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ai-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an ai-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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How Does Software Prefetching Work on GPU Query Processing? 20

How Does Software Prefetching Work on GPU Query Processing?

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20th International Workshop on Data Management on New Hardware, DaMoN 2024

作者： Deng, Yangshen Chen, Shiwen Hong, Zhaoyang Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology AlayaDB Ai Guangdong Shenzhen China

ISBN: (纸本)9798400706677

Improving the performance of GPU query processing is a well-studied problem in database community. However, its performance is still unsatisfactory due to the low utilization of GPU memory bandwidth. In the literature, employing software prefetching techniques to improve the bandwidth utilization is a common practice in CPU database as it overlaps computation cost and memory access latency. However, it was ignored by GPU database even though the software prefetching ability has been provided by modern GPU architecture (i.e., from NVIDIA Ampere). In order to investigate the effectiveness of software prefetching techniques on GPU query processing, we implement four software prefetching algorithms on GPU, i.e., Group Prefetch (GP), Software-Pipelined Prefetch (SPP), Asynchronous Memory Access Chaining (AMAC) and Interleaved Multi-Vectorizing (IMV) in the work. We then adapt them on hash join probe and BTree search tasks with a suite of optimizations. Last, we conduct comprehensive experiments and evaluate the performance of them. The results confirm the superiority of software prefetching techniques on GPU query processing. Specifically, they can achieve up to 1.19X speedup on hash join probe and 1.31X speedup on BTree search when compared with the implementations without software prefetching. © 2024 ACM.

关键词： Graphics processing unit

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PaySafe Al: Intelligent Fraud Detection for UPI Transactions using Machine Learning

PaySafe Al: Intelligent Fraud Detection for UPI Transactions...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Markala Anjali Maddela Ajay Mukkerla Saiteja Baddula Omkar Yadav Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

In the financial world, it is impoartant to have a sound understand of systems against fraud, as they help detect activities such as anomalous and malicious transactions, through the analysis of activity engagement. Decision Tree are the recent form of deep learning models which, in more recent years, have shown advanced capability in the identification of fraudulent transactions, however, sensitivity and accuracy when implemented in real time systems remain an issue. In this research, we provide a new idea of fraud detection of UPI transactions that utilizes a Decision Tree based sequence modeling framework. The model focuses on normalization sets and typical cardholder activities to effectively learn how to detect anomalies in UPI transactions. It then analyzes the sequences of a person’s activities and adds on the probabilities, to then classify if the transaction is fraudulent or not. Several measures are established, however, in order to aid in minimizing false positives, because the aim is to ensure valid transactions are still processed. The experimental results support the proposed fraud detection system’s potential usefulness in monitoring actual UPI transactions and it also shows how it outperforms current systems in related studies. Our model facilitates the UPI transaction environment equally and addresses the fine balance in detection efficiency, accuracy and transaction speed by proposing the integration of deep learning techniques.

关键词： Support vector machines Deep learning Logistic regression Accuracy Sensitivity Predictive models Fraud Decision trees Security Standards

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Proactive Purity: Predicting Water Quality with Precision

Proactive Purity: Predicting Water Quality with Precision

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Sandhya G. Ashitha B. Sharath Kumar B. Varun Kumar T. Bharadwaj Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Water pollution, driven by industrialization and urbanization, threatens public health, aquatic ecosystems, and livelihoods worldwide. Rivers, being the primary source of water for drinking, agriculture, and industry, are highly vulnerable to contamination from untreated sewage, industrial discharge, and agricultural runoff. Traditional water quality monitoring methods are expensive, time-consuming, and lack real-time *** study proposes a technology-driven water quality monitoring system capable of processing large data volumes in real-time, identifying pollution patterns, and supporting timely interventions. By integrating advanced monitoring techniques, the system enhances decision-making for water management, ensuring sustainability and ecological balance. The research highlights the critical role of technology in addressing global water pollution balance, further underlining the very important linkage between technology and *** data volumes from source water can be processed by this system, identifying patterns and trends that indicate fluctuation in water quality. this system is particularly beneficial in areas that experience seasonal and man-made causes of change in water quality. Besides its technological achievements, this project is a giant leap toward the management of water *** technology provides authority to decision makers. who together with environmentalists and enables easy accessibility for various tasks to make sensible decisions for drinking water supply as well as irrigation systems and pollution prevention measures for water sources. measures. It has great importance as it combines science and technology in saving vital natural resources and maintaining long-term ecological balance by tackling one of the most urgent worldwide environmental issues.

关键词： Machine learning algorithms Water quality Prediction algorithms Market research Water pollution Real-time systems Pollution measurement Public healthcare Water resources Monitoring

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Invisible Shield: Real-Time Industrial Threats Detection for IoT-based Systems

Invisible Shield: Real-Time Industrial Threats Detection for...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Kadaganchi Sudhakar Pasula Manish Mavillapally Rohit Shaik Kaleem Ahmed Batharaju Sneha Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and Data Acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.

关键词： Deep learning Accuracy SCADA systems Feature extraction Threat assessment Complexity theory Security Decision trees Cyberattack Industrial Internet of Things

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ai-Powered Ingredient Detector for Allergies: Enhancing Food Safety Through Natural Language Processing and computer Vision

AI-Powered Ingredient Detector for Allergies: Enhancing Food...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Suwetha K Abhiram R Crispin R Nagaraj R Department of Computer Science and Engineering (AI & ML) Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Food allergies pose a significant health risk, affecting millions worldwide and potentially leading to severe reactions, including anaphylaxis. Identifying allergens in food products is crucial for individuals with allergies. However, the increasing complexity of food labeling and hidden allergens makes this process challenging, time-consuming, and error-prone. This paper presents the ai-powered Ingredient Detector for Allergies, an innovative application designed to streamline allergen detection using Artificial Intelligence (ai). The system enables users to input a food item, scan a barcode, or upload an image for ingredient analysis. By leveraging Natural Language Processing (NLP) and computer vision, it accurately identifies potential allergens and provides real-time alerts. This application is integrated with a comprehensive allergen database, which ensures precise detection and enhances food safety. By offering a user-friendly, intelligent, and efficient solution, this ai-driven system empowers individuals with food allergies to make informed dietary choices confidently and efficiently.

