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检索条件"机构=Dept. of Information Engineering and Computer Science and Mathematics"

共 8635 条记录，以下是151-160 订阅

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure Automated Inventory Management system using Abstracted System Design Microservice Architecture 14

Secure Automated Inventory Management system using Abstracte...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Darshan, M. Raswanth, S.R. Chaitanya, V.S.S.K. Kalavalapalli, Venkata Sridhar Sai Kumar, Priyanka Srivastava, Gautam Amrita School of Computing Dept. of Computer Science and Engineering Coimbatore India University of Texas Dept. of Computer Science San Antonio United States Brandon University Dept. of Mathematics and Computer Science Brandon Canada

ISBN: (纸本)9798350335095

Managing inventory has forever been a difficult problem for e-commerce sellers. Even today manual labor is used for the major part of the job. Without much automation in this industry growth has been stagnant because of the same *** proposed system works on a microservice-based architecture that enables automated inventory management with the help of a social-media messaging bot in an abstract manner. The bot comprises an integrated hybrid recommender system at the customer's end for better recommendations based on image and textual data. The entire system is deployed on the cloud following a serverless architecture for resilient service. The proposed solution plays a key role in taking research forward in the direction of micro-service-based system design involving real-world data. The results of the experiment prove to be beneficial for customers and retailers since they are now connected using the micro-service. © 2023 IEEE.

关键词： Systems analysis

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Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

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Video Classification Using CNN and Ensemble Learning 9

Video Classification Using CNN and Ensemble Learning

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Jaswanth, Kunisetty Murali, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350397376

Video classification is the task of producing an index or a label that is relevant to the given video. For this classification input can be in frames, heatmaps of respective videos, etc., depending on the application. Video classification is more than just simple image classification, it can be done using the concept of Convolutional Neural Network (CNN). Based on the application, and dataset limitations there are many extensions to this method such as CNN- Long Short-Term Memory (CNN-LSTM), Convolutional 3D network, Pose detection and LSTM, etc. In this project, we used the UCF11 (YouTube Action) dataset and classified them based on the action. A CNN model has been developed and trained with the dataset and the tested results are improved with a better approach called Ensemble Learning. Using this approach, the same CNN model having the same architecture gave better prediction results. © 2023 IEEE.

关键词： Convolutional neural networks

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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Framework for Optimizing Distribution of Deep Neural Network Inference Tasks across Computing Networks

Framework for Optimizing Distribution of Deep Neural Network...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Preetha Evangeline, D. Sahu, Atma R. Anandhakumar, P. Siva Subramaniya Nadar College of Engineering Dept. of Information Technology Tamil Nadu Kalavakkam India Coppin State University Dept. of Mathematics and Computer Science BaltimoreMD United States Madras Institute of Technology Anna University Dept. Of Information Technology Tamil Nadu India

ISBN: (纸本)9798350364910

Ubiquitous artificial intelligence (AI) is considered one of the key services in 6G systems. AI services typically rely on deep neural networks (DNN) requiring heavy computation. Hence, to support ubiquitous AI, it is crucial to provide a solution for offloading or distributing computational burden due to DNN, especially at end devices with limited resources. This paper proposes an optimization framework for assigning the tasks of DNN layer computations to computing resources in the network, to reduce the inference latency. To this end, we propose a layered graph model with which simple conventional routing jointly solves the problem of selecting nodes for computation and paths for data transfer between nodes. The proposed framework is applied to derive algorithms for minimizing the end-to-end inference latency. The numerical evaluations show that our new formulation can find a solution for DNN inference job distribution much faster than the existing formulation and that our algorithms can select computing nodes and data paths adaptively to the computational attributes of given DNN inference jobs. © 2024 IEEE.

关键词： Computation offloading

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Platform Construction to Protect Portraits of Others

Platform Construction to Protect Portraits of Others

引用

2023 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2023

作者： Cheng, Hui-Jun Zeng, Sheng-Fu Lin, Chun-Yuan Minth University of Science and Technology Dept. of Artificial Intelligence Hsinchu County Taiwan Chang Gung University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan Asia University Dept. of Computer Science and Information Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350302561

As the times change, online video activities and video sharing have become commonplace. However, publicly sharing the images of others can involve risks of violating privacy and portrait rights, especially in today's era of widespread high-resolution cameras that can capture detailed facial features. Therefore, protecting the portrait rights of others has become crucial. In order to balance the protection of portrait and the preservation of image quality, we propose a deep learning-based person tracking system. This system can record and track the time and location of people in videos or video streams. Through image synthesis techniques, we can either place the necessary people in virtual environments or hide people in real videos. In this way, we ensure the portrait and privacy of others while maintaining the authenticity and quality of the image. This approach is not only applicable for individuals protecting their own images but can also be applied to respect the privacy of others, making online video activities safer and more respectful of people rights. © 2023 IEEE.

关键词： Deep learning

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep Learning LSTM Machine learning multi-label classification

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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