Analyzing the Vulnerability of Consumer IoT Devices to Sophisticated Phishing Attacks and Ransomware Threats in Home Automation Systems

Raghu; Tejeswar Reddy; L.; Peruri Venkata

doi:https://doi.org/10.54216/JISIoT.150105

International Journal of Neutrosophic Science | 2025

Operations on Translation of Fermatean Neutrosophic INK-Algebra

This paper investigates the theoretical basis of fermatean neutrosophic sets, which were first introduced by Smarandache, to clarify the relationship between single-valued fermatean neutrosophic sets and their role as specific subsets in the wider context of fermatean neutrosophic sets, particularly in science and engineering. This study investigates fermatean neutrosophic INK-ideals within INK-algebras using the translation concept, which is proposed as an extension of intuitionistic fuzzy sets. First, translation fermatean neutrosophic INKalgebras are presented and their fundamental features are studied. Furthermore, the research investigates properties related to the translation of INK-subalgebras and INK-ideals, as well as the dynamics of their unions, intersections, and multiplications for fermatean neutrosophic INK-ideals. The article adds definitions and theorems to provide a complete grasp of the problems of fermatean neutrosophic INK-algebras.

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Wadei Faris AL-Omeri mail -

M.Kaviyarasu mail -

Rajeshwari M. mail

link https://doi.org/10.54216/IJNS.250411

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

Epanechnikov-pareto Distribution with Application

In this article, we combined the Epanechnikov kernel function with the pareto distribution to produce the Epanechnikov-Pareto distribution (EPD). Some properties of this distribution are studied, like the moments, MLEs, reliability analysis functions, ordered statistics, and quintile function.

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Naser Odat mail

link https://doi.org/10.54216/IJNS.250412

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Vol. Volume 25 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

Neutrosophic Ideal of a Near Algebra

This article introduces the idea of neutrosophic ideal of a near algebra and provides a definition and example. A few fundamental features related to this approach are also explored. We also present the topics neutrosophic near algebra homomorphism, kernel of a neutrosophic near algebra and coset of a neutrosophic ideal of a near algebra. It is been briefed with the appropriate definitions and theorems on it. It is been proved that sum of the right neutrosophic ideal of a near algebra is also a right neutrosophic ideal of a near algebra over a neutrosophic field.

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P. Narasimha Swamy mail -

Bhurgula Harika mail -

T. Nagaiah mail -

L. Bhaskar mail -

K. Vijay Kumar mail

link https://doi.org/10.54216/IJNS.250414

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

UNCA: A Neutrosophic-Based Framework for Robust Clustering and Enhanced Data Interpretation

Accurately representing the complex linkages and inherent uncertainties included in huge datasets is still a major difficulty in the field of data clustering. We address these issues with our proposed Unified Neutrosophic Clustering Algorithm (UNCA), which combines a multifaceted strategy with Neutrosophic logic to improve clustering performance. UNCA starts with a full-fledged similarity examination via a λ-cutting matrix that filters meaningful relationships between each two points of data. Then, we initialize centroids for Neutrosophic K-Means clustering, where the membership values are based on their degrees of truth, indeterminacy and falsity. The algorithm then integrates with a dynamic network visualization and MST (Minimum Spanning Tree) so that a visual interpretation of the relationships between the clusters can be clearly represented. UNCA employs Single-Valued Neutrosophic Sets (SVNSs) to refine cluster assignments, and after fuzzifying similarity measures, guarantees a precise clustering result. The final step involves solidifying the clustering results through defuzzification methods, offering definitive cluster assignments. According to the performance evaluation results, UNCA outperforms conventional approaches in several metrics: it achieved a Silhouette Score of 0.89 on the Iris Dataset, a Davies-Bouldin Index of 0.59 on the Wine Dataset, an Adjusted Rand Index (ARI) of 0.76 on the Digits Dataset, and a Normalized Mutual Information (NMI) of 0.80 on the Customer Segmentation Dataset. These results demonstrate how UNCA enhances interpretability and resilience in addition to improving clustering accuracy when contrasted with Fuzzy C-Means (FCM), Neutrosophic C-Means (NCM), as well as Kernel Neutrosophic C-Means (KNCM). This makes UNCA a useful tool for complex data processing tasks.

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D. Dhinakaran mail -

S. Edwin Raja mail -

S. Gopalakrishnan mail -

D. Selvaraj mail -

S. D. Lalitha mail

link https://doi.org/10.54216/IJNS.250415

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

A Comparative Case Study on Neutrosophic Linear Programming Approach and  -Constraint Method for Fuzzy Multiobjective Solid Cold Transportation Problem with an Improved Preservation Technology

Cold transportation is one among the unquenching needs of people around the globe. Although cost sensitive, refrigerated transportation is preferred globally as it ensures the quality of perishable items in pharmaceutical, food and beverages, chemicals and certain other industries during transportation. However, many refrigerated vehicles fail in offering consistent preservation as most of their cooling units depend on the vehicle’s engine. It is also important to acknowledge that operating a vehicle unceasingly to maintain temperature is impossible in real life. This set up of poor cold logistics and supply chain leads to an increased deterioration of sensitive items. The paper overcomes this complication by adjoining an extra power source that supports freezing during the shutdown time of the vehicle engine by proposing improved mathematical models on Multi-Objective Cold Fuzzy Solid Transportation Problem (MOCFSTP) with an extra time parameter relating to the static and delay condition of the vehicles during various preservation modes (zero, semi, full) and defends them with comparable scrutinizing. The objectives contemplated in the problem are minimizing the cost, time and rate of deterioration. Numerical examples are discussed in detail and solved using reknown methods in LINGO (19.0) to stress on the effectiveness of the models.

