Journal of Systems Engineering and Information Technology (JOSEIT)

Enhancing News Recommendations with Deep Reinforcement Learning and Dynamic Action Masking

2025-04-28T10:57:23+00:00

The news recommender system is crucial in the transmission of news inside new media. A deep reinforcement learning-based recommender system is suggested, intending to integrate the characterization capabilities of neural networks with the strategic selection capabilities of reinforcement learning to enhance news recommendation efficacy. Dynamic action masks enhance the capacity to assess users' short-term interests, an optimized caching mechanism improves the efficiency of the experience cache, and a reward design characterized by region masking accelerates model training, thereby enhancing the performance of the recommender system in news recommendation. Experimental results indicate that the recommendation accuracy of the proposed model on the news dataset is on par with that of prevalent neural network recommendation techniques and surpasses existing state-of-the-art algorithms in ranking performance.

Parallel Clustering Algorithms: Segmenting Chinese A-Share Stocks Using Financial Indicators

2025-04-30T09:30:30+00:00

This study presents a novel application of parallel clustering algorithms for segmenting stocks in the Chinese A-share market based on financial indicators. Using the Hadoop platform and Mahout software library, we implemented and compared the performance of K-means and fuzzy K-means algorithms across five distance measures: Euclidean, squared Euclidean, Manhattan, cosine, and Tanimoto. The analysis utilized 15 financial indicators from 2,544 listed companies, reflecting profitability, solvency, growth capability, asset management quality, and shareholder profitability. Experimental results demonstrate that for stock financial data clustering, the K-means algorithm with Tanimoto distance yields optimal execution efficiency and clustering quality, while the fuzzy K-means algorithm performs best with squared Euclidean distance. However, the K-means algorithm proved more effective overall, successfully categorizing 1,483 stocks into 26 meaningful segments compared to only 511 stocks in 27 segments by fuzzy K-means. The resulting stock segmentation framework divides the market into eight comprehensive categories based on investment value and security, providing investors with practical guidance for stock selection. Our approach enables investors to understand fundamental characteristics of each stock segment, discern their distinctive features, and identify undervalued stocks with appreciation potential. This research represents the first application of parallel big data clustering algorithms to segment the entire Chinese A-share market, offering significant practical value for investment decision-making.

Multi-Class CNN Models for Banana Ripeness Classification

2025-04-30T09:31:28+00:00

This study develops and evaluates Convolutional Neural Network (CNN) models for classifying banana maturity stages using images, addressing a significant challenge in the banana supply chain. The banana industry represents a major agricultural sector worldwide, with Brazil exporting 56.2 thousand tons in 2023. Accurate maturity classification is essential for optimizing harvest timing, reducing post-harvest losses, and extending shelf life. We utilized a public Brazilian dataset of 1,000 Prata Catarina banana images categorized into eight ripening stages based on peel coloration standards established by the Brazilian Program for Horticulture Modernization. The images were preprocessed to a standardized 200x200 pixel resolution, and we evaluated the effectiveness of data augmentation techniques including horizontal flip, vertical flip, rotation, and zoom. Our CNN architecture consisted of five convolutional blocks with a dropout layer prior to flattening. We conducted six experiments comparing three classification scenarios (8, 5, and 2 ripeness classes) with and without data augmentation. The models achieved test accuracy ranging from 45.3% to 89.5%, with optimal precision and recall of 87.2% and 89.6% respectively in the two-class model without data augmentation. Performance improved as the number of classes decreased, highlighting the challenge of distinguishing between visually similar ripening stages. This research provides a fundamental reference for future banana ripeness classification studies and demonstrates the potential for practical applications using mobile device cameras, supporting increased productivity and sustainability in the banana industry.

Machine Learning Models for Air Pollution Health Risk Assessment

2025-04-30T09:32:24+00:00

This study explores the application of machine learning (ML) models and artificial neural networks (ANNs) in the assessment of public health concerns associated with air pollution. Utilizing a dataset comprising over 12,000 records from India and Nepal, encompassing both quantitative measurements and visual data, several classification models were constructed and evaluated to predict air quality index (AQI) categories indicative of varying health risk levels. The implemented models comprise decision tree (DT), support vector machine (SVM), random forest (RF), XGBoost, and deep neural networks (both convolutional and recurrent). The methodology entailed data preprocessing, feature significance analysis, and model assessment utilizing accuracy metrics and ROC curves. The findings reveal a high classification accuracy across all models (>90%), with ensemble-based methods demonstrating enhanced performance. XGBoost attained superior accuracy with optimal resource efficiency; however, artificial neural network (ANN) models, especially long short-term memory (LSTM), obtained accuracy levels of 98% by the 15th training epoch. The feature significance analysis revealed that AQI, PM2.5, and PM10 are the primary predictors of health risk categorization. The correlation analysis demonstrated robust associations between particulate matter measures (PM2.5 and PM10), underscoring their significance in air quality evaluation. This study proposes a methodological framework for automating risk assessment procedures via machine learning approaches, facilitating more effective environmental health monitoring. The findings suggest that ensemble models offer an optimal balance between precision and computing efficiency for real-time air quality classification systems, with potential applications in early warning systems and public health intervention techniques.