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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20250 条记录，以下是481-490 订阅

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Enhancing Real Estate Market Predictions: A Machine learning Approach to House Valuation 3

Enhancing Real Estate Market Predictions: A Machine Learning...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jha, Pradeep Mathur, Manju Purohit, Abhay Joshi, Apoorva Johari, Anantika Mathur, Suhani Department of CSE Global Institute of Technology Rajasthan Jaipur India Department of IT Global Institute of Technology Rajasthan Jaipur India

ISBN: (纸本)9798331527549

Machine learning (ML) has emerged as a versatile technology, transforming various domains such as speech recognition, medical diagnostics, and real estate analytics. This research addresses the critical challenge of accurately predicting house prices in dynamic and volatile real estate markets. Traditional valuation methods often fail to capture complex relationships between features, leading to suboptimal predictions. To mitigate these limitations, this study proposes a predictive model leveraging multiple ML algorithms, including Linear Regression, Decision Tree Regression, K-Means Clustering, and Random Forest Regression. Among these, Random Forest Regression demonstrated superior accuracy, achieving a root mean square error (RMSE) of 2.913. The findings indicate that integrating advanced ML techniques can streamline the home-buying process by enhancing price prediction accuracy, reducing dependency on intermediaries, and enabling informed decision-making for stakeholders. This study provides a robust framework for applying ML in real estate and highlights opportunities for further advancements in predictive modeling. © 2025 IEEE.

关键词： Houses

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Performance optimization of learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC Systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC Systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling learning Tree algorithms of Machine learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 IEEE.

关键词： Parallel processing systems

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Advanced Adaptive Traffic Management System for Urban Environments 3

Advanced Adaptive Traffic Management System for Urban Enviro...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sai Srujan Reddy, P. Naveen Raj, B.U. Jose, Nikhil Tom Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Viswa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Urban traffic congestion is still a major problem that causes delays, collisions, and wasteful use of resources. Conventional fixed-schedule systems, which frequently exacerbate problems at junctions, U-turns, and pedestrian crossings, are insufficient for dynamic traffic situations. The Smart Dynamic Traffic Management System described in this work makes use of servo motors, proximity sensors and ultrasonic sensors, Arduino microcontrollers, and AI-powered cameras. To improve safety and streamline traffic flow, the system prioritizes emergency vehicles, controls U-turns, dynamically modifies traffic signals based on real-time data, and monitors road conditions. Scalability and adaptability for a range of urban scenarios are ensured by the integration of IoT technology and intelligent intercommunication between crossings. A visualization tool based on Pygame was used to simulate and assess the suggested solution. The findings showed that traffic flow had significantly improved, with an average increase in the exit-to-entry ratio of 30.71% for the three roadways. More specifically, traffic on Road A decreased by 15.28%, on Road B decreased by 31.25% and on Road C decreased by 45.61%. The outcomes confirm just how well the system works for reducing traffic and giving priorities to critical traffic situations, such as emergency response and turning. This system uniquely integrates real-time adaptive traffic control algorithms with IoT and machine learning to dynamically prioritize traffic flow and emergency vehicles, overcoming the limitations of existing static systems. © 2025 IEEE.

关键词： Traffic congestion

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Construction of intelligent College Student Management Evaluation System Based on Machine learning Algorithm 3

Construction of Intelligent College Student Management Evalu...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Lv, Junling Student Affairs office Quanzhou preschool education College Quanzhou Fujian362600 China

ISBN: (纸本)9798350376173

With the in-depth development of blockchain, Internet, 5G and other technologies, major universities in China have also paid attention to the application of student management systems. Relevant researchers have also made in-depth investigations in the theory and practical application of information management system. In this paper, the intelligent student management system built by machine learning algorithm is used to collect and analyze the integrated data of teaching classroom information and student information of various faculties and departments on campus, and it is verified that the student management system built by machine learning algorithm has a remarkable effect on the improvement of teaching effect. This paper aims at the design and research of the student management evaluation system, and provides strategies for the creation and improvement of the student management evaluation system with the help of SVM (support vector machines) learning model. The study compared the results of 8 experts in related fields and the use of the evaluation system in S university, which proved the stability and input of the student management system. © 2024 IEEE.

关键词： Adversarial machine learning

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Preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running 25th

Preliminary Investigation on Machine Learning and Deep Learn...

引用

25th international conference on intelligent data engineering and automated learning

作者： Jaiswal, Pranay Kaushik, Abhishek Lawless, Fiona Malaquias, Tiago McCaffery, Fergal Dundalk Inst Technol Dundalk Ireland STATSports Newry North Ireland

ISBN: (纸本)9783031777301;9783031777318

The ability to detect, define, and classify Change of Direction (COD) movements during running plays a crucial role in sports science, as it has been widely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine learning (ML) and Deep learning (DL) models for the classification of COD using accelerometers and gyroscope sensor data, and speed data were calculated from the Global Positioning System (GPS) sensor data. We hypothesized that DL algorithms classify COD better than ML classification algorithms. Comparative analysis showed that the best-performing DL and ML models showed similar behavior. Similarly, the statistical analysis observed no significant difference. This emphasized the importance of accurate model selection.

