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Research on Scheduling algorithms for AI data-Intensive Tasks in Edge Heterogeneous Environments

Research on Scheduling Algorithms for AI Data-Intensive Task...

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2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Liu, Luanqi Xu, Zixian Wang, Yingxun Wang, Xiang School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350309614

Edge computing has become a new computing service model by virtue of its low latency and high performance characteristics. It deploys cloud server resources at the edge of the network to help mobile devices to complete data-intensive, large-scale and complex computing tasks. The heterogeneity of resources and the mobility of devices in the edge environment make the edge heterogeneous environment often suffer from unbalanced distribution of resources and loads, which leads to the reduction of system computing performance and resource utilisation. The CPU-GPU heterogeneous computing system in the edge environment has higher performance than the isomorphic system, and the existing task scheduling strategy still limits the performance of the heterogeneous system. Aiming at the above problems, a task scheduling model for CPU-GPU heterogeneous systems is designed, and a genetic algorithm based on reinforcement learning is proposed to achieve efficient allocation of AI data-intensive tasks. The Q-learning algorithm is used to achieve adaptive adjustment of parameters to solve the complex and high-dimensional task scheduling problem. Simulation experiments are carried out by Edge-cloudsim platform, compared with other methods, the results prove that the QL-GA task scheduling method proposed in this paper can provide an efficient solution to the task scheduling problem in edge heterogeneous computing systems. © 2023 IEEE.

关键词： edge computing heterogeneous computing systems Reinforcement learning task scheduling

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The Smart Application of data Mining in the Detection of Fraudulent Transactions 3

The Smart Application of Data Mining in the Detection of Fra...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Bodhankar, Aniruddha Aswal, Upendra Singh Arivazhagn, D. Pund, Sachin S. Kulkarni, Nandini Chaturvedi, Sudhir Kumar Dr. Ambedkar Institute of Management Studies and Research- Nagpur Department of Data Science & Information Systems Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India Business School Academy of Maritime Education and Training Deemed to Be University Department of Amet Chennai India Upes Department of School of Engineering Dehradun India

ISBN: (纸本)9798350319125

Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown. © 2023 IEEE.

关键词： developments entails reconciliation statistics transaction

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Optimization Strategies for Analyzing Piano Learners' Practice Time and Frequency Using data Mining algorithms 3

Optimization Strategies for Analyzing Piano Learners' Practi...

引用

3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Lin, Yushi Jimei University Normal College Xiamen China

ISBN: (纸本)9798350343922

How to choose the best number of exercises reasonably is the key to improve the learning efficiency in piano learning, but how to effectively optimize the practice of students is still an urgent problem to be solved. Therefore, this paper studied students' learning motivation from the aspects of learners' age, learning experience, practice time, practice plan and so on, and analyzed students' learning motivation. On this basis, the collected experimental data is divided into two groups, and the data Mining (DM) algorithm is used to optimize the experimental data individually, while the control group remains unchanged. The results show that the individualized practice scheme produced by the DM algorithm can obviously improve the practice effect of students. Therefore, the research method in this paper provides a feasible solution for piano learners to optimize the practice time and frequency, which is of great significance for improving the piano learning effect. © 2023 IEEE.

关键词： Contrastive Learning

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Performance Evaluation of KNN & Decision Tree in Prediction of Heart Disease 3

Performance Evaluation of KNN & Decision Tree in Prediction ...

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3rd international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Kumar, Aniket Mathew, Jobin Thomas Gupta, Neha Malhotra, Vivek Manav Rachna International Institute of Research Studies Faculty of Computer Applications Faridabad India

ISBN: (纸本)9781665494007

Heart disease has been the leading cause of death in recent decades. Every five minutes, one person dies unexpectedly from heart disease. Researchers from all over the world are assisting doctors in diagnosing heart problems. However, machine learning techniques can substantially minimize the number of tests required. The main objective of this paper is to predict the heart disease of a person well in advance to minimise the need for expensive treatment and medicines. The present papers have used some of the finest approaches of machine learning like decision tree and KNN classifier for prediction of heart diseases. These algorithms are quite useful in diagnosing cardiac illness without the use of machinery or labs, by figuring out the problem at a far lower cost. These algorithms are very widely utilized and helpful in other domains. The dataset was obtained from the Kaggle website and contain data of 303 patients and 76 attributes. The accuracy of the KNN along with Decision tree algorithms have been compared with existing work of researchers and found to be more in the detection of the heart disease. These algorithms certainly help in reducing the number of deaths which are happening all around the world. © 2023 IEEE.

