Journal title Journal of Applied Data Sciences Initials JADS Abbreviation J. Appl. Data Sci. Online ISSN 2723-6471 Frequency 4 issues per year DOI doi.org/10.47738/jads Editor-in-chief Prof. Dr. Ibrahiem M. M. El Emary, (ID Scopus: 15135499400), King Abdulaziz University, Kingdom of Saudi Arabia Collaboration Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia. Publisher Bright Publisher Citation Analysis Scopus | Web of Science | Google Scholar Established in 2020, the Journal of Applied Data Science (JADS) aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data. The journal publishes research works on the full spectrum of the data science including statistics, computer science, and domain applications. The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data. The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science. Finally, accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases: Scopus Dimensions Google Scholar Index Copernicus GARUDA: Digital Reference Garba by Ristekdikti - Indonesia Indonesia OneSearch Crossref Search ROAD: the Directory of Open Access scholarly Resources OAI Address Journal of Applied Data Sciences has OAI address: https://bright-journal.org/Journal/index.php/JADS/oai Before submission, You have to make sure that your paper is prepared using the JADS paper Template, has been carefully proofread and polished, and conformed to the author guidelines. Online Submissions Already have a Username/Password for JADS? GO TO LOGIN Need a Username/Password? GO TO REGISTRATION Registration and login are required to submit items online and to check the status of current submissions. 7 days First Time Decision 93 days Review Time 100 days Submission to Accepted 32 days Acceptance to Publication
Authors who publish with Journal of Applied Data Sciences agree to the following terms: Authors retain copyright and grant the Journal of Applied Data Sciences right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in Journal of Applied Data Sciences. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in Journal of Applied Data Sciences. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Progressive Massive Fibrosis Detection Using Generative Adversarial Networks and Long Short-Term Memory
Contribution: Progressive Massive Fibrosis (PMF) is a severe form of pneumoconiosis, affecting individuals exposed to mineral dust, such as coal miners and workers in the artificial stone industry. This condition causes significant pulmonary impairment and increased mortality. Early and accurate detection is vital for effective management, yet traditional diagnostic methods face challenges in differentiating PMF from other pulmonary diseases due to variability in clinical presentations and limitations in imaging techniques. Idea: The study introduces a novel diagnostic framework that integrates Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks to enhance the detection and monitoring of PMF. The GAN generates high-fidelity synthetic imaging data to address the issue of limited datasets, while the LSTM network captures temporal patterns in patient data, enabling real-time monitoring of disease progression. Objective: The primary objective of this research is to develop an AI-driven model that improves the accuracy and efficiency of PMF detection and monitoring, facilitating early diagnosis and better treatment planning. Findings: The integrated GAN-LSTM model significantly outperformed traditional diagnostic methods. It proved high accuracy, a Dice coefficient of 0.85, and an Area Under the Curve (AUC) of 0.92, showing precise differentiation of PMF from other pulmonary conditions, such as lung cancer and tuberculosis. Results: The GAN-LSTM framework achieved an accuracy of 91.3%, suggesting that the fusion of GAN and LSTM technologies can effectively address the challenges of limited datasets and heterogeneous disease progression. The model showed promise in enhancing the non-invasive detection and ongoing monitoring of PMF. Novelty: This research stands for a significant advancement in PMF diagnostics by combining GAN and LSTM technologies in a single framework. This approach improves diagnostic accuracy and eases continuous disease monitoring, offering a non-invasive and highly precise solution for PMF detection.
Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor
This article presents the design and research of a modular horizontal tubular bioreactor for efficient biogas production based on anaerobic digestion technology. The study combines a digital twin implemented in the MATLAB/Simulink environment with a physical bioreactor equipped with a sensor and control system. The developed mathematical model describes the biochemical processes of acidogenesis and methanogenesis, the thermal regime and the sensitivity of the system to key parameters. Numerical modeling and visualization methods were used for the analysis. The experiments were carried out for 30 days at a mesophilic temperature of 37 ° C, repeated three times to increase reliability. The raw material used was a mixture of cattle manure and food waste in a 3:1 ratio, with a total volume of 60 liters. Readings from temperature, pH, and methane sensors were taken every 10 minutes. Experimental data confirmed the high efficiency of the design: removal of up to 70.5% of volatile substances and methane yield of up to 80.5%. Predictive analysis has shown that the digital twin is able to predict the behavior of the system and apply corrective actions in real time. The novelty of the work lies in the integration of a digital twin with a physical bioreactor in real time through industrial communication protocols.
Development of a Smart Lung Health Monitoring System Using Sensors and Data Analytics for Early Disease Detection
This study introduces a novel multimodal wearable sensor system for real-time monitoring and analysis of respiratory and cardiac activity. The primary objective is to facilitate the early detection of cardiopulmonary abnormalities by integrating electrical (ECG) and acoustic data. A total of 30 participants, aged 25 to 50 years, were involved in controlled breathing experiments, which included deep (1000 ml, 15 breaths/min), moderate (750 ml, 20 breaths/min), and shallow (500 ml, 30 breaths/min) breathing, as well as coughing simulations. Signal processing using a 7th-order polynomial approximation yielded the lowest modeling error at 6.8%, ensuring precise waveform reconstruction. The system demonstrated a clear differentiation of respiratory patterns via Area Under the Curve (AUC) metrics, with average AUC values increasing from 1200 µV·s during shallow breathing to 3200 µV·s during deep breathing. Further analysis of the first derivative of AUC values revealed a strong correlation (r = 0.89) between respiratory volume and ECG amplitude fluctuations, highlighting robust cardiorespiratory coupling. Notably, the system achieved a 92% accuracy in detecting abnormal breathing events, such as shallow breathing and coughing fits. By combining ECG-derived heart rate variability with respiratory data, the system offers a comprehensive assessment of cardiopulmonary interaction. The key contribution of this work lies in its real-time, continuous monitoring capability using a compact wearable form factor, which distinguishes it from existing single-modality systems. This approach represents a significant advancement in non-invasive health monitoring, with strong potential for application in clinical diagnostics and home-based tracking of chronic conditions, such as asthma, COPD, and cardiac dysregulation.
Unsupervised Neural Networks for Breast Cancer Clustering: A Comparative Study of RBMs and SOMs with Interpretability Metrics
This study presents a comparative analysis of two unsupervised neural network models—Restricted Boltzmann Machines (RBMs) and Self-Organizing Maps (SOMs)—applied to breast cancer data clustering. The primary objective is to evaluate and benchmark these models in terms of their latent feature extraction, clustering accuracy, and interpretability in a medical diagnostic context. Using a preprocessed breast cancer dataset comprising 569 patient records and 30 clinical features, the models were trained and evaluated based on two internal clustering metrics: Silhouette Score and Davies-Bouldin Index (DBI). The proposed methodology, implemented in Python, emphasizes reproducibility and diagnostic relevance. RBMs achieved a Silhouette Score of 0.88 and a DBI of 0.52, indicating compact and well-separated clusters, while SOMs recorded significantly lower performance with a Silhouette Score of 0.34 and a DBI of 1.47. Furthermore, classification performance (based on cluster-label mapping) shows RBMs yielding precision between 0.82 and 0.92, and recall between 0.87 and 0.89 for benign and malignant cases. SOMs, although less accurate, offer superior visualization of high-dimensional data, which aids in exploratory analysis and interpretability. The key contribution of this work lies in the development of a standardized evaluation framework for unsupervised neural clustering in healthcare, combining quantitative clustering metrics with qualitative insights into clinical applicability. The findings demonstrate that RBMs are better suited for diagnostic tasks requiring high pattern recognition, whereas SOMs retain value for data exploration and decision explanation. This research introduces a novel integration of RBM-based clustering into medical analytics, highlighting its potential in supporting decision-making processes in oncology. Future work will extend this approach to hybrid models and multi-modal datasets, aiming to balance performance and explainability in complex diagnostic environments.
Structural Equation Modeling Factors That Influence Online Course Acceptance in University Students
Online courses are currently growing rapidly, influenced by various factors, especially among college students. This study uses the Technology Acceptance Model, by adding the variable of facilitating conditions. This study aims to determine the effects of perceived ease of use, perceived usefulness, and facilitating conditions on the actual use of online courses, mediated by intention to use. This study is the first conducted on students in university in South Sumatra, Indonesia, who have used or are currently using online courses. The data collection was carried out from June 24, 2024, to August 9, 2024. The research instrument used was a questionnaire based on a 5-point Likert scale. The sampling technique used in this study was purposive sampling, targeting students at universities under the Higher Education Service Institution Region II in Indonesia. A total of 360 students participated as respondents. The questionnaire results were analyzed using Structural Equation Modeling – Partial Least Squares (SEM-PLS). Data analysis was conducted using the SmartPLS software, version 4.1.1.2. The results showed that perceived ease of use had a significant influence on intention to use (path coefficient = 0.281, T-statistic = 6.642, P-value = 0.000), and perceived usefulness also had a positive influence on intention to use (path coefficient = 0.155, T-statistic = 4.545, P-value = 0.000). Facilitating conditions also had a positive influence on intention to use (path coefficient = 0.476, T-statistic = 8.880, P-value = 0.000), and intention to use significantly influenced actual use (path coefficient = 0.452, T-statistic = 10.490, P-value = 0.000). These findings highlight the important role of perceived ease of use, perceived usefulness, and facilitating conditions—mediated by intention to use—in significantly influencing the actual use of online courses among students, particularly at universities under the jurisdiction of the Higher Education Service Institution Region II in Indonesia.
