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An Anomaly Detection Method for Multiple Time Series Based on Similarity Measurement and Louvain Algorithm 3

An Anomaly Detection Method for Multiple Time Series Based o...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Li, Shuya Song, Wenbin Zhao, Chao Zhang, Yifeng Shen, Weiming Hai, Jing Lu, Jiawei Xie, Yingshi Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Minist Environm Protect South China Inst Environm Sci Guangzhou 510655 Peoples R China Guangdong Prov Engn Lab Solid Waste Technol & Equ Guangzhou 510330 Peoples R China

Anomaly detection is an important task for data mining Detecting anomalies in the data collected from Municipal Solid Waste (MSW) incineration process is critical for their post processing, which helps to decrease emissions of typical flue gas pollutants and reduce costs. In this paper, we propose an unsupervised multiple time series anomaly detection model based on similarity measurement and Louvain algorithm, which consists of two stages. The first stage aims to find a subset of correlated time series data. The second stage is to detect anomalies in the subset, which can identify anomalous time windows and anomalous time subseries in the anomaly windows. The proposed method is applied on the data collected from an MSW incinerator in South China The results demonstrate the effectiveness of the proposed method. In addition, four similarity measure approaches are conducted and the results show that the model performs the best when using Pearson correlation coefficient, with an F2-score of 0.92. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： municipal solid waste incineration anomaly detection machine-learning Louvain algorithm similarity measurement

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Exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing 1

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Gao, Yanxia Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function is weakened or suppressed, its form is too abstract, and it lacks elaborate design, this paper proposes a design method of course work meta-universe based on extended reality under edge computing. XR technology is used to realize the interactive immersion of the coursework meta-universe, in order to improve students’ learning enthusiasm and ability to solve abstract problems. Based on the international perspective, this paper systematically combs and analyzes the integration architecture, application scenarios and future trends of the meta-universe education application based on Edge computing (MEC). Four key processes are described: teacher-student interaction, data collection, data feedback and comprehensive evaluation. In order to promote the application of the metaverse in the field of education to provide mirror. The validity of the method is illustrated by the data. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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Classification of Rice Leaf using RCNN with Transfer learning 3

Classification of Rice Leaf using RCNN with Transfer Learnin...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Iyswaryalakshmi, K. Ramkumar, M.O. Priyanka, S. Jayakumar, D. Ifet College of Engineering Computer Science of Engineering Villupuram India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Bangladesh, as one of the 10 leading rice suppliers and users in the globe, heavily relies on grain to power its economic system and meet its food demand. Rice is one of the world's most common foods. However, rice production is impeded by a variety of crop illnesses. One of most prevalent paddy diseases is leaf disease. Recognizing leaf illnesses is time-consuming as well as difficult for farmers in remote areas due to the lack of expertise. Despite the presence of experts in some areas, illness detection is achieved by the recognition through human eye, which may result in incorrect diagnosis and requires a lot of effort. The use of an automated process can help to solve these problems and hence having an Artificial Intelligence (AI) system is mandatory. In this study, an AI approach to detect four prevalent rice leaf diseases such as, Blast, Sheath Blight, Tungro, and Brownspot is provided. The input was clear photos of affected rice leaves on a white backdrop. The datasets were getting trained on using a variety of machine learning methods after appropriate preprocessing. When applied to the test datasets, the proposed approach attained an accuracy of over 97% after 10-fold cross validation. Moreover, to preserve the rice plants' healthy and suitable growth, it is vital to detect sickness and administer the needed therapy to the injured plants. Therefore, pesticides and/or fertilizers have been recommended based on the severity of the illness detected. © 2022 IEEE.

