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Autonomous Vehicle Navigation Systems: Machine Learning for Real-Time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 IEEE.

关键词： Traffic congestion

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Discover Crypto-Jacker from Blockchain Using AFS Method 1

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4th International Conference on computer and Communication Technologies, IC3T 2022

作者： Subburaj, T. Shilpa, K. Sultana, Saba Suthendran, K. Karuppasamy, M. Arun Kumar, S. Jyothi Babu, A. Department of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of Information Technology Kalasalingam Academy of Research and Education Krishnan Koil Tamilnadu Srivilliputhur India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil TamilNadu Srivilliputhur India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of MCA Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

The blockchain technology is used throughout the world for digital ledgers of transactions. To maintain all participant transactions on a blockchain, distributed ledger technology (DLT) is used. Blockchain technology has become a popular method of transferring huge amounts due to the pandemic situation. A hacker was able to exploit some part of a chain, smart contract, exchange, or stealing cryptocurrency, that is, a hack or a theft. Such attacks are referred to as crypto-Jack attacks. The Wormhole cryptocurrency platform was hacked in Feb 2022, resulting in a loss of $326 Millions. Many cryptocurrency Web pages are being hacked every day. Most of the attacks are focusing the financial purpose, so banking attackers are globally raised. To identify crypto-jackers and to trace out the hijackers, we proposed the new technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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A Smart Air Pollutants Monitoring System Using IOT Technologies

A Smart Air Pollutants Monitoring System Using IOT Technolog...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Pyingkodi, M. Blessing, N R Wilfred Thenmozhi, K. Kanimozhi, K. Pavithira, L. Balakrishnan, P K Sandhya Kongu Engineering College Department of Computer Applications Erode India IT Department Oman Kristu Jayanti College Department of Computer Science Bengaluru India Sri Krishna Adithya College of Arts and Science Department of Information Technology Coimbatore India PSG College of Arts and Science College Department of Computer Science with Data Analytics Coimbatore India CMS College of Science and Commerce Department of Computer Science Coimbatore India

ISBN: (纸本)9798350335835

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of 'inventive gadgets' which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device. © 2023 IEEE.

关键词： Internet of things

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Predicting Glaucoma Severity Levels in Human Eyes: Integrating CNN and Random Forest for accurate classification 1

Predicting Glaucoma Severity Levels in Human Eyes: Integrati...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Brar, Tejinder Pal Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350328042

This study prompts intervention and effective treatment, it is crucial to accurately categorize the various levels of glaucoma severity in human eyes. To accurately classify glaucoma sickness, this research introduces a novel methodology that combines Convolutional Neural Networks, also known as CNNs, and Random Forests synergistically. Our model displays amazing accuracy in differentiating between intensity classes by taking advantage of CNNs' skill at extracting complex information from medical pictures and Random Forests' talent for ensemble learning. The method includes careful data augmentation, preprocessing, and aggregation, which strengthens the model's durability. The effectiveness of the model over a range of glaucoma levels of severity may be understood by a thorough performance evaluation that takes variables like accuracy, recollection, and F1 score into account. The results highlight its potential as a tool for diagnosis for healthcare practitioners, enabling early detection. This study provides a transformational approach to improving glaucoma diagnosis and demonstrates the possibility of multidisciplinary collaboration between machine learning and medical field expertise. The results highlight the wider significance of technology-driven medical improvements in tackling complicated medical issues, with potential applications to other eye illnesses and beyond. © 2023 IEEE.

关键词： Health care

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Automatic Detection and Epidemiologic Trends in Head and Neck Cancer Using Fast-R-CNN and SVM Methods 7

Automatic Detection and Epidemiologic Trends in Head and Nec...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sakthivel, M. Patil, Sonal Kamarunisha, M. Pokhariyal, Rajesh Pal, Subharun Tiwari, Mohit Malla Reddy College of Engineering Department of CSE-AIML Hyderabad India G H Raisoni Institute of Engineering and Business Management Jalgaon Department of CSE-IT Maharashtra India Department of Computer Applications Tamilnadu Perambalur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350340600

H ead and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in the world. Despite improvements in diagnosis and therapy, the five-year survival rate for patients with advanced HNSCC has risen only slowly. This is mostly due to the high number of patients who present with an advanced disease stage at diagnosis, as well as the high frequency of relapse and secondary tumors. Protein biomarkers that aid in the early diagnosis of primary HNSCC or relapse hold promise for improving clinical outcomes. Finding early changes in the mucosal linings previous to the development of invasive tumors and properly assessing the likelihood of malignant transformation, both currently tough activities, may be important. The proposed procedure makes use of preprocessing, feature extraction, segmentation, and model training. Anisotropic Diffusion Filter and Gaussian Filter are used in preprocessing. Extraction of the techniques follows preprocessing. The Grey Level Run Length matrix is used for feature extraction. Segmenting the nucleus is a highly recommended step in the segmentation process. Training is performed using an RCNN- SVM model. When compared to two other models, RCNN and SVM, the proposed method excels. © 2023 IEEE.

