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

R.; K. Santhosh

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

International Journal of Neutrosophic Science | 2025

Neutrosophic Cordial Labeling on Helm and Closed Helm Graph

The Neutrosophic Cordial Labeling Graph integrates both neutrosophic labeling and Cordial Labeling. Building on our previous work, we have extended our study to include Neutrosophic Cordial Labeling for Helm and Closed Helm Graphs. This extension allows us to explore the application of Neutrosophic Cordial Labeling in more complex graph structures, providing insights into their properties and relationships. One of the key aspects of our research is investigating the relationship between Cordial and Neutrosophic Cordial Labeling. By comparing and contrasting these labeling techniques [4], we aim to uncover similarities, differences, and potential synergies between them. This analysis contributes to a deeper understanding of graph labeling methodologies and their implications in various graph-theoretic applications [18]. Our research contributes to the advancement of graph labeling theory, particularly in the context of Neutrosophic Cordial Labeling and its applications in Helm and Closed Helm Graphs. By exploring these concepts and relationships, we aim to enhance the theoretical foundation and practical utility of graph labeling techniques in diverse domains [16,17].

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Tephilla Joice P. mail -

A. Rajkumar mail

link https://doi.org/10.54216/IJNS.250439

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Fusion: Practice and Applications | 2025

Optimized Gaussian Convolutional Neural Network Framework for Enhanced Detection of Deepfakes in Digital Media

With the latest developments in computer vision, processing, accurate deepfakes (DF) require powerful tools. Recent research has developed a useful technique for identifying DFs in networks. The inter-frame differences of the gathered media streams, however, are beyond the scope of most methods. In this research, an Optimized Gaussian Convolutional Neural Network Framework for Enhanced Detection of Deepfakes in Digital Media (OGCNN-DDF-DM) is proposed. Initially the input images are gathered using the Face Forensics++ (FF++), and Deep Fake Detection Challenge dataset (DFDC) datasets. Then the Multi-Window Savitzky-Golay Filter (MWSGF) is used to improve quality of the DF images and reduce noise. Afterwards, Simple Contrastive Graph Clustering (SCGC) achieves segmentation. Here, the image's facial regions are segmented. Then, the texture features are extracted using Revised Tunable Q-Factor Wavelet Transform (RTQWT) is introduced. The extracted features are fed to Gaussian Convolutional Neural Network (GCNN) to categorize the image as real or fake. Finally, Gooseneck Barnacle Optimization Algorithm (GBOA) is proposed to improve the GCNN classifier. Performance parameters including accuracy, precision, recall, specificity, ROC, and computation time are examined. The introduced method attained an accuracy of 99.6% and the precision of 98.9% on the FaceForensics++ dataset, and 99.5% and 98.6% on the DFDC dataset, respectively.

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Ahmed Alhussen mail

link https://doi.org/10.54216/FPA.180217

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

The Analysis of Pentagonal Fuzzy Numbers in a Neutrosophic Fuzzy Inventory Management Modelling with Minimal Insufficient Supply Required and Fuzzy Consumption

The fuzzy stock administration demonstrates displayed in this work employments neutrosophic set hypothesis, pentagonal fuzzy numbers, and the Graded mean Integration Representation (GMIR) strategy for defuzzification. Request rates, arrange amounts, utilization rates, holding costs, setup costs, and deficiency costs are all spoken to as fuzzy parameters within the demonstrate to account for the inborn instability and vacillation. To reduce by and large costs, the whole cost work is calculated, taking setup, holding, and shortage costs into consideration. In arrange to speak to the combined impacts of a few fetched components, the overall taken a toll work is rearranged and the ideal arrange amount is built up beneath fuzzy conditions utilizing pentagonal fuzzy parameters. The demonstrate is assessed beneath different degrees of instability through a case-based investigation, advertising an exhaustive system for making choices on stock administration in equivocal and dubious circumstances. The results appear how versatile and capable the show is for improving fetched advancement and stock control.

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Kalaiarasi K. mail -

Nasreen Kausar mail -

Said Broumi mail -

Tonguc Cagin mail

link https://doi.org/10.54216/IJNS.250440

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Time-Optical Control Strategies for SIR Epidemic Models in Cattle and Neutrosophic Fuzzy Modelling

The utilization of neutrosophic fuzzy logic with machine learning constitutes a revolutionary way of improving epidemic modelling. With the help of Weka, this method solves the problem of uncertainty and vagueness that is characteristic of epidemic processes with the help of neutrosophic equations. These equations enhance the way how indeterminacy of epidemic levels can be modelled, therefore enhancing predictions of complex networks. The effectiveness of the proposed framework is confirmed by extensive evaluations providing extensive tables and visualizations regarding the improvements in the accuracy and reliability of the models. Further, the work explores time-optimal control strategies of SIR epidemic models. It shows exactly how bang-bang controls work avoiding the duration of outbreaks drastically, especially if introduced with delayed interventions. This finding is especially important for controlling the health of livestock since the response to disease outbreaks has to be done as soon as possible because of stringent measures on animal health. Altogether, the analysis presented therein contains strong recommendations that would help to improve the handling of epidemics and better understand the approaches to employ in decision-making under conditions of risk and ambiguity.

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T. Kavitha mail -

P. V. N. Hanumantha Ravi mail -

K. Meenakshi mail -

S. Shunmugapriya mail -

Shrivalli H. Y. mail -

Elangovan Muniyandy mail

link https://doi.org/10.54216/IJNS.250441

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhancing E-commerce Security through Fake News Detection Using Natural Language Processing and Advanced Feature Engineering Technique

E-commerce has simplified customers' lives and offered a range of items, but it has also made them vulnerable to frauds. Fake news on e-commerce platforms threatens trust, brand image, and economic stability. Researchers have shown that contemporary Natural Language Processing (NLP) and machine learning can stop bogus news. However, e-commerce companies still struggle to distinguish phony news from real information. Fast knowledge diffusion can cause financial loss, reputation damage, and customer distrust. Thus, e-commerce false news identification requires robust and trustworthy methods. This investigation will successfully recognize and discriminate fake news. High Feature Extraction uses Word2vec and Term Frequency-Inverse Document Frequency (TF-IDF) to extract features. The optimum feature subset is determined via feature selection utilizing the least absolute shrinkage and selector operator (LASSO). The study involves four phases: Extraction, selection, classification, and data processing are the four steps. To remove raw data, data preparation utilizes stemming, lemmatization, and stop word removal. The suggested method averages model outputs to reduce overfitting and improve prediction stability. DIstilBERT with multi-stacked LSTM is tested on WELFake and ranked by F1 score, sensitivity, accuracy, and specificity. The multi-stacked LSTM distiller has 99.77% accuracy, far greater than the others do. We can use it to detect bogus news. It boosts customer confidence and Internet commerce legitimacy by improving accuracy and consistency.

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Lama Sameer Khoshaim mail

link https://doi.org/10.54216/JISIoT.150208

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

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

Details open_in_new

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.

groups

Yerraginnela Shravani mail -

Ashesh K. mail

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

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

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

Details open_in_new

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.

groups

R. Parthiban mail -

K. Santhosh Kumar mail

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

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new