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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Design and implementation of a physical security evaluation system for cryptographic chips based on machine learning 3

Design and implementation of a physical security evaluation ...

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3rd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Liu, Jiajing Wei, Congming Wen, Shengjun Wang, An School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Information Center State Administration for Market Regulation Beijing100820 China

ISBN: (纸本)9781510661301

Side-channel attack is a commonly used attack method for recovering cryptographic chip keys, and plays an important role in the field of cryptographic chip physical security evaluation. Combining side-channel attacks with machine learning and replacing some steps of traditional side-channel attacks with machine learning methods can improve the efficiency of key-recovery from side-channel attacks to a certain extent. In practice, there is a problem that most existing cryptographic chip security evaluation systems cannot support the complete key recovery process, and fully improve the utilization of side information generated in the evaluation process. In this paper, we design a cryptographic chip physical security evaluation system based on machine learning. Through the integrated operation of power trace acquisition, preprocessing, analysis and evaluation, the correct key can be successfully recovered. © 2023 SPIE.

关键词： machine learning

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machine learning in Student Health - A Review 3

Machine Learning in Student Health - A Review

引用

3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Gurusamy, Deivanai Chakrabarti, Prasun Chakkaravarthy, Midhun Lincoln University College Faculty of Computer Science and Multimedia Malaysia Itm Sls Baroda University Department of Computer Science and Engineering India

ISBN: (纸本)9781665482684

Students' health is an important research topic today because they are the cornerstone of our society. Researchers have used various technological breakthroughs to address schoolchildren's and college/university students' health issues, and machine learning is now frequently employed. However, to understand the efficacy of machine learning and progress in student health research, a concise review of the influence of machine learning on student health is required, which the paper provides. The paper's primary objective is to examine which of the students' health concerns are efficiently addressed by machine learning algorithms and the outcomes of the approaches. The paper also discusses what leads students to perform poorly in schools, colleges, and universities and if machine learning will improve student health in the future. The review findings can be helpful to researchers in moving forward with unaddressed health-related problems and other solutions concerning the student's health. © 2022 IEEE.

关键词： Students

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A Comparative Analysis of machine learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics 3

A Comparative Analysis of Machine Learning Models: SVM, Naï...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Singh, Malikhan Arora, Kavita Ather, Danish School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India BSA College of Engineering & Technology Mathura India Amity University in Tashkent Uzbekistan

ISBN: (纸本)9798350342338

machine learning models have recently developed into a crucial predictive analytics tool, driving progress in a variety of industries. Support Vector machine (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory are four well-known machine learning algorithms that are thoroughly examined and compared in this work (LSTM). By assessing these models on many parameters, including accuracy, precision, recall, and computing efficiency, the study conducts an in-depth investigation. To ensure the validity and relevance of the findings, a broad dataset is used. The comparison analysis highlights the distinct advantages and disadvantages of each model, providing information about how well suited they are for various applications. The analysis explores efficiency and interpretability as well, offering helpful direction for scholars and practitioners. The work also advances the field of predictive analytics by providing actual data on the effectiveness of these algorithms, paving the path for more in-depth and efficient application of machine learning models across a range of fields. © 2023 IEEE.

关键词： Long Short-Term Memory machine learning Naive Bayes Random Forest Support Vector machine

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Customer Evaluation And Profit Maximization Using machine learning for Offline Stores 3

Customer Evaluation And Profit Maximization Using Machine Le...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Gopinath, Abhijith Joy, Aloysius Wilson, Joyal Saji, Shamjith Devasia, Jeethu V Vimal Jyothi Engineering College Department of Computer Science and Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Proper planning is essential for a long-term business. This can be done by implementing proper marketing strategies from time to time. machine learning can play a key role in decision-making. machine learning can play a key role in decision-making. This paper proposes a systematic approach which can help offline stores target their customers and obtain maximum profit by using the clustering application of machine learning. It helps offline stores get the benefits of the latest technologies in their business. The initial step for this system is to analyse the acquired sales data based on the purchase history, which will be used to group the customers. K-Means clustering is used to segment customers. Later, the most preferred product of each cluster is determined, and the result of this can be used by the shopkeepers to analyse their business and make good decisions for the long life of the business. It can assist offline stores in finding different groups of customers rather than viewing the entire customer as a single unit. © 2022 IEEE.

关键词： Decision making

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Self-triggered control for linear systems based on hierarchical reinforcement learning with RNNs 24

Self-triggered control for linear systems based on hierarchi...

引用

3rd international conference on computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Chen, Wei Wan, Haiying Luan, Xiaoli Liu, Fei Key Laboratory of Advanced Process Control for Light Industry Institute of Automation Jiangnan University Wuxi China

ISBN: (纸本)9798400716942

In this research, we introduce a novel model-free self-triggered control strategy for linear systems, employing a hierarchical reinforcement learning approach. This strategy establishes a dual-Actor Critic hierarchical architecture, utilizing two neural networks that guide each other's decision-making to simulate the triggering and control strategies in self-triggered control. The triggering strategy, serving as the top-level policy, aims to find the maximum triggering interval that maintains system stability based on the initial control strategy. The control strategy, as the bottom-level policy, under the guidance of the triggering strategy, takes the optimization of control performance considering the impact of triggering intervals as a sub-goal. This approach reduces the exploration space of the bottom-level policy and accelerates the reinforcement learning process. In the implementation of the dual-Actor Critic algorithm, the Critic network employs recurrent neural networks to capture the temporal dependencies of expected rewards, thereby enhancing the efficiency and accuracy in approximating the Q-function. The efficacy of our proposed method is demonstrated through a series of numerical simulations. Theory of computation∼Theory and algorithms for application domains∼machine learning theory∼Reinforcement learning∼Sequential decision making. © 2024 ACM.

