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An Emperical Way to Diagnose the Cancer Stages Using Algorithm in AI System 5

An Emperical Way to Diagnose the Cancer Stages Using Algorit...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Muniandi, Balakumar Yennapusa, Haritha Ramakrishnan, Rakesh Gupta, Ketan Logeshwaran, J. Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of Central Missouri Department of Computer Science AustinTX United States University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350383522

This paper gives a unique technique for automated cancer detection the use of AI-based totally prediction algorithms. Cancer often gives as a complicated and heterogeneous set of traits, making prognosis hard. In this novel method, an AI-primarily based set of rules is used to come across most cancers from visible and scientific information. The set of rules utilizes supervised studying to categorize scientific information and photograph features, after which practice a supervised type version to be expecting the presence of most cancers. The set of rules is optimized for accuracy and can discover multiple types of cancer with excessive accuracy. The results demonstrate that this novel approach can stumble on most cancers with an accuracy of up to ninety%, making it an effective device for automatic most cancers prognosis. Additionally, the set of rules might be similarly applied to other scientific diagnosis tasks, in which AI-based totally prediction can be a treasured device. © 2024 IEEE.

关键词： Accuracy Heterogeneous Prediction Supervised learning Valuable tool

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Accuracy Measures for Timed Petri Nets

Accuracy Measures for Timed Petri Nets

引用

international conference on electrical engineering, computing science and automatic control (CCE)

作者： Marina Montes-Partida Ernesto Lopez-Mellado CINVESTAV Unidad Guadalajara. Zapopan Jal. Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

The paper is in the scope of quality assessment of discrete-event process models; accuracy measures of timed Petri nets (TPN) are proposed. A TPN $\boldsymbol{N}$ is assessed concerning a set of dated event sequences $\lambda_{\tau}$ , where the occurrence instant of every event is recorded. The class of TPN addressed assigns real-valued time parameters to transitions as a single value $\boldsymbol{\delta}(\mathbf{t}_{\mathbf{j}})$ or an interval of values $\imath(\mathbf{t}_{\mathbf{j}})$ ; a metric is developed for each kind of timing. The accuracy of $\boldsymbol{N}$ is evaluated on [0, 1] by measuring the deviations between the actual event occurrences in $\boldsymbol{\lambda}_{\boldsymbol{\tau}}$ and the expected firing instant of transitions in the TPN.

关键词： Measurement Schedules Accuracy Firing Computational modeling Petri nets electrical engineering computing Timing Quality assessment Iterative methods

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Synthesis of WFN from Reduced Event Logs Based on Event Precedence Structures

Synthesis of WFN from Reduced Event Logs Based on Event Prec...

引用

international conference on electrical engineering, computing science and automatic control (CCE)

作者： Ernesto López-Mellado Néstor Martínez-Medina César Barrón-Rubio CINVESTAV Unidad Guadalajara Zapopan Jal. Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This paper presents a method to build workflow nets (WFN), a subclass of Petri nets, from sets of event traces (called event logs) based on event precedence structures. An event log $\boldsymbol{\lambda}$ is partitioned into sub-logs $\boldsymbol{\lambda}_{\mathbf{i}}$ containing few traces that have common sub-sequences of length two or more events. Event precedence structures are defined to represents the sub-logs; they are structures similar to partial order relations that are derived from event precedence relations drawn from traces. The method includes several steps regarding the building of the event structures and a technique for the synthesis of the WFN.

关键词： Computational modeling Petri nets Buildings electrical engineering computing Compounds Switching circuits

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Design of Wind Power Generation System Based on Interval Optimization 2

Design of Wind Power Generation System Based on Interval Opt...

引用

2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhu, Jijin Luo, Chan Wu, Qinqin Guangdong University of Science and Technology College of Mechanical and Electrical Engineering Dongguan China Dongguan Guangming Primary School Mathematics Teaching and Research Office Dongguan China

ISBN: (纸本)9798350317558

According to the analysis of the mechanism model of the doubly fed variable speed constant frequency wind power generation system, it can be concluded that the system is a complex system with nonlinearity, uncertainty, and constraints. Therefore, based on the above characteristics, this paper uses predictive control method to control it, and uses interval optimization theory to solve the optimal control quantity in rolling optimization. Based on the mathematical transformation model, develop a nonlinear interval number optimization algorithm with certain engineering practicality. The main difficulty to solve is the low efficiency caused by two-layer nested optimization, and further improve the real-time performance of the algorithm by adopting parallel computing strategy on the basis of designing the solution algorithm. The results show that compared with classical big data mining technology and traditional data analysis methods, interval optimization technology have better accuracy and convergence in feature recognition, and can judge feature recognition. © 2024 IEEE.

