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Enhanced Real-Time Object Detection on Customized Dataset with YOLOv9 Architecture

Enhanced Real-Time Object Detection on Customized Dataset wi...

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Adithiyan, P.V. Kumar, M Ranjith Rahul, K. Nikhil, V. Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engg. Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379525

YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object recognition approach based on the YOLOv9 architecture, trained on a football-specific custom dataset. The 255 annotated images in the dataset are separated into training, validation, and test sets with an 80-15-5 split ratio;there are 204 images in the training set, 38 in the validation set, and 13 in the test set. Our gelan-c model achieves remarkable detection performance with 467 layers, 25,414,044 parameters, and 102.5 GFLOPs. Upon validation, Gelan-c shows an aggregate mAP50 of 75.9%, with mAPs ranging from 32.2% (ball) to 93.7% (goalkeeper) depending on the specified class. The impressive aspect of the Gelan-C is its 31.6 ms picture processing rate, which assures real-time performance. The methodology covers environmental setup, preparation of data, YOLOv9 architecture introduction, and experimental evaluation to demonstrate the model's football-related recognition capabilities. The suggested approach has potential applications in sports event monitoring and evaluation due to its reliability and efficacy in detecting football players, referees, and the ball. © 2024 IEEE.

关键词： Image annotation

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Bijective Soft Set-Based Decision Model for Classification Rule Generation

Bijective Soft Set-Based Decision Model for Classification R...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Deepa, N. Bhuvaneswari, S. Prabadevi, B. Jessintha, J. Persis Department of Computer Science Pondicherry University UT Karaikal Puducherry India School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350325188

Data dissemination is a significant problem in the data mining industry. This is the most difficult task to complete in every subject, including those involving the humanities, social sciences, and the sciences. Tools are needed to efficiently store, retrieve, and analyze the enormous amount of data and information that have been gathered for various objectives. The development of decision models can benefit from a variety of artificial intelligence techniques, such as decision trees, neural networks, rough sets, fuzzy sets and soft sets. One of the cutting-edge techniques is called bijective soft set theory, and it is used to build data reduction, handle information reliance, and deal with data uncertainty. The soft set approach has been used to construct a general model in this research for producing classification rules that can be used for any set of inputs.t. © 2023 IEEE.

关键词： alternatives classification rules Data Mining Decision problem Soft set

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Architecture for Evaluating Customer Retention Strategies 1

Architecture for Evaluating Customer Retention Strategies

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Abdurrahman Agarwal, Chitrang Lokesh Kumar, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9781607685395

The modernizing world with increasing technological developments and the everyday generation of the new innovative strategies has created a competitive era. The advancement of technology, implementation of advanced solutions, and strategies attract many customers, which results in changing market and business. With this Digital Darwinism, organizations face a major pitfall known as Customer Attrition. Addressing this problem is the need of thehour for every organization aiming to increase customer loyalty and its revenue. Understanding the business ecosystem and coming up with a strategy that strengthens the roots of the firm and increasing their customer lifetime is an approach, but the question arises that how good is your strategy when it comes to the comparison with the competitors in the market. The architecture proposed in this paper evaluates business strategy using customer segmentation and customer lifetime value prediction, churn prediction, uplift modelling, and survival analysis. © The Electrochemical Society

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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm

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SN computer science 2023年第6期4卷 718页

作者： Vijayan, R. Mareeswari, V. Pople, Vedant School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore 632014 India Fulton Schools of Engineering Arizona State University Tempe AZ United States

computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places. Graphical Abstract: [Figure not available: see

关键词： computer vision Deep learning Object classification Object detection Social monitoring YOLO

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Framework for Automated Attendance & Attention Tracking to Address Learning Gaps Due to Pandemic 11

Framework for Automated Attendance & Attention Tracking to A...

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11th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2022

作者： Pendyala, Pranavi Reddi, Sriya I Rajasekar, Arjun Falahuddin Quadri, Syed Jaisankar, N. Loganathan, Ramesh Hyderabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665491174

The sudden shift to online education due to the pandemic has led to widespread learning gaps in the student population, increasing the need for individualistic attention to student learning as we transition back to physical classrooms. One can take advantage of the advancements in technology to help in this process by developing tools that can assist teachers decrease the amount of administrative workload and improve their ability to track the learning and understanding of every student in the classroom. The current work proposes a framework for accurate, low-cost, and scalable computer visionbased attendance and attention tracking in physical classroom environment to tackle this problem. © 2022 IEEE.

关键词： Students

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, AI, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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Towards Improving the Packet Delivery Ratio in Mobile Ad Hoc Networks Using DSDV Protocol and Cuckoo Search Algorithm

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International Virtual Conference on Industry 4.0, IVCI4.0 2021

作者： Devi, T. Ganesan, R. Deepa, N. Jaisharma, K. Department of Computer Science and Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811999888

A wireless ad hoc network which is infrastructure less where the communication of the mobile nodes is done through wireless mode without any base station is termed MANET (Mobile Ad hoc Network). One of the main issues of MANETs is routing due to the free movement of nodes and changing topology. As a result of this, there is a change in topology due to failure in the routes thereby decreasing the network performance. Forwarding of data between the transmitter and the receiver is the main aim of routing. Node connection can be made at any time and data can be forwarded to the specifically selected nodes. There is a need for developing an effective routing protocol to find the shortest path for packet forwarding. The proposed work employs cuckoo search algorithm along with destination sequenced distance vector to identify routing paths that are shorter to forward the data packets. In order to perform validation, the proposed work is compared with ad hoc on-demand distance vector routing protocol to show the efficacy of the system. The packet delivery ratio of the proposed system is high which makes the DSDV protocol to be suitable for various communication. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile ad hoc networks

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ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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