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DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph learning 38

DeepScaler: Holistic Autoscaling for Microservices Based on ...

引用

38th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Meng, Chunyang Song, Shijie Tong, Haogang Pan, Maolin Yu, Yang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (纸本)9798350329964

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.

关键词： Cloud computing Microservice QoS Resource Management Holistic Autoscaling Graph Convolution Container

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Bibliographic Exploration of Application of Machine learning and Artificial Intelligence in Solar Energy

Bibliographic Exploration of Application of Machine Learning...

引用

2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Bashir, Faizah Mohammed Dodo, Yakubu Aminu Oyebode, Oluwadare Joshua Culpable, Rex V. De La Calzada, Limic M. Adediran, Anthonia Oluwatosin Ajibade, Temiloluwa Iyanuoluwa Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey University of Hail Department of Decoration and Interior Design Hail55467 Saudi Arabia Najran University Architectural Engineering Department Najran66426 Saudi Arabia Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Cebu Technological University College of Technology Cebu Philippines Cebu Technological University College of Engineering Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Brazil Scholar School System Dept of Business Management Birmingham United Kingdom

ISBN: (纸本)9798350358155

Solar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. Machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. international publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied. © 2024 IEEE.

关键词： Malware

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Cloud-Native Countinous Integration/Continous Deployment (CI/CD) Pipeline 2

Cloud-Native Countinous Integration/Continous Deployment (CI...

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2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Kumar, Shivansh Bala, Er. Neetu Singh, Arun Prakash Raj, Yash Chandigarh University Deparment of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

This paper explores the design and implementation of a cloud-native Continuous Integration/Continuous Deployment (CI/CD) pipeline for automating the deployment of machine learning models. Leveraging modern technologies like Docker, Kubernetes, Jenkins, and cloud platforms such as AWS, Google Cloud, and Azure, the pipeline enhances the efficiency, consistency, and reliability of the development process. Key challenges such as scalability, security, and model drift are addressed, offering solutions to ensure smooth operations in dynamic production environments. Performance evaluations demonstrate the benefits of a cloud-native approach, highlighting improvements in deployment speed and resource optimization. The paper concludes by discussing future directions for further automation and advancements in cloud-native CI/CD processes for machine learning. © 2024 IEEE.

关键词： Cloud platforms

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Sprocket Defect Detection using Deep learning 3

Sprocket Defect Detection using Deep Learning

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Mallika, Sugantha S.S. Bhaggiaraj, S. Nivarshan, T.K. Xavier, Praveen R. Rahul, R. Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798331532420

The detection and recognition of sprockets and springs play an important role in ensuring the optimal functionality and safety of various mechanical systems. This abstract gives an overview of the methodologies and advancements in automated sprocket and spring detection. The significance of sprockets and springs in mechanical assemblies, highlighting their ubiquitous presence across diverse industries, ranging from automotive to aerospace. Manual inspection methods have challenges including time consumption and susceptibility to human error. To overcome the problems, this sprocket defect detection system is developed. Our custom dataset is used in this project. Presence of springs/holes are identified using deep learning methods. This system will be usseful in automated identification, localization, and classification of sprockets within complex and dynamic manufacturing environments, facilitating tasks such as assembly, inspection, and maintenance. © 2024 IEEE.

关键词： Sprockets

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using learning based Data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mercy, S.Sudha Venkatesan, S. Bharaneedharan, M. Nayagan, S. Venkateswarlu, L. Padmaja, Nimmagadda R.M.K. Engineering College Department of Computer Science and Design Chennai India Qis College of Engineering and Technology Department of Electrical and Electronics Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and -Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Department of Ece Tirupati India

ISBN: (纸本)9798331543617

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (ML) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical ML algorithms such as Support Vector Machine (SVM) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Cascaded Multi-Layer Perceptron (MLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded MLP reached 99.2%. Classical models like Random Forest and SVM also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. Data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Modern Trends in Deep learning for Fruit Quality Analysis of Hyperspectral Images: A Review 14

Modern Trends in Deep Learning for Fruit Quality Analysis of...

