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Bitcoin-Blockchain Technology: Security Perspective 3

Bitcoin-Blockchain Technology: Security Perspective

引用

3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Ansar, Syed Anas Arya, Swati Aggrawal, Shruti Yadav, Jaya Pathak, Prabhash Chandra Babu Banarasi Das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India University of Lucknow Department of Computer Science Lucknow India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken. © 2022 IEEE.

关键词： Blockchain

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm 7

Research on Mining Association Rules for Classroom Concentra...

引用

7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Bai, Jingqiu Kong, Huafeng Hu, Wenhan Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China The Main Research Direction Is the Computer Application China

ISBN: (纸本)9798350334197

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future. © 2023 IEEE.

关键词： Association rules

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Exploring Dimensionality Reduction Techniques for On-line Analytical Processing 15

Exploring Dimensionality Reduction Techniques for On-line An...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Anup Inzimam Patel, Srinivasan Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Management - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Exploring Dimensionality discount strategies for Analytical Processing (OLAP) is an area of research that makes a specialty of the use of diverse techniques to reduce the variety of dimensions needed to investigate a records set. That is high-quality because it reduces processing time, garage area requirements, and simplifies the statistics and outcomes acquired. Diverse strategies are to be had for reducing the wide variety of dimensions, and those are based totally upon the application and the associated information set. The strategies include central factor assessment (PCA), characteristic desire, weighted length reduction, and Locality Sensitive Hashing (LSH). This paper provides a comparative evaluation of those strategies and discusses their strengths and obstacles. The outcomes of this observation will help researchers and practitioners understand each method's talents and limitations while using it in OLAP packages. © 2024 IEEE.

关键词： Analytical Processing application Dimensionality Locality Observation Researchers Technique

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A Statistical Review on Machine Learning Based Medical Diagnostic Systems for Chronic Kidney Disease 3

A Statistical Review on Machine Learning Based Medical Diagn...

引用

3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Sharma, Arvind Singh, Dalwinder Lovely Professional University Phagwara Department of Computer Application Punjab India Lovely Professional University Phagwara Department of Computer Science and Engineering Punjab India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The decision making is a very challenging and complex process while doing the diagnosis of any specific illness. Huge ambiguities and problems are faced in the process of diagnosing by using the human visual system, which leads to conclusions that are inappropriate and unsuitable for a chronic kidney disease diagnosis. The employment of artificial intelligence approaches can overcome these limitations as these techniques help an expert doctor to offer precise and high-quality care to the patent suffering from CKD. Numerous publications enlighten the usage of machine learning methodologies along with its benefits, and the number of publications is elevated day by day. The primary focus of this study is to statistically review the various proposed and developed medical diagnostic systems for the diagnosis of chronic kidney disease using machine learning algorithms. The conducted review is categorized based on some criteria such as the objective of research work, its publication year, research gap and results obtained by researchers. © 2022 IEEE.

关键词： Expert systems

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Modelling and Simulation of Autonomous Decision - making Process Based on Proximal Policy Optimization

Modelling and Simulation of Autonomous Decision - making Pro...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Chen Zhou Baoran An Weijian Huang Bing Yu Institute of Computer Application China Academy of Engineering Physics MianYang

The autonomous decision-making problem in the mixed process of medium-range and close-range confrontation stages of agents is studied in this paper, utilizing reinforcement learning theory. The complexity and intensity of the environment resulting from the integration of information technology and system-of-systems (SoS) support in the agent confrontation scenario, as well as the oversimplification of scenarios in traditional research on autonomous decision-making of agents, are taken into account. Firstly, a simulation platform is constructed based on the Agent-Based Modeling and Simulation (ABMS). Then, the state space, action space, and reward function are designed to adapt to the multi-stage confrontation scenario for agents. Finally, an autonomous decision-making method based on Proximal Policy Optimization (PPO) algorithm for agent confrontation is proposed. The experimental results demonstrate that the proposed decision-making algorithm can learn reasonable confrontation tactics in multi-stage confrontation scenarios, verifying the feasibility of studying the problem based on the above modeling and simulation framework.

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

引用

2023 IEEE International Conference on Memristive Computing and applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Sparse Black-Box Inversion Attack with Limited Information

Sparse Black-Box Inversion Attack with Limited Information

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yixiao Xu Xiaolei Liu Teng Hu Bangzhou Xin Run Yang China Academy of Engineering Physics Institute of Computer Application China

Existing black-box model inversion attacks mainly focus on training and attacking surrogate models. However, due to the deployment process of face recognition models, training surrogate models becomes extremely difficult in practice. At the same time, query-based black-box inversion attacks still suffer from low image quality and high computational costs. To bridge these gaps, in this paper, we propose BMI-S, a sparse black-box inversion attack against face recognition models. BMI-S first introduces evolution strategies to perform efficient black-box gradient estimation and achieve query-based attacks. Meanwhile, BMI-S performs sparse attacks on the key styles that contribute most to the face recognition process. By only optimizing key style control vectors, BMI-S further narrows the dimensions of the search space and accelerates the inversion attacks.

关键词： Training Image quality Privacy Face recognition Computational modeling Closed box Estimation

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Classification of Foods based on Ingredients

Classification of Foods based on Ingredients

引用

International Conference on computer Communication and Informatics (ICCCI)

作者： Sudipa Guria Sudarshan Banerjee Suddhasattwa Bhattacharjee Swarup Paul Sobhan Halder Priyanka Das Dept. of Computer Application Institute of Engineering and Management Kolkata

Due to the rise of recipe sharing websites, it is now quite easy to find online cooking instructions that list ingredients and cooking techniques. The development of recipe suggestion systems has also been a focus of several recipe sharing websites. Little has been done to analyse the relationship between cuisines and ingredients, while the majority of contemporary food-related research works concentrate on recipe suggestion systems. The work presented here offers an insight into which cuisines are the most related and what are a cuisine’s important elements because we know that each cuisine is unique and has a separate list of ingredients. While the cuisine of food has been regarded as the class label, the ingredients have been seen as the features. In order to categorise the cuisine of unlabeled data, this work is focused on selecting the most important ingredients using various feature selection strategies and classification approaches. This has been done to investigate the fundamental connection between ingredients and cuisine.

关键词： Feature extraction Informatics

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CLAST: Efficient Certificateless Sequential Aggregate Signature Scheme for Industrial Internet of Things Environments

CLAST: Efficient Certificateless Sequential Aggregate Signat...

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International Conference on Information, Communication and Networks (ICICN)

作者： Siqi Yang Wenqiang Pan Wenqi Zheng Mingyong Yin Fang Lou Teng Hu Jiajun Xie CAEP Institute of Computer Application China Academy of Engineering Physics

The Internet of Things (IoT) has experienced rapid growth in recent years, drawing significant attention from both academics and industry. As IoT gradually gains popularity in the industrial market, industrial internet of things(IIoT) has emerged as a necessary solution to meet the demands of the times. The limitations posed by the small storage space of certain devices, restricted network throughput coupled with timing and reliability requirements, and the need for safety assurance in the IIoT, all present significant challenges. In particular, security threats are constantly increasing, posing a significant risk to the rapid development of IIoT. Therefore, CLAST scheme was proposed to address these issues, which based on elliptic curve cryptography and bilinear mapping to ensure the security of information. The security analysis of the scheme in the random oracle model was proved, affirming that it satisfies the security requirements for signature verification. Furthermore, the scheme reduces the complexity of the cryptographic operation process and reduces the computational overhead of the signature verification algorithm. These benefits have been substantiated through security analysis performance analysis.

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