关键词： Big data Feature recognition Interval Optimization traditional data analysis method Wind Power Generation System

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Research on Abnormal Behavior Identification Method for Autonomous Driving Formation Vehicles in Urban Road Environment

Research on Abnormal Behavior Identification Method for Auto...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Kailong Zhang, Feng Li, Min Wang, Li Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing10041 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang330100 China Beijing Jidao Technology Co. Ltd. Beijing100044 China

ISBN: (纸本)9789819639687

With the development of science and technology and social progress, the landing of automatic driving technology seems to be close at hand, but the reality is that high-level automatic driving vehicles want to drive safely on urban roads, but also need to have the ability to accurately identify abnormal behaviors and accurately assess the risks in their operation. This paper focuses on the scientific issues in the process of abnormal behavior identification and risk assessment of autonomous driving formation vehicles in urban road environments. We constructed a framework for identifying abnormal behaviors of autonomous driving multi-vehicle formations in urban road environments, simulated urban road traffic scenarios using the CARLA-SUMO joint simulation platform, and completed several groups of independent simulation tests, and created a 17-dimensional multi-vehicle formation dataset by using five typical machine learning methods, and completed the identification and classification of six types of abnormal behaviors: sharp acceleration, sharp deceleration, overspeed, sharp steering, discrete, and falling out of line, abnormal behavior identification and classification. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： automatic vehicle identification

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The Technical Ethics used in Bad and SD Methods: A Deep Review 4

The Technical Ethics used in Bad and SD Methods: A Deep Revi...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Aadithyan Shnain, Ammar Hameed Sharma, Vikrant Lafta, Alaa M. Mudhafar, Mustafa Ghobash, Ali Jawad, Ahmed Qusay Hindustan Institute of Technology and Science Department of Computer Applications India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Department of Cse Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq University of Kerbala Faculty of Mediscal Applied Sciences Department of Medical Physics Karbala56001 Iraq Al-kunooze University College Computer Engineer Department Basra Iraq Kut University College Departmen of Electrical Engineering Technologies Wasit Iraq

ISBN: (纸本)9798350360165

Artificial intelligence (AI) is the process by which human-designed systems analyse available data and make judgements based on predetermined criteria in order to accomplish particular objectives. The incorporation of artificial intelligence (AI) has sparked a deep ethical conversation among key players, including tech companies, businesses, educational institutions, and legislators. AI's ethical problems have prompted the creation of ethical frameworks that aim to control its application for the good of society, the defence of human rights, and the maintenance of individual autonomy and privacy. This paper provides a critical evaluation and summary of the fundamental concepts behind the use of artificial intelligence, with a focus on the GDPR's (General Data Protection Regulation) approach. The rights of data subjects, data protection legislation, and data subjects' permission are crucial considerations in the context of 'privacy by design.' © 2024 IEEE.

关键词： Differential privacy

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Modern Trends in Deep Learning for Fruit Quality Analysis of Hyperspectral Images: A Review 14

Modern Trends in Deep Learning for Fruit Quality Analysis of...

引用

14th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2023

作者： Lanke, Neha Chandak, M.B. Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Non-destructive testing techniques for fruit quality analysis have gained importance over the years. Hyperspectral image processing is one of the important non-destructive, cost-effective, rapid, and reliable quality testing techniques that provide both spatial as well as spectral information. Due to the automatic feature learning ability, deep learning is one of the most promising and powerful techniques with better classification accuracy for fruit applications. This paper starts by giving an overview of the quality concept of fruits and vegetables followed by fundamentals of hyperspectral imaging, and its potential applications. Then machine learning and deep learning techniques are highlighted followed by recent research work to illustrate the potential of hyperspectral imaging for fruit and vegetable quality analysis. Lastly, the research gap and future considerations are discussed to improve the analysis and applicability of hyperspectral imaging using deep learning for the quality assessment of fruits, and vegetables. © Grenze Scientific Society, 2023.

关键词： Hyperspectral imaging

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Multi-Robot Active Mapping based on Inverse Reinforcement Learning in Unknown Environments

Multi-Robot Active Mapping based on Inverse Reinforcement Le...