关键词： Change of Direction Machine learning Deep learning Sports Running Classification

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Transforming Healthcare with Federated learning-based Artficial Intelligence: Concepts, Classifications, and Challenges 3

Transforming Healthcare with Federated Learning-based Artfic...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Uma Maheswari, G. Gnana Jeslin, J. Rajasuguna, M. Amutha, S. RMK College of Engineering and Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Pudhuvayal Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The realm of intelligent healthcare has seen a surge in discussions about transformative technologies, with a focus on federated learning (FL), artificial intelligence (AI), and explainable AI (XAI). Traditionally, healthcare data has been centralized, with a limited number of entities sharing raw data across the industry. However, this approach poses significant risks and challenges. Introducing AI into the healthcare ecosystem opens the door to a multitude of agent collaborators, each capable of working alongside their chosen host. The decentralized nature of FL is a noteworthy aspect, as it ensures that raw data remains within its source while enabling model-based communication within the system. The fusion of FL, AI, and XAI offers the potential to address some of the system's limitations and challenges. This article delves deep into the ways in which FL is revolutionizing healthcare facilities through AI integration. We begin by examining the current landscape of futuristic technologies, including FL, AI, XAI, and their relationship with the healthcare sector. We categorize FL in conjunction with AI, exploring a wide array of applications in healthcare IT. Key issues within the healthcare domain, such as privacy, security, stability, and reliability, are addressed. Furthermore, we showcase practical use cases of FL and AI in medical settings. We conclude by shedding light on the various future directions and avenues of research in the management of healthcare using FL-based AI. In this article, we offer insights into the development and implementation of the Contribution-Aware Federated learning (CAreFL) system for intelligent healthcare. This system not only optimizes the FL model aggregation process based on evaluation results but also assesses the contributions of FL participants in a fair and transparent manner, ensuring efficiency and confidentiality. © 2024 IEEE.

关键词： Health care

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Research on AI composition based on deep learning techniques 3

Research on AI composition based on deep learning techniques

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Wang, Wenchu Shandong University School of Mechanical Electrical & Information Engineering Weihai China

ISBN: (纸本)9798350341546

With the recent wave of deep learning networks, its application in generating creative content, including music, grows wider. The research of automatic music generation, as a way of understanding human's artful expression, and a promising field of high commercial value, has been progressed rapidly. Multiple networks have been used for music generation. Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN) are most frequently used because of their advantages in processing time sequences and generating new creations. The results of diverse generative algorithms for intelligent composition vary greatly. This article analyzes some cases of RNN-based and GAN-based music generation methods, and discusses how the application of multiple networks, such as C-RNN-GAN, integrates the advantages of different networks, showing a trend in the future progress of music generation. It also discusses the necessity of building precise methods for evaluating generated music, and describes some cases where deep learning method has helped in this realm. © 2023 IEEE.

关键词： deep learning GAN Music generation RNN

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Sentiment Detection Using X-NLP: Harnessing Advanced NLP Techniques 3

Sentiment Detection Using X-NLP: Harnessing Advanced NLP Tec...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vaneeta, M. Pandey, Aryan Ravi, A. Mahammadinthiyaz Ashwath Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9798331515911

Sentiment analysis is a useful way to gain insights from text, helping organizations make informed decisions based on the emotions in the information they collect. This project aims to create a sentiment analysis system using Explainable Natural Language Processing (X-NLP) to clarify how sentiment predictions are made. The system offers users a look into how certain words and features affect sentiment classification. By using explainable AI techniques, it ensures that users not only get accurate sentiment labels but also understand why these labels were assigned, building trust and ease of use. The models are trained on the Sp1786 database and a larger data-set of 1.6 million tweets, applying both statistical methods (SVM) and deep learning techniques (LSTM). The SVM model achieved a precision of 0.6753, an AUC of 0.8432, and an F1 score of 0.6769, making it effective for real-world use. © 2025 IEEE.

关键词： Deep learning

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SmartLeafNet: An automated Deep learning based Efficient Plant Disease Detection using RNN-GRU Model 3

SmartLeafNet: An Automated Deep Learning based Efficient Pla...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prakash, M. Gnana Sungeetha, D. Saveetha University Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798331509675

Plant leaf disease has consequences for health and productivity maintenance, hence making early interventions on them possible, and preventing loss caused by disease. They minimize the usage of pesticides for agricultural benefits, conserve other valuable resources, and provide much better yield for betterment in agriculture. Most lately, during this enhanced tendency of technological reliance, different deep learning techniques find prominent support in automating this type of plant disease detection process. This paper presents a new approach in detecting diseases in a plant leaf using deep learning, by implementing the structure of the Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU). This methodology uses a PlantVillage dataset for the extraction of images covering a wide range of aspects of plant diseases. Our approach has done extensive preprocessing in the form of resizing, normalizing, and augmenting the data so that real-world diverse plant images could be fed into the model. These models were trained based on the robust strategy for splitting up the training-validation data and further evaluated on separate testing for assessing their generalization capability. Accuracy, precision, recall, F1-score, time, and AUC are used for the performance analysis of the proposed model. It considers deep learning techniques, more precisely the RNN-GRU method, a promising outlook for changing plant disease detection and agricultural management. The model meets scalability and efficiency demands that will be helpful to farmers in the early detection of diseases by applying much more sustainable and efficient agriculture. © 2025 IEEE.

关键词： Recurrent neural networks

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