Improved Correlation Coefficients of Fermatean Quadripartitioned Neutrosophic Sets for MADM

S.; M.; R.

doi:https://doi.org/10.54216/IJNS.270201

Journal of Intelligent Systems and Internet of Things | 2026

A Deep Convolutional Autoencoder with Metaheuristic Optimization based Feature Reduction Framework for Genetic Disorder Detection Model

Genetic disorder is an outcome of transformation in deoxyribonucleic acid (DNA) system, which is progressed or natural from blood relation. Such transformations might lead to deadly illnesses like Alzheimer’s, cancer, and much more. The disorder of single gene kind is affected by a change in a solitary gene in DNA. The chromosomal disorder kind is affected when a genetic material or a portion of chromosome is removed or substituted in the structure of DNA. Complex illnesses are caused by the alteration in over one gene exhibit in the DNA. In recent times, the usage of artificial intelligence (AI)-based deep learning (DL) systems has exposed excellent achievement in the prognosis and prediction of diverse illnesses. The latent of DL models are employed to forecast genetic disorder at an initial phase utilizing the genome data for appropriate treatment. This paper presents a Deep Feature Selection Framework for Genetic Disorder Detection Using Convolutional Autoencoder and Metaheuristic Optimization (DFSFGDD-CAEMO) model. The aim of DFSFGDD-CAEMO model is to develop an accurate DNA-based genetic disorder classification model using advanced techniques for early and reliable disease diagnosis. Initially, the min-max normalization method is employed in the data pre-processing stage for converting an input data into a beneficial format. Besides, the Aquila optimizer (AO) method has been deployed for the selection of feature process in order to select the most significant features from a dataset. For the classification procedure, the proposed DFSFGDD-CAEMO technique designs Convolutional Autoencoder (CAE) method. At last, the hyperactive parameter tuning process is performed through enhanced pelican optimization algorithm (EPOA) for improving the classification performance of CAE model. The experimental evaluation of the DFSFGDD-CAEMO technique occurs using benchmark dataset. The experimentation results indicated out the enhanced performance of the DFSFGDD-CAEMO system when equated to existing approaches.

groups

S. Puvaneswari mail -

G. Indırani mail

link https://doi.org/10.54216/JISIoT.180128

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

An Advanced Immersion Level Prediction Using Ensemble Classifier with Heuristic Search Algorithm in 3D Games Content Generated Virtual Environments

Nowadays, virtual reality (VR) and immersive environments are research fields used in various educational and scientific areas. Immersive digital media desires new techniques for its immersive and interactive features it implies the model of new relationships and narratives with users. VR and technologies related to the virtuality sequence, like digital and immersive environments, are developing media. 3D environments generated with VR compatibility can be skilled from a stereoscopic and egocentric view that outperforms the immersion of the ‘classical’ screen-based view of 3D gamed virtual environments. Recent video games have complete, interactive scenes generated with innovative modeling and animation software and provided with hardware speeded-up graphics and physics. Their communication takes place with body-based sensing and commodity 3D motion controllers, like and in certain ways more progressive, than those discovered in conventional VEs do. Currently, artificial intelligence-based deep learning (DL) methods have been progressively applied to identify and assess user immersion levels in VR environments. In this paper, we present an Advanced Immersion Level Prediction Using Ensemble Classification Model and Metaheuristic Optimization Algorithm (ILPECM-MOA) in 3D Games Virtual Environments. This paper aims to develop a predictive model for assessing advanced immersion levels in 3D game virtual environments using behavioral and contextual data. At the primary stage, the data pre-processing stage uses Z-score normalization to transform input data into a beneficial pattern. Followed by, the presented ILPECM-MOA method designs ensemble models such as the temporal convolutional network (TCN) model, sparse denoising autoencoder (SDAE) method, and stacked long short-term memory (SLSTM) technique for the classification process. At last, the Hybrid ebola and Bald Eagle search optimization (HEBEO) approach fine-tunes the hyperparameter values of ensemble methods and results in the superior performance of classification. The effectiveness of the ILPECM-MOA model has been validated by the detailed studies utilizing the benchmark dataset. The mathematical outcome indicates that the ILPECM-MOA approach has improved performance and scalability in terms of various measures over the recent methods.

groups

Kamalanathan Sundararajan mail -

Prasanna Santhanam mail

link https://doi.org/10.54216/FPA.210120

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Model for the Prediction of Cardiovascular Disease in IoMT Based on AI's Binary and Multi-Class Structures

Heart disease is a severe hazard to the public's health and safety because of the high rates of disability and mortality it causes. Accurate disease prediction and diagnosis are more critical than ever in this era of earlier illness prevention, faster disease detection, and earlier disease treatment. Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) have made it possible to detect, forecast, and diagnose cardiovascular disease more precisely. However, the bulk of these prediction models can only state whether a person is sick; they cannot and do not forecast the severity of the ailment. We present a machine-learning-based technique for predicting cardiovascular disease. Using this strategy, we hope to perform binary and multimodal classifications at the same time. To get things started, we will go through the fuzzy-adaboost approach, which will serve as the foundation for the rest of our work. By combining fuzzy logic and the Adaboost method, this method aims to increase the number of applications that can use binary classification prediction to simplify data analysis. If it is completed, both objectives will be met, and we will eliminate overfitting by merging bagging and fuzzy adaboost into a single approach. It is the ideal solution to the challenge we are currently facing. Because it has a separate classification for the severity of the presentation of heart disease, the bagging fuzzy adaboost can be used for multiclassification prediction. This is because Adaboost's assessment of the severity of the observed heart disease presentations is unclear and imprecise. The results of the experiment reveal that, in addition to a wide range of other classes, the Bagging-Fuzzy-Adaboost can anticipate binary data accurately. When compared to traditional procedures, it is evident that this has significant advantages.

groups

Ahmed A. F. Osman mail -

Nesren Farhah mail -

Rajit Nair mail -

Mohammed Awad Mohammed Ataelfadiel mail -

Rami Taha shehab mail

link https://doi.org/10.54216/FPA.210121

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

HeartLink: IoT Smartwatch for Emergency Alerts

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.

groups

A. V. Adlin Grace mail -

Cherlin Flory Thomas mail -

Anu Sushmitha S. mail

link https://doi.org/10.54216/JCHCI.100101

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Enhanced Real-Time Detection of Cyber Threats through Adaptive Machine Learning in Network Traffic Analysis

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.

groups

C. Meenaloshini mail -

A. R. Darshika Kelin mail -

Keirolona Safana Seles mail

link https://doi.org/10.54216/JCHCI.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Artificial Intelligence in Healthcare: A Review

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.

groups

Sneha K. mail -

Akifulla Kha mail -

Hanan Abdul Razack mail -

Ibaad Khan mail

link https://doi.org/10.54216/JCHCI.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Real-Time Student Identification and Data Retrieval System Powered By Haarcascade and OpenCV

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.

groups

S. Hemamalini mail -

J. Beryl Sharon mail -

M. Dharshini mail -

M. Indu mail

link https://doi.org/10.54216/JCHCI.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Machine Learning Rehabilitation for Stroke Patients

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.

groups

Ramesh Prabhakaran R. mail -

Angel Maanu P. mail -

Niranjan G. mail -

Karthika K. mail

link https://doi.org/10.54216/JCHCI.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Neural Engineering Informatics: A Review

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.

groups

Naheem M. R. mail -

Adithya V. mail -

Dhanush H. S. mail -

Harsh Vishwakarma mail

link https://doi.org/10.54216/JCHCI.100106

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026