Spatio-Temporal Variation in Bus Service Satisfaction and Policy Implications: A Case-Based Study in an Emerging Urban Area
This study investigates the spatio-temporal variation in bus service satisfaction in a rapidly urbanizing city in Southeast Asia. The research addresses a critical gap in urban transit studies, where passenger satisfaction is often treated as a static construct, overlooking how satisfaction may fluctuate across different geographic areas and time periods. By applying a spatio-temporal analytical framework, this study aims to provide a more dynamic and localized understanding of transit service perceptions. The research builds upon existing approaches by integrating a structured survey based on the Customer Satisfaction Survey (CSS) method with Best–Worst Scaling (BWS) to prioritize service attributes. A stratified sampling technique was employed across multiple wards in the study area, with 612 valid responses collected during both peak and off-peak hours. The survey captured data on passenger experiences and preferences, disaggregated by time of day, location, and demographic characteristics. Multinomial Logit (MNL) modeling was used to estimate the relative importance of key service dimensions, such as punctuality, comfort, frequency, and accessibility. The analysis revealed significant spatial and temporal heterogeneity in satisfaction levels. For instance, passengers in peripheral wards rated reliability and onboard conditions more negatively compared to those in central areas. Similarly, satisfaction levels were lower during evening hours, particularly concerning bus overcrowding and wait times. The findings suggest that transit policy must adopt a more flexible and localized strategy, rather than uniform service standards, to address distinct user expectations. Targeted improvements in underperforming routes and time slots could enhance overall user experience and promote public transport usage. This study contributes new insights to the evaluation of urban bus services in emerging cities and underscores the value of incorporating spatio-temporal dynamics into transit planning and customer satisfaction research.
Digital Platform Utilization and ICT Literacy on Global Market Access among MSMEs: The Mediating Role of Digital Business Readiness and the Moderating Effect of Government Support
This study investigates how digital capabilities and institutional support shape global market access among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, with a specific focus on Banyumas Regency. The research integrates technology adoption, organizational readiness, and policy support into a unified structural model to explain MSME internationalization in semi-urban contexts. The objective is to assess the direct effects of digital platform utilization and ICT literacy on global market access, the mediating role of digital business readiness, and the moderating effect of government support. A survey of 125 digitally engaged MSMEs was analyzed using Partial Least Squares Structural Equation Modeling. The findings reveal that digital platform utilization (β = 0.163; p = 0.008) and ICT literacy (β = 0.161; p = 0.004) significantly and directly enhance global market access. Both constructs also positively influence digital business readiness digital platform utilization (β = 0.600; p < 0.001) and ICT literacy (β = 0.497; p < 0.001) which itself contributes to market access (β = 0.154; p = 0.037). Mediation analysis confirms that digital business readiness significantly mediates the relationship between digital capabilities and global market access, with indirect effects of 0.093 (p = 0.045) and 0.077 (p = 0.035) for digital platform utilization and ICT literacy, respectively. Furthermore, government support significantly moderates the effect of digital readiness on market access (β = 0.222; p = 0.002). The model demonstrates strong explanatory power (R² = 0.782 for global market access) and predictive relevance (Q² = 0.507). This study contributes to the digital transformation literature by positioning digital business readiness as a critical enabler of MSME internationalization and highlights the synergistic role of government interventions in amplifying internal digital capabilities. The novelty lies in applying an integrated model to a semi-urban developing economy setting, offering insights for inclusive digital ecosystem design and policy formulation.
Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas
Dengue fever is an endemic disease that poses a serious threat to public health in tropical regions such as Indonesia. Efforts to control this disease require a data-based approach that is able to accurately predict the level of risk so that interventions can be targeted. This study aims to develop a predictive model of DHF risk using ensemble stacking method optimized with Optuna algorithm and integrated into an interactive dashboard based on Streamlit. The dataset used includes environmental, climate, and socio-demographic indicators from 2015 to 2024 with a total of 1,440 data entries. The preprocessing process includes normalization with Standard Scaler, feature selection using LASSO, and label data balancing with the SMOTE method. Model validation was performed using 10-Fold Cross Validation to ensure model generalization to new data. The stacking model is built with three basic algorithms, namely SVM, KNN, and Random Forest, which are combined using Logistic Regression as a meta-learner. The evaluation results show that the model is able to achieve an average accuracy of 97.57%, with high precision, recall, and f1-score values in all three prediction classes (low, medium, high). The ROC-AUC for each class also showed near-perfect performance. The implementation of the model in the Streamlit dashboard allows non-technical users such as health center or health office staff to perform regional risk prediction and obtain data-driven intervention recommendations automatically. This research not only contributes to the development of predictive technology, but also strengthens evidence-based health promotion practices in urban areas. Further research is recommended to integrate IoT-based real-time data and expand the scope of application areas.
A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones
Megathrust earthquakes are one of the most severe threats to countries situated along tectonic subduction zones, particularly Indonesia, where the movement of converging plates frequently triggers large-scale seismic events and tsunamis. Although recent developments in seismology have introduced various predictive tools, many of these models still face challenges, especially due to limitations in hydrogeological data quality. This study aims to investigate how three different machine learning algorithms perform in predicting megathrust earthquake events. The algorithms tested are Support Vector Machine, Random Forest, and Artificial Neural Network, applied to a dataset dominated by earthquake records from the Indonesian and Pacific regions. Each model was evaluated based on accuracy, precision, recall, and F1 score to provide a comprehensive performance analysis. The results show that Random Forest produced the highest accuracy, reaching 96%, followed closely by Support Vector Machine with 95%, while Artificial Neural Network achieved 83%. In terms of the F1 score, Random Forest led with a score of 0.95, indicating balanced performance in classification. However, recall, which is critical in disaster preparedness because it measures the model’s ability to detect high-risk events, Artificial Neural Network reached 92% for tsunami-related classifications. This suggests that while Random Forest is the most accurate overall, Artificial Neural Network could be more appropriate for early warning systems where the cost of missing a true event is much higher than issuing a false alarm. The contribution of this research is the direct comparison of multiple machine learning methods using real earthquake data, focusing not only on accuracy but also on practical disaster management considerations such as recall. This study also presents a novel perspective by analyzing the trade-off between model accuracy and disaster risk, emphasizing the need for probabilistic forecasts that can support timely public decision-making during seismic crises.
Clustering-Based Adaptive UX in E-Learning Systems: Aligning Microservices with the 4C Framework
This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p < 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.
A Data-Driven Mixed Integer Nonlinear Programming Model for Cost-Optimal Scheduling of Perishable Production and Workforce
This study presents a data-driven, Mixed Integer Nonlinear Programming (MINLP) framework for optimizing the multi-period production scheduling of perishable products with integrated workforce planning. Its primary novelty is the holistic integration of a continuous exponential decay function for product deterioration with dynamic workforce planning, creating a unified model that optimizes production, inventory, and labor simultaneously. This approach addresses key challenges in perishable inventory systems by treating labor as a controllable resource rather than a fixed constraint. Mathematically, the model includes nonlinear inventory balance equations with decay terms and resource-dependent capacity constraints. The objective is to minimize total operational cost, comprising production, holding, and spoilage costs. Computational experiments, based on a realistic case study, demonstrate that the proposed model reduces total system cost by 6.2% and spoilage costs by 43.2% compared to a standard heuristic benchmark. The resulting production and labor schedules align closely with demand fluctuations, supporting both economic and operational efficiency. This unified framework advances the mathematical modeling of sustainable production planning and offers a practical tool for real-world industries such as food processing and pharmaceuticals.
Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models
This study develops an advanced forecasting methodology for air pollution levels in Almaty, Kazakhstan, focusing on fine Particulate Matter (PM2.5) and carbon monoxide concentrations. Air pollution poses significant risks to public health, and Almaty’s basin location exacerbates the problem. Addressing the limitations of traditional statistical forecasting methods, we propose an ensemble machine learning approach that integrates Seasonal-Trend decomposition with gradient boosting algorithms to capture complex temporal and nonlinear patterns. The objective is to develop and validate an effective methodology for forecasting atmospheric air pollution in Almaty using machine learning methods, in particular STL decomposition, XGBoost, LightGBM models, and their ensemble combination. The novelty lies in the integration of STL decomposition with an ensemble of gradient boosting models for high-accuracy air pollution forecasting in the complex urban environment of Almaty. The dataset includes hourly measurements from over 20 monitoring stations, enabling seasonal and spatial analysis. Rigorous preprocessing techniques were applied, including outlier removal, normalization, and time series decomposition into seasonal, trend, and residual components. Two gradient boosting models, XGBoost and LightGBM, were trained separately and combined into a weighted ensemble, with optimal weights determined through cross-validation. Figures and tables illustrate data preprocessing flow, model architectures, feature importance analysis, and evaluation of predictive performance. The ensemble outperformed individual models, achieving high accuracy with coefficient of determination values exceeding 0.98 for PM2.5 and 0.83 for carbon monoxide. The findings demonstrate that integrating Seasonal-Trend decomposition with ensemble learning provides a robust and effective approach to forecasting air pollution in complex urban environments. The methodology shows strong potential for practical application in real-time air quality monitoring and warning systems, aiding policymakers and public health authorities. Future research will expand the dataset by incorporating additional factors such as traffic flow, industrial emissions, and satellite remote sensing data to enhance predictive accuracy and model interpretability.
