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检索条件"任意字段=5th Asia Conference on Machine Learning and Computing, ACMLC 2022"

共 595 条记录，以下是111-120 订阅

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Predicting Leakage Current of Distribution Insulators Based Deep learning Gated Recurrent Unit 5

Predicting Leakage Current of Distribution Insulators Based ...

引用

5th asia conference on Energy and Electrical Engineering, ACEEE 2022

作者： thanh, Phuong Nguyen Yeh, Chao-Tsung Cho, Ming-Yuan Phan Van, Cuong National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Nha Trang University Deparment of Electronic - Electrical Engineering Khanh Hoa Viet Nam Nha Trang University Deparment of Physics Khanh Hoa Viet Nam

ISBN: (数字)9781665499330

ISBN: (纸本)9781665499330

the serious problem with distribution insulators is the leakage current resulting from severe contaminant accumulation in coastal areas. To improve the electrical safety operations in distribution system in Taiwan, the application of a deep learning machine is developed to predict the leakage current of insulators. In this project, the deep learning methodology with a large scale of gathered data is developed to predict the insulator leakage current with weather parameters. the hourly leakage currents of 15kV distribution insulators are recorded continuously at outdoor operating conditions as the target variable for more than one year. the gated recurrent unit (GRU) based deep learning machine is utilized to predict the leakage current. the performances of GRU are compared with the RNN model with different benchmarks. the resultant experiments proved that the proposed GRU method is suitable to predict the leakage current. © 2022 IEEE.

关键词： Forecasting

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Cloud computing, machine learning, and Secure Data Sharing enabled through Blockchain 5

Cloud Computing, Machine Learning, and Secure Data Sharing e...

引用

5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Singh, Rajesh Chandra, Akkaraju Sailesh Bbhagat, Babeetta Panduro-Ramirez, Jeidy Gaikwad, Anil Pandurang Pant, Bhasker Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India University Doctoral Scholar School of Business and Management Karnataka Bangalore India Chhatrapati Shivaji Maharaj University Department of Computer Science & Information Technology Navi Mumbai India Universidad Tecnológica Del Perú Department of Business Peru Savitribai Phule Pune University JSPM'S JSCOE-MCA Department Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. this system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. © 2022 IEEE.

关键词： Blockchain

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A Survey of Available Information Recovery of Binary Programs Based on machine learning 5

A Survey of Available Information Recovery of Binary Program...

引用

5th IEEE International conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Shao, Wenqiang Yang, Qichao Guo, Xixi Cai, Ruijie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665499132

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. the rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. this paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed. © 2022 IEEE.

关键词： machine learning

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Forecasting Stock Exchange Trends for Discrete and Non-discrete Inputs Using machine learning and Deep learning Techniques 5th

Forecasting Stock Exchange Trends for Discrete and Non-discr...

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5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Dhondi, Teja Ravi, G. Vazralu, M. Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789811986680

the trend of stock exchange prices is always uncertain for the stockholders, and venture capitalists is there governed by multiple factors that can influence them. In this project, we aim to predict the stock market trends using deep learning and machine learning techniques. We had chosen four distinct stock market groups from current stock exchange to be our dataset for experimentation and evaluation. they are metals, petroleum, non-metallic mineral and financials. In this paper, we try to build machine learning models for stock market correction using 9 different machine learning algorithms like SVM, KNN, ANN, RNN, LSTM, logistic regression, XGBoost, AdaBoost, etc. Ten different technical indicators from the past 10 years are used to train our machine learning models to predict the stock exchange movement in two ways, i.e., using non-discrete and discrete data. Our experimentation results reveal that for non-discrete data, the models built using RNN and LSTM give better performance bi for discrete data deep learning techniques outperform other mechanisms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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machine learning based Code Assessment Systems 5

Machine Learning based Code Assessment Systems

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Ainapure, Bharati Pise, Reshma Singh, Digvijay Vishwakarma University Pune411048 India Uttaranchal Institute of Technology Uttaranchal University India

ISBN: (纸本)9798350398267

Today, there is a huge demand for automatic assessment of computer programs due to an outsized variety of Massively Open Online Courses-MOOCs or online courses. Assessment of computer programs is additionally beneficial to the businesses during their recruitment procedure, where participants are required to solve a series of programming statements. As a result, computerized grading of open-ended responses is becoming a hot topic in academia. In existing techniques assessment of computer programs is carried out based on the scores computed by passing the code through a variety of test scenarios. this does not reflect the programmers' overall abilities. therefore considering the programming style of the programmer, the code evaluation can be made more accurate and easy by combining existing systems with machine learning methods. MOOCs are fundamentally an asynchronous platform and a process for teaching using pre-recorded classes, resource video files, class notes, assignments, and tests, all of which are usually available online and provide self-assessment at frequent intervals throughout the learning process. In this paper machine learning based code assessment system is proposed for programming languages. © 2022 IEEE.

