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Gas anomaly data detection based on hybrid neural network 7

Gas anomaly data detection based on hybrid neural network

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2022 7th International Conference on Electronic technology and Information science, ICETIS 2022

作者： Li, Xiaolong Xu, Junwu Liao, Jiayin Wang, Chaoqun Chen, Xiaohui Li, Fang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Product Development Department Shanghai Academy of Spaceflight Technology Shanghai China Gas Intelligent Application Technology R&D Center Shanghai Aerospace Energy Co. Ltd Shanghai China

ISBN: (纸本)9783800757794

The accurate measurement of gas flow is directly related to the economic benefits and social interests of gas enterprises. Moreover, as a kind of time-series data, there is a serious imbalance in the distribution of positive and negative samples in the data, which leads to low accuracy and difficulty in detecting gas anomaly data. To address these problems, a cost-sensitive hybrid network model is proposed in this paper. The model combines the stronger local feature learning ability of CNN network and the better sequence feature learning ability of GRU network, and then introduces a cost-sensitive loss function to solve the problem of inaccurate classification accuracy due to skewed data distribution. The experimental results show that using this model can find the outliers in the measurement data more accurately, which has some reference significance for the accurate measurement of traffic. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Flow of gases

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

引用

International Conference on Data Engineering

作者： Yuhan Wu Siyuan Dong Yi Zhou Yikai Zhao Fangcheng Fu Tong Yang Chaoyue Niu Fan Wu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users’ privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem—key-value set secure aggregation—that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously.

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Face Recognition and Detection Using Machine Learning Techniques

Face Recognition and Detection Using Machine Learning Techni...

引用

Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Amarinder Kaur Sudha Shanker Prasad Rachit Garg Sudhanshu Maurya Ajay Kumar Yadav Deepak Suresh Asudani School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Nagpur India Department of Computer Application United Institute of Management Prayagraj U.P. India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

One of the most cutting-edge and well-liked technologies, face recognition (FR) has applications in numerous industries and is used for software authentication, verification, and banking. In this paper we explore and evaluate numerous FR approaches and procedures in this work. The paper addresses several algorithms to improve the accuracy system, assessment criteria, and, lastly, recent work in this field, as well as potential future advances

关键词： Technological innovation Machine learning algorithms Image recognition Three-dimensional displays Reviews Face recognition Software algorithms Artificial neural networks Software Sustainable development

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AI for Disaster Management: Predictive Models and Response Strategies

AI for Disaster Management: Predictive Models and Response S...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： A. Shameem Devupalli Saroja Damodar S. Hotkar Bijaya Bijeta Nayak Anand Pandey Sachin Tripathi AMET Business School AMET University Department of Computer Science and Engineering Vignan's Institute of Information Technology (A) Visakhapatnam Department of Computer Science and Engineering R.T.E. Society's Rural Engineering College Hulkoti Karnataka School of Mechanical Engineering KIIT Deemed to be University Bhubaneswar Odisha Computer Science and Application Sharda University Greater Noida U.P India Symbiosis Law School Nagpur Symbiosis International (Deemed) University (SIU) WATHODA Nagpur Maharashtra India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The application of artificial intelligence (AI) in managing severe weather events has become more necessary in recent years owing to their increased frequency. Disaster management is required to control and avoid such occurrences. To successfully aid in better and quicker reactions to catastrophes, AI is widely employed in prediction and disaster preparation, damage mitigation and minimization, and rescue phases. To determine how AI technology can be used to lessen the damaging impacts of different disasters, this article looks into the potential for connecting artificial intelligence methods based on technological innovation. The study potentially contributes in designing an effective framework for disaster management based on the predictive accuracy and response time.