关键词： Decision trees

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Exploring Cryptographic Techniques for data security in Resource-Constrained Wireless Sensor Networks:Performance Evaluation and Considerations 8

Exploring Cryptographic Techniques for Data security in Reso...

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8th IEEE international conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Singarathnam, Dharshika Ganesan, Swathi Pokhrel, Sangita Somasiri, Nalinda St John University Department of Computer Science York London United Kingdom

ISBN: (纸本)9798350310931

Wireless sensor networks (WSNs) play a crucial role in environmental monitoring and data collection. However, ensuring data security in WSNs poses challenges due to the vulnerabilities of wireless communication channels. In this paper, we address this concern by exploring the application of cryptographic techniques to enhance data security in WSNs. Considering the limited sensor power, computing power, and storage resources, we propose a novel approach that evaluates the suitability of symmetric and asymmetric cryptographic algorithms in WSNs. Through performance comparisons based on computation power and storage capacity requirements, we identify key insights for selecting appropriate encryption algorithms in WSNs. Our findings emphasize the importance of considering the specific requirements and constraints of WSN applications, highlighting the efficiency of symmetric key-based encryption algorithms in resource-constrained environments and the stronger security and key distribution mechanisms provided by ECC-based asymmetric encryption algorithms for secure communication among multiple nodes. This research contributes to the existing knowledge by offering an effective solution to enhance data security in WSNs while considering computational and storage limitations © 2023 IEEE.

关键词： Wireless sensor networks

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IoT, Heterogeneous data processing, and AI Automation Synergy for Improved Efficiency and Maintenance: Revolutionizing Hospital Operations

IoT, Heterogeneous Data Processing, and AI Automation Synerg...

引用

2023 IEEE international conference on Signal processing, Communications and computing, ICSPCC 2023

作者： Muqing, Niu Wenting, Liu Xiaole, Gao Zhiyuan, Zhao Jingjuan, Wang First Affiliated Hospital Henan University of Science and Technology Luoyang China Business School Henan University of Science and Technology Luoyang China

ISBN: (纸本)9798350316728

Modern healthcare is going through a big change because of the way that new tools are proposed. This paper works at how the Internet of Things (IoT), heterogeneous data transfer and processing, and Artificial Intelligence (AI) robotics could completely change how hospitals work. IoT technology has become a significant tool because it makes it possible to watch and collect data in realtime from hospital properties like medical equipment, wearables for patients, and smart sensors. This steady flow of different kinds of data brings both possibilities and problems. In our study, we talk about how significant it is to have strong data transmission and processing systems that can handle different data sources well. Intelligent automation that is led by AI is at the heart of this change. AI systems can work with the huge amount of data created by IoT devices and use machine learning, predictive analytics, and cognitive computing to improve different parts of hospital management. This paper gives case studies and real-world examples of how IoT, diverse data processing, and AI automation have been used successfully in hospitals to show how these ideas can be used in the real world. IoT technology, diverse data processing, and AI automation are assembled to change the way hospitals work by making them more effective and easier to maintain. This study offers the significance of using these tools to keep up with the varying needs of current healthcare. As hospitals continue to change, this relationship will play an even bigger role in making sure that patients get superior care while making the best use of their resources. © 2023 IEEE.