关键词： Support vector machines

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Multi-vector Monitoring, Detecting and Classifying GPU Side-Channel Attack Vectors on a Secure GPU Execution Framework 8

Multi-vector Monitoring, Detecting and Classifying GPU Side-...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lungu, Nelson Dash, Bibhuti Bhusan De, Utpal Chandra Dash, Bibek Bikram Singh, Satyendr Patra, Sudhansu Shekhar University of Zambia Electrical and Electronical Engineering Lusaka Zambia Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India Bml Munjal University Computer Science & Engg Department Gurugram India

ISBN: (纸本)9798350376425

Graphics Processing Units (GPUs) are increasingly deployed in security-sensitive domains, but their parallel architectures introduce vulnerabilities enabling side-channel attacks to steal sensitive data. Recent works have shown feasible timing, contention, power, and access pattern side-channel attacks against cryptographic implementations, randomness protections, and other workloads on GPUs, highlighting risks of intellectual property theft, cryptographic key extraction, and spying. However, existing academic defences remain inadequate in systematically protecting real-world shader execution. This research aims to design, develop, and evaluate a secure GPU shader execution framework incorporating runtime monitoring, anomaly detection, and response mechanisms for commercial GPUs. The methodology involves implementing and assessing the framework against simulated side-channel attacks on shader benchmarks;this can help secure emerging GPU adoption in sectors like finance, healthcare, and government against rising data breaches. © 2024 IEEE.

关键词： Parallel architectures

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Reduce Low-Frequency Distributed Denial of Service Threats by Combining Deep and Active Learning 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Shukla, Aditya Kumar Sharma, Ashish Sengar, Sandeep Singh Department of Computer Engineering and Applications GLA University NH#2 Delhi Mathura Highway Post Ajhai Uttar Pradesh Mathura India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

This study introduces substantial contributions to the field of Low-Rate Detection of Distribution Denial of Service (DDoS) Attacks, leveraging convolutional neural networks (CNNs) with an attention mechanism and incorporating active learning with semi-labelled data. These contributions collectively enhance the accuracy, efficiency, and adaptability of DDoS detection systems. Additionally, the study introduces active learning strategies into the learning process. By actively selecting instances for manual labelling, the study reduces the burden of extensive manual labelling efforts and enhances the model’s scalability. In the dynamic realm of cybersecurity, where threats evolve rapidly, active learning ensures the model’s adaptability with minimal human intervention. Furthermore, this research addresses the challenge of scarce labelled data in real-world cybersecurity contexts. By harnessing semi-labelled data efficiently and pairing it with active learning, the study streamlines the detection and defence against DDoS assaults with low attack rates. This is particularly relevant in situations where procuring an abundance of labelled data is impractical or cost-prohibitive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Active learning Attention mechanism CNNs Cybersecurity Low-rate DDoS detection Semi-labelled data Threat mitigation

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A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images 2

A Systematic Review of Deep Learning Approaches for Vessel S...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Hegde, Govardhan Prabhu, Srikanth Gupta, Shourya Prabhu, Gautham Manuru Palorkar, Anshita Srujan, Metta Venkata Bhandary, Sulatha V Department of Computer Science and Engineering India Department of Data Science and Computer Applications India Department of Information and Communication Technology Manipal Institute of Technology Karnataka Manipal576104 India Department of Ophthalmology Kasturba Medical College Karnataka Manipal576104 India

Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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A Novel Architecture for Developing IoT Solutions Applied to Healthcare 2

A Novel Architecture for Developing IoT Solutions Applied to...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Levina Kaliyamoorthi, P. Dattu, Vinjamuri S.N.Ch. Dineshnath, G. Jaiganesh, S. Dobhal, Dinesh Chandra Robin, C. R. Rene Kns Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle517325 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Department of Master of Computer Applications Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels;(2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed. © 2023 IEEE.

关键词： Internet of things

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