2771-1463ISSN (Online)
2771-1471ISSN (Print)
This paper introduces HeartLink, an IoT-based health monitoring system designed to provide real-time heart rate tracking and emergency alerts using the Huawei Band 9 smartwatch. The system integrates Huawei's Health Kit with a Flutter-based Android application, enabling seamless data collection and processing. The backend, built on Java Spring Boot or Node.js, utilizes a hybrid database architecture combining MongoDB and Firebase for efficient data storage and real-time synchronization. HeartLink features threshold- based alert mechanisms, where heart rate deviations trigger SMS notifications to pre-selected contacts via Twilio and emergency calls to ambulance services in critical scenarios. Firebase Cloud Messaging (FCM) ensures timely push notifications, while Firebase Authentication secures user access. The system's modular design allows for real-time heart rate analysis, dynamic threshold configuration, and automated emergency responses, making it a robust solution for individuals requiring continuous health monitoring. By leveraging advanced IoT and cloud technologies, HeartLink bridges the gap between wearable health devices and emergency response systems, offering a scalable, reliable, and user-friendly platform for real-time health tracking and life- saving interventions.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100101
Vol. 10 Issue. 1 PP. 01-13, (2025)
As cyber threats become more complex, real-time systems are needed to detect and eliminate attacks. Traditional network intrusion detection systems based on rule based static method tend to be ineffective against novel emerging threats. In this paper, we propose an improved real time cyber threat detection system using adaptive machine learning techniques used to analyze network traffic and find anomalies. Our proposed approach uses a blend of supervised and unsupervised learning models such that the system maintains high detection accuracy with minimal false positives, while maintaining continuous adaptation to constantly evolving threats. On critical network traffic features like packet size, flow duration, source and destination IP addresses, transmission protocols, the system is then trained. They show experimentally better detection accuracy, responsiveness and adaptability than conventional IDS. In this work, contributions of adaptive machine learning for robustness against dynamic and evolving threats in network environments are highlighted as significant strides towards improving real time cybersecurity infrastructure.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100102
Vol. 10 Issue. 1 PP. 14-22, (2025)
Artificial Intelligence (AI) is reshaping healthcare by transforming disease diagnosis, treatment planning, and preventive care. Its origins trace back to the 1970s with expert systems like MYCIN, which pioneered the integration of computational intelligence into clinical decision-making. Today, AI harnesses machine learning, natural language processing, and computer vision to process large-scale medical data, detect intricate patterns, and generate precise insights. This paper presents a detailed review of AI’s progression in healthcare, focusing on its foundational technologies, significant applications, and persistent challenges. Key aspects explored include AI’s contributions to medical imaging, drug development, robotic-assisted procedures, and patient care, emphasizing its role in improving accuracy and efficiency in healthcare services. Additionally, this review examines pressing concerns such as data security, ethical dilemmas, and biases in AI models, while discussing strategies to address these challenges. By analyzing current advancements and future possibilities, this study highlights AI’s expanding role in shaping healthcare innovations and enhancing global medical outcomes.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100103
Vol. 10 Issue. 1 PP. 23-32, (2025)
Face recognition technology is increasingly integrated into daily life, from unlocking smartphones to taking attendance in classrooms, despite challenges like lighting, occlusion, and posture variety in real-world scenarios. Therefore, this study aims to develop an Automated Face Recognition System for Data Retrieval and Management using OpenCV. Using a camera, the system records users' photos in real time. Computer vision techniques are then applied, particularly the face identification and recognition functions of the Local Binary Pattern Histogram (LBPH) and the Haar Cascade algorithm, which are implemented using OpenCV. The system correctly recognizes people and makes it easier to handle student information by comparing the faces it detects with a database of photographs of students that has been stored. Improved face recognition accuracy, real-time data retrieval, and efficient data management procedures are the main goals. Although the system performed satisfactorily in normal lighting, difficulties with low light were shown to affect the accuracy of detection and recognition. The primary causes of these constraints were changes in the quality of the camera and lighting. Subsequent developments will concentrate on optimizing the accuracy and overall performance of the system, maybe by incorporating better cameras and more sophisticated processing. The study highlights how computer vision and facial recognition technology can revolutionize data management procedures in a variety of applications. In conclusion, the suggested system effectively makes use of cutting-edge methods for dependable and effective data retrieval.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100104
Vol. 10 Issue. 1 PP. 33-38, (2025)
This study explores the use of algorithmic for learning (ML) techniques in stroke rehabilitation to enhance patient outcomes and care. Machine learning offers potential uses in outcome prediction, progress tracking, customized treatment planning, and assessment. Algorithms based on machine learning (ML) can assist doctors with seriousness of stroke assessment, which is treatment plan customization, monitoring of progress, and long-term result prediction by leveraging a range of data sources, such as sensor data, doctor's notes, and medical images. Through personalized interventions and timely feedback, machine learning (ML) can optimize rehabilitation efforts and improve the standard of life for stroke patients. Interdisciplinary cooperation and ethical considerations are required to ensure the responsible and effective application of ML in physiotherapy after a stroke treatment. This study highlights the significant impact on the treatment of patients and their outcomes as it investigates the potential applications of algorithms for learning (ML) in recovery from stroke. These applications include result prediction, customized treatment planning, assessment methods, and progress monitoring. Through a convergence of current research findings and technological advancements, we illustrate how machine learning (ML) approaches can exploit many information modalities to assist professionals in providing tailored rehabilitation therapies and optimizing patient care. Despite the benefits that seem obvious, adoption needs to be fair and responsible. Problems like algorithmic bias, concerns about data privacy, and barriers to integrating clinical information need to be fixed.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100105
Vol. 10 Issue. 1 PP. 39-55, (2025)
Neuroengineering Informatics (NEI) is an interdisciplinary field combining neuroscience, engineering, data science, and informatics to understand and control neural systems. It leverages advanced technologies like brain-computer interfaces (BCIs), neuroimaging, and artificial intelligence (AI) to decode brain function and drive clinical breakthroughs. BCIs enable direct communication between the brain and devices, aiding individuals with neurological conditions, while neuroimaging methods such as fMRI, EEG, and MEG generate vast data used to uncover neural patterns linked to cognition, emotion, and disease. AI, particularly deep learning, enhances data analysis, enabling disease prediction, personalized treatment, and decision- making insights. NEI also employs neuroinformatics platforms for data sharing and collaboration, advancing innovations like adaptive neuroprosthetics and brain stimulation techniques such as TMS and DBS to treat conditions like epilepsy, Parkinson’s, and depression. Computational neuroscience contributes further by modeling brain functions to explore learning, memory, and decision-making mechanisms. Despite challenges like integrating diverse datasets and ethical concerns around privacy and fair ness, advancements in cloud computing and parallel processing are addressing these issues, accelerating discoveries while ensuring responsible innovation. NEI’s transformative applications ex tend beyond healthcare to rehabilitation, cognitive enhancement, and human-machine integration, reshaping our understanding and interaction with the brain.
Read MoreDoi: https://doi.org/10.54216/JCHCI.100106
Vol. 10 Issue. 1 PP. 56-66, (2025)