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共 35127 条记录，以下是4551-4560 订阅

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Boosting Multi-Class Emotion Detection Through Hyperparameter Optimization 5

Boosting Multi-Class Emotion Detection Through Hyperparamete...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Nooji, Pavitha Savithramma, R.M. Ashwini, B.P. Pinto, Merwin Vishwakarma University Department of Artificial Intelligence Maharashtra Pune India Siddaganga Institute of Technology VTU Department of CSE Karnataka India

ISBN: (纸本)9798331518097

This Research Study is based on examining various techniques and methodologies from machine learning to detect more than one emotional state in text data, this is further enhanced by hyperparameter tuning. There are various challenges faced in today's world in the context of emotion detection, it includes ambiguous text as well as cross-cultural differences in emotional expression. Presently models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are widely used in various text classification tasks but there is no efficient optimization method is commonly used to tune the results properly. Proper data tuning is very important to incorporate accurate predictions. This study shows how these limitations could be solved by implementing hyperparameter tuning to improve both training and validation accuracies. Advanced Natural Language Processing (NLP) is implemented to capture unique emotional entities from text data, especially in fields like mental health, customer service, and marketing. It may also open up opportunities for more enhanced and upgraded deep learning techniques. © 2024 ieee.

关键词： Adversarial machine learning

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Secure and Trusted Copyright Protection for Educational Data on Redactable Blockchains 29

Secure and Trusted Copyright Protection for Educational Data...

引用

29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Li-E Wei, Yi Liu, Peng Li, Xianxian Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin China

ISBN: (纸本)9798350330717

Due to the explosion of online educational resources, the protection and management of educational multimedia data have become more challenging. Blockchain has emerged as a promising technology for copyright management due to its decentralized and traceable nature. However, it still faces problems such as difficulties in copyright maintenance and delays in the consensus process, especially for educational multimedia data with large amounts of data, high demand for privacy, and many participants. This paper proposes a secure and trustworthy copyright protection method based on a redactable blockchain to address these issues. We use a decentralized chameleon hash function for copyright maintenance to enable trusted blockchain editing. This can promptly modify infringing copyrights to complete efficient copyright maintenance. For the delay during consensus, we design a consensus mechanism called Proof of Behavior (PoB) based on the Bayesian network (BN), which can achieve fast and reliable consensus by predicting user behavior and selecting highly trusted nodes to participate in consensus. Finally, We conduct simulation experiments to validate the performance of our proposed approach. Specifically, our approach effectively reduces storage space by 5%-10% comparable to other blockchain solutions and enhances system scalability and security. © 2023 ieee.

关键词： consensus mechanism copyright educational multimedia data Proof of Behavior Redactable blockchain

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Impact Factors and Safety Analysis of Takeover Behavior in Remote control of Maritime Autonomous Surface Ships 7

Impact Factors and Safety Analysis of Takeover Behavior in R...

引用

7th ieee international conference on Transportation Information and Safety, ICTIS 2023

作者： Chen, Guanglin Li, Chen Liu, Jialun Lin, Nan Hu, Xinjue State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan China Wuhan University of Technology School of Transportation and Logistics Engineering Wuhan China Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan China National Engineering Research Center for Water Transport Safety Wuhan China

ISBN: (纸本)9798350308532

With the development and application of artificial intelligence, advanced control theory, mobile communication, and IoT technologies, the Maritime Autonomous Surface Ship (MASS) has gradually entered the development characteristics period of remote control (Level 2). Based on the review of development dynamics and application cases of the remote-controlled ship in recent years, it analyzes the impact factors of operator takeover behaviors from three aspects: network communication, system control performance, and traffic scenarios, it proposed a new development direction of the remote-controlled ship, which could provide theoretical support for scholars in related fields. © 2023 ieee.

关键词： Remote control

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Self-Organized Multi-Hop Bridge Reservation Medium Access control for cyber Physical Autonomous Vehicles 6

Self-Organized Multi-Hop Bridge Reservation Medium Access Co...