引用

2024 international automatic control conference, CACS 2024

作者： Hsieh, Ping-Heng Wong, Kai-Yi National Sun Yat-sen University Electrical Engineering Department Kaohsiung Taiwan

ISBN: (纸本)9798350354904

Multi-robot active mapping aims to efficiently explore and construct complete maps of unknown environments using multiple robots. The key to solving this problem lies in frontier-based exploration, which enables more efficient robot movements. Recent approaches have employed Reinforcement Learning (RL) to optimize robot policies for efficient exploration. However, designing a suitable reward function for R L in this context is challenging because it requires balancing multiple objectives, such as rapid exploration, efficient coordination, and complete coverage. To address this challenge, we propose an Inverse Reinforcement Learning (IRL) approach that automatically updates the underlying reward function from expert demonstrations of effective multi-robot mapping. We use the performance of NeuralCoMapping on specific maps as expert demonstrations and take NeuralCoMapping as the RL policy. By learning the potential reward function optimized by these expert demonstrations, we aim to discover a more comprehensive and balanced reward function that captures the key features of efficient multi-robot mapping. The proposed IRL-based approach not only offers a promising direction for automatically learning effective reward functions and policies in complex multi-robot systems but also instills optimism for the future of robotics, reducing the need for manual reward engineering. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Authentication Methods for Access and control Management in Cloud computing 10

Enhanced Authentication Methods for Access and Control Manag...

引用

10th IEEE Uttar Pradesh Section international conference on electrical, Electronics and Computer engineering, UPCON 2023

作者： Teegala, Shyam Prasad Vijai, C. Nagpal, Amandeep Anuradha, R. Aljbori, Ali Swathi, S.B. Anurag University Department of Cse Telangana Hyderabad India Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala R&d Department of Commerce and Business Administration Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India Technical Institute of Babylon Al-Furat Al-Awset Technical University Babylon Iraq New Horizon College of Engineering Department of Information Science Engineering Bangalore India

ISBN: (纸本)9798350382471

Cloud computing has become a disruptive technology that provides both consumers and organizations with scalable and affordable solutions. But serious security concerns have been brought up by the growing use of cloud services, especially when it comes to access and control management. Conventional authentication techniques, such two-factor authentication and passwords, have shown to be insufficient to protect cloud resources from a constantly changing array of cyberthreats. This study investigates improved authentication techniques created to handle the particular difficulties associated with cloud computing access and control administration. The study looks into a number of methods, such as adaptive access control, biometrics, multi-factor authentication, and behavioral analysis based on machine learning. By making the authentication process more reliable and easier to use, these techniques hope to lower the dangers of data breaches and unauthorized access. Additionally, the study explores how these improved authentication techniques might be used in a cloud computing context while taking data protection laws, scalability, and user friendliness into account. In order to achieve a balance that maximizes security without sacrificing the user experience, the study also looks at the trade-offs between security and user convenience. The results of this study have the potential to greatly help both individuals and businesses that use cloud services by providing enhanced security against unwanted access while preserving a smooth and intuitive user experience. Improved authentication techniques are becoming more and more necessary to guarantee the availability, confidentiality, and integrity of vital data and resources as cloud computing remains an essential component of contemporary IT architecture. © 2023 IEEE.

关键词： access Authentication Cloud computing control management protection

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NLP-assisted information extraction for layout criteria of substation 6

NLP-assisted information extraction for layout criteria of s...

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6th international conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Wu, Bing Song, Yuanbin Cao, Jinhao Economic Research Institute of State Grid Zhejiang Electrical Power Company 1 Nanfu Road Zhejiang Province Hangzhou310008 China School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

The automatic compliance review of substation design model greatly depends on the computer-understandable layout criteria. Unfortunately, most of these layout criteria are depicted in a series of design codes written in natural language. Meanwhile manual conversion from unstructured code text to layout rules is often costly and time consuming. In this regard, automatic extracting layout information can be developed utilizing Natural Language Processing (NLP) tools based on deep learning. The layout texts are collected and manually labeled to establish the experimental corpus for further study, and then the NLP-assisted information extraction method is developed mainly using the Bidirectional Long Short-Term Memory (BiLSTM) networks and Conditional Random Field (CRF). Finally, the information extraction model is trained on the labeled experimental corpus. The test results indicate that the developed model is feasible to extract semantic information of layout constraints residing in design code. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Random processes

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