关键词： Federated learning

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Research on the High Resolution Remote Sensing Image Target Detection Based on machine learning 3rd

Research on the High Resolution Remote Sensing Image Target ...

引用

3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Fu, Yanli Sun, Yingying Li, Shuyao Deng, Rui Linlin, Hou Engineering University of CAPF Shaanxi Xian China

ISBN: (纸本)9783031631351

With the development of computer technology and the national aerospace industry, remote sensing satellites have been successively projected into the air, and the resolution of remote sensing images is becoming higher and clearer. The surface information contained in remote sensing images can be used not only in civil fields such as forestry, agriculture, water conservancy, but also in the military and national defense fields. Current target detection technologies have achieved good results in natural image detection, but their performance in remote sensing images is poor. This is due to the high resolution of remote sensing images themselves, the small size of key targets in the image, and the dense distribution in multiple directions, while the background information is very complex and covers the entire range of the image, resulting in low detection accuracy and slow speed issues when using traditional methods for target detection. High resolution remote sensing images have a wide range of utilization scenarios. The traditional remote sensing image target detection based on artificial vision recognition and processing has been difficult to effectively cope with and meet the practical needs of large-scale high-resolution remote sensing impact target recognition. Based on this, this paper first analyzes remote sensing image information extraction methods based on machine learning, then studies high-resolution remote sensing image target detection methods based on machine learning, and finally presents the development trend of high-resolution remote sensing image target detection based on machine learning. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Remote sensing

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Proceedings - 3rd international conference on Information Systems and Software Technologies, ICI2ST 2022

Proceedings - 3rd International Conference on Information Sy...

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3rd international conference on Information Systems and Software Technologies, ICI2ST 2022

ISBN: (纸本)9781665455176

The proceedings contain 25 papers. The topics discussed include: a convolutional neural network-based web prototype to support melanoma skin cancer detection;person re-identification system in a controlled environment based on soft biometric features: clothing color and body silhouette collected on short video sequences using computer vision and machine learning algorithms;identifying defective fruits and vegetables with hyper-spectral images: a brief tutorial;development of a video surveillance web application for the use of biosafety equipment with image recognition for workspaces;person re-identification using soft-biometric features: body silhouette and clothing texture in a multi-camera video surveillance environment;systematic mapping: translator language from sign language to Colombian formal language;evaluating extended reality application for a virtual museum. case study: Remigio Crespo Museum;and development of a system to detect stress using electrocardiographic signals and machine learning models.

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Proceedings of 3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

Proceedings of 3rd International Conference on Latest Trends...

引用

3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

ISBN: (纸本)9781665487061

The proceedings contain 23 papers. The topics discussed include: an intelligent QR code scanning system for visually-impaired users;a computer aided technique for classification of patients with diabetes;an applied artificial intelligence technique for early prediction of diabetes disease;polarization-insensitive metalens with high efficiency for optical fiber communication;electrocardiograph based obstructive sleep apnea diagnoses;an application of artificial intelligence for an early and effective prediction of heart failure;an integrated machine learning framework for classification of cirrhosis, fibrosis, and hepatitis;heart attack prediction using machine learning approach;design of unit cells for groove gap waveguide at millimeter wave spectrum;future position estimation in case of GPS outages;design of customized high-performance on-chip and off-chip inductor to transmit power in millimeter distance compared with contrasting electromagnetic simulators;and application of digital twin technology in the field of autonomous driving test.

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Predicting A New Hotel Rating System by Analysing UGC Content from Tripadvisor: machine learning application to Analyse Service Robots Influence 3

Predicting A New Hotel Rating System by Analysing UGC Conten...

引用

3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Calero-Sanz, Jorge Orea-Giner, Alicia Villace-Molinero, Teresa Munoz-Mazon, Ana Fuentes-Moraleda, Laura Rey Juan Carlos Univ Signal & Commun Theory & Telemat Syst & Comp Madrid Spain Tech Univ Madrid Dept Appl Math & Stat EIAE Madrid Spain Rey Juan Carlos Univ High Performance Res Grp OPENINNOVA Madrid Spain Univ Paris 1 Pantheon Sorbonne EIREST Paris France Rey Juan Carlos Univ Business Econ Madrid Spain Rey Juan Carlos Univ Ctr Univ Estudios Turist Madrid Spain

Industry 4.0 tools permit computerized creation measures, and Artificial Intelligence (AI) approaches are pivotal in investigating the travel industry. Applying these devices to decipher User Generated Content (UGC) is fundamental to understand better client's necessities, opinions, and assumptions regarding tourism services. Through this research, an exploratory analysis of results is developed through machine learning Models to understand better the role played by robot and traveler typologies on the rating given to hotels considering TripAdvisor reviews of 74 hotels. The purpose of this exploratory research is to develop a methodology focused on analyzing online reviews related to service robots in hotels using machine learning techniques to train the data collected from TripAdvisor. Preliminary results show a link between the hotel rating given in TripAdvisor and the robot typology implemented in hotels. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： robot typology traveler TripAdvisor Rating machine learning

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application of Deep Reinforcement learning in Quantum Control 3

Application of Deep Reinforcement Learning in Quantum Contro...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Cheng, Yiming Yin, Yixi Lin, Jiaying Wang, Yaobin Officers College of PAP Sichuan Chengdu610213 China School of Science Beijing University of Posts and Telecommunications Beijing100876 China Southwest University of Science and Technology Sichuan621010 China

machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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