关键词： Technological innovation Storms Disasters Prevention and mitigation Disaster management Predictive models Software Time factors Personnel Artificial intelligence

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction

Leveraging the Strengths of Long Short-Term Memory and Gated...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Prem Purusottam Jena Kaberi Das Debahuti Mishra Arundhati Lenka Sashikala Mishra Pradeep Kumar Mallick Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to be University Bhubaneswar India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

The study recognizes the multifaceted nature of the stock market and the challenges inherent in predicting market movements accurately. Focused on the Indian stock market, the research harnesses the capabilities of deep learning, specifically the long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, known for their prowess in processing sequential data with predictions over a 10-day horizon. The models are systematically evaluated, considering parameters such as root mean square error (RMSE), mean absolute error (MAE), regression variance, and R2-score, with a comparative analysis highlighting the effectiveness of the proposed hybrid models—LSTM-GRU and GRU-LSTM. The experimental analysis reveals that the GRU-LSTM model demonstrates superior performance on the HDFC bank Ltd. dataset, while the LSTM-GRU model excels on the TATA Motors Ltd. dataset, aligning with the predictive performance and accuracy metrics observed in earlier experiments.

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Characteristic Mode Analysis of Nanostructures using Hydrodynamic Volume Integral Equation

Characteristic Mode Analysis of Nanostructures using Hydrody...

引用

Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Meruyert Khamitova Ran Zhao Doolos Aibek uulu Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China Department of Mathematics and Natural Sciences University of Central Asia (UCA) Naryn Kyrgyzstan

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The characteristic mode analysis (CMA) is formulated and implemented for the hydrodynamic volume integral equation (HDVIE) that is used to mathematically model electromagnetic field interactions and conduction current dynamics on nanoantennas and nanoscatterers. The proposed method produces excitation-independent characteristic hydrodynamic currents and the corresponding modal significance curves, providing useful information that can be used to optimize the performance of a nanoantenna. Numerical results demonstrate the reliability and the applicability of the proposed approach.

关键词： Solid modeling Nanoantennas Integral equations Hydrodynamics Mathematical models Characteristic mode analysis Numerical models

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User Satisfaction Evaluation for ASEAN Entrepreneurship Profiling application Improvement

User Satisfaction Evaluation for ASEAN Entrepreneurship Prof...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Sasmoko Yasinta Indrianti Napoleon Chandra Sonya Rapinta Manalu Davy Ronald Hermanus Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Universitas Agung Podomoro Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia Mobile Application & Technology School of Computer Science Bina Nusantara University Jakarta Indonesia School Of Electric and Informatic Institut Technologi Bandung Binus University Bandung Indonesia

ASEAN Entrepreneurship Profiling is a survey tool in the form of an application to help entrepreneurs identify gaps in their profile by comparing it with the profile of an ideal entrepreneur. Through the application, entrepreneurs can increase their capacity so that they can become successful entrepreneurs. This study aims to evaluate the applications developed by assessing user satisfaction by determining user experience when using the website. Therefore users will feel comfortable and continue to use the application as a method of self-development as an entrepreneur. The evaluation method used in this research is the Human Center Design (HCD) method. In HCD, the user will be used as the human aspect in the evaluation, because the nature of this evaluation is about developing the system from the user's point of view. For this study, the evaluation method will use System Usability Scale (SUS) questions to assist the evaluation. The main reason for using SUS in this research is because SUS questions are implemented using a Likert Scale to collect data that will produce integer results that can help the analysis process more easily. The aim of this research is to produce results that can be used to improve the User Interface of the AEP website and in return will have a positive impact on the user experience which will directly increase user satisfaction when using the application.

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Exploring User Attitudes and Innovative System Design for Remote Lighting Control Systems in Thailand’s Creative Industries

Exploring User Attitudes and Innovative System Design for Re...

