Precision Driven Human Recognition Model for Adaptive Information Retrieval in Learning Environments

S.; J. Beryl; M.; M.; SK; C. Sathish

doi:https://doi.org/10.54216/JISIoT.160102

Journal of Intelligent Systems and Internet of Things | 2025

Energy Efficiency and Practical Implications of IoT-Based Static vs. Single-Axis Solar Tracking Systems: A Comparative Analysis

The objective of this research is to offer a comparative evaluation of IoT based static and single-axis solar tracking systems with respect to energy efficiency, economic viability, and impediments in the implementation of both static and single-axis solar tracking systems. In order to fill in the gaps in the current literature on their performance comparison. In this research work, IoT technology has been used to monitor both systems in real time over a period of 30 days in comparable under the similar environmental conditions for data collection and analysis. The research also implements the Fuzzy Logic Controller-based algorithm, developed for the single-axis solar tracking system provides a dynamic and flexible mechanism to optimize solar energy capture. It intelligently adjusts the solar panel's angle based on real-time sensor data, ensuring that the panel is always positioned to maximize sunlight exposure. The data characteristics like solar radiation, temperature, voltage and these different effects were monitored to help in the determination of energy output and the overall efficiency of the system. The findings confirm that the IoT-based single-axis tracking system considerably improved the average system efficiency by 7% as compared to the static system. However, the high installation and maintenance costs of IoT-based single-axis systems increase complexity, posing challenges for mass adoption, particularly in small-scale applications. This paper demonstrates how IoT tracking systems offer improved efficiency of single axis trackers to achieve higher energy efficiency. This work will help in the decision making process for the future solar energy projects where there will be a need to consider the costs against the operational and performance advantages to balance performance benefits with cost and operational consideration. Studies have shown that IoT technology application enhances efficiency and energy operational parameters of solar photovoltaic (PV) systems.

groups

Indra Kishor mail -

Udit Mamodiya mail -

Bright Keswani mail

link https://doi.org/10.54216/JISIoT.150212

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Securing IoT through Intrusion Detection Systems: An Overview

Internet of Things (IoT) has emerged as a new paradigm for integrating internet resources and physical objects. It provides a better standard of living in different domains, like industrial processes, home automation, and environmental monitoring. The growth of IoT depends on the need to connect more devices via the Internet. However, anywhere internet connectivity is involved, security poses as an enormous challenge. Intrusion Detection Systems (IDS) can protect IoTs by applying rules related to IoTs operation. This paper reviews some of the mechanisms of IoT-related IDS, which protect IoT devices against various attacks. The paper includes a summary of the recent developments of IDS against many security threats. A review is presented regarding various IDS designs developed in the last decade with different methods, ideas, and approaches toward a better understanding of suitable IDS platforms that provide security against the global growth of attacks and intruders. It also involves the examination of the IDS basics, types, and components of the previously proposed systems, as well as discussing the pros and disadvantages of each.  We organize the taxonomy of investigated IDS approaches using the detection approaches. This work aims to provide a thorough summary of the existing IDS designs and issues to empower research and development for IDS about IoTs.

groups

Razan Abdulhammed mail -

Shaima Miqdad Mohamed Najeeb mail -

Rabei Raad Ali mail -

Mohammed Ahmed Jubair mail

link https://doi.org/10.54216/JISIoT.150213

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion Based Learning Approach for Melanoma Skin Cancer Detection through Multi-Stage Convolutional Neural Networks

Melanoma is one of the forms of skin cancer that affects people worldwide. Research indicates that nearly 75% of the global population has been impacted by melanoma. Early detection and treatment of melanoma significantly increase survival rates. However, detecting melanoma in its early stages can be challenging because dermatologists typically rely on visual examination and biopsy analysis, which is both time-consuming and labor-intensive. This highlights the need for automated, efficient methods to identify melanoma at earlier stages. Skin cancer is generally classified into two categories: melanoma and benign tumors. The goal of this study is to facilitate the early detection of melanoma by employing deep learning techniques, specifically convolutional neural networks (CNNs), to distinguish between melanoma and benign lesions using the ISIC dataset. The proposed model achieves an accuracy of 80.80%, outperforming previous approaches by offering faster and more accurate melanoma detection.

groups

Hamsalekha R. mail -

Glan Devadhas George mail -

T. Y. Satheesha mail

link https://doi.org/10.54216/FPA.180220

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Comprehensive Methodology to the Detection and Classification of Emotion in Human Face using EMOTE-Net

