the likelihood of achieving high-quality video transmission. Depending on the assessed probabilities, the algorithm recommends one of the following actions: initiating a new video stream transmission, reducing the average length of wireless channels, or discontinuing the transmission of low-information video streams. Computational experiments demonstrate a significant improvement in the accuracy of decision-making regarding the management of video stream transmission to FANET when utilizing the proposed algorithm.
Copyright (c) 2025 Salah M. M. Alghazali, Wisam K. Madhloom Aljeazna, Murtadha N. Rasol, Konstantin A. Polshchykov, Rodion V. Likhosherstov http://creativecommons.org/licenses/by-sa/4.0
Facial features extraction using active shape model and constrained local model: a comprehensive analysis study
Human facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements.
Copyright (c) 2025 Musab Iqtait, Marwan Harb Alqaryouti, Ala Eddin Sadeq, Suhaila Abuowaida, Abedalhakeem Issa, Sattam Almatarneh http://creativecommons.org/licenses/by-sa/4.0
Unveiling critical features for failure prediction in green internet of things applications
The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Copyright (c) 2025 Ouiam Khattach, Omar Moussaoui, Mohammed Hassine http://creativecommons.org/licenses/by-sa/4.0
Building change detection via classification in high-resolution aerial imagery
This research investigates the detection of changes in building structures within high-resolution aerial images of Baghdad, Iraq, over two years, 2007 and 2024. Employing advanced remote sensing techniques and sophisticated image processing algorithms, this study aims to identify and quantify alterations in the urban landscape accurately by addressing the key challenges inherent in the image registration process, as well as the availability associated with change detection (CD) techniques. We examined the data collection strategies, evaluated matching methods, and compared CD approaches. Aerial images were accurately analyzed to detect changes in building footprints, construction activities, and destruction. We developed a comprehensive annotation methodology tailored to the complex urban environment of Baghdad. These findings emphasize the rapidly evolving nature of Baghdad’s urban fabric and the critical need for ongoing monitoring to inform urban planning and management strategies. The results demonstrate the efficacy of utilizing high-resolution aerial imagery with object-based CD techniques for detailed urban analysis. This research advances the existing knowledge by providing a robust framework for urban CD, with implications for enhancing urban planning and policy-making processes. Future research will focus on refining the annotation processes and incorporating additional data sources to enhance the accuracy and comprehensiveness of urban CD methodologies.
Copyright (c) 2025 Hayder Mosa Merza, Ihab Sbeity, Mohamed Dbouk, Zein Al-Abidin Ibrahim http://creativecommons.org/licenses/by-sa/4.0
Heart disease prediction optimization using metaheuristic algorithms
This study explores metaheuristics hyperparameter tuning effectiveness in machine learning models for heart disease prediction. The optimized models are k-nearest neighbors (KNN) and support vector machines (SVM) using metaheuristics to identify configurations that minimize prediction error. Even though the main focus is utilizing metaheuristics to efficiently navigate the hyperparameter search space and determine optimal setting, a pre-processing and feature selection phase precedes the training phase to ensure data quality. Convergence curves and boxplots visualize the optimization process and the impact of tuning on model performance using three different metaheuristics, where an error of 0.1188 is reached. This research contributes to the field by demonstrating the potential of metaheuristics for improving heart disease prediction performance through optimized machine learning models.
Copyright (c) 2025 Zaid Nouna, Hamid Bouyghf, Mohammed Nahid, Issa Sabiri http://creativecommons.org/licenses/by-sa/4.0
Multi-class stock market forecasting with deep learning models: an explainable artificial intelligence
In this research, we investigated the influence of different deep learning techniques on time series stock market data, especially for all Nifty50 companies in the Indian stock market. Our proposed method of stock market prediction focused on multi-class classification with explainable artificial intelligence (XAI). Our proposed model incorporates convolutional neural network (CNN) for operational feature extraction and long short-term memory (LSTM) to capture time-based dependencies. Predicted value is classified with multiclass classes-very bullish, bullish, neutral, bearish, very bearish signals for all Nifty50 stocks. The model integrates essential technical indicators to find patterns from basic price data. XAI techniques are also used to find feature contributions to model prediction. It improves the clarity of the model’s administrative procedure by figuring out how technical indicators influence stock estimates. The outcomes highlight the model’s ability to generate actionable trading signals, reinforced by performance progress metrics, contributing to more well-informed and planned venture decisions. The proposed model reveals greater performance, reaching an average accuracy of 96%, beating LightGBM at 89%, random forest at 85%, and support vector machine at 60%.
Copyright (c) 2025 Chhaya Patel, Ashwin Raiyani http://creativecommons.org/licenses/by-sa/4.0
Transformation of Islamic values in the era of artificial intelligence
The emergence of artificial intelligence (AI) such as ChatGPT has brought significant changes in the way humans’ access and understand information, including in the religious field. This research aims to examine how the transformation of Islamic values occurs through ChatGPT responses in the aspects of educational ethics, Islamic law, da'wah, and Qur'anic interpretation. This study applied a qualitative case study method and data was collected from indexed scientific articles from academic databases, ChatGPT responses, and online news articles. The study findings show that the use of ChatGPT in the context of Islam requires caution. While technology can answer a variety of questions, there are fundamental flaws related to the accuracy of citations, unverified sources of information, and a lack of understanding of the sharia context. In fact, there are errors in the mention of Qur'anic verses that have the potential to cause confusion. This emphasizes the importance of the sanad principle in Islamic scholarship as a valid reference. The paper proposes the need to develop more ethical and contextual AI systems in understanding religious questions, as well as the involvement of scholars and academics in training machines to conform to Islamic values.