AI-Driven Mobile Application for Self-Monitoring Personalized Premenstrual Symptoms and Risk Assessment of Depressive Crises in Female University Students
Premenstrual Syndrome (PMS) and depressive symptoms are common concerns for female university students, often triggered by hormonal fluctuations before menstruation. These conditions can severely impact academic performance, interpersonal relationships, and overall well-being, particularly when symptoms escalate into severe depressive episodes. Even though the prevalence, awareness, and self-management strategies among students are on the rise, they remain limited, particularly in cultural contexts where women's health and emotional well-being receive little attention. This study presents the development of an AI-driven mobile application designed to facilitate personalized tracking of premenstrual symptoms and assess the risk of depressive episodes. The application integrates machine learning models trained on self-reported psychological and physiological data, using validated instruments such as DASS-21 and PSST-A. The research adopted a mixed-methods approach, involving survey-based symptom identification, model training and validation, system design, and user satisfaction evaluation. This research contributes to the development of artificial intelligence-assisted self-care technology for the purpose of monitoring personal health and taking preventative psychological measures. The findings indicate that the application that was developed is beneficial in terms of forecasting the likelihood of someone suffering from depression and fostering self-awareness regarding mental health among college students. Considering this, the system has the potential to develop into a useful tool for providing aid to female students attending universities.
Application of Convolutional Neural Networks for Automated Iris Edge Detection in Sleepiness Monitoring during Blended Learning
This study introduces a novel lightweight Convolutional Neural Network (CNN) model, T-Net, designed for real-time drowsiness detection based on eye closure patterns. The model was developed to address the prevalent issue of student fatigue in resource-constrained environments, such as during prolonged online learning or blended learning sessions. Unlike traditional deep learning models, T-Net prioritizes efficiency while maintaining high accuracy, making it suitable for deployment on devices with limited computational resources. The model uses a 68-point facial landmark detection technique to extract the eye region and accurately classify eyelid states (open or closed). Evaluated on two benchmark datasets, Dataset-1 (342 eye images) and Dataset-2 (1,510 eye images), T-Net demonstrated superior performance, achieving classification accuracies of 99.33% and 99.27%, respectively, outperforming other pre-trained models such as VGG19, ResNet50, and MobileNetV2. Usability testing revealed a high acceptance rate, with a System Usability Scale (SUS) score of 84.5, indicating the system’s practicality for real-world use. Additionally, statistical analysis showed a significant correlation (r = 0.67, p < 0.01) between prolonged screen time and the emergence of visual fatigue symptoms. This study highlights the effectiveness of a lightweight CNN approach for real-time fatigue monitoring, offering a balance between performance and computational efficiency. The results suggest that T-Net can be effectively integrated into student monitoring systems to ensure alertness during learning sessions. Future research will focus on expanding the dataset, integrating infrared imaging for low-light environments, and incorporating additional fatigue indicators such as yawning and head pose.
Optimizing The XGBoost Model with Grid Search Hyperparameter Tuning for Maximum Temperature Forecasting
This study presents a novel comparative approach to maximum temperature forecasting in Surabaya, Indonesia, by integrating Extreme Gradient Boosting (XGBoost) with Grid Search Hyperparameter Tuning and benchmarking it against Autoregressive Integrated Moving Average (ARIMA) and Neural Prophet models. The main idea is to evaluate the capability of XGBoost in capturing nonlinear patterns in environmental time series data, which traditional models often fail to address. Using 15,388 historical daily maximum temperature records from the BMKG Juanda weather station spanning 1981–2022, the objective is to identify the most accurate predictive model for short- and medium-term forecasts. The modeling process involved four stages: data acquisition, preprocessing, training, and evaluation, with performance assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings show that, after hyperparameter tuning, XGBoost achieved the best performance with MAE = 0.32 and RMSE = 0.65, outperforming ARIMA (MAE = 0.85, RMSE = 1.20) and Neural Prophet (MAE = 0.70, RMSE = 0.98). Prediction results for 2025 indicate peak maximum temperatures in January, October, and November, aligning with recent climate patterns. The contribution of this research lies in demonstrating the superiority of a tuned XGBoost model for complex environmental datasets, offering a practical tool for urban climate planning, agricultural scheduling, and heatwave risk mitigation. The novelty of this work is the systematic integration of Grid Search-based optimization with XGBoost for meteorological forecasting in a tropical urban context, producing higher accuracy than both classical statistical and modern hybrid time series methods. These results highlight the model’s adaptability and potential for broader climate-related applications, with future research recommended to incorporate additional meteorological variables such as humidity and wind speed for even greater predictive capability.
Moodle-based Blended Learning: Factors Influencing the Behavioral Intention of Undergraduate Students
The demand for blended learning by higher education has increased since COVID-19. Blended learning combines the advantages of both face-to-face and online learning. Many HEIs in developing countries have started to depend on Moodle to offer blended courses to their students, as it is freely available and open source. The current study aims to explore the factors that influence the intentions to use Moodle-based Blended Learning (MBBL) by higher education students in a public university in Jordan, a developing country. For this purpose, we used a modified version of the UTAUT2 model. Data were gathered through a survey that targeted undergraduate students. The study used 319 valid response samples and analyzed the data using SmartPLS 4 software that implements PLS-SEM analysis. The data analysis results show that the factors that influence the students’ behavioral intention to use MBBL are performance expectancy (β = .18), effort expectancy (β = .21), social influence (β = .16), and habit (β = .25). However, the results indicate that facilitating conditions and hedonic motivation factors do not have a significant influence. In addition, the results reveal that result demonstrability has significant effect on both performance expectancy (β = .58) and effort expectancy (β = .52). Also, effort expectancy is found to influence performance expectancy (β = .17). Among the influential factors, habit is identified as the strongest predictor of intentions followed by effort expectancy, whereas social influence is the weakest predictor. The proposed model was able to explain 50% of variance in students’ intentions to use MBBL. The current study provides HEIs with valuable insights needed to improve the MBBL process and enhance the performance of students. It also suggests future research directions that build on this study to reach more generalized and stable results.
A Data-Driven MINLP Approach for Enhancing Sustainability in Blockchain-Enabled e-Supply Chains
Modern e-supply chains are characterized by increasing complexity and a critical need for enhanced sustainability, transparency, and traceability. Blockchain technology emerges as a significant enabler, offering decentralized, immutable ledgers and smart contracts that can support more secure, verifiable, and environmentally responsible operations through trustworthy data. Despite blockchain's potential, a notable gap exists in the availability of quantitative, data-driven optimization models that rigorously assess the operational and sustainability impacts of its integration into e-supply chains, particularly for complex, non-linear system interactions. This study aims to address this gap by presenting an in-depth analysis of a specific Mixed-Integer Non-Linear Programming (MINLP) optimization model. The goal is to clarify its structure, evaluate its application for an e-supply chain incorporating blockchain features (like transaction costs and conceptual smart contract enforcement for compliance) and sustainability objectives (such as carbon emission reduction), and derive practical insights. The methodology involves a detailed exposition of the MINLP model, followed by its application to a defined e-supply chain scenario. The analytical approach includes computational experiments focusing on a base case analysis to demonstrate model functionality. The broader evaluative framework for this study encompasses benchmarking the model’s performance against a conventional system and conducting sensitivity analyses on key parameters to understand performance trade-offs. The initial base case analysis demonstrates the model's capability to optimize supplier selection and operational plans while adhering to sustainability constraints, such as maintaining carbon emissions at or below 300 kg CO₂ per period, and accounting for blockchain-specific costs like a per-supplier usage fee of 500. The structure of the model and preliminary insights suggest its potential to achieve improved environmental impact compared to conventional systems, balanced against associated blockchain implementation costs. This research provides a detailed examination of a complex MINLP structure, offering a data-driven analytical approach for assessing blockchain's role in sustainable e-supply chains. It furnishes a foundational framework and insights that can guide managerial decisions and strategic planning for industries transitioning towards greener, more transparent, and digitally advanced supply chain operations.
Enhancing Autonomous Vehicle Navigation in Urban Traffic Using CNN-Based Deep Q-Networks
This research proposes a CNN-based Deep Q-Network (CNN-DQN) model to enhance the navigation capabilities of autonomous vehicles in complex urban environments. The model integrates CNN for spatial abstraction with reinforcement learning to enable end-to-end decision-making based on high-dimensional sensor data. The primary objective is to evaluate the impact of CNN-DQN state abstraction on the quality and stability of the resulting policy. Using a grid-based simulator, the agent is trained on a synthetic dataset representing urban traffic scenarios. The CNN-DQN model consistently outperforms standard DQN in multiple metrics: cumulative reward increased by 14.3%, loss convergence accelerated by 22%, and mean absolute error (MAE) reduced to 0.028. Furthermore, the model achieved a Pearson correlation coefficient of 0.94 in predicted actions and demonstrated superior robustness under Gaussian noise perturbation, with reward loss limited to 6.18% compared to 18.7% in the baseline. Visualizations of CNN feature maps reveal spatial attention patterns that support efficient path planning. The action symmetry index confirms that the CNN-DQN agent exhibits consistent left-right decision behavior, validating its policy regularity. The novelty of this study lies in its combined use of deep spatial encoding and value-based reinforcement learning for structured, rule-based environments with real-time control implications. These findings indicate that CNN-enhanced reinforcement learning architectures can significantly improve autonomous navigation performance and robustness in dynamic urban settings.
