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70 articles for “Traffic Detection”
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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Traffic Detection Algorithms Analysis using ML
Abstract: It is difficult to watch traffic on crowded roads. Traffic monitoring procedures are time-consuming, expensive, labor-intensive, and require human operators. The limited accessibility hindered the storing and processing of large-scale video streams. Nonetheless, it is now possible to employe video feeds from traffic monitoring systems for number plate recognition, object tracking, traffic behavior analysis, and surveillance. Static image recognition and vehicle identification in a traffic surveillance system are very useful …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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An approach of Computer Vision Methods for Driver’s Drowsiness and Yawn Detection
Abstract: Numerous studies have demonstrated that 4,444 traffic crashes are primarily caused by driver drowsiness. Due to advancements in digital computer systems, tiredness behaviour may now be studied by researchers worldwide. The goal of this project is to increase road safety by preventing accidents caused by sleepy drivers. To view the driver's face, use real-time facial recognition technology. A driver's attentiveness and reaction time may be impacted by weariness, which raises …
Published in International Journal of Optical Innovations & Research · Vol. 1, Issue 1, 2023 · pp. 21–26 Read article
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Real-Time Object Detection and Tracking in Traffic Surveillance: Implementing Algorithms That Can Process Video Streams for Immediate Traffic Monitoring
Abstract: The rapid growth in urban development and traffic congestion calls for adopting high standards of traffic surveillance systems for monitoring. This paper reviews the current advancement and future trends of real-time object detection and tracking technology and its implications for traffic surveillance. Conventional approaches to traffic monitoring can provide more or less accurate data, but they are not easily scalable and cannot cope with rapidly changing conditions typical within urban …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 18–39 Read article
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Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 Read article
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Advanced Helmet Recognition System with Integrated Number Plate Detection for Enhanced Traffic Monitoring Using Deep Learning
Abstract: This study focuses on the crucial problem of non-adherence to traffic regulations, particularly with the compulsory use of helmets by motorcyclists. Motorcycle accidents have a greater mortality rate compared to other types of accidents, indicating a need for a more effective enforcement strategy. Current procedures depend on traditional techniques where traffic officers manually observe traffic rule infractions through patrols and monitoring CCTVs, requiring substantial labor and time resources. The inherent …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 1, 2024 · pp. 9–18 Read article
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Dynamic Traffic Control System with Emergency Vehicle Detection
Abstract: The project's goal is to create a density-based dynamic traffic light system in which the timing of the signal changes autonomously based on the density of traffic at any given junction. Traffic congestion is a major issue in most places around the globe, so it is time to transition from a more manual or set timer method to an automatic system with decision-making capabilities. The current traffic signaling system is …
Published in Journal of Microcontroller Engineering and Applications · Vol. 11, Issue 1, 2024 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration
Abstract: The “Intelligent Traffic Monitoring: YOLO v8 and CSV Data Integration” project is a cutting-edge solution for intelligent traffic monitoring, with YOLO v8 (You Only Look Once) serving as the fundamental technology for real-time vehicle detection and traffic counting on roads. In addition to these features, the system interfaces effortlessly with data pipelines and machine learning projects by storing gathered traffic data in CSV (Comma-Separated Values) format. The major goal of …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 20–24 Read article
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Design and Implementation of a Real-Time ANPR-OCR Based Embedded System for Automated Vehicle Compliance Verification-Checking Valid PUC and Insurance
Abstract: With the rapid increase in vehicle ownership, ensuring adherence to traffic regulations has become essential, particularly concerning vehicle insurance and Pollution Under Control (PUC) certification. Non-compliance with these requirements can lead to financial risks, legal violations, and environmental damage. Traditional enforcement methods rely on manual inspections, which are time-consuming, inefficient, and prone to human error. To address these challenges, this paper proposes an automated system that utilizes Automatic Number Plate …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 2, 2025 · pp. 1–7 Read article
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VERONICA: AI-Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion
Abstract: Road safety for bike riders remains a significant concern, with accident rates highlighting the need for advanced solutions to ensure rider protection and awareness. This paper presents “VERONICA: AI-Driven Edge System for Comprehensive Bike Safety and Assistance Through Multi-Source Data Fusion”, a voice-activated, continuously operating assistance system designed to provide real-time, intelligent solutions for various riding scenarios. VERONICA integrates accident detection, low-traffic route navigation, traction control advisories, and weather updates …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 09–17 Read article
