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Virtual machine Allocation in Cloud Computing Using Reinforcement learning: DDPG 4

Virtual Machine Allocation in Cloud Computing Using Reinforc...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

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

ISBN: (纸本)9798350393248

Virtualization and cloud computing can be used together in new ways to help cloud-based data centres (DCs) make better use of their limited resources and use less power. A big problem with cloud data centres is that they use a lot of power. this is bad for the environment and raises the costs for people who use the cloud. VM consolidation is something that some people think could help solve this problem. Moving VMs to different real hosts is part of this method. this spreads them out and makes them use less power. Using a method called Deep Deterministic Policy Gradient (DDPG) for reinforcement learning to group virtual machines (VMs) together is a new way to make cloud data centres work better. When you use DDPG to group virtual machines together in a cloud data centre, you save money and time. Common methods like the Genetic Algorithm, Particle Swarm Optimisation, and the Ant Colony System are not the same as the work that is being offered. this method and the VM transfer work better in simulations than popular ones like the genetic algorithm (GA), particle swarm optimisation (PSO), and adaptive control system (ACS). If you do things the way that is suggested, you will use 72% less material and 68 KWh of energy. the tests also show that the proposed way cuts down on the number of running physical machines (PMs) by a large amount. © 2023 IEEE.

关键词： Virtual machine

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machine learning-Based application for Predicting 5G/B5G Service 13

Machine Learning-Based Application for Predicting 5G/B5G Ser...

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13th international conference on Cloud Computing, Data Science and Engineering, Confluence 2023

作者： Khan, Md. Tabrej Adholiya, Ashish Pacific Academy of Higher Education and Research University Faculty of Computer Science Rajasthan Udaipur India

ISBN: (纸本)9781665462631

In a 5G/B5G (Beyond 5G) network, Service level agreement (SLA), network efficiency, and service management are key issues for a network provider. A user equipment requests services (UE) are based on a key performance indicator (KPI) and key quality indicator (KQI) while selecting the network slice. Earlier predicting the benefits of 5G/B5G will be helpful for the service provider to improve the quality of service (QoS). therefore, we aim to build a data-driven predictive application to screen the multiple services of 5G/B5G. In this context, multi-classification supervised machine learning models are applied to the publicly available dataset to classify the services of 5G/B5G. We performed different simulations with the ML algorithm, first with all features (KPI and KQI parameters), second with alone KPI, and finally with features selection methods. the multiclass Decision jungle (MDJ) model shows better performance in terms of accuracy of 90%, precision, and recall. Moreover, an application programming interface (API) of the MDJ model implemented and deployed in https://***/ and source code are available at https://***/tabrejmsc/5GServicePredictor. © 2023 IEEE.

关键词： Quality of service

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Logic-Based machine learning with Reproducible Decision Model Using the Tsetlin machine 12

Logic-Based Machine Learning with Reproducible Decision Mode...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Tarasyuk, Olga Gorbenko, Anatoliy Rahman, Tousif Shafik, Rishad Yakovlev, Alex Odesa Technological University Step Ukraine School of Engineering Newcastle University Newcastle upon Tyne United Kingdom Leeds Beckett University Leeds United Kingdom National Aerospace University 'KhAI' Ukraine

ISBN: (纸本)9798350358056

Tsetlin machine (TM) is a recent automaton-based algorithm for reinforcement learning. It has demonstrated competitive accuracy on many popular benchmarks while providing a natural interpretability. Due to its logically underpinning it is amenable to hardware implementation with faster performance and higher energy efficiency than conventional Artificial Neural Networks (ANNs). this paper provides an overview of Tsetlin machine architecture and its hyper-parameters as compared to ANN. Furthermore, it gives practical examples of TM application for patterns recognition using MNIST dataset as a case study. In this work we also prove reproducibility of TM learning process to confirm its trustworthiness and convergence in the light of the stochastic nature of TAs reinforcement. © 2023 IEEE.