Enhanced Detection of Consumer Behavioral Shifts in E-Commerce Platforms with Transformer-Based Algorithms
This research aims to analyze changes in consumer behavior on e-commerce platforms using consumer interaction data such as view, add to cart, and purchase. Identifying changes in consumer behavior on e-commerce platforms is very important because it can provide deeper insight into consumer motivations and preferences. By better understanding how consumers interact with products, companies can design more targeted strategies to increase conversions, reduce cart abandonment, and improve the overall customer experience. The DistilBERT based prediction model is applied to detect and predict changing patterns of consumer behavior in the purchasing process. DistilBERT was chosen because of its more efficient capabilities compared to previous models which enable faster data processing and lower resource usage, which is very important for real-time applications on e-commerce platforms with big data. The data used includes consumer interactions during a certain period, with model evaluation using precision, recall, F1-score, and accuracy metrics. The results showed that despite an increase in the number of actions such as View and Add to Cart, conversion to Purchase was still hampered, indicating a cart abandonment problem. The model used managed to achieve 90% accuracy, with a precision value of 0.87, recall of 0.85, and F1-score of 0.86, showing excellent performance in predicting changes in consumer behavior. Based on the results of this analysis, companies can optimize marketing strategies by targeting consumers who have added products to their basket but have not yet made a purchase, as well as making price adjustments, discounts, and limited time offers. This research also emphasizes the importance of using real-time data to dynamically adjust marketing strategies and improve customer experience.
An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data
Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Stacking Ensemble with SMOTE for Robust Agricultural Commodity Price Prediction under Imbalanced Data
The volatility of agricultural commodity prices presents a substantial obstacle in the agribusiness sector, especially in supporting timely and data-driven decision-making. This volatility is primarily caused by the imbalanced distribution of historical price data and the complex, often nonlinear nature of price patterns. To address this challenge, this study proposes a novel predictive modeling approach by integrating Stacking Ensemble Learning and Synthetic Minority Over-sampling Technique (SMOTE). The dataset used in this research consists of 5,558 records and 9 features, sourced from a publicly available Kaggle dataset. The target variable daily price was transformed into three classes: low, medium, and high, using a quartile-based discretization approach to enable multiclass classification. The main objective is to evaluate whether stacking combined with SMOTE can improve model performance compared to baseline models that use individual algorithms. A total of eight models were constructed and compared: four baseline models using SMOTE only, and four stacking models integrating SMOTE. The experimental results demonstrate that the proposed model Decision Tree Regression with Stacking and SMOTE achieved the highest performance, with 98.68% accuracy, an F1-score of 0.9868, Cohen’s Kappa of 0.9803, MCC of 0.9803, ROC-AUC of 0.9995, and a log loss of 0.0529. Other optimized models also performed well, such as Random Forest (98.37% accuracy) and Gradient Boosting (98.56%). In contrast, baseline models such as Linear Regression and Decision Tree without stacking achieved only around 67–68% accuracy, with log loss exceeding 0.97. The key contribution of this study is the empirical evidence that combining stacking and SMOTE significantly enhances classification accuracy and model robustness in imbalanced datasets. The novelty lies in applying a deep learning-optimized stacking framework specifically for agricultural commodity price classification, along with a comprehensive multiclass evaluation, offering new insights for practical implementation in agricultural decision support systems.
Enhancing U-Net for Wrist Fracture Segmentation in X-ray Images using Adaptive Callbacks and Weighted Loss Functions
The detection of wrist fracture through medical imaging is causing considerable challenges due to the subtle and variable manifestation of such ruptures, necessitating precise and reliable segmentation methods. Therefore, this research aimed to propose an improved U-Net model for detecting wrist fracture. The model incorporated two innovations, namely adaptive callback training and weighted loss combination. The adaptive callback mechanism could be performed by dynamically adjusting the training parameters based on the model performance to prevent overfitting and accelerate convergence. At the same time, the loss function combined Dice Loss and Binary Cross-Entropy (BCE) Loss with linear as well as non-linear exponential weighting strategies, ensuring balanced optimization between region-based accuracy and pixel classification. During this analysis, a series of experiments were conducted on a curated wrist X-ray image dataset, and the results showed that the proposed method expressed superior performance in terms of segmentation accuracy when compared with previous U-Net and other state-of-the-art procedures. The proposed method achieved 91% accuracy, 87% precision, 86% recall, and 87% F1 score. Following this discussion, the findings showed the efficacy of the adaptive training design and loss function in improving the strength and sensitivity of the model in detecting wrist fracture
Mobile-Based AI Platform Integrating Image Analysis and Chatbot Technologies for Rice Variety and Weed Classification in Precision Agriculture
This work presents the development of an intelligent chatbot system capable of identifying rice plants and weeds from aerial photographs captured by smartphones, thereby enhancing precision agriculture. The study involves creating an AI model that utilizes image processing and deep learning techniques. Users can access the model through a LINE chatbot, and the study will also assess users' satisfaction with the model. Researchers gathered 12,000 pictures of rice fields in Phayao Province, Thailand, to train a modified InceptionV3 model using transfer learning. The dataset included images of rice plants and various types of weeds. The model was trained using image data collected under natural lighting and augmented to improve generalization. It achieved training, validation, and testing accuracies of 98.79%, 96.08%, and 97.83%, respectively. When deployed through a LINE Chatbot, it analyzed user-submitted images to estimate rice-to-weed ratios, yielding 73.33% average accuracy with consistent rice detection. Thirty individuals who used the system reported that it functioned well, was user-friendly, and provided significant benefits for farming in real-world applications. These results suggest that the system could leverage easily accessible AI tools to enhance farming efficiency, reduce costs, and positively impact the environment.
A Hybrid LSTM–Stacking–SMOTE Model for Weather-Aware Palm Oil Price Prediction Addressing Data Imbalance and Forecast Accuracy
Accurate forecasting of palm oil prices is crucial for agribusiness decision-making due to high market volatility influenced by dynamic weather conditions. This study proposes a novel hybrid deep learning model combining Long Short-Term Memory (LSTM), Stacking Ensemble, and Synthetic Minority Over-sampling Technique (SMOTE) to improve predictive accuracy and handle class imbalance in price trend classification. The model was trained using a multivariate time-series dataset sourced from Kaggle, consisting of daily records of temperature, humidity, rainfall, and palm oil prices. A binary classification scheme was applied by labeling instances as either price increase (class 1) or price stable/decrease (class 0), based on a 0% price change threshold. Four experimental configurations were evaluated: standard LSTM, LSTM + SMOTE, LSTM + Stacking, and the proposed LSTM + SMOTE + Stacking. The proposed model outperformed all baselines, achieving the highest accuracy of 83.12%, an F1-score of 0.8466, MAE of 0.1688, RMSE of 0.4109, and a perfect recall of 1.0000, indicating excellent sensitivity to minority class trends. In contrast, the standard LSTM achieved only 77.32% accuracy and an F1-score of 0.7224, showing limited ability in handling imbalanced data. Visualization of loss curves and confusion matrices confirmed the model’s learning stability and classification effectiveness. This study contributes a novel integration of ensemble learning and oversampling in time-series commodity forecasting and demonstrates the effectiveness of this approach in capturing weather-driven price patterns, offering a robust framework for predictive analytics in agriculture.
YOLOv12 Model Optimization for Monitoring Occupational Health and Safety in Hospital Archive Rooms
The application of artificial intelligence technology in occupational safety monitoring systems within healthcare facilities has become an urgent necessity, particularly to support compliance with Occupational Safety and Health (OSH) standards in hospitals. This study aims to develop an automated detection model based on YOLOv12 to identify visual OSH elements in hospital archive rooms, such as APAR, evacuation signs, windows, and Personal Protective Equipment (PPE) including masks, gloves, and shoes. The initial dataset consisted of 2,866 documented images, which were expanded through augmentation to 6,886 images to increase data diversity and prevent overfitting. The YOLOv12 model was trained over 100 epochs using SGD as the optimization technique. The dataset was divided into three subsets training, validation, and testing in a proportional manner. Model evaluation employed metrics such as precision, recall, mAP@0.5, and mAP@0.5–0.95, supported by visualizations including the confusion matrix, F1-confidence curve, and precision-recall curve. One of the key advantages of YOLOv12 lies in its architectural efficiency and enhanced generalization capability, enabled by the integration of R-ELAN, Area Attention Mechanism, and FlashAttention. These components allow for broader receptive field processing with reduced computational complexity. Furthermore, the removal of positional encoding and adjustment of the MLP ratio make the model lighter and faster without compromising accuracy. Compared to previous versions (YOLOv8–YOLOv11), YOLOv12 demonstrates more stable and accurate performance in detecting complex OSH objects in indoor environments. The system was also implemented in a real-time user interface using Streamlit, automatically displaying personnel PPE completeness and room safety compliance status. In conclusion, the optimized YOLOv12 model has proven effective for real-time visual detection in OSH contexts. Future studies are recommended to incorporate data balancing approaches, spatial segmentation, and IoT sensor integration to expand the system’s coverage and resilience across diverse workplace conditions.
