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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6977 条记录，以下是501-510 订阅

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Research on Fast Adaptive Transmission Models for international Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics international Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua international Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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Exploring data as Quantum Bits: Analysis of Quantum Image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

引用

2023 IEEE international conference on Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Multi-objective Task Scheduling in Cloud data Center using Cat Swarm Optimization Framework

Multi-objective Task Scheduling in Cloud Data Center using C...

引用

2023 Asia conference on Power, Energy Engineering and Computer Technology, PEECT 2023

作者： Srichandan, Sribas Jena, R.K. Shiv Nadar University Department of Electrical Engineering Greater Noida India Institute of Management Technology Nagpur India

ISBN: (纸本)9798350313376

Cloud computing is a burgeoning computing paradigm that offers reusability and cost-effectiveness, including a diverse array of autonomous systems. The business offers consumers customizable and expandable services on a pay-per-use basis, according to their needs and preferences. The efficacy of cloud infrastructure is contingent upon the allocation and arrangement of tasks. Efficient task scheduling plays a crucial role in minimizing power consumption within cloud infrastructures, while simultaneously maximizing the profitability of service providers through the reduction of user job processing time. The primary purpose of this study is to investigate the effectiveness of the multi-objective Cat Swarm Optimization (TA-CSO) in optimizing job scheduling for improved efficiency. The algorithm presented in this study aims to enhance the efficiency of the cloud environment by optimizing many factors such as energy consumption, cost, resource utilization, and processing time. The outcome achieved through the utilization of TA-CSO is likewise replicated by the implementation of an open-source cloud platform known as CloudSim. The findings derived from this study are contrasted with the current leading scheduling algorithms, revealing that the suggested algorithm (TA-CSO) yields an optimal equilibrium of outcomes for various objectives. © 2023 IEEE.

关键词： Cloud computing

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Application and Innovation of Cloud computing Technology in Computer data processing 1

Application and Innovation of Cloud Computing Technology in ...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Xiaolian, Zhao Hubei Three Gorges Polytechnic Hubei China

ISBN: (纸本)9798350323795

Cloud computing technology has completely changed the way computer data is processed. It involves using remote servers to store, manage, and process data, rather than relying on local servers or personal computers. The application of cloud computing technology in computer data processing has many advantages. Firstly, it allows easy access to data from any location through an internet connection, making it easier for businesses and individuals to collaborate and share information. Secondly, cloud computing technology provides scalability as users can easily increase or decrease storage capacity according to their own needs. Thirdly, it provides a cost effective solution, as users only pay for what they use. However, there are also some concerns about the security and privacy of data stored in the cloud. Therefore, appropriate measures must be taken to ensure that sensitive information is not subject to unauthorized access. In short, the application of cloud computing technology in computer data processing has changed the way we store and manage information. Its benefits outweigh the challenges, making it a popular choice for businesses and individuals. The main advantage of using this technology is that it can provide multiple functions for multiple people to use simultaneously. This can more effectively utilize resources and reduce costs related to hardware maintenance. Cloud computing also provides access to information from different sources, allowing users to obtain the information they need faster than before. © 2023 IEEE.

关键词： Personal computers

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data Mining in Healthcare using Machine Learning Techniques

Data Mining in Healthcare using Machine Learning Techniques

引用

2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Goel, Honey Kumar, Deepak Amity Institute of Information Technology Aiit Amity University Noida India

ISBN: (纸本)9798350333602

Classification techniques have become increasingly important in healthcare due to the need for accurate and efficient disease diagnosis, treatment planning, and patient care. Supervised learning algorithms, such as decision trees, logistic regression, and support vector machines, are used for disease diagnosis, predicting patient outcomes, and identifying potential risk factors. Classification techniques are also used in image recognition and analysis, such as in radiology and pathology. Classification is a supervised learning technique used to predict the class or category of an instance based on the given set of attributes. This research study explores the use of classification techniques in data mining for healthcare applications. The goal of this study is to apply classification algorithms such as Naive Bayes, Logistic Regression and Random Forest to healthcare datasets and evaluate their performance. The datasets used in this study include patient information such as demographics, medical history, and diagnosis. The findings suggest that the classification techniques can be effective in data mining for healthcare applications, enabling healthcare professionals to make more informed decisions based on patient data. © 2023 IEEE.

关键词： Python

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Research on Virtual Display Space System Based on Computer Big data Technology

Research on Virtual Display Space System Based on Computer B...

引用

2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Zeng, Yuan Wu, Xukun Liu, Ying School of Art and Design Wanjiang University of Technology Ma'anshan China Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9798350359244

Using big data analysis method, a virtual environment demonstration system is designed. In the hardware part of the system, the data acquisition in the system is optimized to ensure the high integration of the system. By expanding the sensing circuit around the memory, the effective expansion of the memory is realized. Big data processing method was used to classify the images. High quality edge-texture parallax is obtained by collecting the key information in the image. We need to complete the transformation of pixels in the image to 3D reconstruction. And through color rendering and noise reduction processing, we finally realize the three-dimensional reconstruction stereo display based on the image. Experiments show that this method can improve the efficiency of 3D reconstruction display while reducing the error of 3D reconstruction. © 2023 IEEE.

