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检索条件"任意字段=International Conference on Advanced Computer Science Applications and Technologies"

共 55374 条记录，以下是81-90 订阅

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international conference on Information science and Communications technologies: applications, Trends and Opportunities, ICISCT 2019

International Conference on Information Science and Communic...

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2019 international conference on Information science and Communications technologies, ICISCT 2019

ISBN: (纸本)9781728125640

The proceedings contain 205 papers. The topics discussed include: the importance of mobile applications in the use of standard water measurements;virtual fence moving algorithm for circulated grazing;mathematical simulation and the methods to assess the strength of earth dams;innovative approach of distance learning in the form of online courses;electronic invoice system database architecture issues and the possible solutions;development of creativity of learners in the courses of advanced training and retraining of pedagogical staff in the process of studying the course 'innovative educational technology';and the research of reliability indicators transport systems multi -service network based fuzzy set theory.

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Machine Learning for Wireless Network Optimization: Algorithms and applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Srinivas, Madhira Surya, S. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine Learning Network Elasticity Predictive Models Resource Allocation Scalability Signal Strength Wireless Networks

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AI-Driven Pedagogical Approaches in science Classrooms: Enhancing Digital Literacy and Learning Outcomes 1

AI-Driven Pedagogical Approaches in Science Classrooms: Enha...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Agrawal, Kajal Shahin, Asma B. Kumar, Sushil Shilpa Thamizharasan, S. Babu, T. Rajan Nath School of Business & Technology BCA MGMU Maharashtra Aurangabad India Maharashtra Institute of Technology Department of Emerging Sciences and Technology Maharashtra Aurangabad India Bhagat Phool Singh Mahila Vishwavidyalaya Department of Education Haryana Sonepat India Rajiv Gandhi College of Engineering and Technology Department of Computer Applications Puducherry India Rajiv Gandhi College of Engineering and Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798331518578

A high quality education in science classroom is crucial to foster equitable societal progress. However significant disparities for undeserved and marginalised community. Solutions using artificial intelligence bridges the cap through enhanced teaching methodologies to promote fair access to learning in science classroom. The present study explores the AI powered pedagogical approaches in science education. It examines the AI technologies including computer vision, natural language processing and machine learning techniques utilised for personalised learning delivering feedback at real time and boost a student engagement across various background. AI driven approaches can Taylor instruction to individual paces and learning style that ensure personalised support for every student. Platforms also offer additional assistance to learners facing challenges fostering equity and inclusivity in science education. Moreover, AI driven assessment tools provide teachers with valuable insights in student performance helping them to pinpoint the areas that requires improvement and targeted interventions. It enables collaborative learning environment that allows the student to interact regardless of geographical constraints thereby expanding access to science education with enhanced digital literacy and learning outcomes. © 2025 IEEE.

关键词： Federated learning

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Employee Retention Analysis: Insights and Predictive Modeling With Machine Learning Techniques 1

Employee Retention Analysis: Insights and Predictive Modelin...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Prajapati, Jyoti Vetrivendan, L. Mishra, Kanderp Narayan Radhakrishnan, R. Ranjith, M. School of Computer Science and Engineering Galgotias University Greater Noida India Alliance University India Sharda University India School of Computer Science Galgotias University Greater Noida India

ISBN: (纸本)9798331518578

Brain tumors are one of the most serious and potentially fatal diseases in the world, to have any hope of being successfully treated it is imperative that they be discovered early. Conventional diagnostic methods such as MRI and CT images often rely on manual interpretation;this can take a long time and is prone to human error. To address this situation, we present a deep learning-based approach which uses a Deep Convolutional Neural Network for automatic detection of brain tumors. The resultant model takes advantage of the powerful feature extraction abilities of CNNs in order to divide brain images into tumor and non-tumor categories with high precision. The dataset consists of MRI scans and has been preprocessed to enhance image quality while removing noise. Employing advanced techniques such as data augmentation, transfer learning and hyperparameter tuning, the structure of this CNN network for brain tumor detection is optimized. Experimental results prove that the combination is superior to traditional machine learning methods. In medical imaging, this research shows that the potential for deep learning is enormous and will help pave the way for easier, accurate brain tumor detection systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Exploring blockchain solutions: Integrating cryptocurrency with IoT for enhanced security

Exploring blockchain solutions: Integrating cryptocurrency w...