关键词： Grading

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Detection of Phishing Websites Using machine learning 5th

Detection of Phishing Websites Using Machine Learning

引用

5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Kumar, Rahul Kumar, Ravi Sahu, Raja Kumar Patra, Rajkumar Ghosh, Anupam Department of CSE Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789811986680

In the cyber-world, phishing attack is increasing rapidly and is a major matter of concern, which is resulting in losing large amount of money of Internet users every year. Our study on this field develops and creates a model which will predict whether a URL is phishing or not, i.e., it can predict whether a URL is phishing or legitimate. the goal of this study is to develop a web application software that detects phishing URLs from the collection of over 5000 phishing URLs and 5000 legitimate URLs, which are collected from open source and are fragmented into 80% training samples and 20% testing samples. the study provided a model for URL classification into phishing and legitimate URLs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Recognition Technology with machine learning into Swallow Ecological Education 5

Recognition Technology with Machine Learning into Swallow Ec...

引用

5th IEEE Eurasian conference on Educational Innovation, ECEI 2022

作者： Wu, Wen-Chuan Wang, Tsai-Chu Wang, Chao-Yuan Yu, Yu-Han National Ilan University Dept. of Computer Science and Information Engineering Yilan Taiwan

ISBN: (纸本)9781665433181

In recent years, artificial intelligence (AI) technology has become the hottest industry trend due to the improvement of computing capability for computer hardware. AI refers to making machines have the same logical thinking and behavior patterns as humans, including learning, perception, reasoning, self-correction, and so on. At present, many industries, such as marketing, finance, transportation, agriculture, and medical care, have integrated AI applications. In this paper, we intend to apply recognition technology to ecological education of swallows. the computer is able to recognize the swallow's behavior and record it, instead of only using the nest-cam. When students observe the swallows, they are informed of relevant knowledge immediately for learning, without the teacher's explanation. this way of learning through technology is easy to attract the attention of students and is more effective. © 2022 IEEE.

关键词： Students

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Plant Seedling Classification Using machine learning 5

Plant Seedling Classification Using Machine Learning

引用

5th International conference on Artificial Intelligence, Big Data, computing and Data Communication Systems (ICABCD)

作者： Khoza, Nokuthula Mahlangu, thabo Khosa, Marshal Ndlovu, Nathi CSIR Next Generat Enterprises & Inst Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Precision agriculture is a farming approach that uses artificial intelligence and information technology to improve crop yield, preserve the environment and maximize profits. Farmers need to follow precision agriculture to improve their crop quality and production Weed control is one of the challenges that agriculture faces. the growth of weed leads to a decrease m crop yield and to prevent that, weed must be identified and achieved earlier to avoid the adverse effects on the crops. Applying deep learning techniques has become an important field of study in precision agriculture. In this paper, we presented two deep learning models to classify crops and weeds in their early growth stages. From the comparison of the two models ResNet50 and MobileNetV2, MobileNetV2 with 500x500 pixel size gave the best performing results with average f1-score of 88% and accuracy score of 88% which shows that this deep learning model can successfully classify 12 segmented plant seedlings in their early growth stages and this tool can be useful to farmers in identifying weeds.

关键词： Convolutional neural network pre-trained models plant seedling classification

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Secure Localization Techniques in Wireless Sensor Networks Against Routing Attacks Using machine learning Models 5th

Secure Localization Techniques in Wireless Sensor Networks A...

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5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Gebremariam, Gebrekiros Gebreyesus Panda, J. Indu, S. Department of Electronics and Communication Engineering Delhi Technological University Shahbad Daulatpur Main Bwana Road Delhi110042 India

ISBN: (纸本)9789811986680

Security is an essential hot research topic for detecting and localization of malicious nodes and improving the quality of service in wireless sensor networks (WSNs). WSNs are exposed to different routing attacks, including wormhole attacks, Sybil attacks, and blackhole attacks that manipulate the network resources and routing protocols, affecting the localization accuracy and quality of service in WSNs. this work proposed secure localization and detection of routing attacks in WSNs using hybrid optimized machine learning techniques with an average detection accuracy of 100% using binary classification with the application of cluster labeling K-means clustering technique. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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2022 5th International conference on computing and Big Data, ICCBD 2022

2022 5th International Conference on Computing and Big Data,...

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5th International conference on computing and Big Data, ICCBD 2022

ISBN: (纸本)9781665457163

the proceedings contain 35 papers. the topics discussed include: research of data-driven iterative optimization scheme for machine learning models;unified analysis of algorithms for dictionary learning;Bayesian network for base station energy consumption data completion;rapid processing technology of massive data based on XMA new collector in flight test;a novel method for product quality control based on production IoT data;digital twin for air conditioning systems based on electric vehicle operating data;data streams management: multidimensional summary with big data tools;ensemble summarization models to leverage performance;empirical study on equity incentive of core employees, enterprise innovation and financial performance-based on big data of gem listed companies;numerical simulation on drag reduction of micro-grooved surface;and hybrid neuro-particle swarm optimization model for predicting depression in asphalt pavements.

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