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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Decrease of Communication Workloads Over Green Scheduling Algorithm Based Federated learning with Erlang in Big Data Using IoT 4

Decrease of Communication Workloads Over Green Scheduling Al...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Anitha, G. Jegatheesan, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300888

F-SLA(Federated service-level agreement) is a promise towards a service benefactor and a customer in which the prior undertakes to supply a service to the final under certain conditions. Time or performance are examples of specialized phrases. To abide by the F-SLA benefactor must keep the focus on the situation. Cloud processing performance should be regularly monitored. F-SLA-related research and determination-associated concerns provide a significant barrier. GS (Green Scheduling) was created to reduce power usage as much as feasible. the primary goal of this work is to progress the methods of resource scheduling to increase the power proficiency of data sectors by turning them off and leaving them idle. Federated learning (FL) recently commences as a capable concept to quarter these concerns, by resources of learning a universal model by gathering local apprises from various devices without allotment the privacy-complex data. the ever-growing quantity of Internet of things (IoT) devices are endlessly producing enormous masses of data, but the at present cloud-centric method for IoT big data analysis has raised public concerns on both data confidentiality and network cost. In the meantime, GS aims to provide a negotiated F-SLA as well as power-aware solutions, setting aside other concerns. © 2023 IEEE.

关键词： Internet of things

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7th EAI international conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023

7th EAI International Conference on Future Access Enablers o...

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7th EAI international conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023

ISBN: (纸本)9783031500503

the proceedings contain 14 papers. the special focus in this conference is on Future Access Enablers of Ubiquitous and Intelligent Infrastructures. the topics include: Possibilities of Applying Augmented Reality Elements in the Concept of Lean Management;detection of Energy Consumption Cyber Attacks on Smart Devices;an Efficient Strategy for Deploying Deception Technology;the Use of Data in BIM Technology and Effects on Profitability of Construction Projects in Slovakia, Slovenia and Croatia;a Blockchain Patient-Centric Records Framework for Older Adult Healthcare;analysis of Serious Challenges Faced by the Aviation Industry;internet of Vehicle Moving Objects Detection System for the Rural Road Networks;photogrammetry in a Virtual Environment;enhancing Circular Economy Using Expert Systems;Digital Twin Ecosystem Built with PLM Software for Smart Factory;empirical Evaluations of machine learning Effectiveness in Detecting Web application Attacks;parameters of Sustainability in the Context of Decarbonization and Circular Construction Sector.

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Unsupervised Deep learning Approaches for Anomaly Detection in IoT Data Streams 4

Unsupervised Deep Learning Approaches for Anomaly Detection ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suresh, Helina Rajini Neelaveni, R. Kumar, Bonda Kiran Arunadevi, R. Indhumathi, G. Varun, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Tamilnadu Chennai India Srm Institute of Science and Technology Department of Cse Tamilnadu Chennai India Kl School of Architecture Kl Deemed to Be University Andhrapradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

the proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. this suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. the results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification. © 2023 IEEE.

关键词： Internet of things

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Reinforcement learning-based traffic signal control 4

Reinforcement learning-based traffic signal control

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Sheng, Liu Tian Azman, Afizan Bin Khan, Navid Ali Raja Sindiramutty, Siva Singh, Suresh Verma, Sahil School of Computer Science Taylor's University Selengor Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

Traffic signal control (TSC) is an effective method for easing traffic congestion and enhancing traffic efficiency in urban areas, particularly with the growing urbanization. Conventional traffic signal control techniques are unable to adapt swiftly to the intricate and dynamic road environment, necessitating a more intelligent approach to signal control. In recent years, there has been an increasing interest in employing reinforcement learning (RL) for TSC, which has displayed considerable potential in optimizing control strategies for complex traffic conditions. this article focuses on the application of RL in TSC for research purposes. To recreate real urban traffic conditions, a traffic simulation system was used in this study. Extensive experiments and comparative studies conducted in a simulated environment have illustrated the effectiveness and superiority of reinforcement learning methods in traffic signal control. the article primarily employs Deep Q-Network (DQN) in reinforcement learning to build the model and examines the variations in traffic signal timing under different reward mechanisms. In conclusion, this study delves into traffic signal adaptation strategies, conducts experiments that integrate the virtual and real world, and effectively addresses urban traffic congestion issues. the research findings demonstrate that reinforcement learning can significantly impact traffic signal control, effectively resolving current traffic signal control challenges and advancing the development of urban road network traffic efficiency. © 2024 IEEE.

关键词： Traffic congestion

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Miniaturisation of Binary Classifiers through Sparse Neural Networks 4th

Miniaturisation of Binary Classifiers Through Sparse Neural...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Cavallaro, Lucia Serafin, Tommaso Liotta, Antonio Free University of Bozen-Bolzano Piazza Domenicani 3 Bolzano39100 Italy Radboud University Toernooiveld 212 Nijmegen6525 EC Netherlands

ISBN: (纸本)9783031812408

the continuous advances of Artificial Intelligence (AI) techniques have created new application domains for smaller and more efficient machine learning (ML) models. In the context of embedded ML, network sparsification strategies became crucial steps to fit models with severe space constraints. Hence, the aim of this research is to evaluate Neural Network (NN) sparsification and compression on embedded systems. To do so, we investigate the problem of miniaturised binary classifiers (i.e., disease detection) in the computer vision domain. We applied a constant pruning technique during the training process of three architectures: a standard Convolutional Neural Network, CNN in short, (i.e., AlexNet), a residual network (i.e., ResNet), and a densely connected CNN (i.e., DenseNet). We varied: network sparsity (up to 95%), image resolution (from 8×8 up to 32×32), and quantisation. the results indicate that the use of sparse networks has a significant impact on the accuracy of miniaturised binary classifiers. With a 70% of sparsity, it was reached an accuracy improvement of 4% in low-resolution images (i.e., 8×8) compared to the standard dense approach. Our findings suggest that sparse NNs can significantly reduce both the size and computational demands of the models while increasing their accuracy on these edge cases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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6th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2023

6th EAI International Conference on Emerging Technologies fo...