关键词： Efficiency

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Spatial data Analysis on On-Demand Cab Services using Spark 3

Spatial Data Analysis on On-Demand Cab Services using Spark

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kokkiligadda, Manohar Raj Shaik, Fathimabi Likhitha, Poritigadda Velagapudi Ramakrishna Siddhartha Engineering College Department of Information Technology Vijayawada India Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350343632

Spatial data analysis is a technique used to analyze large amounts of spatial data generated by on-demand cab services such as Uber, Lyft, and Grab. This type of data includes information on the pickup and drop-off locations of riders, the trajectories of drivers, and the time when demand for cabs is highest. By analyzing this data, the on-demand cab services can gain insights on the customer demand, optimize driver allocation, improve pricing strategies, and enhance overall system efficiency. Spark is an open-source framework used for processing large-scale spatial data. It provides a set of APIs for processing and analyzing spatial data in distributed computing environments by using Apache Spark as its computing engine. This research explores the utilization of Spark, an open-source framework tailored for large-scale spatial data processing. Spark offers a comprehensive set of APIs designed for distributed computing environments, leveraging the power of Apache Spark as its underlying computational engine. By using Spark, on on-demand cab services can efficiently analyze large amounts of spatial data generated by their systems, enabling them to make data-driven decisions and improve the efficiency of their services. There by, we did analyze and visualize, handle the spatial data through spark with the help of graphs. © 2023 IEEE.

关键词： Spark Spatial data frame Spatial RDD

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Streaming IoT data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case

Streaming IoT Data and the Quantum Edge: A Classic/Quantum M...

引用

29th international conference on Parallel and Distributed computing (Euro-Par)

作者： Herbst, Sabrina De Maio, Vincenzo Brandic, Ivona Vienna Univ Technol Vienna Austria

ISBN: (纸本)9783031506833;9783031506840

With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machine learning could be a solution for the increasing demand of urgent analytics, providing potential theoretical speedups and increased space efficiency. However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics. In this work, we investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum, identifying the main challenges and possible solutions. Furthermore, exploring the data encoding and hyperparameter tuning challenges, we present preliminary results for quantum machine learning analytics on an IoT scenario.

关键词： Machine learning

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Exploring Possibilities And Methodologies for Big data and 5G Convergence

Exploring Possibilities And Methodologies for Big Data and 5...

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Alam, Intekhab Fiza, Samreen Sunil, M.P. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka 562112 India

ISBN: (纸本)9798350357769

The advantages and benefits of big data and cellular networks are discussed in this essay. First, a summary of their interactions, together with big data-driven and big data-assisted communication networks, is given. The erstwhile caters for big data characteristics like number, pace, and variation by utilizing diverse means such as transmission, storage, and processing in cellular networks in order to accommodate big data apps and services. In order to gather wireless big data and gain detailed information about the networks and users for better network management and scheduling, the latter makes use of big data methods. Two examples that focus on channel data collection and big data-aided peripheral information storage are offered that will further highlight the reciprocal advantages. The discussion concludes with a few intriguing open study questions. © 2023 IEEE.

关键词： 5G Convergence Big data Cellular Networks Cloud computing data Analytics data Collection data processing data Storage and data Security data Transmission Edge computing Network Infrastructure Network Management Resource Allocation Wireless Communications

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Exploration on Accounting data Encryption processing System Based on DES Algorithm 1

Exploration on Accounting Data Encryption Processing System ...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Zhi Intelligence Finance and Economics Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350316469

The confidentiality of accounting data is crucial for the security and trade secrets of enterprises. The aim of this study is to explore an accounting data encryption processing system that improves the DES algorithm, enhancing the efficiency and security of data encryption processing. This article optimized the system architecture and algorithm design by introducing safer AES (Advanced Encryption Standard) algorithms, and combined hardware to accelerate the technological process. The experimental results showed that under the same amount of data, the AES algorithm reduced the encryption time by an average of 40% compared to the DES algorithm. This indicated the need to further strengthen key management. It needs to establish a data backup and recovery mechanism, and strengthen security training and awareness enhancement to improve the security performance of the accounting data encryption processing system of DES algorithm. These improvements would provide enterprises with more reliable and effective accounting data protection solutions, ensuring the confidentiality and integrity of accounting data. © 2023 IEEE.

关键词： Accounting data Encryption processing System Apriori Algorithm DES Algorithm Safety Assessment

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