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L. Brigith Gladys mail -

J. Merline Vinotha mail

link https://doi.org/10.54216/IJNS.250416

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

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Journal of Intelligent Systems and Internet of Things | 2025

Brain Tumor Detection: Integrating Machine Learning and Deep Learning for Robust Brain Tumor Classification

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

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Hassan Al Sukhni mail -

Qusay Bsoul mail -

Fadi yassin Salem Al jawazneh mail -

Raghad W. Bsoul mail -

Diaa Salama AbdElminaam mail -

Magdy Abd-Elghany mail -

Yasmin Alkady mail -

Ibrahim A. Gomaa mail

link https://doi.org/10.54216/JISIoT.150101

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Deployment of Hybrid Chaotic Hashes for Blockchain Driven Internet 4.0 applications

The evolution of Internet 4.0 demands robust, secure, and scalable solutions to meet the growing needs of digital transactions and interconnectivity, and blockchain technology has emerged as a foundational enabler for these applications. However, blockchain's reliance on traditional cryptographic methods presents vulnerabilities that can be exploited in increasingly sophisticated cyber landscapes. This paper introduces the deployment of Hybrid Chaotic Hashes for enhanced security and efficiency in blockchain-driven Internet 4.0 applications. By integrating chaotic systems with hash functions, hybrid chaotic hashes provide a more unpredictable, complex cryptographic layer that enhances data integrity, confidentiality, and resistance to attacks. The unique properties of chaotic functions—nonlinearity, ergodicity, and sensitivity to initial conditions—make them advantageous for hashing in blockchain environments. This study highlights the practical applicability and resilience of hybrid chaotic hashes which is nonlinear technique in Internet 4.0.

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P. Vinayasree mail -

A. Mallikarjuna Reddy mail

link https://doi.org/10.54216/JISIoT.150102

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

The Impact of Big Data on the Nexus between Financial Leverage and Stock Price Prediction

This research explores the impact of financial leverage on stock price prediction among listed industrial Jordanian companies. Moreover, the effect of big data as a moderating variable on the relationship between financial leverage and stock price prediction. The study uses two types to measure financial leverage according to the terms [short-term and long-term]. The study results point out that only short-term leverage influences stock price prediction among listed industrial Jordanian companies, which it maybe because short-term leverage has a direct impact on a firm situation compared with long-team leverage that resorts it to achieve long-term goals. Furthermore, the findings provide an original contribution by asserting that big data plays a main moderating role when making decisions regarding investment, where it helps in expecting stock prices in companies with financial leverage.

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Ahmad Ibrahim Karajeh mail -

Khaled Aldiabat mail

link https://doi.org/10.54216/JISIoT.150103

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Exploring the Use of Deep Learning Models for Image Compression in Embedded Systems: Encoder and Decoder Architectures

With the growing demand for efficient image processing in embedded systems, the exploration of deep learning-based image compression methods has emerged as a promising avenue. Traditional image compression techniques, such as JPEG and PNG, face challenges in achieving optimal performance for constrained environments due to their reliance on handcrafted algorithms and limited adaptability. This study investigates the use of deep learning models for image compression tailored to embed systems, focusing on encoder and decoder architectures. By leveraging convolutional neural networks (CNNs) and variational auto encoders (VAEs), we design lightweight models capable of achieving high compression ratios while maintaining visual fidelity. The research emphasizes computational efficiency, ensuring compatibility with the resource constraints of embedded hardware. Key contributions include the development of streamlined architectures optimized for low memory and power usage, along with a comprehensive evaluation of compression quality, reconstruction accuracy, and real-time performance. The results demonstrate that deep learning-based approaches can outperform traditional methods in terms of adaptability and efficiency, paving the way for their integration into next-generation embedded systems.

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Abhiram Potlapalli mail -

Seetharam Khetavath mail

link https://doi.org/10.54216/JISIoT.150104

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Analyzing the Vulnerability of Consumer IoT Devices to Sophisticated Phishing Attacks and Ransomware Threats in Home Automation Systems

This research presents a new and elaborate security model for IoT devices used in home automation systems. The framework comprises five algorithms: The following models were identified: Vulnerability Assessment (VA), Anomaly Detection with Machine Learning (ADML), Behavior Analysis (BA), Intrusion Detection System (IDS), and Adaptive Security Framework (ASF). Ablation study brings out the specificity of each algorithm and underlines the synergy of the algorithms for IoT device protection. Comparisons with similar procedures confirm higher levels of sensitivity and specificity of the proposed method, as well as enhanced efficiency and tunability. Animated charts give crisp information about the total effects of security methods on different parameters. The proposed security framework has therefore been presented as now a viable solution to complex threats and continuous security for the IoT devices used in home automation systems.

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Raghu Dhumpati mail -

Tejeswar Reddy Velpucharla mail -

L. Bhagyalakshmi mail -

Peruri Venkata Anusha mail

link https://doi.org/10.54216/JISIoT.150105

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new