关键词： Image classification

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Technologies for Hybrid Cloud Computing in Renewable Energy Associated with the Proposed Algorithm 3rd

Technologies for Hybrid Cloud Computing in Renewable Energy ...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Kumar, Yogendra Gupta, Hemant Department of Electrical Engineering GLA University Mathura India

ISBN: (纸本)9789819918935

Sharing the information with cloud computing allows many users to easily communicate accumulation, increasing the ability of work in collaborative scenarios and having a wide range of applications. However, maintaining data sharing security inside a group and properly disseminating data that has been leased inside a bunch context are significant to take exception. Keep a close eye on the vital deal. C-Cloud enables Internet customers to rent out excess system resources valued millions of dollars. This sort of resource sharing allows resource owners to benefit from unused resources while also giving cloud clients with a more cost-effective option than large cloud providers. Scientific computer applications, especially molecular modeling calculations, were create a wonderful information as well as computer vision methodologies to aid, expedite, and enhance. Cloud computing platforms, on the other hand, are gaining popularity in scientific computing because they provide "unlimited" processing power, easier programming and deployment techniques, and access to computer accelerators. Alternative against basic power derived from fossil fuel in the short and medium haul. However, because renewable energy sources are variable in their availability, it’s really challenging to schedule work automatically and effectively within renewable energy constraints and deadlines. This same goal of this research would be to develop a novel scheduling scheme based on neural supervised learning that applies scheduling techniques like work flow displacement as well as fog exploding automatically in a public cloud. Our main goals are to maximize the utilization of renewable energy while avoiding missed deadlines. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Chemical Fault Diagnosis Modeling Optimization Based on machine learning 1

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3rd international conference on Big Data and Security, ICBDS 2021

作者： Zong, Han Dongying Vocational Institute Shandong Dongying257091 China

ISBN: (数字)9789811908521

ISBN: (纸本)9789811908514

With the increasing complexity of modern chemical production processes, chemical operating conditions have become more and more diversified, which has improved the production efficiency of chemical companies and increased the probability of chemical production failures. With the expansion of the chemical industry’s scale of development, traditional manual diagnosis methods have been difficult to detect complex fault information. Therefore, the introduction of modern technology has become the key to chemical fault diagnosis. This paper studies chemical fault diagnosis based on machine learning, uses SVM classifier to perform chemical fault diagnosis, and then optimizes the parameters of SVM classifier through three methods of GS, GA and PSO machine learning algorithms. It is found that the diagnosis accuracy rate after GS algorithm optimization is 99.1273%, GA algorithm optimized diagnosis accuracy rate was 99.3548%, PSO algorithm optimized diagnosis accuracy rate was 99.0626%, but from the perspective of fault diagnosis running time, GS algorithm optimization running time is shorter, so GS algorithm is used to optimize SVM classification the parameters of the device more effectively improve the efficiency of diagnosis. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Detection of Alzheimer's Disease Using machine learning Models: The Impact of Pre-processing 3

Detection of Alzheimer's Disease Using Machine Learning Mode...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Al Zoubi, Rouaa Elnagar, Ashraf Dept. of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839538186

Alzheimer's disease is a brain ailment that causes memory loss, disorientation, personality changes, and a loss of independence over time. People with this disease initially experience minor memory loss and confusion. This is referred to as cognitive deterioration. However, as time passes, these symptoms get more acute. On the other hand, as there is no cure for this disease, medical professionals concentrate on detecting dementia in its early stages, this helps them determine the best treatments and allows patients to take precautionary measures before any damage happens to their brain which is irreversible. As a result, researchers focus on using machine learning models that can give significant results in identifying symptoms of dementia in the early stages. In this paper, we employed machine learning methods to identify the various phases of dementia using brain MRI scans. We used multiple traditional algorithms such as XGBoost, AdaBoost, Gradient Boosting, Decision trees, SVM, Logistic regression, Random forest, and KNearest Neighbor. We compared the performance of these algorithms with and without the use of preprocessing techniques. Our best model was the SVM model with an accuracy of 100% after applying preprocessing. In addition, we used a transfer learning model that achieves an accuracy of 90%. Thus, our work demonstrated the effect of preprocessing on enhancing the results of the traditional algorithms and transfer learning models in identifying the stages of dementia. © IET conference Proceedings 2022.

