Research ArticleOpen accessIn the abstractAdaptive Cross-Validation Under Concept Drift for Time Series ForecastingKunjira Kingphai, Prapakorn Kanjina, Kamol Sanittham, Wacharong WongsanurakTime-series forecasting often involves non-stationary data, making i.i.d. validation unreliable and fixed-window protocols vulnerable to leakage and biased error estimates. We propose Adaptive Time-Series Cross-Validation (ATSCV), a drift-aware evaluation framework that uses statistical change-point detection to partition each series into contiguous, approximately stationary regimes, followed by forward-chaining folds that respect those boundaries. By aligning train–validation splits with distributional changes (with … …Journal of Computer Science, Vol. 22, Issue 7 · 7 Jul 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Machine Learning-Based Sentiment Analysis of Article 370 Tweets to Support Government Policy DecisionsSubhasis Mohapatra, Sudhir Kumar Mohapatra, Sweta Samantaray et al.This study proposes a robust sentiment analysis framework to evaluate public opinion on the abrogation of Article 370 using Twitter data. The methodology begins with tweet collection through the Twitter API, followed by systematic preprocessing. Sentiment labels were generated using the Text Blob lexicon-based polarity scoring approach to facilitate the construction of a large-scale sentiment dataset. Features are extracted, and … …Journal of Computer Science, Vol. 22, Issue 6 · 2 Jul 2026 · 914 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid BiGRU-BiLSTM Model for Real-Time ECG Arrhythmia Detection Using Wearable SensorsPrem Narayan Singh, Rajendra Prasad MahapatraAccurate and real-time detection of cardiac arrhythmias is essential for timely medical intervention. Advances in wearable devices and deep learning have made it feasible to continuously monitor electrocardiogram (ECG) signals, facilitating early identification of abnormal heart rhythms. For arrhythmia detection, this study presents a hybrid deep learning architecture combining Bidirectional Gated Recurrent Units (Bi-GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM). … …Journal of Computer Science, Vol. 22, Issue 5 · 5 Jun 2026 · 1,331 accessesView PDFCite
Research ArticleOpen accessIn the abstractAI-Integrated Probabilistic Optimization for Inventory Control Under Stochastic DemandJanardan BeheraThe identification and operationalization of increasingly nonstationary and volatile demand patterns challenge the conduct of inventory management in contemporary supply chains. The classical models, especially EOQ and static policies, rest on the stability of demand assumptions, either deterministically or distributionally, and are hence less apt under real-world uncertainty. In view of these limitations, the present study puts forward an integrated … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 6 May 2026 · 2,217 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent Hybrid Machine Learning Model for Paddy Disease DetectionS. Hrushikesava Raju, S. Adinarayna, U. Sesadri et al.Most countries rely on paddy/rice as a preferred staple crop due to its desirable agronomic characteristics. However, during the growth cycle, undetected or delayed identification of diseases can significantly reduce crop yield. To address this, machine learning approaches are increasingly employed for early-stage disease detection. Traditional Recurrent Neural Networks (RNNs), while useful for sequential data, suffer from limitations such as … …Journal of Computer Science, Vol. 22, Issue 4 · 29 Apr 2026 · 1,747 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Deep Learning Model for Evaluating Subjective Answers Based on Semantic Textual SimilaritySiddhesh Kudtarkar, Kavita ShirsatSubjective answer evaluation requires accurately identifying semantic similarities between student and reference responses. This study introduces a Hybrid Deep Learning Model (HDLM) that integrates CNN, GRU, and LSTM architectures to assess Semantic Textual Similarity (STS) more effectively. The HDLM employs two parallel branches-CNN-GRU and CNN-LSTM to capture both local syntactic features and long-range contextual dependencies, followed by Manhattan distance for … …Journal of Computer Science, Vol. 22, Issue 4 · 7 Apr 2026 · 1,690 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Study of BERT Based Architectures for Multi Task News Classification and Threat DetectionA. Mustain Billah, Sani M IsaWe present a comparative analysis of single-task and multi-task BERT-based models for Indonesian news classification across two objectives: category prediction (five classes) and threat detection (binary). Using 8,951 annotated news titles, single-task baselines achieved weighted F1 scores of 0.84±0.01 (category) and 0.87±0.00 (threat). Multi-task hybrids integrating CNN, LSTM, and Bi-LSTM layers performed comparably overall and improved minority class threat detection, … …Journal of Computer Science, Vol. 22, Issue 3 · 25 Mar 2026 · 1,124 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intelligent System for Cardiovascular Disease Prediction Using Deep LearningDeepika Arora, Deepak Dudeja, B K AgrawalHeart disease remains among the major causes of death in the world. Hence, accurate and reliable prediction schemes are key. To detect the presence of diseases in early stages, data mining and deep learning algorithms offer powerful tools to determine important trends in large and complex data. To enhance the prediction of cardiac diseases, this paper proposes a hybrid deep … …Journal of Computer Science, Vol. 22, Issue 2 · 9 Mar 2026 · 1,607 accessesView PDFCite
Research ArticleOpen accessIn the abstractEdge Computing Enabled Human Activity Recognition (ECEHAR) Using LSTM and CNNSuresh Kumar, M Y. Mohamed ParveesHuman Activity Recognition (HAR) is an important research area for various application domains such as healthcare, gaming, telemonitoring, and sports. However, executing HAR algorithms on remote servers or in the cloud have challenges in terms of high latency, increased bandwidth demand, and high energy consumption. Moving the computation to edge-assisted HAR is more effective and flexible solution to address the … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,578 accessesView PDFCite