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87 articles for “Learning and Memory”
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Research ArticleOpen accessIn the keywordsAutomated Medical Image Captioning with Soft Attention-Based LSTM Model Utilizing YOLOv4 AlgorithmPaspula Ravinder, Saravanan SrinivasanThe medical image captioning field is one of theprominent fields nowadays. The interpretation and captioning of medical imagescan be a time-consuming and costly process, often requiring expert support. Thegrowing volume of medical images makes it challenging for radiologists tohandle their workload alone. However, addressing the issues of high cost andtime can be achieved by automating the process of medical image … …Journal of Computer Science, Vol. 20, Issue 1 · 16 Dec 2023 · 6,651 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Scalable Hybrid CNN-LSTM and SDN Based Architecture for Real-Time DDoS Defence in IoT and Cloud NetworksSujit Sutradhar, Joy Lal Sarkar, Rajesh Kumar VermaDistributed Denial-of-Service (DDoS) is one of the most significant threats to the security of IoT, cloud, and Software-Defined Network (SDN) environments because it can aggravate network unavailability and disrupt the continuity of services. This study presents a hybrid CNN-LSTM-based DDoS detection and mitigation system for real-time DDoS detection in the context of heterogeneous network environments, which is integrated with SDN … …Journal of Computer Science, Vol. 22, Issue 10 · 19 Oct 2026 · 508 accessesView PDFCite
Research ArticleOpen accessIn the abstractChronic Lung Disease Identification in Chest CT Scans Through Hybrid Deep Transfer Learning FrameworkGarima Jain, Amit Kumar Goel, Rahul KumarAccurate and timely diagnosis is essential for successful treatment of chronic pulmonary conditions, particularly lung tumours. To capture more complicated pathological characteristics, diagnostic algorithms based on current data sets are restricted to using older databases and single branch systems. This research introduces a new solution: A hybrid deep learning model applied to a recently released quantitative database of CT scans … …Journal of Computer Science, Vol. 22, Issue 8 · 9 Sep 2026 · 651 accessesView PDFCite
Research ArticleOpen accessIn the abstractHMA-RAG: A Hierarchical Memory Augmented Retrieval Augmented Generation Framework for Fake News DetectionAfnan Altarjami, Sawsan AlshattnawiExisting methods for detecting fake news, such as traditional machine learning and transformer-based models, often work as separate classifiers that do not use outside evidence, historical knowledge, or ways to make the results easier to understand. This paper presents a framework that combines retrieval augmentation and hierarchical memory to overcome three primary constraints: Insufficient external evidence, the absence of memory, … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 735 accessesView PDFCite
Review ArticleOpen accessIn the abstractHuman Activity Recognition at the Edge: A Systematic Review of Machine Learning Models, Deployment Constraints, and Latency Energy Accuracy Trade offsJose Antonio Rojas Guillén, Wini Ebelin Quispe Bautista, Arturo Gamarra MorenoHuman Activity Recognition (HAR) has become a key component of intelligent healthcare monitoring, assisted living, sports analytics, smart environments, and wearable computing. However, the migration of HAR models from cloud-centered architectures to edge, embedded, mobile, and wearable platforms introduces constraints related to computational capacity, memory footprint, battery consumption, inference time, and real-time responsiveness. This study presents a systematic literature review … …Journal of Computer Science, Vol. 22, Issue 8 · 7 Sep 2026 · 593 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing DevOps Pipelines With Viral Spread Optimization and Hybrid Deep LearningDileepkumar S R, Juby MathewModern software teams rely on Continuous Integration and Continuous Deployment (CI/CD) pipelines to automate testing and delivery. However, these pipelines often waste resources by running unnecessary tests and failing to predict which builds will fail. This study presents a new framework that addresses three key challenges: (1) deciding which tests to run first, (2) predicting which builds will fail, and … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jul 2026 · 935 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 abstractAddressing Emergency Communication Challenges: Deep Learning Solutions for the Speech and Hearing- ImpairedPoornima B V, Srinath S et al.Emergency communication plays a very important role in ensuring that help reaches people promptly and safely in case of any emergency. However, the biggest problem faced by those who cannot speak and hear properly is to convey their message clearly and understand others. In this regard, the proposed research work focuses on recognizing emergency gestures made in the Indian Sign … …Journal of Computer Science, Vol. 22, Issue 5 · 1 Jun 2026 · 1,063 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