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Human-Machine Interaction in Advanced Driver Assistant Systems: Design and Evaluation
Abstract: An autonomous vehicle, also known as a self-driving car or self-driving car, is one that is equipped with sensors and cameras to monitor its environment and can operate independently without the need for human intervention. It recognizes objects in the immediate environment, such as people, animals, and barriers, using a combination of cameras and sensors. reducing fuel waste, improving mobility, safety, and customer pleasure, among other things. Cost savings is …
Published in Journal of Microelectronics and Solid State Devices · Vol. 11, Issue 1, 2024 · pp. 1–8 Read article
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Automatic Car Controller Based on Sign Board using Deep Learning and IOT
Abstract: The rapid growth of intelligent transportation systems has increased the demand for safer and more efficient driving solutions. Conventional vehicles often rely heavily on human intervention, which can lead to accidents due to negligence, fatigue, or poor visibility of traffic signs. This project proposes an automated car control system that utilizes deep learning and Internet of Things (IoT) technologies to recognize traffic signboards and respond accordingly. The primary objective is …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 Read article
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Self-Driving Cars and Computer Vision: Enhancing Computer Vision for Autonomous Vehicle Navigation
Abstract: Autonomous vehicles, commonly known as self-driving cars, are transforming the transportation sector by aiming to enhance road safety, ease traffic congestion, and boost overall efficiency. Central to the operation of these vehicles is computer vision, which enables them to perceive and understand their environment. This paper examines how computer vision contributes to the navigation of autonomous vehicles and highlights its continuous developments. Specifically, it examines key challenges such as real-time …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 · pp. 1–10 Read article
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Acoustic Sensing for City Flow: Quasi-Supervised Recognition of Sirens and Traffic for Urban Mobility Intelligence
Abstract: This paper frames environmental audio as a mobility telemetry source, extending a benchmark urban-sound corpus with transportation-critical classes—ambulance, firetruck, police, and traffic—and training spectrogram-based models under a quasi-supervised regime to support real-time city operations; leveraging 10-fold protocols, class-weighted objectives, and audiospecific augmentations (time stretch, pitch shift, SpecAugment, PatchAugment), the system benchmarks multiple CNN backbones combined with self-supervised learning paradigms enable the extraction of rich, discriminative acoustic representations, achieving strong multi-class …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 42–50 Read article
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Future-Ready Communication Systems: Exploring High-Speed, Adaptive, and Secure Network Solutions
Abstract: The domain of electronics communication systems has experienced rapid transformation due to the growing demand for high-speed, reliable, and intelligent communication networks. This paper presents a comprehensive analysis of emerging trends such as Fifth Generation (5G) communication systems, Internet of Things (IoT), Artificial Intelligence (AI)-enabled networks, Software-Defined Networking (SDN), optical communication advancements, and cybersecurity mechanisms. The combination of cloud computing, edge computing, and network virtualization which improve system flexibility, allow …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 1, 2026 Read article
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Swarm-Enabled AI for Smart Mobility and Sustainable Transport
Abstract: The significant issues facing the modern urban infrastructure are the management of road traffic problems, such as severe traffic congestion, the detection of unsafe driving behavior, and road safety. The traditional ground-based surveillance systems will be helpful, but they will reach their limits in large and dynamic environments. It is because of predetermined perspectives, blindness, and the inability to scale. To designate these problems, the present study proposes a traffic …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 33–38 Read article
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Robust Classification of Traffic Signs Using Relief Feature Reduction Technique
Abstract: Ensuring driver safety amidst the rapid growth of global population and vehicular density continues to be a paramount challenge for transportation authorities and governments worldwide. With the rise of smart mobility solutions and autonomous driving technologies, the ability to detect, classify, and respond to traffic signs accurately has become critically important, especially under diverse and adverse environmental conditions such as rain, fog, or poor lighting. Reliable traffic sign recognition not …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 30–37 Read article
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The Evolution of Bio Crypt Keys: From Concept to Implementation
Abstract: With the rapid increase in data exfiltration due to cyber-attacks, Covert Timing Channels (CTCs) have emerged as a significant and sophisticated network security threat. These channels exploit inter-arrival times of data packets to exfiltrate sensitive information from targeted networks. Detecting CTCs increasingly relies on machine learning techniques, which use statistical metrics to differentiate between malicious (covert) and legitimate (overt) traffic flows. However, as cyber-attacks become more adept at evading detection …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 2, 2024 · pp. 38–45 Read article