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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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Dynamically Contrastive Clustering For Sentence Embedding 6

Dynamically Contrastive Clustering For Sentence Embedding

引用

6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Shi, Haidong Zhou, Xiaotang Li, Lingbo School of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798331541750

U nsupervised contrastive learning to obtain sentence embedding has become a widely adopted and effective method. However, existing techniques mainly focus on the construction of positive-negative sample pairs, where instance contrastive learning is performed on samples by determining the positive and negative examples, while these methods ignore the effect of temperature coefficients on the model and tend to use fixed temperature coefficients. To address these issues, we propose a new approach named DCCSE(Dynamically Contrastive Cluster for Sentence Embedding), our model smooths the sample embedding while enhancing the sample linkage. Specifically, we use sample embeddings to dynamically update the cluster embeddings, combine contrastive learning and cluster centroid while employing cluster embed dings to smooth the sample embeddings and design a dynamic temperature coefficient function to improve the linkage between sample pairs. We evaluate our approach on a standard STS dataset, and the experimental results show that our model has relatively significant advantages over other baseline models. © 2024 IEEE.

关键词： Contrastive Learning

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Prediction of Knee Osteoarthritis Severity from X-Ray Images Using Ensemble Learning 5

Prediction of Knee Osteoarthritis Severity from X-Ray Images...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Navyeesh, Kothoju Hinduja, Kanakala Jani Anbarasi, L. Charitha, Nagavarapu Divya Varshitha, Pasala Sri Varshitha, Uppuluri Venkata Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

Millions of people worldwide are affected by knee osteoarthritis (OA), a prevalent condition involving the deterioration of joint cartilage and underlying bone. Consequently, it causes discomfort, restricted movement, and diminished mobility. It is crucial to accurately and promptly assess the severity grading of the disease for effective clinical management. In this study, the important issue of predicting knee OA severity using X-ray images is addressed through the utilization of deep learning models, specifically convolutional neural networks (CNNs). An ensemble model that combines the strengths of different architectures was proposed to show the enhanced accuracy in Knee OA. The proposed work used the best features extracted using EfficientNet, DenseNet and ensemble models to attain an accuracy rate of 94.76%. The exceptional performance of the ensemble model underscores its reliability in classifying knee OA severity and provides a comprehensive and precise solution for health assessment. The proposed systematic approach demonstrates how deep learning can enhance diagnostic capabilities for knee osteoarthritis and improve clinical decision-making. © 2024 IEEE.

关键词： Grading

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Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM Attention and MediaPipe 2

Flowing Asanas: Enhancing Yoga Posture Detection With BiLSTM...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Noel Jeygar Robert, V. Aakash Kumar, P. Rani, Abha Pandey, Shraddha Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Yoga is an ancient practice that the combines harmony of mind, body, and soul with discipline, relaxation, and happiness. Besides its breathing and meditation exercises, a central aspect of yoga is its asanas (postures). Keeping the importance of asanas of yoga in mind, this research paper aims to describe a novel and compact posture detection system for yoga that integrates MediaPipe for real-time skeletal landmark placement and deep learning models for posture analysis and accuracy evaluations. The architecture acquires live yoga poses and hand gestures directly from a webcam through an RGB camera where MediaPipe recognizes key body points such as the knees, eyes, hands, etc., to create a skeleton representation of the pose taken by the user. Next, these skeletal structures are evaluated by deep learning algorithms against correct yoga postures that have been pre-trained and can tell how aligned or accurate the user's postures may be. It automatically stops the timer once it identifies the wrong posture to ensure enhanced safety and efficiency. The technique helps expand the horizons of yoga to greater potential in physical training and rehabilitation while enhancing the practice itself. © 2024 IEEE.

关键词： Deep learning

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ResNet50 Integrated Vision Transformer for Enhanced Plant Disease Classification 3

ResNet50 Integrated Vision Transformer for Enhanced Plant Di...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohnish Kumaar, D. Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This study introduces an innovative hybrid model that integrates the powerful feature extraction capabilities of ResNet 50 with the advanced pattern recognition and contextual understanding afforded by the Vision Transformer (ViT) for the classification of plant diseases. By embedding a class token into the input feature sequence and leveraging a novel multi-head attention mechanism, this model captures both local and global image features, leading to a significant enhancement in diagnostic accuracy and efficiency. Our approach involves a meticulous configuration process that adjusts hyperparameters such as the number of transformer encoder layers and attention heads to optimize performance. Rigorous testing on a meticulously curated and pre-augmented dataset underscores the model's superiority over existing methodologies, demonstrating its readiness for real-world agricultural applications. The findings from this research not only mark a substantial advancement in plant disease diagnostics but also set a new benchmark for the application of hybrid deep learning models in precision agriculture, paving the way for sustainable farming practices in the face of escalating global food demands. © 2024 IEEE.