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Lanke, Neha Chandak, M.B. Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Non-destructive testing techniques for fruit quality analysis have gained importance over the years. Hyperspectral image processing is one of the important non-destructive, cost-effective, rapid, and reliable quality testing techniques that provide both spatial as well as spectral information. Due to the automatic feature learning ability, deep learning is one of the most promising and powerful techniques with better classification accuracy for fruit applications. This paper starts by giving an overview of the quality concept of fruits and vegetables followed by fundamentals of hyperspectral imaging, and its potential applications. Then machine learning and deep learning techniques are highlighted followed by recent research work to illustrate the potential of hyperspectral imaging for fruit and vegetable quality analysis. Lastly, the research gap and future considerations are discussed to improve the analysis and applicability of hyperspectral imaging using deep learning for the quality assessment of fruits, and vegetables. © Grenze Scientific Society, 2023.

关键词： Hyperspectral imaging

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Enhancing Predictive Power in Personalized Breast Cancer Treatment through the Integration of Multi-Omics Data and Clinical Information with Deep learning Models

Enhancing Predictive Power in Personalized Breast Cancer Tre...

引用

2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Varpe, Aishwarya Bhujbal, Rutuja Kumar, Ranjit Ajeenkya D y Patil University School of Engineering Maharashtra Pune412105 India

ISBN: (纸本)9798350362879

The paper explores the complexities of personalized breast cancer treatment, by integration of multi-omics data, clinical data, and advanced computational tools. The heterogeneous nature of breast cancer causes challenges to conventional therapies, requiring the need for tailored approaches. The literature review highlights the relevance of predictive analytics in identifying detailed tumor patterns and enhancing personalized treatment efficacy. Integrating multi-omics data and clinical information data with utilising deep learning improves diagnostic precision and prognosis accuracy, forming a robust framework for predicting disease-related genes and influencing treatment decisions. Despite challenges with predicting recurrence risk and comprehending tumor complexity, the incorporation of multi-omics data and clinical information with modern computer techniques shows potential. © 2024 IEEE.

关键词： Tumors

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Anomaly Detection in Surveillance Camera using Deep learning Techniques 15

Anomaly Detection in Surveillance Camera using Deep Learning...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Bizu, B. Saumya, S. Sowmiya, N. Sowndhariya, J. Kongu Engineering College Perundurai India

ISBN: (纸本)9798331300579

The purpose of safeguarding public safety,protecting vital infrastructure, and preventing security breaches, anomaly detection in surveillance camera systems is an essential responsibility. Deep learning techniques have made significant progress in recent years in terms of increasing the precision and effectiveness of anomaly identification. The DenseNet architecture is used in this paper to provide a unique technique for anomaly identification in surveillance camera data. Convolutional neural networks (CNNs) like DenseNet are renowned for their dense connection and capacity to recognize intricate elements in images. To improve the functionality of the system, we suggest utilizing its benefits in the context of anomaly detection. Our method's primary objective is to effectively find anomalies through the utilization of the spatial correlations that already exist between pixels in images. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Development of Virtual Reality Platform through Human Computer Interaction using Artificial Intelligence 3

Development of Virtual Reality Platform through Human Comput...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Saranya, S. Channarayapriya, B. Harshavardhini, U. Nandhini, A.Sunitha Revathi, J. Venkatesan, R. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Deparment of Information Technology Chennai India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Mathematics Erode India Bannari Amman Institute of Technology Deparment of Information Technology Erode India

ISBN: (纸本)9798350375190

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments. © 2024 IEEE.

关键词： Virtual reality

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Research on Facial Expression Recognition Algorithm Based on Improved StarGAN Network 2

Research on Facial Expression Recognition Algorithm Based on...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Yang, Dawei Jiang, Simiao Wang, Weinian Shenyang Ligong University School of Information Science and Engineering Shenyang China Shenyang Sport University School of Social Sports Shenyang China

ISBN: (纸本)9798350368888

TO address the issue of overfitting in deep learning testing and pattern collapse in traditional generative adversarial networks (Gans),an based on deep learning improved Star Generative Adversarial network (StarGAN) facial expression recognition method is proposed. For this method, the regular term of pattern search and reconstruction loss are combined to improve the quality of the generated images and solve the problem of model pattern collapse. spatially separable convolution is used to extract image features to make a large number of parameters and operation cost is reduced. SVM is used for multi-task classification to improve the model generalization ability. Good recognition results were obtained on FER2013 data sets. © 2024 IEEE.

关键词： Generative adversarial networks

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