SAGOMECON: An Adaptive ε-Constraint-Based Optimization Method for Multi-Criteria Decision-Making in Collaborative Industrial Networks
In collaborative industrial networks, decision-making processes often involve conflicting objectives such as minimizing operational costs and risks, while simultaneously maximizing efficiency and inter-organizational collaboration. Existing multi-objective optimization methods, including the ε-constraint and its variants, face significant challenges in handling dynamic constraints and achieving computational efficiency in real-world scenarios. To address these limitations, this study introduces SAGOMECON (Simplified Adaptive Optimization with Modified ε-Constraint), an enhanced optimization approach designed to support adaptive and efficient multi-criteria decision-making in dynamic environments. SAGOMECON extends the conventional SAUGMECON framework by incorporating real-time constraint updates, adaptive slack handling, and iterative refinement mechanisms, enabling it to maintain solution feasibility under shifting priorities and evolving operational conditions. The proposed method was evaluated using simulated datasets representing partner selection scenarios in collaborative networked organizations (CNOs). Comparative analysis against the traditional ε-constraint and SAUGMECON methods demonstrates that SAGOMECON consistently delivers Pareto-optimal solutions with reduced computational time and superior adaptability to dynamic changes. The findings suggest that SAGOMECON offers a practical and scalable solution for decision-makers in collaborative industrial settings, particularly where trade-offs between competing objectives must be navigated under uncertainty. This contribution is significant for industries seeking intelligent optimization strategies that align with agile and data-driven decision-making frameworks.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms
This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
Study of Machine Learning Techniques for Predicting Panic Attacks with EEG and Personalized Binaural Beat Frequencies
Panic attack detection and intervention remain critical challenges in mental health care due to their unpredictable nature and individual variability. This study proposes a machine learning-based framework for early detection of panic attacks using EEG-derived physiological signals, coupled with real-time personalized auditory intervention through binaural beat frequencies. Data were collected under controlled conditions using wearable biosensors to capture features such as heart rate variability, electrodermal activity, and skin temperature. A Gradient Boosting Classifier achieved 96% accuracy in detecting panic states, while an Isolation Forest algorithm effectively identified anomalous patterns preceding attacks. Based on physiological profiles, the system dynamically recommends individualized binaural beat frequencies to promote relaxation and emotional stabilization. The results demonstrate the feasibility of combining predictive modeling and neuroadaptive sound therapy to deliver scalable, non-invasive, and personalized mental health interventions. This approach aligns with global preventive health strategies, particularly those promoting digital therapeutics and early intervention for anxiety-related conditions.
Comparative Analysis of Novel Deep Reinforcement Learning Methods for Food Distribution Optimization
Uneven food distribution across various regions in Indonesia often results in supply-demand imbalances, leading to price surges, stock shortages, and overall market instability. This challenge is compounded by the limitations of conventional distribution systems, which are ill-equipped to respond to rapidly changing market dynamics. In response, this study introduces a novel, AI-driven approach by implementing Deep Reinforcement Learning (DRL) to optimize food distribution policies using real-world data. Specifically, we perform a comparative evaluation of four emerging DRL models—Double Deep Q-Network (Double DQN), Dueling DQN, Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—to determine their effectiveness in learning adaptive distribution strategies from national food logistics data provided by Indonesia’s Central Bureau of Statistics (BPS). Each model was trained within a custom simulation environment based on the Markov Decision Process (MDP) framework and evaluated using five core performance metrics: cumulative reward, average reward, success rate, sample efficiency, and best reward. The results reveal that A2C consistently outperformed the other models, delivering the highest average reward and most stable training performance, while PPO demonstrated strong efficiency and success rate. These findings underscore the potential of policy-gradient methods—particularly A2C—as robust and intelligent solutions for dynamic food logistics management. This research offers one of the first comparative benchmarks of DRL methods in the food distribution domain and highlights their applicability for future integration into national AI-powered logistics systems.
Clustering Cadet Training Performance Using K-Means and Ward's Method Evidence from OTMon Maritime Monitoring System
This study investigates cadet performance segmentation during on-board maritime training using clustering analysis of data from the On Training Monitoring (OTMon) system. Grounded in the competency-based education framework and experiential learning theory, the research aims to identify behavioral patterns and competency levels among 80 maritime cadets over a twelve-month sea-based training program. The OTMon application continuously recorded task completion rates, feedback interactions, sign-on consistency, and report submissions. K-Means clustering and Principal Component Analysis (PCA) revealed three distinct cadet profiles: Cluster 1 (high-performing) with average task completion of 92.4% and feedback frequency of 15.2 times/month; Cluster 2 (administratively consistent) with 88.1% completion but only 6.3 feedback interactions/month; and Cluster 3 (at-risk) with 67.5% completion and 3.8 feedback interactions/month. Linear Discriminant Analysis (LDA) validated the clusters with 98.8% resubstitution accuracy and 97.6% cross-validation accuracy, supported by generalized squared distances above 9.5 between all cluster pairs, indicating strong separation. These findings demonstrate that unsupervised clustering can reliably distinguish high-performing cadets from those needing targeted intervention, enabling data-informed mentoring and adaptive learning strategies in maritime education. The contribution of this study lies in integrating digital monitoring data with both unsupervised and supervised machine learning methods to enhance competency assessment. The novelty is in applying maritime-specific learning analytics for real-time performance segmentation, offering a scalable diagnostic framework for improving supervision quality and supporting individualized cadet development in vocational training contexts.
Modernizing Medicinal Plant Recognition: A Deep Learning Perspective with Data Augmentation and Hybrid Learning
This study proposes a deep learning-based solution to address the longstanding challenge of accurately identifying Indian medicinal plants, which are vital to Ayurvedic pharmaceutics but often misidentified due to their morphological similarities. The objective is to develop a reliable, automated classification system using image processing and advanced neural network architectures. A dataset of 5,945 images representing 40 distinct medicinal plant species was sourced from Kaggle and augmented to 11,890 images using techniques such as flipping, rotation, and scaling to enhance diversity. The models tested include a baseline Convolutional Neural Network (CNN), transfer learning with DenseNet121, DenseNet169, and DenseNet201, a voting ensemble of these DenseNet variants, and a hybrid DenseNet201-LSTM architecture. Experimental results show that the CNN model achieved the lowest accuracy at 69.58%, while the hybrid DenseNet201-LSTM model reached the highest validation accuracy of 93.38%, with a precision of 94.74%, recall of 93.38%, and F1-score of 93.42%. These findings confirm the hybrid model’s superior ability to capture spatial and sequential dependencies in leaf features. The novelty of this work lies in the integration of DenseNet201 with LSTM for medicinal plant classification, which has not been widely explored in this domain. The study also acknowledges dataset scalability as a limitation and proposes future work involving dataset expansion through botanical collaborations, integration of environmental metadata, and deployment of a mobile application using TensorFlow Lite for real-time, low-resource implementation. Overall, the research contributes a robust and scalable framework for medicinal plant identification, promoting trust in traditional medicine, supporting conservation efforts, and enabling practical field-level applications in both rural and clinical settings.
Analyzing the Efficacy of Pose Recognition, YOLOv3, and Deep Learning Techniques for Human Activity Recognition
The global increase in life expectancy, driven by increased nutrition, healthcare, and living conditions, has resulted in a significant growth in the senior population, notably in Kazakhstan, where the number of people aged 60 and more currently exceeds 2.7 million. This demographic transition poses considerable public health problems, particularly the high prevalence and severity of falls in older persons. Falls are currently the second largest cause of unintentional mortality for more than 87% of the elderly, with 28-34% falling at least once per year. As the worldwide population of people aged 65 and more is predicted to exceed 1.5 billion by 2050, there is an urgent need for precise, real-time fall detection systems. This work uses standardized datasets to conduct a complete evaluation of three fall detection methodologies: posture recognition, YOLOv3-based detection, and deep learning. Deep learning models attained the best accuracy of 92.0% by utilizing their capacity to learn complex spatial-temporal information, but at the cost of increased computing burden and slower inference times (40 ms). YOLOv3 provided competitive accuracy (90.2%) and quicker processing (25 ms), making it suitable for real-time deployment, although with a larger false positive rate. Pose identification, while highly interpretable due to its emphasis on skeletal key points, performed less well in crowded or obscured settings. The findings highlight the possibility for combining the capabilities of each technique to create hybrid systems with adaptive, resource-efficient architectures. Future research should focus on sensor fusion and optimization methodologies to improve accuracy and scalability across a variety of scenarios.
A Data-Driven Training Kit to Enhance the Note Recording Skills of Music Learners
Music note recording is a fundamental skill in music education, yet many undergraduate learners struggle due to the lack of structured, self-guided practice resources. This study aimed to develop and evaluate a data-driven training kit designed to Enhance the Note Recording Skills of Music Learners. A total of 20 first-year students from a Bachelor of Education program in Western Music and Vocal Education were selected through purposive sampling. Research instruments included the training kit, expert evaluation forms, pre- and post-tests, student satisfaction surveys, and behavior observation checklists. Quantitative data were analyzed using mean, standard deviation, and paired sample t-tests. Results showed a statistically significant improvement in students' performance after the intervention (t = 18.789, p < .001), with a large effect size (Cohen’s d = 2.53). Experts rated the kit highly (M = 4.86, SD = 0.29), and students reported very high satisfaction (M = 4.94, SD = 0.14). These findings support the kit’s effectiveness as an engaging and pedagogically sound tool for developing music note recording skills in higher education settings.