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2025 international conference on Pervasive Computational technologies, ICPCT 2025

作者： Shravan, M. Kakad, Shital Mulani, Umar M Sharma, Geeti Tiwari, Mohit Mary Shiba, C. Sr University School of Business Telangana Warangal India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Maharashtra Pune India Kj College of Engineering and Management Research Pune India India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India R.M.K Engineering College Department of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798331508685

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem. © 2025 IEEE.

关键词： Blockchain Cryptocurrency Internet of Things privacy Security

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The interactive impact and integration of AI, big data, and cloud computing combined with photonics technology 2

The interactive impact and integration of AI, big data, and ...

引用

2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Zeng, Changxi Xu, Yixuan Yang, Lei School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

This article aims to analyze the interactive impact and integration development trend of three technologies: artificial intelligence, big data, and cloud computing. Overview of its basic concepts and technical characteristics, analysis of its interconnections and impacts, the development of AI technology relies on the support of big data, and cloud computing provides powerful infrastructure and computing capabilities for the application of AI and big *** optical and photonics technologies have also played an important role in this fusion process, particularly in terms of data security and privacy *** analyzing the current integration status of these technologies in practical applications, such as intelligent transportation management, personalized medical and health services, further discussions were held on the challenges encountered in the process of technology integration, such as data security and privacy protection, and future development directions were also discussed. The analysis results indicate that the deep integration of AI, big data, and cloud computing will bring more innovation opportunities and development momentum to various industries, while also emphasizing the importance of strengthening interdisciplinary cooperation and improving laws and regulations to ensure that technological progress is balanced with social ethics and public interests. © 2025 SPIE.

关键词： Electronic health record

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

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

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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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Machine Learning-Enhanced Voice Interaction: Revolutionizing Windows Desktop applications 1

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Vora, Raj Chakrawarti, Rajesh Kumar Raghuwanshi, Namrata Sushila Devi Bansal College of Technology Computer Science and Engineering Indore India

ISBN: (纸本)9798331518578

The use of voice interaction is changing the way human being communicate with devices as it offers a simple and effective way of interaction. To implement voice interaction within desktop applications and make it more effective as it is integrated with the operating system, there is a various need models, to particularly improve Convolutional its neural quality. Networks This (CNNs), paper Recurrent considers Neural how Networks (RNNs), and transformers, can enhance the effectiveness of voice interaction in the context of Windows desktop platforms. The research focuses on several major issues: enhancing the speech recognition performance in the presence of background noise, handling the differences in user's accent and speech manner, and real-time operation with reduced delay. It also examines issues of privacy relating to handling of sensitive voice information and ways of reducing these vulnerabilities. Using and enhanced user machine intent learning and results operating encompass connecting detection strategies, the indicate system the in this user's and that voice possibility developing voice research experience provides the interaction the of recognition in is an application with next increasing systems. Different designed better of other these generation the practical user to machine current models of multilingual applications experience. Improve learning and are desktop capabilities, including the enhances future assessed apps. Implementing productivity current The AI from the tools, limited paper effectiveness solutions, the ability accessibility capabilities also and thus perspective to features of provides quality stating of recognize and contextual recommendations of that performance, users' intelligent understanding for voice flexibility emotions, assistants. Future control is and the research, commands the which on key them to Windows. © 2025 IEEE.

关键词： Speech recognition

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Investigating Flavors of RAG for applications in College Chatbots 17

Investigating Flavors of RAG for Applications in College Cha...

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17th international conference on computer Supported Education, CSEDU 2025

作者： Sarmiento, Christian Lauría, Eitel J.M. School of Computer Science and Mathematics Marist University PoughkeepsieNY12601 United States

ISBN: (纸本)9789897587467

Retrieval Augmented Generation (RAG) has become a growing area of interest in machine learning (ML) and large language models (LLM) for its ability to improve reasoning by grounding responses in relevant contexts. This study analyzes two RAG architectures, RAG's original design and Corrective RAG. Through a detailed examination of these architectures, their components, and performance, this work underscores the need for robust metrics when assessing RAG architectures and highlights the importance of good quality context documents in building systems that can mitigate LLM limitations, providing valuable insight for academic institutions to design efficient and accurate question-answering systems tailored to institutional needs. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Chatbots

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