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6th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2023

ISBN: (纸本)9783031639982

the proceedings contain 11 papers. the special focus in this conference is on Emerging Technologies for Developing Countries. the topics include: Comprehensive Review of Smart Parking Occupancy Prediction Models in Nairobi City: Strengths, Weaknesses, and Research Gaps;Integration of IK, Satellite Imagery Data, Weather Data and Time Series Models in Season Behaviour Predictions. Case of Swayimane, KZN, South Africa;preface;state-of-the-Art Review on Recent Trends in Automatic Speech Recognition;predicting Malaria Outbreak Using Indigenous Knowledge and Fuzzy Cognitive Maps: A Case Study of Vhembe District in South Africa;A Systematic Review on the Use of AI-Powered Cloud Computing for Healthcare Resilience;Towards a Smart Healthcare System for Non-Communicable Diseases (NCDs) Management: A Bibliometric Analysis;a machine learning Approach to Mental Disorder Prediction: Handling the Missing Data Challenge;adopting Blockchain for Enhancing Data Security and Privacy in Service-Based Digital Platforms: A Case Study of a Distributed application (Dapp) Global Mission Services.

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COMPARATIVE ANALYSIS OF DIFFERENT machine learning MODELS FOR HUMAN ACTIVITY RECOGNITION 50

COMPARATIVE ANALYSIS OF DIFFERENT MACHINE LEARNING MODELS FO...

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50th international conference on computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Sawalha, G. Abuouelezz, W. Alhammadi, E. Alteneiji, A. Poon, K. Ali, N. Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781713886952

the application of machine learning (ML) algorithms to Human Activity Recognition (HAR) is a rapidly growing field of research with potential applications in elderly care. Recently, Deep learning (DL) approaches have emerged as powerful alternatives, exhibiting significantly better recognition accuracy and superior performance. As a result, deep learning techniques have gained increasing attention and interest in the field of HAR, as they offer a promising way forward for advancing our understanding of human activity patterns and behaviors. this paper presents an in-depth comparative analysis of different machine learning models for the purpose of identifying the best model for an AI Elderly Activity Recognition System. through this study, we analyze the performance of different models such as Neural Network (NN), 1D Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) network to identify activities such as walking, standing/idle positions, running, falls, Parkinson's and antalgic gait using both raw and preprocessed data. © 2023 computers and Industrial Engineering. All rights reserved.

关键词： Long short-term memory

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Overview of Image Classification Methods Based on Deep learning 4

Overview of Image Classification Methods Based on Deep Learn...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Huang, Jinke Yang, Tiancai Jia, Zhenze Air Force Engineering University Equipment Management and UAV Engineering College Xi'an710038 China Chinese Unit 93525 of the People's Liberation Army Shigatse857000 China Air Force Communication NCO School Dalian116600 China

ISBN: (纸本)9798350342710

Image classification is a method of classifying different categories of objects based on the different characteristics of objects in an image. Most of the traditional image classification algorithms use shallow structures, which have obvious deficiencies in performance and generalization ability. the image classification algorithm based on convolutional neural network has good performance indicators and can solve the image classification problem well. © 2023 IEEE.

关键词： Image classification

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application of an Improved YOLOv7 Model in Disease and Pest Detection for Lychee Trees 4

Application of an Improved YOLOv7 Model in Disease and Pest ...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Ma, Cuihua Li, Zhenwan Long, Haixia Hainan Normal University College of Information Science Technology Haikou China

ISBN: (纸本)9798350373233

Disease and pest detection grounded in the principles of deep neural network architectures has become a prominent research direction in the field of agricultural technology, attracting widespread attention and emphasis from researchers globally in recent years. this paper investigates the application of an improved YOLOv7 algorithm in the detection of diseases and pests in lychee trees. It introduces a new Efficient Complete Intersection over Union (ECIoU) regression loss function, which, compared to the original CIoU loss function, enhances detection accuracy and convergence speed. By comparing with six other advanced models, the improved algorithm introduced in this manuscript exhibits superior performance compared to existing leading-edge models, in terms of mean average precision (mAP) curves, loss curves, and Precision-Recall curves. Finally, the improved model delineated in this document holds the potential to aid lychee growers in effectively managing pests and reducing the use of pesticides, providing practical benefits for sustainable agriculture. © 2023 IEEE.

关键词： Deep neural networks

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machine learning-Based Detection of Cardiovascular Disease using Classification and Feature Selection 12

Machine Learning-Based Detection of Cardiovascular Disease u...

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12th IEEE international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Jain, Anuj Kumar Kumar, Kamal Tiwari, Raj Gaurang Jain, Nitin Gautam, Vinay Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India

ISBN: (纸本)9781665462617

the application of ameliorative datasets to foretell chronic diseases has grasped considerable attention amongst data scientists all around the globe. the study is based on the Cardiovascular disease dataset. Cardiovascular disease is a broad term concerning disorders of the heart and blood vessels and is ranked to be the topmost cause of demise worldwide. Various feature selection techniques have been applied to each of the given classification algorithms and the features which give the best accuracy are then utilized for that particular algorithm. © 2023 IEEE.

关键词： Neural networks

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