引用

6th international conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Srithar, S. Sivaraju, S.S. Vetrimani, E. Kanaga Priya, P. Athinarayanan, S. Kalyan, G. Ramesh Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India R V S College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641 402 India Presidency University Department of Computer science and Engineering Bangalore India KPR Institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Computer science and Engineering Tirupati517520 India SNS College of Technology Department of Computer science and Engineering Coimbatore India

ISBN: (纸本)9781665483285

The cyber-physical intelligent systems (CPIS) are integrated with internet-connected network devices for communication and content sharing. The CPIS is widely used in intelligent transportation and unmanned ariel vehicle. In an autonomous cyber-physical vehicle system to coordinate and connect with the other vehicle to enhance the perception is a challenging task. The ad-hoc network of nodes should be well connected with the control unit and other authorization entities for dropping less transmission. Mostly the ad-hoc vehicle suffers from connectivity issues whenever the handover from one network into another network. The current self-organized vehicle suffers in terms of hard and soft handover because of its dynamic nature. The proposed approach is named Multi-hop Bridge Reservation Medium Access control (MBR-MAC) for optimal handover between the roadside unit. The approach MBR can handle hidden and exposed terminal problems. Mostly the failure handover happens when there is no channel to carry over the current connectivity. The proposed scheme utilizes the time and code division multiplexing for optimum bandwidth utilization. The experimental result shows the better results in terms of byte transmission rate and handover failure rate. © 2022 ieee.

关键词： Embedded systems

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Development of intelligent Fault Diagnosis and Predictive Maintenance Algorithm in Photovoltaic Power Generation System

Development of Intelligent Fault Diagnosis and Predictive Ma...

引用

2024 international conference on Power, Electrical Engineering, Electronics and control, PEEEC 2024

作者： Guan, Junning Lei, Guosheng Jiang, Daofang Zhou, Yifan Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangdong Zhuhai519000 China

ISBN: (纸本)9798350378917

With the global awareness of renewable energy and environmental protection rising, photovoltaic power generation system (PPGS), as an important part of clean energy, its stable operation and fault management has become a research hotspot. Aiming at the problem of fault diagnosis and predictive maintenance in PPGS, this paper proposes an intelligent fault diagnosis and predictive maintenance algorithm based on deep learning and time series analysis technology. By monitoring the operation data of key equipment in PPGS in real time, the algorithm uses features extraction techniques such as wavelet transform (WT) and deep neural network (DNN) model to automatically learn the deep-level features in the data, thus realizing accurate fault prediction and health status assessment of photovoltaic inverter. In order to quantitatively evaluate the health status of inverter, this study constructs the health status index (HSI) based on the output of deep learning model, and uses ARIMA model to predict the trend of HSI, so as to warn the possible faults in advance. The experimental results show that the proposed algorithm can effectively reflect the health state changes of the inverter, which provides strong support for preventive maintenance. This algorithm is superior to the comparison algorithm in the accuracy and timeliness of fault prediction, and has high feasibility and practical application value. © 2024 ieee.

关键词： Predictive maintenance

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Smart Fan with Sensor based Activation and Environment control System 5

Smart Fan with Sensor based Activation and Environment Contr...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Devi, Y. Sarada Chaitanya, Anyam Kumar, Katari Sanjay Chandra, Potnuru Bharath Gopal, K. Venu Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Electronics and Communication Engineering Vijayawada India Lakireddy Bali Reddy College of Engineering Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798331504403

With the increasing demand for smart home-automated systems, there is a growing need for efficient and intelligent control mechanisms that enhance comfort and energy efficiency. A smart fan control system is proposed, utilizing ultrasonic and PIR sensors for reliable person detection and an LM35 temperature sensor for dynamic fan speed regulation. The system ensures immediate fan activation upon detecting a person, contributing to a comfortable environment. The LM35 sensor adjusts the fan speed based on room temperature, maximizing energy efficiency. Unlike existing approaches that rely on complex fuzzy logic systems, this method prioritizes simplicity and effectiveness. The widespread availability of sensors enhances user-friendliness without compromising functionality. The proposed system offers valuable insights into smart room control. It presents a robust solution, particularly beneficial for individuals with physical disabilities by improving their quality of life through sensor-based activation and environmental control. © 2024 ieee.

