Research ArticleOpen accessIn the abstractCustomized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworkingKayal Padmanandam, KVN Sunitha, Behafarid Mohammad Jafari et al.Named Entity Recognition (NER) is an information extraction task and one of the most researched applications to extract knowledge from massive data. Conventional NER systems identify predefined entities like name, person, location, organization, time, etc. However, there is a limitation to identifying user-defined entities that are specific to an application. This challenge introduces the concept of customized NER. For instance, … …Journal of Computer Science, Vol. 20, Issue 1 · 15 Dec 2023 · 6,985 accessesView PDFCite
Research ArticleOpen accessIn the abstractMeasurement of Cooling Load-Based Occupant Behavior in Under-Actuated Zones: A Time-Variance ApproachYaddarabullah, Yusuf Maulana Akbar, Aedah Binti Abd Rahman, Amna SaadThis research is dedicated to the enhancement of Heating, Ventilation, and Air-Conditioning (HVAC) systems through an innovative investigation of occupant behavior within under-actuated zones. These zones are strategically positioned to elevate indoor air quality and comfort through the intelligent utilization of HVAC systems. However, accurately measuring the cooling loads within these zones, which are inherently influenced by the presence of … …American Journal of Applied Sciences, Vol. 20, Issue 1 · 11 Dec 2023 · 6,158 accessesView PDFCite
Research ArticleOpen accessIn the abstractTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDFCite
Research ArticleOpen accessIn the abstractReal-Time Monitoring and Injury Risk Warning Algorithm Based on Data Fusion of Motor Muscle ContractionGuomin Wang, Jingying HuangThe existing sports injury warning algorithms are difficult to achieve dynamic injury risk modeling based on individual muscle characteristics while ensuring real-time fusion efficiency of multimodal signals. This article proposes a real-time muscle contraction state perception and injury risk warning algorithm based on adaptive weighted feature fusion and lightweight temporal modeling. The system synchronously collected surface electromyography (sEMG), Inertial Measurement … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 3 · 10 Aug 2026 · 1,089 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnxiety-Focused Classification Using Smartphone-Based Gait Analysis and Deep Learning ModelsMinchan Kang, Gou-Sung Degbey, Hyunhwa Lee et al.Mood disorders, such as anxiety, significantly impact individual well-being and present challenges to healthcare systems globally. Traditional screening methods, including self-administered symptom questionnaires, provide valuable insights into symptoms but may miss dynamic fluctuations. This study explores the potential of smartphone inertial sensors and deep learning models for anxiety risk classification. Using gait data from 36 participants, collected via smartphone accelerometers … …Journal of Computer Science, Vol. 22, Issue 7 · 16 Jul 2026 · 821 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntrusion-Resistant Multi-Hop Communication Protocol for Secure Wireless Sensor NetworksRakesh Ranjan, Vaishali Singh, Hitendra SinghModern Internet of Things (IoT) systems are based on Wireless Sensor Networks (WSNs), which can be distributed to various environments to perform a range of sensing and data aggregation. Their intrinsic deficiencies in computation, energy, and bandwidth, however, make them very vulnerable to routing-layer attacks like black hole and selective forwarding as well as Sybil intrusion. The current studies on … …Journal of Computer Science, Vol. 22, Issue 6 · 1 Jul 2026 · 935 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 abstractAdaptive and Neural Network Based Control of Pitch of Unmanned Aerial VehiclesMackenzie T. Matthews, Sun YiThe objective is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) that assist in closing the existing research gaps between the undesired uncertainties and current control systems. This research investigates a system modeling and the effectiveness of error elimination through adaptation in the change of the plant/system’s output. The UAV is controlled via Wi-Fi … …Journal of Mechatronics and Robotics, Vol. 3, Issue 1 · 18 Nov 2019 · 6,489 accessesView PDFCite