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Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

CAAI Transactions on Intelligence Technology 2021年第2期6卷 147-154页

作者： Priyadarsini Karthik Karthik Sekhar Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies(VISTAS)ChennaiIndia Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennaiIndia

Many organizations around the world use cloud computing Testing as Service(Taas)for their *** computing is principally based on the idea of on-demand delivery of computation,storage,applications,and additional *** depends on delivering user services through Internet *** addition,it uses a pay-as-you-go business design to deliver user *** offers some essential characteristics including on-demand service,resource pooling,rapid elasticity,virtualization,and measured *** are various types of virtualization,such as full virtualization,para-virtualization,emulation,〇S virtualization,and application *** scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and *** of the cloud environment,uncertainty,and perhaps heterogeneity,resource allocation cannot be addressed with prevailing *** situation remains a significant concern for the majority of cloud providers,as they face challenges in selecting the correct resource scheduling algorithm for a particular *** authors use the emergent artificial intelligence algorithms deep RM2,deep reinforcement learning,and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.

关键词： Service cloud prevailing

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Cloud-Native Hybrid Deep Learning Pipeline for Network Traffic Classification using Convolutional Recurrent Neural Network

Cloud-Native Hybrid Deep Learning Pipeline for Network Traff...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Satish, E.G. Sudheer Kumar, Komuravelly Adnan, Myasar M. Krithika, M. Sundari, MandapatiVenkata Rama Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vels Institute of Science Technology and Advanced Studies Department of BCA/IT Pallavaram India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally Hyderabad India

ISBN: (纸本)9798350375442

Identifying encrypted traffic from emerging applications is important and challenging as traditional traffic classification approaches fail to achieve the desired level of accuracy. This necessitates the elaborate strategies to learn about the encrypted network flow. The research focuses on developing a cloud native hybrid Deep Learning (DL) pipeline for Network Traffic Classification (NTC). Initially, the ISCXVPN2016 dataset is considered that consists encrypted and non-encrypted traffic where the raw data is pre-processed with packet length normalization. The features are extracted applying Convolutional Neural Networks (CNNs) to define statistical flow features including inter-arrival time, packet size and flow duration. The data from network traffic is processed and classified using Convolutional Recurrent Neural Network (CRNN) with microservices executed in serverless containers. The hyper parameters in the DL models are then tuned using grid search and the random search algorithms. Experimental results showed that the proposed cloud native CRNN pipeline achieved accuracy of 98.67%, precision of 95.46%, and 94.32% when compared to the existing NTC models Stacked Autoencoders (SAE) based CNN (SAE-CNN) and Autoencoders based CNN (AE-CNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends 6

Artificial Intelligence Techniques for the Performance Analy...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Priscila, S. Silvia Sakthivanitha, M. Yogeshwari, M. Devakirubai, T. Shirley Sirajudeen, M. Mohamed Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu 600126 India Department of Information Technology VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Data Analyst Saveetha Liberal Arts and Science College Tamil Nadu Chennai India Department of Commerce Madras Christian College Tamil Nadu Chennai India Thaloor Tamil Nadu The Nilgiris643239 India

ISBN: (纸本)9798331522667

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. This study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including Data Mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. The purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques. © 2025 IEEE.

关键词： Decentralized finance

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Deep learning based Crop Monitoring for effective Agricultural-IoT Management

Deep learning based Crop Monitoring for effective Agricultur...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Mir, Mahmood Hussain Kovilpillai J, Judeson Antony Mohamed, Sulaiman Syed Pragya Mir, Tawseef Ahmad Paul, Banibrata Alliance School of Advanced Computing Alliance University Bangalore India Department of Computer Science GITAM University Bangalore India Department of Computer Science & Engineering Birla Institute of Technology Ranchi India

Achieving sustainable crop production to meet the increasing demand for food depends on the efficient management of agricultural greenhouses. Conventional methods of greenhouse management typically depend on human observation and intervention, resulting in a lack of scalability, efficiency, and precision. In this research, a deep learning technique for managing agricultural greenhouses is proposed by utilizing IoT sensor data obtained from a smart greenhouse. The proposed method aims to enhance crop monitoring and control, hence improving the overall efficiency of greenhouse operations. The utilization of several deep learning models with various activation functions, such as ELU, Swish, ReLU, SELU, and Mish, enables the ongoing surveillance and independent regulation of greenhouse conditions throughout the day. The proposed methodology can be used to enable real-time tracking and autonomous control, leading to resource optimization, increased productivity, and reduced manual intervention. Empirical validation demonstrates the efficacy of utilizing deep learning and IoT in agriculture to improve crop management and sustainability. © 2025 The Author(s).