引用

IEEE International Conference on Industrial Engineering and Engineering Management

作者： Thammanoon Jeerasottikule Nagul Cooharojananone Kanokwan Atchariyachanvanich Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand School of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

In this paper, the authors investigate the current state of the lighting design and control sector in Thailand’s creative industry. The government aims to promote the creative industry as a key source of income, but there needs more skilled professionals in the industry. The authors have found that successful cases have used the Internet for remote controlling in the creative industry in other countries. Therefore, the paper will explore the potential of using technology to improve professional efficiency and assess the feasibility of implementing remote lighting control systems via the Internet with Thai lighting designers and lighting console operators. Data will be collected through qualitative in-depth interviews and quantitative surveys. The results have shown a promising acceptance rate of wireless devices for lighting control due to their mobility, flexibility, cost-effectiveness, and positive attitudes toward adopting Internet technology. The authors have also proposed a concept design for an internet-based control system tailored to Thai users, focusing on simplicity, ease of connection, and user-friendliness to accommodate those with limited network configuration knowledge. The proposed system aims to reduce professionals, save time, and increase convenience, leveraging Thailand’s extensive wireless internet coverage. These systems could significantly benefit Thailand’s creative industry by addressing the shortage of skilled professionals and improving efficiency.

关键词： Industries Wireless communication Surveys Knowledge engineering Attitude control Lighting Lighting control Internet Interviews System analysis and design

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Utilizing Natural Language Processing to Enhance Information Capture and Retrieval

Utilizing Natural Language Processing to Enhance Information...

引用

Communication & Computing (WCONF), World Conference on

作者： Shikhar Gupta Ravikumar V Dharmesh Dhabliya Preeti Naval Mahesh TR Zahid Ahmed Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This venture focuses on using natural Language Processing (NLP) to facilitate information capture and retrieval. NLP is a complex and fast strategy used for analyzing text and voice data. It is utilized in a ramification of applications, including machine translation, spell checking, sentiment extraction, and statistics extraction. This mission aims to broaden a gadget that could seize, shop, and retrieve information for users based totally on their herbal language queries. The gadget will use NLP algorithms to become aware of and extract applicable facts from text and voice statistics and shop them in a dependent database. A web-based user interface will be developed for searching and retrieving statistics from the database. The gadget can be utilized by businesses to allow efficient retrieval of data via herbal language queries and can also be used by individuals for obligations along with summarizing files. Standard, this task hopes to offer advanced information capture and retrieval with the assistance of NLP.

关键词： Accuracy Databases User interfaces Natural language processing Machine translation Indexes Standards Business

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Machine Learning based model for Loan Amount Prediction and Distribution

Machine Learning based model for Loan Amount Prediction and ...

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2022 Workshop on Advances in Computational Intelligence, its Concepts and applications, ACI 2022

作者： Sharma, Hitesh K. Choudhury, Tanupriya Ahlawat, Prashant Mohanty, Sachi N. Jain, Sarika Dehradun248007 India Dept. of Computer Science Chandigarh University Punjab 140413 India Department of Computer Science Singidunum University Serbia and and School of Computer Science & Engineering VIT-AP University Andhra Pradesh Amaravati India Dept. of Computer Application National Institute of Technology Haryana Kurukshetra136119 India

With the enhancement of the technology, expand of businesses and thoughts more and more people are applying for the loans. Both for their personal use and for business use. But due to the limited amount of assets the bank cannot grant loans to each and every person. Finding out those right people is a typical and time-consuming process. Banks desire to deliver the loan to an individual who can be recompensate the loan on time and can afford maximum profit to the bank. So there is a need of a system which could do this analysis and save the banks time and resources. This can be done using Machine Learning. The objective of this paper is to create a more accurate loan prediction model using machine learning to reduce the risk behind selecting of appropriate people for the loan. For this we'll mine the previous records of the people to whom the bank has granted loan. Using these records variables and bank loan rules we will train a machine learning model which will predict that a person is eligible for loan or not. We will use sklearn for our model and train_test_split for splitting the data set into train dataset and test dataset. Here we are going to use various models like Logistic Regression, Decision Tree(DT) and Random Forest(RF) so as to fetch more accurate results as the given problem is a supervised classification problem. © 2020 Copyright for this paper by its authors.

关键词： Forecasting

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