Presenting the network architecture EMOTE-Net is a method of enhancing the face emotion recognition and classification in video data for this work. The suggested model merges the use of DenseNet to extract features with the SVM (support vector machine) to categorize the data by specifying SVM here. This feature of EMOTE-Net is highly outstanding because SVM and DenseNet are combined and are thus capable of sophisticated classification and effective feature extraction. The first process to come in methodology is preprocessing of video data. Bounding Box detection is able to extract regions that are of interests (ROIs) and that Densenet is great at the feature representation with high dimensions. Henceforth, feed these features into a classifier from SVM for intelligent categorization. Evaluation has provided clear evidence regarding the efficiency of this model, which has obtained the accuracy of 0.9890, precision of 0.9900, sensitivity of 0.9877, specificity of 0.9972, and F1 score of 0.9886. The pertinence of EMOTE-Net to real life applications, such as video analytics, human-computer interaction, and surveillance, will be highlighted in the chapter through the references from the installation and evaluation processes. The work presents a viable approach for object detection and classification in changeful visual arenas.

groups

Asif Hussain Shaik mail -

Shaik Karimullah mail -

Mudassir Khan mail -

Fahimuddin Shaik mail

link https://doi.org/10.54216/FPA.190102

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Blockchain-Based Secure Framework for Interoperability of Patient Data in Electronic Health Records (EHR)

The intersection of the Electronic Health Records (EHR) is the main factor that makes healthcare delivery and the patient outcomes better. On one hand is the seamless combination of the EHR systems of different departments in preserving data security and privacy is a great achievement, but on the other hand, the integration of the EHR systems of different departments while maintaining data security and privacy is still an important concern This paper suggests a new blockchain-based secure framework that may be used to improve the interoperability of patient data among the EHR systems. The blockchain technology, which is immutable and decentralized, supports the major principles of the framework such as data integrity, security, and privacy.  The proposed model comes with a strong recommender system, which makes the patient-doctor consultations, specialist suggestions, and the laboratory test requests according to the symptoms and doctors' recommendations more efficient. Thus, the system, when linked with Google Maps, recognizes local laboratories, and allows for direct test requests; consequently, the healthcare process is made more effective. The analyzed system optimizes the data exchange, protection, and the functionality of the informational system in contrast to the current EHR systems. It is therefore apparent that this blockchain-based technique is one that can efficiently address the challenges of EHR integration and therefore goes down well with the future of secure and efficient healthcare systems. Assessment of the framework demonstrates the effectiveness of the proposed adjustments in various aspects, such as data security and data compatibility and system; tests affirm the improvement of the user’s satisfaction and the improvement of the data management

groups

Priyanka Sharma mail -

Tapas Kumar mail -

S. S. Tyagi mail

link https://doi.org/10.54216/FPA.190103

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Adverse Drug Reaction Classification of Attention Deficit Hyperactivity Disorder Diagnosis Data Using Deep Learning with Optimization Algorithm

Adverse Drug Reaction (ADR) is a significant global public health issue and the main cause of death. Generally, the effects of ADR are complex. Clinically, they can cause major patient damage and, in some cases, death. Besides, this outcome in significant healthcare costs financially owing to enlarged hospital visits, extra treatments, and harm to productivity. Therefore, early recognition and mitigation of ADRs are vital for the patients. Enhancing the early detection of ADRs and deadliness could severely reduce the harm to patients, improve patient safety, decrease healthcare costs, and increase the efficacy of the drug development procedure. Conventional pre-clinical toxicity tests are expensive, time-consuming, and frequently fail to forecast human-specific toxic effects. Artificial Intelligence (AI)-based deep learning (DL) has been quickly adopted in numerous areas, with healthcare, for its latent to manage huge datasets, find out patterns, and generate predictions. This study presents a new Adverse Drug Reaction Detection through Deep Learning and Improved Red-Tailed Hawk Algorithm (ADRD-DLIRTHA). The main intention of the ADRD-DLIRTHA model is to enhance the detection and classification process of ADR using advanced hybrid and optimization techniques. At first, the data normalization stage applies z-score normalization for converting input data into a beneficial set-up. Furthermore, the proposed ADRD-DLIRTHA method designs a convolutional neural network and long short-term memory (CNN-LSTM) technique for the classification process. At last, the improved red-tailed hawk (IRTH) algorithm-based hyperparameter selection process has been applied to optimize the classification results of the CNN-LSTM system. A wide range of experimentation was led to authorize the performance of the ADRD-DLIRTHA system. The simulation results specified that the ADRD-DLIRTHA model emphasized advancement over other existing techniques

groups

N. Deepaletchumi mail -

R. Mala mail

link https://doi.org/10.54216/FPA.190104

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Gated Recurrent Fusion in Long Short-Term Memory Fusion

