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22 articles for “anomalies detection”
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Review of Anomaly Detection Techniques in MANETs: AMachine Learning Approach to Intrusion Detection, Attack Prediction, and Routing Security
Abstract: Mobile Ad hoc Networks (MANETs) are decentralized, self-organizing networks that offer flexibility and scalability for a variety of applications. However, their dynamic topology and limited resources make them highly susceptible to security threats. Traditional security measures often struggle to address the complexities of evolving and sophisticated attacks within such networks. This paper reviews the role of machine learning (ML) techniques in enhancing anomaly detection capabilities in MANETs, focusing on their …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 1–5 Read article
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Anomaly Detection Network for Surveillance Video
Abstract: Anomaly Activity is the prediction of a suspicious activity from a picture or video. This project would include using neural networks to detect suspicious human activity from real-time CCTV video. Human Anomaly Activity is a central issue in computer vision that has been researched for over 15 years. It is significant due to the large number of applications that can benefit from Activity detection. Human pose estimation, for example, is …
Published in International Journal of Software Computing and Testing · Vol. 7, Issue 2, 2021 · pp. 13–20 Read article
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Intelligence Video Surveillance Using Deep Learning
Abstract: Intelligent video surveillance powered by deep learning represents a transformative paradigm in security and surveillance. This new approach uses the power of complex neural networks to revolutionize the analysis and management of video data. Through the seamless incorporation of deep learning technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN) into observation systems, automated identification, tracking, and classification of objects can be accomplished, alleviating storage constraints on the …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 1, 2024 · pp. 9–20 Read article
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CyberSentinel: A Proposed Hybrid AI Framework for Real-Time Cyber Threat Intelligence
Abstract: Modern cyberattacks have grown far more sophisticated, frequent, and fast-moving than the signature-based intrusion detection systems many organizations still rely on. This paper proposes CyberSentinel, a hybrid artificial intelligence framework for real-time cyber threat intelligence (CTI) that combines three complementary techniques into a single detection pipeline: a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) module for network traffic anomaly detection, a Random Forest ensemble for structured log classification, and a transformer-based …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Machine Learning Approach for Analysis of Ionosphere Parameters for Earthquake Precursors
Abstract: This research explores the relationship between seismic events and predictive indicators, focusing on machine learning-based strategies for early earthquake prediction. It dissects existing prediction approaches, highlighting ionospheric anomalies' correlation with seismic occurrences, particularly preceding earthquakes of magnitudes higher than 5.5. By identifying a lack of comprehensive long-term analyses in the field, the study emphasizes future trends in machine learning-driven EQ-PD techniques using GPS-TEC data for real-time anomaly detection. The methodology …
Published in International Journal of Geological and Geotechnical Engineering · Vol. 9, Issue 2, 2023 · pp. 37–46 Read article
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Unsupervised Machine Learning:Revealing Hidden Data patterns
Abstract: Machine learning helps computers learn from data and make smart decisions. There are two main ways they do this: supervised and unsupervised learning. Supervised learning is like teaching a child with flashcards—you show the computer examples with the right answers, and it learns to recognize patterns. This helps it make predictions, like guessing whether an email is spam or estimating house prices. Unsupervised learning, on the other hand, is like …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 38–47 Read article
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Cloud Security Using Machine Learning: A Study
Abstract: The rapid adoption of cloud computing has revolutionized IT infrastructure, offering unprecedented scalability, flexibility, and cost efficiency. However, this paradigm shift introduces a new spectrum of sophisticated security challenges that traditional, signature-based security mechanisms often struggle to address effectively given the dynamic, multi-tenant, and distributed nature of cloud environments. This paper explores the critical role of Machine Learning (ML) as a potent paradigm for strengthening cloud infrastructure. ML emerges as …
Published in International Journal of Distributed Computing and Technology · Vol. 11, Issue 2, 2025 · pp. 21–30 Read article
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Advances in Machine Learning and Data Analytics: A Multidisciplinary Synthesis
Abstract: Machine learning has emerged as a key driver of innovation across a wide range of fields. Its transformative potential is evident in natural language processing, computer vision, and real-time automation, where it addresses intricate challenges with remarkable efficiency. This paper delves into recent developments in machine learning, spotlighting techniques, such as semi-supervised learning frameworks, stable diffusion models for generative tasks, and advanced lightweight architecture for real-time defection detection. By integrating …
Published in International Journal of Distributed Computing and Technology · Vol. 11, Issue 1, 2025 · pp. 12–17 Read article
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IoT-Based Fault Detection in Distribution Lines
Abstract: Quickly detecting and pinpointing failures in electrical distribution lines are important for shortening the duration of service interruption, reducing maintenance costs and increasing safety. In this work, we propose a realistic architecture for an Internet-of-Things (IoT)-enabled fault detection system designed for distribution networks. In order to continually assess electrical characteristics including voltage, current, frequency, temperature, and line status, the suggested architecture combines inexpensive, dispersed sensor nodes placed along feeders and …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation
Abstract: Environmental monitoring requires intelligent, scalable, and autonomous instrumentation systems capable of continuously sensing, processing, and communicating heterogeneous data under dynamic conditions. This study proposes an Autonomous Multi-Agent Sensor Network for Distributed Environmental Instrumentation, integrating distributed sensor nodes, edge computing, multi-agent collaboration, adaptive data fusion, and intelligent decision-making to achieve reliable real-time monitoring of environmental parameters. Each sensor agent autonomously performs local sensing, anomaly detection, energy-aware communication, and cooperative information exchange …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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Transportable Water and Air Quality Monitoring Equipment Karishma
Abstract: The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of …
Published in International Journal of Environmental Chemistry · Vol. 12, Issue 1, 2026 Read article
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Predictive Maintenance of 6G Infrastructure Using Artificial Intelligence
Abstract: The transition to Sixth Generation (6G) wireless infrastructure introduces unprecedented complexity, characterized by massive densification, reliance on the Terahertz (THz) spectrum, dynamic cell topologies driven by Reconfigurable Intelligent Surfaces (RIS), and stringent requirements for Ultra-Reliable Low-Latency Communication (URLLC). Traditional reactive or fixed-schedule maintenance protocols are fundamentally inadequate for sustaining the necessary service continuity and energy efficiency in this hyper-connected, heterogeneous environment. This paper explores the critical role of Artificial Intelligence …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 2, 2025 Read article
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Industrial IoT Based Health Monitoring System of Motor and Transformer
Abstract: This research presents an Industrial IoT-based health monitoring system tailored for motor and transformer components in industrial environments. Integrating advanced sensors, microcontrollers, and MQTT communication, the system enables real-time monitoring and predictive maintenance. Accurate measurement of critical parameters, coupled with effective anomaly detection algorithms, empowers operators to pre-emptively address potential equipment failures. The system's reliability and effectiveness in optimizing equipment reliability and minimizing downtime offer a promising solution for industrial …
Published in International Journal of Mechanics and Design · Vol. 10, Issue 1, 2024 · pp. 13–19 Read article
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Intrusion Detection System Using Gated Recurrent Neural Network
Abstract: In the realm of cybersecurity, the constant evolution of threats demands sophisticated intrusion detection systems (IDSs) capable of discerning intricate patterns in network traffic. This study proposes an IDS leveraging the capabilities of gated recurrent neural networks (GRNNs) to enhance the detection of anomalies and potential security breaches. The GRNN architecture, employing mechanisms like long short-term memory (LSTM) and gated recurrent unit (GRU), demonstrates efficacy in capturing long-range dependencies within …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 1, 2024 · pp. 1–8 Read article
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Intelligent Data Analytics on Cloud Infrastructure:Real-Time Insights and Managed Service Models
Abstract: This paper examines the integration of advanced data analytics and machine learning within modern cloud infrastructure, highlighting how organizations can transform large volumes of unstructured data into actionable business insights that drive competitive advantage. Cloud platforms leverage scalability and elasticity to efficiently process and analyze massive datasets in real time, allowing enterprises to identify hidden patterns, detect operational anomalies early, and generate accurate forecasts of future outcomes. A key factor …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 36–46 Read article
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Design and Implementation of Autonomous Robots for Crack Detection in Dam Walls: Analyzing Vision-Based Algorithms and Structural Health Monitoring
Abstract: AbstractThe maintenance of dam walls is critical to ensure structural integrity, safety, and efficient water management. Cracks and other structural deficiencies can lead to severe consequences if left undetected. Traditional inspection methods are labor-intensive, time-consuming, and often hazardous, necessitating the development of autonomous systems for effective monitoring. This study focuses on the design and implementation of autonomous robots equipped for crack detection in dam walls, with an emphasis on vision-based …
Published in International Journal of Structural Mechanics and Finite Elements · Vol. 11, Issue 1, 2025 · pp. 14–26 Read article
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AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection
Abstract: One of the key factors in detecting early diseases is medical image segmentation. Medical image segmentation can be very complex due to the differences in image quality, noise, and specific anatomical structures of individual patients. A novel approach for a hybrid neural network structure that combines the power of convolutional neural networks, and the transformer-based models is described here for improved accuracy and robustness of medical image segmentation. Unlike traditional …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 1–7 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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A Study on AI in 7G Mobile Communication
Abstract: The imminent transition from 5G to the seventhgeneration (7G) mobile ecosystem promises to redefine connectivity through unprecedented data rates, pervasive ultralow latency, and holistic integration of terrestrial, aerial, and spaceborne networks. Central to this evolution is Artificial Intelligence (AI), which is poised to become the nervous system that orchestrates, optimizes, and secures the hyperdynamic 7G fabric. This paper surveys the emerging AIdriven paradigms that will underpin 7G, focusing on three …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 1, 2026 · pp. 29–39 Read article