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

共 6952 条记录，以下是331-340 订阅

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Design of Artificial Intelligence Information System Using NoSQL database and Ajax Technology

Design of Artificial Intelligence Information System Using N...

引用

2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Bai, Danyang Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350359244

With the rapid evolution of the Internet and artificial intelligence, big data has emerged as an indispensable asset in today's societal advancement. Given the monumental task of processing and analyzing massive amounts of data, traditional relational databases have proven inadequate in meeting the demand. Consequently, the present work sets forth a novel approach aimed at formulating an artificial intelligence information system design grounded in big data, leveraging NoSQL database technology and Ajax technology. Through harnessing the distributed and horizontally scalable performance of NoSQL databases and the asynchronous communication characteristics of Ajax technology, the proposed method aims to achieve efficient data storage and processing, thereby expediting the design and development process of artificial intelligence information systems. © 2023 IEEE.

关键词： Big data

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New Cryptographic Bent Functions from Permutations and Linear Translator in Cyber Security

New Cryptographic Bent Functions from Permutations and Linea...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Liu, Qian Chen, Yan Chen, Zhixiong Liu, Ximeng Lin, Hui College of Computer and Data Science Fuzhou University Fuzhou China Putian University Fujian Key Laboratory of Financial Information Processing Putian China College of Computer and Data Science Fuzhou University Key Laboratory of Information Security of Network Systems Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (纸本)9798350304602

Cryptographic bent functions are maximally nonlinear Boolean functions with an even number of variables. They are not only closely related to some interesting combinatorial objects, but also have important applications in coding, sequence design, cryptography and cyber security. In this paper, we firstly investigate the Walsh transform of two families of functions via Maiorana-MacFarland's class. Secondly, we present new infinite families of permutations. We show that those families have nice property that one can select three elements among them which can be used to construct bent functions. Finally, by using two linear translators, we construct bent functions from the class of Maiorana-MacFarland. © 2023 IEEE.

关键词： Walsh transforms

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Semantic Optimization in Lossless data Compression for IoT-Driven Smart Healthcare Solutions 2

Semantic Optimization in Lossless Data Compression for IoT-D...

引用

2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Manju Bargavi, S.K. Hashmi, Hina University Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

In this study, we explore the potential of the Internet of Things (IoT) within the domain of smart healthcare management applications, an area of growing importance in Computer Science, Artificial Intelligence (AI), Machine Learning (ML), and data Mining. IoT's capacity to efficiently amass, process, and transfer data is revolutionizing smart city initiatives, healthcare, energy distribution, and vehicular technology. Our primary aim is to construct a robust framework for smart healthcare management that not only harnesses the power of IoT but also integrates ontological models for enhanced data compression without compromising data quality. Our proposed theoretical framework is structured around three integral components: the pre-processing module, the ontological module, and the data compression module. Leveraging a comprehensive dataset from a Kaggle repository, we applied this tripartite approach to smart healthcare data, seeking to refine the data transformation process into a digital format - a process traditionally fraught with challenges due to the voluminous nature of healthcare data. We introduce a novel compression method and evaluate its performance against traditional compression algorithms such as Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), and Huffman coding. The efficacy of our approach is measured using metrics like compression factor (CF), compression ratio (CR), and compression time, providing a quantitative assessment of its viability. The integration of IoT into the ontological approaches and data compression techniques presented in this study not only enhances healthcare services, but also highlights the potential of AI and ML to transform data processing and data storage solutions. This study seeks to demonstrate the benefits of this multi-functional approach and its surprising results in the future of smart healthcare. © 2023 IEEE.

关键词： Efficient data Storage for Medical IoT Systems IoT in Healthcare Lossless Compression algorithms Semantic data Compression Smart Healthcare data Management

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An Extended Agglomerative Hierarchical Clustering Techniques 2

An Extended Agglomerative Hierarchical Clustering Techniques

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Maravarman, M. Babu, S. Pitchai, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattangulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350315905