关键词： architecture hyperparameters learning automaton learning reproducibility logic-based artificial intelligence machine learning tsetlin machine

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Agro Kart - Transforming Agriculture through Innovative Mobile App Development 4

Agro Kart - Transforming Agriculture Through Innovative Mobi...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sreeja, G.G. Saraswathi, V. Sathish, V. Swetha, S. Rajkumar, A. Sri Eshwar College Of Engineering Department Of Cce Coimbatore India

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384628

Nearly 60% of the people in our nation work in agriculture as their primary occupation, but only a small percentage of them make a profit. this is because they are unable to set the price of their own goods, are unable to sell them to consumers directly, and must instead rely on middlemen to do so. In reality, they are paying the farmers very little for it and charging the consumers a premium amount for it. As a result, farmers lose out and only brokers profit more. Farmers and customers will both benefit from using our Agro Kart application. Without the use of an intermediary, farmers can sell directly to consumers. By supporting them, we may prevent businessmen from making investments in a particular farmer in exchange for a portion of the profits. No matter their age, anyone can use this application because of its simple and attractive UI (User Interface), which was developed using flutter and Firebase for the backend. In addition, we used machine learning to evaluate the products' quality. the app must gather information on user behavior, including search history, purchase history, and other pertinent app interactions. the software must constantly assess user behavior and adjust recommendations as necessary. this guarantees that the suggestions endure as accurate and up-to-date. the unique feature of this app is that farmers can apply for sponsorship. Any user of this app can sponsor a farmer, and in exchange, they will receive a specific percentage of the farmer's profits or a specific quantity of their products, depending on the terms of the agreement they entered into. Farmers that use this tool can receive funding for their farming operations with ease. © 2023 IEEE.

关键词： application programs

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4th international conference on machine learning for Networking, MLN 2021

4th International Conference on Machine Learning for Network...

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4th international conference on machine learning for Networking, MLN 2021

ISBN: (纸本)9783030989774

the proceedings contain 10 papers. the special focus in this conference is on machine learning for Networking. the topics include: Distance Estimation Using LoRa and Neural Networks;One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN;multi-Armed Bandit-Based Channel Hopping: Implementation on Embedded Devices;cross Inference of throughput Profiles Using Micro Kernel Network Method;machine learning Models for Malicious Traffic Detection in IoT Networks /IoT-23 Dataset/;application and Mitigation of the Evasion Attack against a Deep learning Based IDS for IoT;dynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering;unsupervised Anomaly Detection Using a New Knowledge Graph Model for Network Activity and Events.

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Research on the Construction of an Accurate Procurement System for Library e-Resources in Foreign Language Under Dig Data Analysis 4th

Research on the Construction of an Accurate Procurement Syst...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Luo, Ke Zhang, Xiujun Department of Library Shaoyang University Hunan Shaoyang China Network Information Centre Nanchang Institute of Technology Jiangxi Nanchang China

ISBN: (纸本)9783031201011

Due to the limited funds for the acquisition of university library resources, in order to improve the collection resource profitability and reduce the labor cost of libraries, a method of text classification using natural language processing is proposed to build an accurate procurement system for electronic book resources. Feature extraction is performed for the text information of the book collection, a feature vector matrix is established, and classification prediction is performed by machine learning algorithms. By optimizing the prediction model and evaluating the results with ten cross-validations, the accuracy of the LightGBM model testing reached 77.49%, so the acquisition system constructed based on this model can make effective decisions and thus optimize the structure of the collection resources. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Deep-Reinforcement-learning-Based Proportional Computation Offloading With User Equipment in Smart Home Edge Computing 4

Deep-Reinforcement-Learning-Based Proportional Computation O...

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

作者： Xie, Haohang Tian, Yunlong Niu, Li Wang, Miao Li, Yonghua Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Qingdao Haier Technology Co. Ltd China National Engineering Research Center for Digital Home Network Qingdao China Qingdao Haier Intelligent Home Appliance Technology Co. Ltd Qingdao China Co. Ltd Beijing China

ISBN: (纸本)9798350373233

the growing adoption of Internet of things (IoT) applications has resulted in a surge in the offloading of time-sensitive and computationally intensive tasks from IoT devices to mobile edge computing (MEC) servers. In this article, we survey the task partition strategy in a smart home edge computing network with multiple user equipments (UEs) and edge servers (ESs), where each UE runs IoT application and generates computation task periodically. the generated task is allowed to be broken down into several proportions then offloaded to ESs following a task partition strategy designed to minimize both execution latency and energy consumption. We first formulate the task partition problem as a nonlinear programming problem. Subsequently, we introduce an algorithm based on multiagent deep deterministic policy gradient (MADDPG) for UEs to optimize their offloading strategies. In the proposed approach, each User Equipment leverages its local observations as input for an actor network, which generates a task partitioning strategy. Simultaneously, a critic network is trained to enhance the performance of task partitioning. through our simulations, the performance of the proposed algorithm has been confirmed to surpass that of alternative methods in terms of minimizing both delay and energy consumption. © 2023 IEEE.