AMIKOM-RECSYS: Enhancing Movie Recommender System using Large Language Model (ChatGpt), Deep Learning and Probabilistic Matrix Factorization
E-commerce has become one of the most widely used digital applications globally, enabling personalized product discovery and purchasing. To support these services, recommender systems are essential, offering item suggestions based on user preferences. Most recommender systems rely on machine learning algorithms to estimate user-item relevance scores, often utilizing product ratings. However, a persistent challenge in this domain is the issue of data sparsity, where only a small fraction of users provides explicit ratings, leading to reduced accuracy in recommendation results. In this study, we introduce a novel hybrid recommendation algorithm, named AMIKOM-RECSYS, designed to address the sparsity problem and enhance rating prediction. Our model integrates three main components included a Large Language Model (LLM) using ChatGPT, a Transformer-based encoder (BERT), and Probabilistic Matrix Factorization (PMF). The LLM generates descriptive information about movies based on specific prompts, which is then passed to BERT to encode the content into meaningful 2D vector representations. These enriched embeddings are subsequently utilized by the PMF algorithm to predict missing user-item ratings. We evaluate the proposed model on two benchmark datasets, ML-1M and ML-10M using Root Mean Squared Error (RMSE) as the evaluation metric. The AMIKOM-RECSYS model achieved RMSE values of 0.8681 on ML-1M and 0.7791 on ML-10M under a 50:50 data split, outperforming several baseline models including CNN-PMF, LSTM-PMF, and Attention-PMF. These results highlight the effectiveness of integrating LLM and Transformer-based contextual understanding into matrix factorization frameworks. In future work, we plan to extend this framework by incorporating other matrix factorization techniques such as Singular Value Decomposition (SVD) and integrating additional sources of user information, including social media activity, to further improve recommendation performance.
Toward Improving Knee MRI Image Quality for Single Image Super-resolution
This study aims to improve image super-resolution techniques by balancing distortion reduction with perceptual quality improvement. It introduces a new framework called Toward Improving Super-Resolution, which focuses on producing high-quality knee magnetic resonance images. The framework uses a lightweight, resolution-independent, feedforward convolutional network with 266,000 parameters, which includes smoothing and denoising preprocessing and Leaky Rectified Linear Unit activations for stable training. The model builds on a baseline deep learning architecture to improve training stability and visual quality while maintaining computational efficiency. The fast Magnetic Resonance Imaging knee dataset was compared to established super-resolution methods like Super-Resolution Convolutional Neural Network, Very Deep Super-Resolution, and Enhanced Deep Super-Resolution. Toward Improving Super-Resolution achieved a peak signal-to-noise ratio of 38.405 ± 0.129 decibels and a structural similarity index of 0.9815 ± 0.0021, surpassing Super-Resolution Convolutional Neural Network, Very Deep Super-Resolution, and Enhanced Deep Super-Resolution. It maintained high performance at scales three and four, demonstrating accuracy and statistical robustness. The study shows that the proposed framework can enhance diagnostic imaging, reduce the need for repeated scans, and speed up clinical decision-making.
Brushstroke Classification from Oil Painting Images Using Convolutional Neural Networks for Tool Optimization
This research introduces an integrated framework that applies Convolutional Neural Networks (CNNs) to classify brushstroke types from oil painting images and utilizes the classification results to inform the design and optimization of painting tools. Researchers will conduct the brush testing activities in four sessions: Session 1: Still life painting test, Session 2: Portrait painting test, Session 3: Landscape painting test, and Session 4: Rose painting test.The classification results were mapped to specific ergonomic and functional brush design parameters, resulting in the production of ten custom-designed brush prototypes. These brushes were fabricated using precision prototyping techniques and evaluated by twenty art students and five professional artists. Quantitative user feedback revealed high satisfaction across all performance categories, including ergonomic comfort, stroke control, and paint handling. The findings confirm that CNN-based analysis of brushstroke characteristics can directly support the practical innovation of art tools, bridging computational visual analysis and traditional artistic practice. This study offers a data-driven approach to creative tool design and presents a new interdisciplinary pathway that combines deep learning, material design, and fine arts.
Hybrid CNN Approach for Post-Disaster Building Damage Classification Using Satellite Imagery
Accurate post-disaster building damage assessment is critical for timely response and effective reconstruction planning. This study proposes a hybrid deep learning architecture that integrates Inception-ResNet-v2 and EfficientNetV2B0, designed to enhance post-disaster damage classification from high-resolution satellite imagery. The model leverages dual-stream feature extraction, followed by concatenated fully connected layers optimized with dropout and batch normalization to improve generalization and reduce overfitting. The objective is to outperform standard Convolutional Neural Network (CNN) models in terms of classification and segmentation performance across multiple damage categories: no damage, minor damage, major damage, destroyed, and unclassified. The model was trained and validated on the publicly available xView dataset, covering over 12,000 annotated images from various natural disasters. Comparative evaluation against ResNet, GoogleNet, DenseNet, and EfficientNet demonstrates that the proposed model achieves the highest accuracy (86%), precision (85%), recall (86%), and F1-score (84%). Furthermore, it outperforms all baseline models in segmentation metrics, achieving an Intersection over Union (IoU) score of 0.7749 and a Dice Similarity Coefficient (DSC) of 0.8726. The model also significantly reduces misclassification rates in critical categories such as “major damage” and “destroyed.” A Wilcoxon signed-rank test confirmed that these improvements are statistically significant (p < 0.05) across all major performance indicators. The novelty of this study lies in the fusion of two state-of-the-art CNN backbones with tailored architectural modifications, yielding a robust and generalizable model suitable for automated disaster damage assessment. This research contributes a scalable deep learning approach that can be integrated into real-time or semi-automated disaster response systems, offering improved decision-making support in emergency contexts. The results affirm the model’s potential as a reliable tool in post-disaster scenarios and set a foundation for future work in multi-modal and real-time AI-based disaster management.
An Explainable Credit Card Fraud Detection Model using Machine Learning and Deep Learning Approaches
This study proposes an adaptive, interpretable real-time fraud detection and prevention system designed for high-risk financial environments, capable of processing over 1.6 million imbalanced credit card transactions with low latency. The objective is to build a unified framework that integrates predictive accuracy, explainability, and adaptability. The methodology follows four phases: exploratory data analysis to reveal structural and behavioral fraud patterns, feature engineering with domain-informed attributes and ADASYN oversampling to mitigate the 1:174 imbalance, training of multiple models (XGBoost, LightGBM, Random Forest, Gradient Boosting, and MLP), and an ensemble architecture evaluated with SHAP-based explainability. The system introduces three key contributions: stability-aware SHAP caching that reduces explanation latency to 41.2 ms, reinforcement learning–based threshold tuning that dynamically adapts to evolving fraud patterns, and out-of-distribution detection to enhance resilience against data drift. Results demonstrate strong performance, with XGBoost achieving 99.86% accuracy, 96.36% precision, 80.59% recall, F1-score of 0.878, and ROC-AUC of 0.9988, outperforming other models. The full system attained 93.2% accuracy, 90.2% F1-score, and 96.1% AUC at the system level, successfully blocking 91% of fraudulent transactions while maintaining a false positive rate of 7.8%. Novelty lies in combining explainability and adaptivity in a production-ready architecture, where reinforcement learning enables continuous threshold self-regulation and SHAP stability analysis validates interpretability across models. These findings show that high fraud detection accuracy and transparency are not mutually exclusive, offering a scalable blueprint for financial institutions and other critical domains requiring real-time, explainable, and adaptive decision-making.
Explaining Students' Digital Entrepreneurial Behavior: The Role of Social Media Adoption in an Integrated TPB–UTAUT Model
Amidst digital transformation and demographic bonuses in Indonesia, the emergence of digital entrepreneurship among the younger generation has become a promising yet challenging phenomenon. The main objective of this study is to develop and empirically evaluate an integrated model that explains students' digital entrepreneurial behavior by integrating psychological and technological viewpoints and combining the Theory of Planned Behavior (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT) approaches. TPB has been widely used to predict entrepreneurial intentions and behavior. However, TPB is not yet considered to be able to capture the role of comprehensive technology adoption in the context of digital entrepreneurship. To bridge this gap, this study integrates the UTAUT approach, which focuses on technology acceptance factors. This integration addresses the shortcoming of the TPB by completely including the impact of digital technology adoption on entrepreneurship, while the UTAUT fails to include psychological motivation. PLS-SEM analyzed data from 322 student entrepreneurs who run social media-based enterprises. The study found that the TPB-UTAUT framework explains 62.2% of the variation in social media adoption (R² = 0.622) and 62.6% of the variance in entrepreneurial activity (R² = 0.626). Eight out of nine hypotheses were supported: attitudes (β = 0.330, p < 0.001) and perceived behavioral control (β = 0.189, p = 0.008) significantly influenced social media adoption, while attitudes (β = 0.155, p = 0.006), perceived behavioral control (β = 0.295, p < 0.001), performance expectancy (β = 0.149, p = 0.011), and social media adoption (β = 0.225, p = 0.001) directly enhanced entrepreneurial behavior. Effort expectation influenced adoption (β = 0.183, p = 0.005) but not behavior (β = 0.101, p = 0.069). The novelty of this study lies in demonstrating that among digital-native students, effort expectancy loses significance in predicting entrepreneurial behavior, indicating a generational shift in technology adoption dynamics. These insights offer theoretical enrichment and practical implications for designing digital entrepreneurship curricula and policies in developing countries.
