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48 results for “Context-Aware”
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Research ArticleOpen accessIn the keywordsUsing MVCA to Improve Architecture Modularity of Smart SpacesChakib Tadj, Moeiz Miraoui, Somia BelaidouniThere has been increasing interest in the use of context awareness, as a technique for designing architectures dedicated to smart spaces in order to adapt and produce suitable services according to user context. In recent years, various architectures have been developed to support context-aware systems. The major challenge with these systems is decomposing the entire architecture into smaller, modular components … …Journal of Computer Science, Vol. 13, Issue 10 · 20 Oct 2017 · 7,162 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAN INTELLIGENT VERTICAL HANDOVER DECISION ALGORITHM FOR WIRELESS HETEROGENEOUS NETWORKSV. Anantha Narayanan, A. Rajeswari, V. SureshkumarThe Next Generation Wireless Networks (NGWN) should be compatible with other communication technologies to offer the best connectivity to the mobile terminal which can access any IP based services at any time from any network without the knowledge of its user. It requires an intelligent vertical handover decision making algorithm to migrate between technologies that enable seamless mobility, always best … …American Journal of Applied Sciences, Vol. 11, Issue 5 · 13 Feb 2014 · 7,284 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Comparative Analysis of Various Approaches for Incorporating Contextual Information into Recommender SystemsQuang-Hung Le, Son-Lam Vu, Anh-Cuong LeRecommender systems are being widely applied in many fields, such as e-commerce, e-documents, places and travel, multimedia, news and advertising and transportation. These systems are similar to an information filtering system that helps to identify a set of items that best satisfy the users’ demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions and … …Journal of Computer Science, Vol. 18, Issue 3 · 28 Mar 2022 · 6,822 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 accessesView PDFCite
Research ArticleOpen accessIn the abstractBenchmarking Lightweight Deep Learning Models for Field Based Sorghum Leaf Disease DetectionPrince Daughin Ngqabutho NcubeSorghum is a climate-resilient cereal crop central to food and livelihood security across Southern Africa, yet its productivity is frequently reduced by foliar diseases that smallholder farmers struggle to diagnose reliably in the field. While deep learning has demonstrated strong potential for automated plant disease identification, sorghum remains underrepresented in the literature, and no prior study has provided a comprehensive, … …Journal of Computer Science, Vol. 22, Issue 10 · 29 Sep 2026 · 535 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 abstractA Coding Schema-Based Mobile System for Children With ASD in MauritaniaCheikhne Mohamed Mahmoud Seyidi, Ahmed Mohameden, Mohamed Lamine Diakité, Mohamedade Farouk NanneChildren with Autism Spectrum Disorders (ASD) in many African countries face persistent barriers, including limited diagnostic awareness, scarce specialized resources, and the absence of educational interventions adapted to local contexts. In Mauritania, these challenges are exacerbated by the extreme centralization of autism services, with only two care centers available nationwide. Unlike existing ASD interventions that rely on imported pedagogical models … …Journal of Computer Science, Vol. 22, Issue 5 · 25 May 2026 · 1,312 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegration of Local Large Language Models, Retrieval-Augmented Generation, and Adaptive LearningAnass Belcaid, Kamal ReklaouiIn recent years, many schools and teachers have started using closed Large Language Models (LLMs) to help with learning. These tools can be very helpful for tutoring and personal learning, but they also bring serious problems. One big issue is that they use cloud systems, which means student data is sent to outside servers. This can put privacy at risk … …Journal of Computer Science, Vol. 22, Issue 4 · 26 Mar 2026 · 3,923 accessesView PDFCite
Research ArticleOpen accessIn the abstractSystematic Literature Review of Graph Neural Networks in Disaster Management: Methods, Applications, and Future DirectionsSularno, Wendi Boy, Putri Anggraini et al.The increasing frequency and complexity of natural disasters underscore the urgent need for intelligent systems that support timely and effective decision-making. Graph Neural Networks (GNNs) have emerged as a powerful deep-learning paradigm for modeling spatial and relational data, offering distinct advantages for disaster management. This study presents a Systematic Literature Review (SLR) of GNN-based approaches for disaster mitigation, emergency response, … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 2,036 accessesView PDFCite
Research ArticleOpen accessIn the abstractMining of Social Media on Covid-19 Big Data Infodemic in IndonesiaFaisal Binsar, Tuga MauritsiusCovid-19 is an unprecedented disaster that is still difficult to contain. During the pandemic, there were a lot of cases that were reported to increase exponentially. In this situation, the dissemination of messages and information was very important. The social media platform has contributed as a channel of communication with unprecedented speed. However, the uncontrolled and irresponsible dissemination of information … …Journal of Computer Science, Vol. 16, Issue 11 · 16 Nov 2020 · 7,533 accessesView PDFCite