关键词： Information management

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Evolving Distributions Under Local Motion

arXiv

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arXiv 2024年

作者： Acharya, Aditya Mount, David M. Department of Computer Science University of Maryland College Park United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States

Geometric data sets arising in modern applications are often very large and change dynamically over time. A popular framework for dealing with such data sets is the evolving data framework, where a discrete structure continuously varies over time due to the unseen actions of an evolver, which makes small changes to the data. An algorithm probes the current state through an oracle, and the objective is to maintain a hypothesis of the data set’s current state that is close to its actual state at all times. In this paper, we apply this framework to maintaining a set of n point objects in motion in d-dimensional Euclidean space. To model the uncertainty in the object locations, both the ground truth and hypothesis are based on spatial probability distributions, and the distance between them is measured by the Kullback-Leibler divergence (relative entropy). We introduce a simple and intuitive motion model where with each time step, the distance that any object can move is a fraction of the distance to its nearest neighbor. We present an algorithm that, in steady state, guarantees a distance of O(n) between the true and hypothesized placements. We also show that for any algorithm in this model, there is an evolver that can generate a distance of Ω(n), implying that our algorithm is asymptotically optimal. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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SALIENCY MAP-BASED IMAGE RETRIEVAL USING INVARIANT KRAWTCHOUK MOMENTS

arXiv

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arXiv 2024年

作者： Nejad, Ashkan Faraji, Mohammad Reza Qi, Xiaojun Department of Computer Science Institute for Advanced Studies in Basic Sciences Zanjan Iran Department of Computer Science Utah State University LoganUT United States

With the widespread adoption of digital devices equipped with cameras and the rapid development of Internet technology, numerous content-based image retrieval systems and novel image feature extraction techniques have emerged in recent years. This paper introduces a saliency map-based image retrieval approach using invariant Krawtchouk moments (SM-IKM) to enhance retrieval speed and accuracy. The proposed method applies a global contrast-based salient region detection algorithm to create a saliency map that effectively isolates the foreground from the background. It then combines multiple orders of invariant Krawtchouk moments (IKM) with local binary patterns (LBPs) and color histograms to comprehensively represent the foreground and background. Additionally, it incorporates LBPs derived from the saliency map to improve discriminative power, facilitating more precise image differentiation. A bag-of-visual-words (BoVW) model is employed to generate a codebook for classification and discrimination. By using compact IKMs in the BoVW framework and integrating a range of region-based features-including color histograms, LBPs, and saliency mapenhanced LBPs-our proposed SM-IKM achieves efficient and accurate image retrieval. Extensive experiments on publicly available datasets, such as Caltech 101 and Wang, demonstrate that SM-IKM outperforms recent state-of-the-art retrieval methods. The source code for SM-IKM is available at ***/arnejad/SMIKM. © 2024, CC BY.

关键词： Digital devices

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Predictive Modeling for Asset Bubble Detection in Financial Markets

Predictive Modeling for Asset Bubble Detection in Financial ...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： Suganiya Murugan Pradeep Kumar Sivakumar Arpit Goyal Pranay Goenka Department of Computing Technologies SRM Institute of Science and Technology Chennai India Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

In response to the volatile nature of financial markets, our project, titled ‘A Comprehensive Approach to Stock Market Crash Prediction using Time Series Forecasting, Machine Learning, and Deep Learning Techniques,’ endeavors to create a robust prediction model for anticipating stock market crashes. Leveraging a diverse dataset gathered from various open-source links, with a primary source being YahooFinance, we employ an extensive feature set including time stamps, economic indicators such as unemployment, consumer price index (CPI), price-to-earning ratio (P/E), and market yields. Our innovative approach involves labeling major crashes on the S&P 500 chart and correlating them with diverse economic data. By filtering and marking significant crash phases, we create a binary classifier. Forecasting S&P 500 values for 24 months using ARIMA and SARIMAX models allows for better evaluation and economic analysis. The project integrates various machine learning algorithms, with random forests and decision trees emerging as top performers. Deep learning techniques using Keras, such as Convolutional Neural Networks (CNNs) and Dense Neural Networks (DNNs), achieve notable accuracy. Additionally, real-time stock-related tweets obtained through the Twitter API and market sentiments from the Alpha Vantage API enhance the predictive capabilities. The model employs ARIMA and SARIMAX for forecasting, alongside a CNN architecture for crash classification. The UI/UX showcases data visualizations, sentiment analysis, and predictive models. The technology stack includes REACT, NODE JS, TENSORFLOW, A WS, and more. This work presents a holistic strategy to equip investors with valuable insights and aid in making informed decisions during potential market downturns.

关键词： Deep learning Sentiment analysis Economic indicators Time series analysis Predictive models computer crashes Real-time systems

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Kamalakkannan A. Yasmin Arunkumar. R P. Kavitha Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Science JBAS College for women Chennai India Faculty of Computing Engineering and Science University of South Wales Treforest/Newport Campus United Kingdom Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price.

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 IEEE.

关键词： Proteins

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Bajpai, Chhavi Gaur, Manish Suganthi, J Roselin Vinayagam, S. Kaliappan, S. Manoj Senthil, K. Centre for Advanced Studies Department of Computer Science & Engineering Lucknow India Institute of Engineering and Technology Department of Computer Science & Engineering Lucknow India K. Ramakrishnan College of Engineering Department of Ece Trichirapalli India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioscience Chennai India Lovely Professional University Department of Research and Development Jalandhar India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350375442

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM. © 2024 IEEE.

关键词： Graph theory

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