A Hybrid GRG-Neighborhood Search Model for Dynamic Multi-Depot Vehicle Routing in Disaster Logistics
In disaster relief logistics, timely and adaptive routing is critical to meet fluctuating demands and disrupted infrastructure. This paper proposes a Hybrid GRG–Neighbourhood Search (NS) model for solving the Multi-Depot Vehicle Routing Problem with Capacity and Time Dependency (MDVRP-CTD). The model integrates the Generalized Reduced Gradient (GRG) method for handling nonlinear capacity constraints and NS for local route refinement. The objective is to minimize total travel distance, delay penalties, and maximize vehicle utilization under dynamic disaster scenarios. Tested using the SVRPBench dataset, the hybrid model achieved up to 96.5% demand fulfillment, an 11% improvement in vehicle utilization, and a reduction in total distance by 7%, outperforming Tabu Search and ALNS in three simulation scenarios. The model demonstrates enhanced adaptability and responsiveness to time-sensitive, capacity-constrained environments. Its novelty lies in the integration of nonlinear optimization with adaptive local improvement tailored for disaster contexts, providing a robust decision-support tool for real-time humanitarian logistics.
ACLM Model: A CNN-LSTM and Machine Learning Approach for Analyzing Tourist Satisfaction to Improve Priority Tourism Services
Tourist satisfaction is a key proxy for destination service quality, yet automatic sentiment analysis of online reviews still faces class imbalance, overfitting, and limited deployability. This study proposes ACLM, a hybrid sentiment classification pipeline that learns semantic and temporal features with a CNN-LSTM backbone and evaluates three classifier heads (Softmax, Logistic Regression, XGBoost) on a three-class corpus (neutral, satisfied, dissatisfied). The objective is to deliver an accurate and operational model for decision support in tourism services. The idea combines Word2Vec embeddings, a compact CNN for local patterns, an LSTM for sequence dependencies, and a training workflow with text cleaning, SMOTE based balancing, and regularization to curb overfitting; outputs are exposed through a simple Streamlit interface. Results show that CNN-LSTM with a Softmax head attains accuracy 0.89, macro precision 0.89, macro recall 0.84, and macro F1 0.86, outperforming Logistic Regression (accuracy 0.87, macro precision 0.84, macro recall 0.82, macro F1 0.82) and XGBoost (accuracy 0.85, macro precision 0.80, macro recall 0.82, macro F1 0.80). The findings indicate that deep sequence features paired with a simple Softmax head provide the best tradeoff between accuracy and stability for three-way sentiment classification. The contribution is a reusable, end to end blueprint from preprocessing and balanced training to quantitative evaluation and an inference GUI, and the novelty lies in testing interchangeable classifier heads on a single CNN-LSTM feature extractor while explicitly addressing data imbalance and deployment constraints. The GUI is implemented using the highest accuracy model, namely CNN-LSTM with Softmax.
Classification of Batak Toba Ulos Motifs Based on Transfer Learning with MobileNetV2
Indonesia possesses a rich cultural heritage, including the traditional Batak Toba Ulos textile, which is known for its diverse motifs and deep philosophical meanings. However, the preservation and visual recognition of Ulos remain challenging, particularly in terms of systematic documentation and automated classification. This study presents a visual recognition system for Batak Toba Ulos motifs using a transfer learning approach based on the MobileNetV2 architecture. The methodology involves the construction of a curated dataset of Ulos images, the application of data augmentation and preprocessing techniques, and model training utilizing ImageNet pre-trained weights. The system’s performance was evaluated using accuracy, precision, recall, and F1-score metrics. Results show that the model is capable of accurately classifying all 12 Ulos classes, achieving F1-scores ranging from 0.93 to 0.97. These findings demonstrate that transfer learning is effective in overcoming the limitations of culturally specific, small-scale datasets. This research contributes to the development of artificial intelligence tools for cultural preservation and supports the digital documentation and promotion of Batak Toba Ulos to younger generations and broader audiences in an efficient and scalable manner.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection
Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction
Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
IoT-Enabled Supervised Learning-Based Prediction Model for Smart Instrumentation Controllers in Signal Conditioning Systems
This study proposes an intelligent Machine Learning (ML)-based smart controller for industrial flow process systems to enhance accuracy, adaptability, and robustness compared to conventional Proportional–Integral–Derivative (PID) controllers. The main idea is to replace reactive PID tuning with a proactive data-driven control strategy capable of predicting deviations and adjusting process parameters in real time. The objective is to develop and evaluate supervised learning models that can replicate and improve PID performance using real-time operational data collected from a flow process station. The proposed system integrates Internet of Things (IoT) sensors and edge computing to continuously acquire and process flow rate, pressure, and valve position data for model training and testing within the WEKA platform. Four classifiers—Linear Regression, Multilayer Perceptron (MLP), Sequential Minimal Optimization Regression (SMOreg), and M5P model tree—were compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and model-building time as key evaluation metrics. Experimental results demonstrated that the M5P pruned tree model achieved the best overall performance with an MSE of 0.0024, RMSE of 0.0577, and model-building time of only 0.03 seconds, outperforming Linear Regression (RMSE = 0.0028), MLP (RMSE = 0.026), and SMOreg (RMSE = 0.0279). The findings show that the M5P-based controller closely replicates PID behavior while offering superior predictive accuracy, faster computation, and self-adaptive learning capabilities. The novelty of this research lies in demonstrating that an IoT-enabled, data-driven smart controller can achieve real-time predictive control without requiring explicit mathematical models, thereby simplifying tuning complexities and paving the way for autonomous, scalable, and intelligent control systems in Industry 4.0 environments.
Image-Based Fish Freshness Classification Using Two-Phase Transfer Learning with Deep Learning Fusion Model
This study introduces a novel deep learning approach for automated fish freshness classification using image analysis. The objective is to design and validate a Deep Learning Fusion Model that combines the strengths of EfficientNetB0 and InceptionV3 architectures to improve accuracy and robustness in classifying fresh and non-fresh fish. Input images were subjected to extensive augmentation, including RandomFlip, RandomRotation, RandomZoom, RandomContrast, RandomBrightness, and RandomTranslation, applied exclusively to the training dataset to enhance generalization, followed by backbone-specific pre-processing. Extracted features were fused via global average pooling and forwarded to a newly designed classification head with dropout and L2 regularization to mitigate overfitting. A two-phase transfer learning strategy was employed: initially training the classification head with frozen backbones, followed by fine-tuning the backbone layers using the Adam optimizer with a reduced learning rate. To highlight the contribution of the fusion strategy, ablation studies were conducted with single-backbone models. The EfficientNetB0 model achieved 89.17% validation accuracy, 85.83% test accuracy, and an F1-score of 85.69%, while the InceptionV3 model achieved 86.67% validation accuracy, 81.67% test accuracy, and an F1-score of 81.59%. In contrast, the proposed Fusion Model achieved 93.33% validation accuracy, 95.00% test accuracy, and an F1-score of 94.95%. Additional evaluations with confusion matrices, ROC curves, AUC, and precision-recall curves confirmed the model’s superiority. The findings demonstrate that integrating features from diverse CNN architectures enables the model to learn richer representations, resulting in significantly improved classification performance. The novelty of this work lies in the effective fusion of complementary backbones through global average pooling and fine-tuned transfer learning, establishing a human-centric computational approach that offers a reliable solution for practical fish freshness assessment in food safety and market scenarios.
Hybrid Ensemble Learning with SMOTEENN and Soft Voting for Stunting Risk Prediction: A SHAP-Based Explainable Approach
Stunting remains a critical public health concern in Indonesia, with long-term consequences for physical growth, cognitive development, and human capital. This study introduces a hybrid machine learning framework to predict household-level stunting risk by integrating Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN), soft voting ensemble, and SHapley Additive exPlanations (SHAP). The objective is to enhance both predictive accuracy and interpretability in identifying high-risk households. A dataset of 115,579 household records from West Sumatra, comprising 20 demographic, socioeconomic, health, and housing predictors, was utilized. Preprocessing steps included handling missing values, categorical encoding, and applying SMOTEENN exclusively on the training set to mitigate class imbalance. The baseline models demonstrated limited sensitivity, with XGBoost performing best at 74.56% accuracy and 71.08% F1-score on imbalanced data. After applying SMOTEENN, performance improved substantially, with XGBoost achieving 91.82% accuracy and 91.74% F1-score. Further improvements were obtained through hybridization, where the Random Forest and XGBoost soft voting ensemble reached 91.95% accuracy and 92.46% F1-score, representing a notable gain over individual classifiers. SHAP analysis added interpretability by identifying family members, education level, diverse food consumption, occupation, and drinking water source as dominant predictors of stunting risk. The novelty of this study lies in the integration of SMOTEENN with ensemble learning and SHAP, providing not only robust performance but also transparency in feature contributions. The findings demonstrate that the proposed framework improves sensitivity to minority classes, delivers superior predictive accuracy compared to baseline models, and offers interpretable insights to guide targeted interventions. By combining methodological rigor with explainability, this research contributes a practical decision-support tool for policymakers, supporting early detection of at-risk households and accelerating stunting reduction efforts in Indonesia.