关键词： Computation offloading

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Driver Drowsiness Detection Using computer Vision and machine learning Techniques 4

Driver Drowsiness Detection Using Computer Vision and Machin...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Essel, Ebenezer Albalooshi, Fatema A. Elmedany, Wael Lacy, Fred Ismail, Yasser Southern University and A&M College Department of Electrical Engineering Baton RougeLA United States University of Bahrain College of Information Technology Bahrain

ISBN: (纸本)9798350369786

It is alarming that car crashes resulting in damage to automobiles, injuries to passengers, and loss of lives occur daily. the incidence of road crashes is rapidly increasing worldwide and has drawn many road safety commissions and concerned individuals to discuss ways to drastically reduce this menacing situation. the introduction of artificial intelligence and technological advancement has prompted government agencies and state commissions to call upon universities and research institutions to develop methods to curb the rise of automobile crashes. these crashes can be attributed to various factors, including drunk driving and, most notably, drowsiness, which affects a wide range of individuals. Drowsiness detection can be categorized into three main techniques;behavioral-based, vehicular-based, and physiological-based. In this research, the behavioral-based approach was studied, with significant consideration being the cost of implementation, execution time, and accuracy. three machine learning (ML) classifiers were considered: Support Vector machine (SVM), Naïve Bayes (NB), and Random Forest (RF). A dataset of 1448 images was used for training and testing these classifiers: 70% for training and 30% for testing. Random Forest classifier gave the best accuracy of (92.41) compared to SVM (90.34) and Naïve Bayes (69.43). © 2023 IEEE.

关键词： Support vector machines

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Detection and Analysis of Gene Functioning and Manipulation using Deep learning 4

Detection and Analysis of Gene Functioning and Manipulation ...

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

作者： Bhutto, Jameel Ahmed Sunkari, Venkateswarlu Patil, Amit Madhukar Vanathi, D. Loganayagi, S. Nayak, Manjushree College of Computer Science Huanggang Normal University Huanggang438000 China School of Information Technology and Engineering Addis Ababa Institute of Technology Addis Ababa University Ethiopia Bharati Vidyapeeth Deemed University Department of Applied Science Department of Engineering and Technology Navi mumbai400614 India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Department of Computer Science and Engineering Tamilnadu Chennai602105 India Department of Computer Science and Engineering Odisha Berhampur761008 India

ISBN: (纸本)9798350300888

In the field of genomics, the analysis of gene functioning and manipulation is critical for understanding the underlying mechanisms of genetic diseases and developing effective treatments. Deep learning algorithms have shown great potential in processing large volumes of genomic data and predicting gene function. However, deep learning models require large amounts of labeled data for training, which can be a challenge in genomics where data labeling is time-consuming and expensive. To get over this restriction, deep learning and evolutionary algorithms have been used to optimize model hyperparameters and boost accuracy. through recurrent candidate selection and modification, genetic algorithms apply evolutionary biology principles to optimize complicated solutions. By using this method, the quantity of labeled data needed to train deep learning models can be greatly decreased, and gene function predictions can be made more accurately. Overall, deep learning and genetic algorithms provide a potent tool for the investigation and modulation of gene function. these methods could result in quicker drug development, more precise gene function predictions, and personalized therapy. To ensure the ethical use of these technologies, it is important to recognize and resolve the ethical issues raised by genetic alteration. Furthermore, by deciding on the best guide RNAs for the editing process, genetic algorithms can be utilized to optimize gene editing research like CRISPR-Cas9 gene editing. this strategy can improve the effectiveness and precision of gene editing while minimizing unwanted side effects and potential ethical issues. © 2023 IEEE.

关键词： Genome

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Design and Implementation of a Power Grid Fault State Analysis System Based on Deep learning 4

Design and Implementation of a Power Grid Fault State Analys...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Wen, Xuan Sun, Haimeng Xiamen Institute of Technology School of Data and Computer Science Xiamen China Chengyi College Jimei University Electronic Information Engineering Xiamen China

ISBN: (纸本)9798350308082

the aim of this paper is to propose an intelligent solution to power equipment fault. the emphasis is to improve the feature extraction for different types of power equipment fault Based on the deep learning technology, a method of acquiring feature map and constructing knowledge map for Power Equipment Fault Operation Detection is proposed. this method can analyze and diagnose the running state of power equipment, quickly identify and locate equipment faults, and provide corresponding maintenance schemes, so as to improve the ability of fault treatment and upgrade the level of equipment operation and maintenance services. In addition, this research also designed a kind of immersive full-color hybrid AR virtual environment electrical equipment fault teaching drill and Training Mode method, it enables operators to master the operation principle of power equipment and the skills of handling power equipment faults, so as to improve their practical operation ability and the accuracy of fault handling. © 2023 IEEE.

关键词： Neural network models

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