关键词： Deep learning

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Optimising Makespan Minimization and Performance at the Same Time with a Dynamic Cloud Task Scheduling Comparative Study of MOSOS and PSO 5

Optimising Makespan Minimization and Performance at the Same...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Jain, Anegha Nath, Megha Rama Parvathy, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The practice of allocating available resources, such as personnel, money, time, or materials, to different tasks, projects, or activities is known as resource allocation. Effective resource allocation is crucial for optimizing productivity, achieving goals, and maximizing efficiency within an organization or project. Resource allocation in cloud computing can face several challenges. These include over-allocation, where resources are provisioned excessively, leading to increased cost, and under-allocation, which can result in performance bottlenecks. Cloud environments often have to support a variety of different workloads, each with its own resource requirements. This can make it difficult to allocate resources efficiently whilst maintaining the Quality of Service (QoS). Tackling such issues, our paper demonstrates the workings of the MOSOS (multi-objective symbiotic organism search) algorithm to optimize the performance and cut down on makespan and cost. We simulate MOSOS alongside the Particle Swarm Optimization Algorithm (PSO) to understand whether PSO outperforms MOSOS or whether MOSOS accomplishes similar results to PSO. The results back up MOSOS in terms of makespan but fail when compared against PSO's execution times. We find that PSO uses a lesser number of parameters and makes fewer function calls as opposed to MOSOS. Thus, we can argue that in dynamic cloud environments where time is essential, PSO may perform better as compared to MOSOS. © 2024 IEEE.

关键词： Cloud computing Evolutionary algorithms makespan optimization resource scheduling

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Alzheimer's Disease Classification Using CUDA-Accelerated DenseNet201 and VGG16 5

Alzheimer's Disease Classification Using CUDA-Accelerated De...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Munugala, Shravya Marathe, Atharva Prashant Vijayakumar, Rahul Kumaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Automated classification of Alzheimer's disease through MRI analysis presents significant computational challenges due to complex deep learning architectures and largescale image processing requirements. This paper introduces a GPU-accelerated multi-branch neural network framework that combines modified DenseNet201 and VGG16 architectures for efficient Alzheimer's disease stage classification. The proposed model employs parallel feature extraction pathways, with each branch independently processing MRI inputs before feature fusion. CUDA optimization techniques are implemented across the processing pipeline, including data loading, augmentation, and model training phases, resulting in significantly reduced computational overhead. The framework classifies brain MRI scans into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Experimental results demonstrate that the proposed hybrid architecture achieves 98% classification accuracy while substantially reducing training time through GPU acceleration. The model's performance is further enhanced through adaptive learning rate scheduling and selective layer fine-tuning. This work establishes an efficient approach for deploying complex neural architectures in clinical neuroimaging applications where computational resources and processing time are critical constraints. © 2024 IEEE.

关键词： Neurodegenerative diseases

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E-Waste Management using Deep Learning 6

E-Waste Management using Deep Learning

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, Boddu Manoj Sujitha, Kaduru Abinaya, K. School of Computer Science Engineering with Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science Engineering with Internet of Things Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331522667

Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-waste detection, simplifying its incorporation into current waste management systems. Through utilizing deep learning, our goal is to transform the way that electronic waste is categorized, from tiny parts to larger appliances, allowing for more efficient as well as environmentally friendly management techniques. When it comes to aiming for higher performance, privacy comes first. Methods of preservation guarantee the accuracy of sensitive data correlated with pictures of e-waste. By means of execution utilizing data anonymization and encryption methods, we ensure compliance with legal requirements while safeguarding against potential privacy threats and ethical issues. The YOLO v8 model achieved an average precision (Map) of 97.1% in detecting e-waste items, demonstrating its effectiveness and scalability for real-world applications. © 2025 IEEE.

关键词： Differential privacy

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API Completion Recommendation Algorithm Based on Programming Site Context 10

API Completion Recommendation Algorithm Based on Programming...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Chen, Jun Xiao, Lei Shen, Yuxiang Xiamen University of Technology School of Computer and Information Engineering Xiamen China

ISBN: (纸本)9798350362930

In the process of software development, developers often face a large number of APIs. However, due to the numerous APIs and issues such as missing or incorrect documentation, developers find it difficult to learn and organize various APIs. Nowadays, development tasks cannot be separated from feature-rich and extensive third-party libraries. To address these problems, a feasible solution is to utilize deep learning to extract features of the API's associated object types from the programming context and make API completion recommendations. Based on this, the top N recommendations can be provided to programmers. This paper proposes an API completion method that takes the object types of APIs in the programming context as features. This method first extracts the API call sequence from the programming context and groups them according to object types. Then, it uses a deep learning model to encode them individually, generates the context state of the programming scene through the object state, calculates and analyzes the APIs contained in the object type of the API to be completed, and thus obtains the predicted top N API recommendation output. Experimental results show that this method can recommend relevant APIs to developers based on the analysis of object types in the programming context, with a top-10 recommendation accuracy of up to 95.72%, which is significantly better than several compared API completion models. © 2024 IEEE.

关键词： Software design

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Intrusion Detection System Using Hybrid Model of Denoising Autoencoder and Ladder Variational Autoencoder 10

Intrusion Detection System Using Hybrid Model of Denoising A...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Navyakala, SriramVenkata Rathinam, Rajesh Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

In the realm of cyber-security, the identification and mitigation of network intrusions remain critical challenges. The deep learning based approach for intrusion detection was introduced on reconstructing network traffic dataset using 3 autoencoder architectures and classification using Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The proposed technique is assessed using 10% of the CIC-IDS-2017 dataset. Three autoencoder architectures (GDAE, LVAE, and Gaussian Noise-injected LVAE) are used to remove noise and extract relevant features. This technique successfully identifies network intrusions, with an overall performance of 99% across all assessment measures. © 2024 IEEE.

关键词： Intrusion detection

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