关键词： computer vision Databases Scalability Detectors Streaming media Natural language processing Real-time systems Food safety Artificial intelligence Allergies

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Solar Radiation Prediction Enhancement Via Ensemble Learning Method

Solar Radiation Prediction Enhancement Via Ensemble Learning...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P. Ravi Kishore G. Sai Kiran Reddy A. Shinjith P. Vikas Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Efficient forecasts of solar radiation are very important for optimizing solar energy systems, enhancing energy management, and ensuring the reliability of all renewable energy solutions. This study scrutinizes the performance evaluation of machine-learning algorithms as Decision Trees (DT), Support Vector Machines (SVM) and K - Nearest Neighbors (KNN), Linear Regression (LR), and Logistic Regression (LRg) to forecast solar radiation in relation to urban energy planning. Logistic Regression, usually used in binary classification problems, is modified in this study to demonstrate its regressive capacity. Its simplicity and modeling of linear relationships provide in-depth hungry comparative insights in conjunction with an ensemble. The analysis is made applying the year 2020 data at a high resolution sourced from the Solar Radiation Research Laboratory (SRRL) at the National Renewable Energy Laboratory (NREL). And the dataset considered metropolitan cities, including Delhi, Bangalore, and Hyderabad in the year 2020, where Global Horizontal Irradiance (GHI) is the core predictor variable in this study. Metrics of performance such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), are similarly utilized to appraise the accurate model of the highest predictive power. Further, the current study incorporates weather parameters, such as temperature, humidity, and wind speed, to augment the forecasting operation and make a thoroughly vast analysis. The findings present an exhaustive comparison of the algorithms along with a highlighter showing their strengths and limitations within an urban energy planning context. The research has brought to light the potential for implementing machine learning algorithms in forecasting solar energy while offering key insights to enhance the efficiency, scalability, and reliability of solar energy systems in highly populated areas.

关键词： Support vector machines Logistic regression Renewable energy sources Machine learning algorithms Wind speed Solar energy Planning Solar radiation Reliability Forecasting

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Titanic Survivors Prediction Analysis Using Machine Learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Detection of Machine Failure in Automated Industries using Long-Short Term Memory and LightGBM

Advanced Detection of Machine Failure in Automated Industrie...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Muppidi Mahesh Ponnagani Sreeja Sunnapu Swetha Madala Ritwik Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In the rapidly evolving industrial landscape, companies are using various technologies to optimize their operations which thereby reduce costs, and they focus on increasing their profits. Proactive maintenance has emerged as an important strategy that enable industries to foresee and prevent machine failures beforehand. This proactive approach not only extends the machine lifespan but also leads to cost savings. Machine failure detection is very essential in automated industries, medical fields, aerospace, transportation and many more industries where machinery is central for the production process. Traditional reactive maintenance methods only focus on repairing the machines after the occurrence of the failure which greatly affects the production. Machine learning, with it’s ability to analyze large datasets and identify the unusual patterns based on the historical data. This paper mainly proposes using a hybrid approach of the ml algorithms to increase the accuracy and efficiency of the detection of machine failures. The algorithms are Long-Short Term Memory (LSTM) networks and Light Gradient Boosting Machine (LightGBM). This approach is mainly used for early fault detection in machines. We combine the features of LSTM to find temporal patterns in sensor data with LightGBM’s capacity to refine predictions and analyze feature importance. This approach accurately finds the failure predictions, then classify failure severity that can be visualized using graphs and trigger actionable maintenance alerts. By this proposed approach we achieved a result of 98% accuracy on real sensor data when compared to traditional methods, outperforming them by 7%. This research fills the gap between the traditional reactive maintenance and the current predictive maintenance.

关键词： Industries Machine learning algorithms Costs Accuracy Transportation Production Predictive models Maintenance Long short term memory Predictive maintenance

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Food Nutritional Detection Personalized Dietary Recommendations for Health Monitoring using Image Processing with Machine Learning

Food Nutritional Detection Personalized Dietary Recommendati...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： M. Venkateswarlu M. Vinay M. Likhitha G. Shivaramu Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

A good nutrition monitoring that involves promoting healthy lifestyle and dietary challenges. In this paper, we propose an ai driven nutrition monitoring system utilizing deep learning, computer vision, and natural language processing to handle dish detection, freshness analysis and ingredient recognition. The proposed system takes into account the accuracy of food classification using a fine-tuned convolutional neural network, OpenCV based techniques to measure ingredient quality, and NLP to pull out detailed nutritional information. The system applies real time feedback and actionable insights to the limitations of manual logging and traditional dietary tracking. Built with a modular architecture, it is adaptable to different cuisines, and dietary needs, and can be used by individuals as well as healthcare professionals. One of the innovations of such an approach is to make nutrition management easier, to promote healthier eating habits, and to create a launchpad for future personalized health monitoring advances.

关键词： Deep learning computer vision Technological innovation Shape Medical services Manuals Natural language processing Real-time systems Pattern recognition Monitoring

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