关键词： Deterioration

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Detecting Distributed DoS attacks in autonomous vehicles external environment using machine learning Techniques 3

Detecting Distributed DoS attacks in autonomous vehicles ext...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： El-Ghamry, Amir Elhoseny, Mohamed Faculty of Computers and Information Mansoura University Mansoura Egypt University of Sharjah Sharjah United Arab Emirates School of Engineering and Computer Science University of Hertfordshire Global Academic Foundation Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9781839538186

The use of autonomous vehicles rapidly growing, especially iu smart cities. Autonomous vehicles exchange information with an external network through wireless communication, and hence security and privacy challenges become a major challenge for all user of autonomous vehicles. Attackers try to exploit the vulnerabilities in the wireless network that connect autonomous vehicles these by performing different types of attacks such as denial of service attack (DDOS) and injection attacks. To mitigate the catastrophic effect of these attacks, intrusion detection process is a must. In this paper, A novel intrusion-detection method is proposed to detect distributed denial of service attack (DDOS) in the wireless network of autonomous vehicles. In this method, the wireless network intrusion detection system (WNIDS) is applied on "CICDDoS2019" dataset for training and testing our model. First the dataset is cleaned and the most important features are selected using recursive feature elimination method which decrease the number of features to only 8 features. The reduced dataset is tested by two machine learning classifiers (Random Forest (RF) and Stochastic Gradient Descent (SGD)). The evaluation results showed that both RF and SGD classifiers can be used to detect DDOS attack with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Relying only on 8 features only make the proposed method to be applicable to any IoT application such as internet of autonomous vehicles. © IET conference Proceedings 2022.

关键词： Denial-of-service attack

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Research and application of Stereo Image Fusion Based on Dome 3

Research and Application of Stereo Image Fusion Based on Dom...

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3rd international conference on Image Processing, computer Vision and machine learning, ICICML 2024

作者： Yan, Ren Jinneng Holding Group Virtual Simulation Center Datong China

ISBN: (纸本)9798350355413

The dome system is capable of providing an all-around immersive visual experience and has been widely applied in various fields such as entertainment, education, scientific research, and more. This paper investigates the implementation method of stereo image fusion technology within the dome system, and resolves issues such as image chromatic aberration and tearing through signal frame synchronization and image frame synchronization. Practical verification has demonstrated that this research can create an immersive virtual reality environment, significantly enhancing the sense of stereo and immersion of the dome system, thereby enabling a more realistic and awe-inspiring visual experience. © 2024 IEEE.

关键词： Stereo image processing

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Defect detection method for key components of transmission line based on incremental learning 3

Defect detection method for key components of transmission l...

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3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Yang, Wei Hao, Jingchang Yu, Jinhe Customer Service Center of State Grid Company Limited Tianjin China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

With the continuous development of deep learning, how to use computer vision technology to accurately locate key components of transmission lines and identify defects in complex natural backgrounds has become a common problem faced by both academia and industry. Aiming at the problems of slow detection speed and high false detection rate in the existing defect detection methods of key components of transmission lines, a method for defect detection of key components of transmission lines based on incremental learning is proposed. First, a 34-layer ResNet network is used, two 3×3 convolutional networks are concatenated to form a residual network module, and Pytorch is used to build the ResNet network. Second, when training the network, the parameters of the pre-trained model ResNet34 are used as the initialization parameters of the classification network model. Then, after the training is completed, the network model is trained with a new set of data to obtain a model with incremental learning ability. Finally, the application verification is carried out in the inspection scheme of a power company's transmission line above 110kV. The test results show that the method proposed in this paper can assist inspectors to quickly screening and locating defective bolts in high-resolution pictures to a certain extent, thereby significantly improving the work efficiency of the inspection process. © 2022 IEEE.

关键词： Electric lines

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Migration Techniques for Virtual machine Distribution Using machine learning Heuristics application in Cloud Computing

Migration Techniques for Virtual Machine Distribution Using ...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Gurpreet Singh Panesar Raman Chadha Computer Science and Engineering Chandigarh University Chandigarh India

Cloud computing has revolutionized the way IT resources are provisioned, deployed, and managed. Virtual machine (VM) migration is a crucial aspect of cloud computing that facilitates resource optimization, load balancing, and fault tolerance. In recent years, machine learning (ML) heuristics have emerged as effective tools for VM distribution, allowing dynamic and intelligent allocation of resources. This research review paper presents a comprehensive analysis of various migration techniques that leverage ML heuristics for VM distribution in cloud computing environments. The paper reviews relevant literature, compares different methodologies, and discusses their advantages, limitations, and potential applications.

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