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

Hassan Al Sukhni , Qusay Bsoul , Fadi yassin Salem Al jawazneh , Raghad W. Bsoul , Diaa Salama AbdElminaam , Magdy Abd-Elghany , Yasmin Alkady , Ibrahim A. Gomaa

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.

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

Vol. 15 Issue. 1 PP. 01-16, (2025)

Deployment of Hybrid Chaotic Hashes for Blockchain Driven Internet 4.0 applications

P. Vinayasree , A. Mallikarjuna Reddy

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.

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

Vol. 15 Issue. 1 PP. 16-28, (2025)

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

Ahmad Ibrahim Karajeh , Khaled Aldiabat

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.

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

Vol. 15 Issue. 1 PP. 29-36, (2025)

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

Abhiram Potlapalli , Seetharam Khetavath

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.

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

Vol. 15 Issue. 1 PP. 37-52, (2025)

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

Raghu Dhumpati , Tejeswar Reddy Velpucharla , L. Bhagyalakshmi , Peruri Venkata Anusha

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.

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

Vol. 15 Issue. 1 PP. 53-63, (2025)

Transmuting Detached Patient Consideration through Secure and Private Healthcare Monitoring Systems