Copyright (c) 2025 Nur Faizin, Abul Ma`ali, Muhammad Fahmi Hidayatullah, Ahmad Munjin Nasih, Rohmatul Faizah, Moh. Fauzan http://creativecommons.org/licenses/by-sa/4.0
The effectiveness of ChatGPT in extracting architectural patterns and tactics
This work investigates the potential of ChatGPT, a cutting-edge large language model (LLM), for software design analysis specifically in detecting architectural patterns and tactics. The evaluation involves comparing ChatGPT’s performance with that of Archie, a traditional Eclipse plugin designed for architectural analysis. The study uses the source code of five open-source software systems as the testing ground. Results reveal that ChatGPT achieves noteworthy performance in both pattern and tactic detection tasks. Specifically, for pattern detection, ChatGPT demonstrates an accuracy of up to 47.06%, while for tactic detection, it achieves a precision of 28.25%. While ChatGPT’s current capabilities are not yet a replacement for specialized tools like Archie, it offers significant potential as a complementary tool in architectural analysis workflows. By bridging the gap between natural language understanding and software engineering, ChatGPT could pave the way for more intelligent and automated solutions in the field. However, a key limitation is its difficulties in handling foundational or traditional tactics, resulting in a lower detection rate in certain areas. This research contributes valuable insights into the application of LLMs in software engineering, highlighting both the strengths and the limitations of ChatGPT in addressing complex architectural tasks.
Copyright (c) 2025 Hind Milhem, Naderah Al-Jawabrah, Raghad Abu Wadi http://creativecommons.org/licenses/by-sa/4.0
Intelligent assessment of harmonic distortion compliance in reverse osmosis systems
This study explores the critical challenge of harmonic distortion compliance in reverse osmosis (RO) desalination systems, with a focus on aligning with international standards, specifically IEC 61000, IEEE 519, and EN 50160. High-power equipment, particularly high-pressure pumps (HPP), introduces significant harmonic distortions, threatening power quality and operational reliability. To address this issue, we integrated advanced machine learning (ML) algorithms, namely decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) to assess harmonic compliance and predict total harmonic distortion (THD) under four operational scenarios. All data used for training and testing were obtained from real-time measurements taken at a large-scale desalination plant using a power quality analyzer (QUALISTAR CA 8336), which guarantees the practical relevance of the analysis. The models were trained on harmonic order and amplitude data and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models, MLP demonstrated superior performance, achieving an accuracy of 99.11% and an F1-score of 98.9%, making it a robust tool for harmonic compliance assessment. SVM and RF also showed commendable results, while DT proved effective for basic analysis. This research underscores the potential of AI-driven approaches in mitigating harmonic-related challenges, optimizing power quality, and enhancing operational efficiency in RO plants. These findings offer a pathway toward more reliable and energy-efficient industrial operations.
Copyright (c) 2025 Cherki Lahlou, Belaid Bouikhalene, Jamaa Bengourram, Hassan Latrache http://creativecommons.org/licenses/by-sa/4.0
Accelerating solder joint classification using generative artificial intelligence for data augmentation
Despite advancements in computer vision, deploying deep learning algorithms for automated optical inspection (AOI) in printed circuit board (PCB) manufacturing remains challenging due to the need for large, diverse, and high-quality training datasets. AOI programs must be developed quickly, often as soon as the first PCB is assembled, to meet tight production timelines. However, deep learning models require extensive datasets of defect images, which are both scarce and time-consuming to collect. As a result, AOI software developers frequently resort to traditional rule-based methods. This study introduces a novel framework that leverages generative AI and discriminative AI to address dataset limitations. By applying a diffusion model to systematically add and remove Gaussian noise, the framework generates realistic defect images, expanding the available training data. This data augmentation accelerates the learning process of deep learning models, enhancing their robustness and generalizability. Experimental results demonstrate that this approach improves AOI system performance by producing balanced datasets across various defect classes. The framework shortens training times while maintaining high inspection accuracy, facilitating faster deployment of AOI systems in manufacturing. This advancement enhances quality control processes, contributing to more efficient, and reliable mass production of PCBs.
Copyright (c) 2025 Teng Yeow Ong, Ping Chow Teoh, Koon Tatt Tan http://creativecommons.org/licenses/by-sa/4.0
Designing an intelligent system for vibration diagnosis of centrifugal water-cooling pumps using Bayesian networks
Implementing monitoring methods is a viable method to reduce substantial damage to cooling water centrifugal pumps. Engaging in manual vibration analysis requires considerable time and a requisite level of competence. Small datasets pose challenges when applying classification systems that utilize linear classification models and deep learning. Given these issues, our proposal entails developing a system capable of autonomously, precisely, and accurately diagnosing vibrations using a limited dataset. The system is anticipated to possess the capability to detect multiple categories of mechanical defects, such as static imbalance, dynamic imbalance, misalignment, cavitation, looseness, and bearing corrosion. The Bayesian network (BN) structure was constructed using the MATLAB software. The input data parameters comprise vibration signals measured in the frequency domain and values representing phase differences. The constructed intelligent system was subsequently assessed using a dataset including 120 samples. The smart system can rapidly anticipate and precisely identify every form of harm with exceptional accuracy and sensitivity, relying on test outcomes. The test data analysis reveals that the intelligent system attained an average accuracy of 94.74%, precision of 95.32%, sensitivity (recall) of 93.67%, and F-score of 94.36%.
Copyright (c) 2025 Wiwit Suprihatiningsih, Dedik Romahadi, Aberham Genetu Feleke http://creativecommons.org/licenses/by-sa/4.0