Daily Archives: June 29, 2026

Authors: Shivam Namdev, Bhanu Pratap Singh

Abstract: The rapid increase in urbanization, public security challenges, and criminal activities has accelerated the development of intelligent surveillance systems for real-time violence detection and criminal activity identification. Traditional surveillance systems often depend heavily on manual monitoring, which limits detection efficiency, increases response time, and reduces reliability in complex environments. Recent advancements in deep learning, machine learning, computer vision, sensor networks, and predictive analytics have significantly improved automated surveillance capabilities for public safety management. This review presents an intelligent deep learning framework for violence detection and criminal activity identification in smart surveillance systems by analyzing recent developments in convolutional neural networks (CNNs), 3D-CNNs, ConvLSTM architectures, transfer learning, optimization techniques, and sensor-based monitoring systems. The framework integrates video analytics, spatiotemporal feature extraction, facial recognition, object detection, anomaly detection, and predictive threat analysis into a unified intelligent surveillance ecosystem. Furthermore, the study highlights the role of real-time monitoring, smart city technologies, and intelligent decision-support systems in improving public security operations. The review indicates that deep learning-based surveillance frameworks significantly improve violence detection accuracy, reduce false alarms, enhance predictive threat identification, and support automated emergency response systems in modern smart environments.

Published by: vikaspatanker

Authors: Prakash Gahora, Bhanu Pratap Singh

Abstract: The rapid growth of digital communication, cloud computing, Internet of Things (IoT), and smart infrastructures has significantly increased cybersecurity threats and network vulnerabilities. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving cyber-attacks due to their dependence on static rule-based mechanisms. To address these limitations, Machine Learning (ML) and Explainable Artificial Intelligence (XAI) have emerged as promising solutions for intelligent and adaptive intrusion detection. This research explores the integration of ML and XAI techniques in intrusion detection systems to improve attack detection accuracy, transparency, and real-time threat response. The study reviews various machine learning approaches, including supervised learning, deep learning, reinforcement learning, and federated learning methods used in modern IDS frameworks. Additionally, the role of explainable AI in enhancing trust, interpretability, and decision-making within cybersecurity systems is examined. The proposed approach emphasizes intelligent threat detection, reduced false alarm rates, and improved adaptability in IoT, industrial, and distributed computing environments. The findings indicate that AI-driven IDS frameworks provide efficient and scalable cybersecurity solutions capable of addressing emerging cyber threats while ensuring transparency and reliability in security operations.

Published by: vikaspatanker

Authors: Jyoti Gahora, Bhanu Pratap Singh

Abstract: Brain tumors are among the most critical neurological disorders that require early and accurate diagnosis for effective treatment and improved patient survival. Magnetic Resonance Imaging (MRI) is widely used for brain tumor diagnosis because of its superior soft tissue visualization capability. However, manual tumor detection and classification are time-consuming and highly dependent on radiologists’ expertise. To overcome these limitations, this research proposes an intelligent MRI-based brain tumor detection and classification system using deep learning techniques. The proposed framework integrates preprocessing, segmentation, feature extraction, deep learning classification, and performance evaluation into a unified automated system. Initially, MRI images undergo preprocessing steps such as artifact removal, noise reduction, intensity normalization, and bias field correction to improve image quality. Segmentation techniques including thresholding, region growing, and watershed algorithms are then applied to isolate tumor regions from healthy brain tissues. Histogram-based, texture-based, and shape-based features are extracted to improve discriminative learning. The EfficientNetB3 deep learning model is employed for tumor and non-tumor classification due to its efficient feature learning and lightweight architecture. Hyperparameter tuning techniques such as optimized learning rate, batch size, dropout regularization, and data augmentation are used to improve classification performance and reduce overfitting. The proposed model achieves high performance with improved accuracy, precision, recall, and F1-score compared to existing approaches. Experimental results demonstrate that the proposed framework provides accurate and reliable brain tumor detection with enhanced segmentation and classification capability. The system also supports intelligent clinical decision-making and has the potential for future real-time healthcare applications.

Published by: vikaspatanker