Clustering is a significant method of data analytics in real world environments since human labelling of the data is often costly. Clustering was developed as an alternative to manual tagging. In the field of data analytics, hierarchical clustering is of critical significance, particularly in light of the exponential rise of data derived from the normal world. You may derive a variety of hierarchical agglomerative clustering algorithms from this architecture by providing an inter-cluster semantic similarity, an expression patterns of the -similarity graph, and a cover procedure. These three pieces of information are required. According to the findings of our experiments, our approaches are not only more efficient than conventional hierarchical algorithms, but they also produce smaller agglomerative hierarchical clustering while maintaining the same level of clustering effectiveness. It is generally agreed that topology management is an effective strategy for addressing these challenges. This method groups nodes together for the purpose of managing them and/or carrying out a variety of duties in a dispersed way, such as resource management. There are many quality-driven goals that may be accomplished by clustering, despite the fact that approaches for clustering are mostly renowned for their ability to reduce energy usage. The purpose of this study is to provide a comprehensive explanation on various enhanced agglomerative hierarchical clustering techniques. In addition to this, the authors have provided certain criteria, on the basis of which one may also assess which of these previously described algorithms is the most effective. © 2023 IEEE.

关键词： data Analytics

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Analysis of Sentimental on Twitter using Content Aware Support Vector Machines

Analysis of Sentimental on Twitter using Content Aware Suppo...

引用

2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Manikandan, B. Krishna Kumar, T. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India MNR Engineering and Technology Sangareddy Department of Computer Science and Engineering Hyderabad India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai 73 India

ISBN: (纸本)9798350319200

In the era of the Internet's rapid expansion, social networking platforms have emerged as a crucial medium for individuals to convey their emotions and opinions to a global audience. Text, images, audio, and video are all ways for people to express themselves. Twitter, a popular social network, allows users to tweet about their daily feelings and opinions. These platforms generate vast amounts of unstructured data continuously, necessitating prompt processing to gain insights into human psychology. This processing is accomplished through sentiment analysis, a technique that assesses the emotional polarity within texts, determining whether the author holds a -Ve, +Ve, or neutral stance toward a subject, service, individual, or place. This article investigates the use of four well-known data mining classifiers, specifically the decision tree, K-nearest neighbor, naive Bayes, and support vector machine, to analyze tweet sentiments. Our findings reveal that the support vector machine consistently yields superior results compared to other algorithms, demonstrating an improvement of 4.01% in accuracy for two-class datasets and 8.05% for three-class datasets. © 2023 IEEE.

关键词： Decision Tree Sentimental Support Vector Machine Twitter data Analysis

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Optimization of Supply Chain Management System Based on Big data Analysis Technology

Optimization of Supply Chain Management System Based on Big ...

引用

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

作者： Hu, Jiacai Harbin University of Commerce School of Economics Harbin China

ISBN: (纸本)9798350313413

With the advent of the era of big data, the supply chain management system has also ushered in a huge opportunity for optimization and change. This paper first analyzes the digital application status of China's supply chain management system. It is pointed out that China's supply chain management system has some obvious characteristics, such as remarkable digital application results, continuous expansion of scale, continuous improvement of enterprise production efficiency, multiple types of digital technology and obvious differentiation of application degree, and obvious changes in labor market demand. Then it points out the problems of supply chain management system in product design and development, strategic procurement, production, storage, logistics and transportation, sales and after-sales service, and puts forward corresponding optimization measures based on big data analysis technology according to the problems existing in China's supply chain management system. © 2023 IEEE.

关键词： big data analysis technology problem and optimization supply chain management

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Evolution of Consensus algorithms in Blockchain Technology

Evolution of Consensus Algorithms in Blockchain Technology

引用

2023 international conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Harshini Poojaa, K. Ganesh Kumar, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India

ISBN: (纸本)9798350335835

Blockchain being a decentralised, distributed ledger, has attracted industries to implement the solutions on blockchain. The transactions are agreed in a deterministic manner because of the availability of consensus algorithms. The state of the machine is concluded based on the agreement generated by the consensus algorithm. The performance of the blockchain system can be increased to the order of magnitude when the right consensus is used. In this paper, the limitations and performance of various consensus algorithms have been reviewed along with the characteristics of the same. In addition to that, this paper compares the consensus algorithms based on various parameters such as speed, double-spending, 51% attack and many more. © 2023 IEEE.

关键词： Blockchain

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Enhancing Cryptanalysis of DES Encryption using Neural Networks and Firefly algorithms

Enhancing Cryptanalysis of DES Encryption using Neural Netwo...