Fusion techniques on enhancing the efficiency of Long Short-Term Memory (LSTM) networks are dominating across a variety of domains. To handle sequential data while integrating from various sources is often challenging using LSTM techniques. Fusion methods that integrate different models enhances LSTM’ ability to handle complex correlations in the data. This paper examines early, late and hybrid fusion techniques. The study provides fusion approaches to enhance LSTM networks to efficiently handle complex multimodal data across self-navigating models. The findings reveal that the hybrid fusion techniques outperform traditional methods in terms of accuracy and generalization of various tasks. This paper proposes the Gated Recurrent Fusion (GRF) approach to demonstrate its performance to handle multimodal and temporal models in a supervised recurrence. The findings report 10% enhancement in terms of precision rate

groups

Anita Venugopal mail -

Aditi Sharma mail -

Preetish Kakkar mail -

Daya Nand mail -

Arvind R. Yadav mail -

Gaurav Kumar Ameta mail

link https://doi.org/10.54216/FPA.190105

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Optimizing Diabetes Diagnosis: HFM with Tree-Structured Parzen Estimator for Enhanced Predictive Performance and Interpretability

This study proposes the novel machine learning concepts to enhance both prediction accuracy of diabetes detection and interpretation of diagnostic models. First, the methodology uses multiple imputations by chained equations (MICE) to complete data before analysis through missing data imputation procedures. The class imbalance problem is solved through the implementation of Synthetic Minority Over-sampling Technique (SMOTE). The Interquartile Range (IQR) outlier detection method helps remove outliers because it enhances model robustness. The hybrid RFE-WWO selection process combines Recursive Feature Elimination (RFE) with Water Wave optimization (WWO) to select important features that strike the right balance between model complexity and prediction accuracy. The HFM framework contains the Hybrid Fusion Model as its essential component, which merges AdaBoost's and CatBoost's most favorable aspects. The hyperparameter optimization with TPE leads to model tuning which reaches a prediction accuracy of 97.84% through the application of Tree-Structured Parzen Estimator. The entire approach delivers enhanced accuracy and it improves precision along with recall metrics and F1 score performance of the predictive model. The framework shows significant potential for early diagnosis by merging these advanced techniques since ensemble methods are essential for healthcare data analysis while accurate interpretable models are vital to create dependable diagnostic tools.

groups

Hemalatha Dendukuri mail -

Kachapuram Basava Raju mail -

S. Phani Praveen mail -

Janjhyam V. Naga Ramesh mail -

Vahiduddin Shariff mail -

N. S. Koti Mani Kumar Tirumanadham mail

link https://doi.org/10.54216/FPA.190106

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Multiple Feature-Based Recurrent Neural Network for Highly Accurate Ransomware Detection in Android Devices

Ransomware or crypto-ransomware is a big headache to digital media and transactions nowadays. Generally, Ransomware affects the operating system and transfers the valuable information and data stored in the system. Some ransomware attacks the system and corrupts the system file, making it useless to the user. Data encryption with a private key is also one of the attaching fashions of some types of ransomwares. Most ransomware attacks are reported in android operating system-based devices. The solution to ransomware is only the earlier identification of an attacked pattern in the operating system and removal of it. Artificial Intelligence (AI) plays a major role in various kinds of attack detection and classification processes. Machine learning (ML) technique can be used to train and classify the presence of ransomware in android-based devices. Various parameters, such as the characteristics of applications' permission access to various inputs of the devices. The data can be used to train the Recurrent Neural Network (RNN), the most popular and highly accurate ML module that performs a highly accurate classification process. The performance can be evaluated using various sensitivity evaluation metrics such as accuracy, sensitivity, specificity, and precision.

groups

Vyom Kulshreshtha mail -

Deepak Motwani mail -

Pankaj Sharma mail

link https://doi.org/10.54216/FPA.190107

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Precision Driven Human Recognition Model for Adaptive Information Retrieval in Learning Environments

Face recognition technology plays a vital role in modern educational systems by enabling efficient and accurate student identification. The growing demand for efficient and accurate student identification systems has highlighted the limitations of conventional face recognition methods, particularly in handling variations in pose, lighting, and occlusions. To address this, our Precision-Optimized Human Recognition Model builds an Adaptive Information Retrieval System utilizing a Histogram of Oriented Gradients (HOG)-based detector for face detection and a ResNet-34-based Deep Metric Learning Model for face recognition. The system encodes facial features and performs identity verification using Euclidean distance for precise and reliable student identification. By integrating these techniques, the model ensures real-time data retrieval with high accuracy and adaptability to diverse conditions. The proposed approach enhances computational efficiency while maintaining robust recognition performance, making it a scalable and practical solution for identity verification in educational institutions.

groups

S. Hemamalini mail -

J. Beryl Sharon mail -

M. Dharshini mail -

M. Indu mail -

SK Mithun mail -

C. Sathish Kumar mail

link https://doi.org/10.54216/JISIoT.160102

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new