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

Priyanka; Tapas; S. S.

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

Journal of Intelligent Systems and Internet of Things | 2025

Grasshopper-Inspired Deep Neural Network for Enhanced Breast Cancer Classification

Early-stage disease diagnosis is critical for effective treatment, and software-aided design can analyze disease architecture for timely detection. Many fail to identify disease severity before it becomes chronic, contributing to global mortality rates. Breast cancer, a prime reason of death among women, can be treated if detected early. Computer-aided diagnosis aids practitioners in accurately assessing disease criticality. This paper introduces an automated diagnosis system utilizing an enhanced Grasshopper Optimization technique and a Deep Neural Network (DNN) classifier. The Grasshopper Algorithm optimally selects features from segmented images, extracted through SIFT and BRISK hybrid techniques. The DNN classifies breast cancer using a partitioned dataset for training and testing. Performance metrics, including accuracy, precision, F-measure, and recall, demonstrate that the proposed system significantly outperforms existing methods, with an F-measure improvement of 5.1% and an accuracy increase of 11.19%.

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Bhawna Utreja mail -

Reecha Sharma mail -

Amit Wason mail

link https://doi.org/10.54216/JISIoT.150209

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Behavior of SPEA2 Algorithm to Resolve Scheduling Problem for IoT Cloud

The (SPEA2) Strength Pareto Evolutionary Algorithm 2 is a capable technique for managing multi-objective optimization problems. In IoT-cloud systems, this is particularly true with regard to task scheduling. Task scheduling and efficient resource allocation are necessary to improve performance and service quality as the Internet of Things (IoT) grows. SPEA2, which is especially helpful for cloud computing frameworks, is excellent at handling competing goals, such minimizing executing duration while increasing the usage of resources. The capacity of SPEA2 to keep a large collection of solutions allows for the exploration of various scheduling approaches in IoT-cloud scenarios, where tasks generated by several devices need to be handled effectively. In dynamic contexts where resource availability varies, this IoT-CS (IoT-Cloud_Scheduling) adaptability is essential. With SPEA2, researchers are able to create algorithms that enhance system responsiveness and dependability overall while also optimizing task scheduling. The management of resource distribution and task prioritizing difficulties is exemplified by the use of SPEA2 to scheduling problems in IoT-cloud infrastructures. Thus, by guaranteeing that computing resources are used efficiently while respecting performance limitations, SPEA2 makes a substantial contribution to the development of intelligent scheduling solutions that satisfy the changing requirements of IoT applications

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Syed Mutiullah Hussaini mail -

T. Abdul Razak mail -

Muhammad Abid Jamil mail

link https://doi.org/10.54216/JISIoT.150210

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Optimizing Heart Attack Predictions Models using Innovative Machine Learning Methods

Cardiopathy is a critical health issue worldwide, accounting for a significant number of fatalities each year. Early and precise prediction of heart-related conditions can substantially reduce mortality rates and improve healthcare outcomes. Although traditional machine learning models have been employed in this domain, their performance often falls short due to challenges like overfitting, limited scalability, and difficulty in capturing intricate, non-linear data patterns. This paper introduces an improved methodology for heart disease prediction by employing advanced machine learning techniques, including deep learning networks, ensemble methods such as CNN and VGG16. Key components of the proposed framework include advanced data pre-processing methods for addressing class imbalance, sophisticated feature engineering driven by domain-specific insights, and comprehensive hyperparameter tuning for enhanced model performance The results of this study reveal significant improvements in predictive accuracy and reliability compared to conventional methods, paving the way for better integration of predictive analytics in cardiovascular healthcare. Future research will focus on integrating dynamic patient data from wearable devices and broadening dataset diversity to enhance the generalizability and fairness of these predictive models.

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Yerraginnela Shravani mail -

Ashesh K. mail

link https://doi.org/10.54216/JISIoT.150211

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Integrating Clustering and Regularization for Robust LSTM-Based Stock Price Prediction

Stock price forecasting has oftentimes interested several researchers around the world. Making predictions for the future largely depends on the data that will be used to train the model. In general, historical data are used to train models, which contain a features of different types, out of which, not all are necessarily helpful in making predictions. It is, hence, crucial to select the features that can be most useful to make precise predictions. This article proposes a feature selection approach based on the K-means clustering algorithm and elastic net regularization. We have used the K-means algorithm to cluster all the correlated features together and apply elastic net regularization to select the most predictive features within each cluster. We use the selected features to train an LSTM model which predicts the future closing price of a stock for the upcoming trading day. We evaluate the performance of our proposed approach in comparison to the existing approach and observe performance improvement.

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Dhruvin Padsala mail -

Rutvij H. Jhaveri mail -

Ashish D. Patel mail -

Faisal Mohammed alotaibi mail -

Thippa Reddy Gadekallu mail

link https://doi.org/10.54216/FPA.180218

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

A Novel Blockchain-Enabled Fuzzy CLSTM Model for Secure and Scalable Heart Disease Prediction in Healthcare

The emerging field of healthcare has taken severe measures to safeguard sensitive patient health-related information especially the information taken from the predictive model. In this study, a novel blockchain-based solution is proposed in correlation with the Fuzzy-enhanced CLSTM model (FCLSTM) for storing and transmitting the data securely for heart disease prediction systems by ensuring data integrity, confidentiality, and access control. The proposed model uses a blockchain-based network which is implemented to prevent the tampering or unauthorized access to patients’ health-related data. The process begins with techniques that incorporate the predicted heart disease information from the patient’s data and is encrypted by using the hashing algorithm. A secure hybrid blockchain-based data management framework (SHB-DMF) is designed for exchanging the patient’s health data which enhances scalability and accessibility to the healthcare environment. The system incorporates a SHAES-256 hybrid model for enhancing the data confidentiality and integrity before transmitting to the neural network (FCLSTM). The proposed model uses a smart contract for regulating data access by ensuring the entry of the authorized entities by providing a suitable decrypting mechanism and interacting with the patient’s data. The smart contracts can automate the data retrieval workflows by integrating the blockchain seamlessly with the prediction model. The security process is a three-phase process that includes defining the nodes, selecting of consensus mechanism, and establishing the governance structure for facilitating secure operations. The security and load testing ensure resilience to potential cyber threats and the scalability required for handling high transaction volumes of medical data. Deploying the proposed system provides a robust infrastructure that is tamper-resistant thus advancing the reliability of the cardiovascular prediction system.

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R. Parthiban mail -

K. Santhosh Kumar mail

link https://doi.org/10.54216/FPA.180219

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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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.

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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.

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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.

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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.

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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

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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