Egg Weight Estimation Based on Image Processing using Mask R-CNN and XGBoost
Manually measuring egg weight in the context of livestock and the food industry can pose various problems, including time and labor requirements, the risk of egg damage, consistency and accuracy, and limitations on production scale. To address these issues, an automated egg weight estimation system is essential. This study proposes integrating computer vision and machine learning into a unified workflow that combines segmentation, classification, and regression for practical weight estimation. The proposed pipeline employs Mask R-CNN for egg segmentation, Random Forest (RF) classifier for egg type classification based on color features, and XGBoost for regression using morphological, geometric, color features, and egg type as predictors. The dataset used is 720 images, consisting of 20 eggs (10 chicken and 10 duck), each photographed from 36 rotational angles, and was collected with Ground Truth (GT) weights obtained from a digital scale. Experimental findings show that the RF classifier achieved perfect accuracy (precision, recall, and F1-score = 1.00) in distinguishing chicken and duck eggs. The XGBoost regressor obtained a training performance of MAE = 1.07 g and R² = 0.68, and a validation performance of MAE = 0.23 g and R² = 0.80 under 10-fold grouped cross-validation. Although a Support Vector Regressor baseline reached higher training accuracy (MAE = 0.22 g, R² = 0.96), it failed to generalize on validation (R² < 0), highlighting XGBoost’s robustness. The feature importance analysis revealed that there are 4 (four) important features for building an estimation model, namely: Hu moments, eccentricity, elongation, and diagonal length, while color statistics played a complementary role. The novelty of this work lies in combining deep segmentation, color-based classification, and feature-driven regression into a unified framework specifically for egg weight estimation, showing its feasibility as a proof of concept and laying the foundation for future large-scale, calibrated, and externally validated deployment.
Muhammad Fadlansyah Zikri Akhiruddin Rawosi
Understanding Teacher Retention through the Lens of Job Satisfaction: An Empirical Study of Organizational and Human Resource Management in Chinese Universities
This study investigates the determinants of teacher retention in public universities in Sichuan Province, China, emphasizing the mediating role of job satisfaction between organizational management (OM) and human resource management (HRM) on job retention (JR). The research aims to (1) identify the key factors influencing the retention of non-established teachers and (2) propose effective strategies to enhance their job stability. A quantitative approach was applied using a questionnaire survey administered to 1,400 teachers from 27 public universities, yielding 1,335 valid responses (95% response rate). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that both OM (β = 0.031, p < 0.001) and HRM (β = 0.029, p < 0.001) significantly and positively affect JR, while job satisfaction fully mediates these relationships (β = 0.030, p < 0.001). The explanatory power of the model was moderate, with R² = 0.630 for job satisfaction and R² = 0.545 for job retention, indicating that the proposed model accounts for over 50% of the variance in both variables. Descriptive statistics further revealed that 50.3% of respondents were non-established teachers, and 61.5% were female, highlighting a balanced and diverse sample. The findings indicate that well-structured OM and HRM practices, such as transparent promotion systems, career development opportunities, and inclusive institutional participation, substantially enhance teacher satisfaction and retention. This study’s novelty lies in its integrated model combining OM, HRM, and job satisfaction to explain teacher retention, a topic rarely explored in the context of Chinese public universities. The research contributes to the literature by offering empirical evidence and actionable recommendations for policymakers and administrators to strengthen human resource strategies and ensure the long-term stability of university faculty.
Exploring User Experience of an Interactive LMS for Green Entrepreneurship: An Empirical Study of the GEJUR Platform
This article aims to assess the design, development, and User Experience (UX) of GEJUR, a web-based interactive Learning Management System (LMS) designed to foster green entrepreneurship among youth in Nusa Tenggara Timur (NTT), Indonesia. This study defines green entrepreneurship as entrepreneurial activities that combine economic value creation with environmentally responsible practices that promote sustainability in business. This research enhances the domains of Human–Computer Interaction (HCI) and entrepreneurship education by illustrating how user-centered digital platforms may cultivate youth business competencies while promoting the Sustainable Development Goals (SDGs). GEJUR incorporates interactive learning modules, e-mentoring, and specific cultural features like weaved motifs (pola tenun), providing contextual relevance alongside essential LMS functions. A mixed-methods approach was employed, integrating surveys, Focus Group Discussions (FGDs), semi-structured interviews, and usability testing. Quantitative data were gathered via the System Usability Scale (SUS), whilst qualitative data documented user impressions of obstacles and anticipations. The usability evaluation, conducted with 33 individuals, resulted in a mean SUS score of 48.9, which corresponds to approximately 71.9% of the benchmark score of 68. This positions GEJUR beneath the established threshold, classified as having “poor” usability, although it remains functionally workable. Participants effectively accomplished critical tasks including authentication, course access, and quizzes, and offered favorable feedback regarding the system’s cultural integration. The results highlighted critical areas for enhancement, such as interface uniformity, minimization of response times, and more explicit user feedback. The study finds that GEJUR offers a viable yet improvable foundation for digital entrepreneurship training platforms in underprivileged areas. Subsequent study ought to broaden testing to encompass bigger and more heterogeneous user populations and implement sophisticated UX evaluation criteria beyond the SUS. Through iterative refinement, GEJUR can develop into a scalable platform that facilitates youth empowerment, sustainable business practices, and extensive socio-economic advancement.
Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing
Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.
Optimized AI-IoT Solution for Real-Time Pest Identification in Smart Agriculture
Pest detection and identification play a crucial role in reducing the damage caused by pest, insect and diseases. Timely detection and response are essential to increase the quality and quantity of crop production. Efficient pest management strategies are important for achieving optimal crop quality and promoting sustainable agricultural practices. This research proposes a framework that can automatically detect pests and offer timely solutions to farmers. The proposed approach integrates intelligent computing methods with connected device networks to identify and classify pests in real time with high precision. The methodology focuses on efficiently segmenting the pest from the captured leaf image using a novel region growing based segmentation algorithm. The threshold for region growing based segmentation algorithm is based on the adaptive local region entropy which contributes to the efficient segmentation. Stacked Ensemble Classifier (SEC) is used for the classification. The metrics used for evaluating the performance of the pest detection framework are accuracy, Area Under the Receiver Operating Characteristic Curve, F1-Score and Mean Average Precision (mAP). The results indicate that the proposed SEC with region growing based segmentation framework achieves 98 % of classification accuracy and mAP of 0.96 proving that it is very effective in both classification and segmentation task. The comparative analysis further reveals that the SEC outperforms the existing machine learning models and ensemble learning models like majority voting and weighted average models for process innovation.
Dynamic Model for Budget Allocation in via Multi-Criteria Optimization
This research introduces a dynamic multi-criteria optimization framework for fair budget distribution across four districts in Kazakhstan’s Almaty region. Its main objective is to promote transparency, equity, and efficiency in allocating a constrained regional budget of 42,656,543 thousand tenge across seven activity areas (AA): education, healthcare, transport, infrastructure, digitalization, culture, and ecology. The framework incorporates four weighted criteria: citizen satisfaction (0.2 weight), strategic development priorities (0.2), basic needs fulfillment (0.3), and urbanization level (0.3). Two optimization techniques were employed: Sequential Quadratic Programming (SQP) in MATLAB, converging in 100 iterations with an objective function value of 18,519,864.85 thousand tenge, and Genetic Algorithm (GA) in Python, achieving a slightly higher value of 18,520,000.00 thousand tenge after 500 generations. The minimal difference of 135.15 thousand tenge (0.0007% of the budget) underscores the reliability of both methods. All seven sectors received funding, with healthcare (22.05%) and transport (21.11%) allocated the largest portions, and education (7.03%) the smallest. Fairness is evidenced by a standard deviation of sectoral shares at 5.69%, a coefficient of variation of 0.398, and a Gini coefficient of 0.223. Participatory budgeting was simulated using synthetic citizen voting data derived from demographic factors. Visualizations depict the optimization process’s convergence and budget distribution across feasible solutions. A proposal for pilot testing within Kazakhstan’s e-government system (Egov) has been submitted to the Ministry of Digital Development. Future enhancements will include explainable AI, stakeholder-driven weight adjustments, and real demographic and budgetary data to foster transparency and public confidence. This framework provides a scalable, data-driven approach to participatory budgeting, harmonizing strategic objectives, socio-economic demands, and citizen preferences. SQP and GA methods achieved near-optimal solutions with objective function values of 18,519,864.85 and 18,520,000.00 thousand tenge, respectively. The 135.15 thousand tenge difference (0.0007% of the budget) is negligible, confirming their robustness.
Enhancing VIX Shock Prediction via a Probabilistic Attention Transformer
This study proposes a Probabilistic-Attention Transformer for forecasting abrupt shifts in the Volatility Index (VIX), advancing volatility modeling by directly embedding externally estimated shock probabilities into the attention mechanism. The core idea is to modify similarity-based attention scores with daily shock probabilities derived from stochastic diffusion equations, thereby enhancing the model’s sensitivity to extreme-value dynamics. The primary objective is to improve predictive accuracy during market stress, particularly under warning (20 ≤ VIX ≤ 30) and shock (VIX > 30) regimes where conventional models often fail. Using 35 years of historical VIX data (1990–2024), the framework is benchmarked against GARCH (1,1) and a standard Transformer under distinct volatility regimes. Empirical findings show that the proposed model consistently outperforms alternatives: during warning regimes, prediction error is reduced by over 40% relative to both benchmarks, while in shock regimes, improvements exceed 50%, with performance gains validated by Diebold–Mariano tests at the 1% significance level. These results demonstrate both statistical and practical significance, offering risk managers and investors more reliable forecasts during periods of heightened market instability. The contribution of this research lies in providing not only empirical evidence of improved predictive performance but also a generalizable framework for integrating probabilistic indicators into deep learning architectures. The novelty is in showing that probabilistic weighting of attention can transform standard neural architectures into early-warning systems capable of capturing regime shifts in financial markets. Beyond VIX forecasting, this methodological contribution has broader applicability to equities, exchange rates, and commodities, where identifying and responding to volatility shocks is critical for risk management and investment decision-making.