引用

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

作者： Balaji, Varshini Krishnamurthy, Vallidevi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350313413

In today's cryptographic landscape, evaluating encryption algorithms' integrity is crucial. Neural networks offer promise for extracting information from data Encryption Standard (DES) encrypted data. This report explores diverse methods of DES attacks using deep learning techniques. The primary aim is to decode DES-encrypted data with neural networks, extending this to contemporary encryption algorithms. The proposed system uses Long Short-Term Memory (LSTM) networks to improve plaintext reconstruction from DES-encrypted data. It evaluates LSTM network performance metrics and seeks to predict critical patterns. Findings reveal the neural network's impressive ability to predict plaintext with 98% accuracy after 250k training iterations. However, performance varies with the encryption key, and complete plaintext recreation remains challenging. This method also experiments using the Firefly algorithm to enhance key prediction, improving overall efficiency. Artificial Neural Networks (ANNs) for classification tasks on encrypted data show promise for practical applications. Future work can focus on enhancing classification accuracy, exploring diverse data types, and studying the critical impact on performance metrics. © 2023 IEEE.

关键词： Backpropagation Cryptanalysis Cybersecurity data Encryption Standard - DES Attacks Encryption algorithms Long Short-Term Memory (LSTM) Neural Networks

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Application of Mathematical Statistics Method Based on YOLOv3 Algorithm in Measurement and Detection data

Application of Mathematical Statistics Method Based on YOLOv...

引用

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

作者： Wang, Yunfan Zhongtai Securities Beijing China

ISBN: (纸本)9798350313413

With the development of Internet visual algorithms, the target demand of measurement and detection data analysis is optimized constantly. In the view of wide application of target detection technology in various industries, mathematical statistics based on YOLOv3 (You Only Look Once) algorithm is increasingly important for the analysis of network detection data in different industries. In the construction of the entire target metric detection model, how to enhance accuracy of data detection and decrease model data processing time is currently significant issue that requires to be instantly resolved. This article studied the optimization model of target detection using the YOLOv3 algorithm and the analysis of metric detection, aiming to enhance the accuracy and efficiency of model detection. In this paper, through the simulation experiment analysis of COCO (Common Objects in Context) dataset, the mathematical statistics method based on YOLOv3 algorithm was applied to research, and the flow chart design was optimized by combining multi-scale improvement and loss function. Based on the discussion of data results, the following conclusion can be drawn. In the context of internet big data, optimization model for object detection using YOLOv3 algorithm has improved detection accuracy in the four sampled test domain parameters, and overall average improvement is 6%. During this time, there have been improvements in detection efficiency, and the overall average improvement is 10.25%. This shows that the mathematical statistics method based on YOLOv3 algorithm has a good practical application effect in the measurement and detection data analysis. This method combines deep learning technology and mathematical statistical methods to make the analysis of metrological detection data more robust, and can cope with complex environmental conditions and diverse measurement targets. It is expected to play a key role in fields such as industrial quality inspection, traffic monitoring,

关键词： data Analysis Metrological Detection Simulation Experiments YOLOv3 Algorithm

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AI for Finance (AIFF): from Abnormal data Recognition to Information System Intrusion Detection 2

AI for Finance (AIFF): from Abnormal Data Recognition to Inf...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Zhang, Xiaoyuan Shandong Youth University of Political Science Accounting School Jinan250103 China

ISBN: (纸本)9798350347579

With the development of the data mining and the intelligent information technology, the complex information processing paradigm based on artificial intelligence has become a mainstream trend. In this paper, starting from the discussions of abnormal data detection, the novel information system intrusion detection is designed for the construction of the AIFF model. The model starts with the recognition accuracy improvement, in the designed scheme, the weighted Naive Bayes algorithm and the intelligent clustering model are combined to effectively detection the abnormal information. Then, by adopting the rule that modification of the firewall rules on the proxy server makes it possible to directly filter out the next intrusion address from the firewall, the pre-processing will be achieved. Furthermore, this study proposes a scheduling strategy based on resource availability to finalize the detection system. Through the experiment under different scenarios of the self and also comparison testing, the performance is validated. © 2023 IEEE.

关键词： Information systems

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