关键词： Big data

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Energy Efficient Speed Planning of Electric Vehicles for Vehicle-Following Scenarios Using data-driven Koopman Model 21

Energy Efficient Speed Planning of Electric Vehicles for Veh...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Bin Wang, Miaoben Zhou, Feng Yan, Haoyang Du, Ronghua Changsha University of Science and Technology College of Automotive and Mechanical Engineering Changsha China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350329223

In the context of urban road following scenarios, ensuring the precise speed prediction and control of electric vehicle while rigorously maintaining the safe driving conditions has persistently presented a formidable challenge. To address such issues, a novel data-driven approach for the energy efficient speed planning of electric vehicles in vehicle-following scenarios is proposed. Incorporating Koopman operator theory, the developed control-oriented vehicle dynamics prediction model leverages real-world vehicle-following data in various vehicle-following scenarios. Subsequently, a data-driven Koopman model predictive control (MPC) method is developed based on the established prediction model, which focuses on reducing the energy consumption through speed planning while ensuring safe following distances. The comparative simulation results ultimately demonstrate the effectiveness of the proposed method under various driving scenarios. © 2023 IEEE.

关键词： Model predictive control

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The Secret Sauce of Student Success: Cracking the Code by Navigating the Path to Personalized Learning with Educational data Mining 2

The Secret Sauce of Student Success: Cracking the Code by Na...

引用

2nd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2023

作者： Alam, Ashraf Rekhi Centre of Excellence for the Science of Happiness Indian Institute of Technology West Bengal Kharagpur India

ISBN: (纸本)9798350348002

The growing need for tailored learning experiences in post-secondary education has resulted in the adoption of educational data mining (EDM) methodologies to derive significant insights from educational data. The existing scholarly literatures suggest the utilisation of adaptive learning algorithms that integrate various data sources, such as student demographic information, academic performance, and physiological data, to offer individualised learning experiences for students. The algorithms have the capability to modulate the tempo of educational content in response to the cognitive burden experienced by students, which is gauged by their brainwave activity. This study explores the application of predictive models, such as classification, regression, and time-series analysis, in detecting patterns and trends in past data for the purpose of forecasting students' forthcoming academic achievements. Predictive models have the potential to assist educators in making well-informed decisions aimed at enhancing course outcomes. This research introduces an approach to course improvement analytics that utilises diverse data sources, including student academic records, demographic data, and external platforms such as social media and online forums, to optimise educational results. Through the examination of this data, academic professionals can acquire valuable knowledge regarding student involvement, achievement, and conduct. The present study establishes that the utilisation of course improvement analytics yields valuable information regarding student engagement and behaviour, thereby enabling educators to make informed decisions aimed at enhancing students' learning outcomes. © 2023 IEEE.

关键词： Students

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Intrusion Detection Technology Based on Large Language Models

Intrusion Detection Technology Based on Large Language Model...

引用

2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Lai, Hsiaofan Sichuan University Pittsburgh Institute Chengdu China

ISBN: (纸本)9798350313413

This paper explores the application of large language models, particularly BERT, to intrusion detection technology. The urgency of developing sophisticated intrusion detection systems (IDS) has grown with the rise of complex cyber threats. The paper proposes a novel framework that employs BERT to extract meaningful features from network data and identify anomalous behavior. By transforming network data into natural language text format, the model effectively discerns patterns often overlooked by traditional IDS. The research findings reveal that the BERT-based IDS significantly outperforms conventional machine learning methods in terms of accuracy, detection rate, and false positive rate. Experimental results demonstrate the model's robustness in various network environments, showcasing its versatility. Specifically, the model excels in identifying unknown threats, reducing false positives, and enhancing attack detection accuracy. These findings emphasize the potential value of large language models in the field of network security. © 2023 IEEE.

关键词： BERT cybersecurity Intrusion detection systems large language models machine learning network data

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Advancing Singular Value Decomposition Techniques for Enhanced data Mining in Recommender Systems 22nd

Advancing Singular Value Decomposition Techniques for Enhanc...

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22nd international conference on Artificial Intelligence and Soft computing (ICAISC)

作者： Beshley, Mykola Hordiichuk-Bublivska, Olena Beshley, Halyna Ivanochko, Iryna Lviv Polytech Natl Univ Dept Telecommun UA-79013 Lvov Ukraine Comenius Univ Dept Informat Syst Fac Management Bratislava 82005 Slovakia

ISBN: (纸本)9783031425073;9783031425080

The development of effective algorithms for processing large volumes of information is a very urgent problem for modern information systems. The work examines the problems of content analysis and data mining by recommender systems. Features of automatic systems management using machine learning algorithms are presented. The importance of using recommender systems for more accurate prediction of goods or services of interest to users, determination of interrelationships between various production factors, etc. have been determined. The operation of the FunkSVD algorithm, which allows accurate and fast processing of sparse data sets of a large volume, has been studied. A modification of FunkSVD is proposed to use fewer data about users and items when forming recommendations. The simulation of the proposed algorithm was carried out and it was determined that it works faster than the usual one, while maintaining fairly high accuracy, so it can be used in the processing of large data. It is also proposed to increase the privacy and accuracy of FunkSVD recommendations in distributed systems by using the Fed SVD algorithm. The results of the experiments showed the high accuracy of the proposed algorithm but at the expense of a certain increase in the duration of calculations. According to the conducted research, it was concluded that both modifications can be used in systems with different requirements for data mining and distributed architecture.

关键词： Big data Recommender Systems data Mining Funk SVD Fed SVD

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