关键词： Smart manufacturing

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Ensemble Learning Based Electrical Load Prediction

Ensemble Learning Based Electrical Load Prediction

引用

2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Chandna, Manisha Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350357769

Electrical load is a stochastic variable;hence its prediction can be a difficult task. The 'curse of dimensionality' can also reduce a predictor's capacity for generalization. In contrast to choosing a subset of features that could lead to lost information, the ensemble system for load forecasting in this paper is based on random forests and uses all available features. Regression systems are built using various feature sets in the proposed method, and they are then combined using the average method. The system is trained on a large dataset using various machine learning algorithms and evaluated using various performance metrics. The results show that the proposed system is able to achieve high accuracy and outperform traditional forecasting methods. The developed system has the potential to improve the efficiency of power systems and enable better decision-making in the energy sector. © 2023 ieee.

关键词： ensemble methods random forest regression Short-term load forecasting time series analysis

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Predicting Heart Disease with Machine Learning: A Comparative Study 5

Predicting Heart Disease with Machine Learning: A Comparativ...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Swetha, R. Joy, Helen K. Yassin, Warusia Sridevi, R. Drakshayini, M.N. Pohrmen, Fabiola Hazel CHRIST University Department of Computer Science Bangaluru560029 India Universiti Teknikal Malaysia Melaka Serdang Selangor Darul Ehsan Malaysia T. John Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798331518097

Heart disease is considered as the leading causes of death worldwide. Detection in early stage helps to prevent the disease. Nearly 17 million heart disease deaths occur every year. If heart disease is detected early, it could save many lives. Using machine learning algorithms to detect heart disease early will benefit many people. The present study explains the utilization of machine learning algorithms and artificial intelligence in the detection of cardiac illness. Data science plays a crucial role in healthcare by facilitating the analysis and processing of large volumes of data, therefore enabling the application of Artificial Intelligence (AI) and Machine Learning (ML). This study aims to identify the optimal model for predicting heart disease by employing different classification methods of machine learning, including Logistic Regression (LR) and Support Vector Machine. keywords Artificial intelligence, machine learning, prediction of heart disease, selection of features, decision trees, logistic regression, and accuracy. © 2024 ieee.

关键词： Adversarial machine learning

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Research and Application of Information System Vulnerability control technology Based on Runtime Self-Protection technology 2

Research and Application of Information System Vulnerability...

引用

2nd international conference on Advanced Sensing, intelligent Manufacturing, ASIM 2023

作者： Du, Huijun Li, Wenjing Zhao, Lina Peng, Jia Yuan, Chuanxin Dong, Yang State Grid Shandong Electric Power Company Taian Power Supply Company Shandong China Information&Telecommuniccations Company State Grid Shandong Electric Power Company Shandong China

ISBN: (纸本)9798350385571

With the rapid development of information technology, information systems are becoming increasingly complex, which also brings more security challenges. Vulnerabilities are weaknesses in information systems that can be maliciously exploited to attack the system. In order to effectively protect the information system from attack, the vulnerability elimination technology based on runtime self-protection technology is developed. Therefore, this paper focuses on the overview of the run-time self-protection technology, the demand analysis of the vulnerability elimination technology, the key mechanism of the run-time self-protection technology, and describes the implementation process of the run-time self-protection vulnerability elimination technology in detail. Finally, the results of two groups of simulation experiments are as follows: After optimization, the vulnerability detection rate of the information system is increased by 12.2% on average, and the comprehensive average vulnerability false positive rate is reduced by 7.35%. The development of this technology improves the overall security of information systems, enabling systems to respond more intelligently and adaptively to emerging threats. © 2023 ieee.

关键词： Information systems

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A Keypoint-based Object Representation for Generating Task-specific Grasps 18

A Keypoint-based Object Representation for Generating Task-s...

引用

ieee 18th international conference on automation Science and Engineering (ieee CASE)

作者： Robson, Mark Sridharan, Mohan Univ Birmingham Sch Comp Sci Intelligent Robot Lab Birmingham W Midlands England

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation that is acquired for each object class from a small number of exemplars of objects, tasks, and relevant grasps. The representation encodes task-specific knowledge for each object class as a relationship between a keypoint skeleton and suitable grasp points that is preserved despite intra-class variations in scale and orientation. The learned models are queried at run time by a simple sampling-based method to guide the generation of grasps that balance task and stability constraints. We ground and evaluate our method in the context of a Franka Emika Panda robot assisting a human in picking tabletop objects for which the robot does not have prior CAD models. Experimental results demonstrate that in comparison with a baseline method that only focuses on stability, our method is able to provide suitable grasps for different tasks.

关键词： Robot grasping visual object representation learning and adaptive systems

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