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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Xu, Jiachen Li, Yushuai Zhang, Xiaoyu Guan, Peiyuan Zhang, Shiliang Li, Tianyi Northeast University Department of Information Science and Engineering Shenyang China Aalborg University Department of Computer Science Aalborg Denmark University of Oslo Department of Informatics Oslo Norway

ISBN: (纸本)9798350373318

The synthesis of digital twin (DT) technology into smart grid systems has emerged as a promising approach for enhancing operational efficiency, reliability, and resilience in en-ergy management. DTs offer a virtual representation of physical assets and processes, enabling real-time monitoring, predictive analytics, optimization, and control. This survey paper provides an overview of the recent advances in designing DTs to upgrade the smart grid. We mainly focus on aspects of DT concept, DT empowered economic dispatch, DT empowered prediction and diagnosis, and DT empowered frequency and voltage regulations. Finally, we pinpoint potential challenges and conclude this paper. © 2024 IEEE.

关键词： Power grids

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms 3

Sentiment Polarity Analysis of Bangla Food Reviews Using Mac...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Uttara University Department of Computer Science and Engineering Dhaka Bangladesh National Institute of Technology Silchar India University of Oslo Department of Informatics Oslo Norway Maharishi International University Department of Computer Science Iowa United States CCN University of Science & Technology Department of Computer Science and Engineering Comilla Bangladesh

ISBN: (纸本)9798350388282

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024 IEEE.

关键词： Sentiment analysis

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DocFlow: A Visual Analytics System for Question-Based Document Retrieval and Categorization

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IEEE Transactions on Visualization and computer Graphics 2024年第2期30卷 1533-1548页

作者： Qiu, Rui Tu, Yamei Wang, Yu-Shuen Yen, Po-Yin Shen, Han-Wei The Ohio State University Department of Computer Science and Engineering ColumbusOH43210-1132 United States National Yang Ming Chiao Tung University Department of Computer Science HsinChu300 Taiwan Washington University School of Medicine Institute for Informatics St. LouisMO63110 United States

A systematic review (SR) is essential with up-to-date research evidence to support clinical decisions and practices. However, the growing literature volume makes it challenging for SR reviewers and clinicians to discover useful information efficiently. Many human-in-the-loop information retrieval approaches (HIR) have been proposed to rank documents semantically similar to users' queries and provide interactive visualizations to facilitate document retrieval. Given that the queries are mainly composed of keywords and keyphrases retrieving documents that are semantically similar to a query does not necessarily respond to the clinician's need. Clinicians still have to review many documents to find the solution. The problem motivates us to develop a visual analytics system, DocFlow, to facilitate information-seeking. One of the features of our DocFlow is accepting natural language questions. The detailed description enables retrieving documents that can answer users' questions. Additionally, clinicians often categorize documents based on their backgrounds and with different purposes (e.g., populations, treatments). Since the criteria are unknown and cannot be pre-defined in advance, existing methods can only achieve categorization by considering the entire information in documents. In contrast, by locating answers in each document, our DocFlow can intelligently categorize documents based on users' questions. The second feature of our DocFlow is a flexible interface where users can arrange a sequence of questions to customize their rules for document retrieval and categorization. The two features of this visual analytics system support a flexible information-seeking process. The case studies and the feedback from domain experts demonstrate the usefulness and effectiveness of our DocFlow. © 1995-2012 IEEE.

关键词： Information retrieval

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CNEG-VC: Contrastive Learning Using Hard Negative Example In Non-Parallel Voice Conversion 48

CNEG-VC: Contrastive Learning Using Hard Negative Example In...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Prihasto, Bima Lin, Yi-Xing Le, Phuong Thi Huang, Chien-Lin Wang, Jia-Ching National Central University Department of Computer Science and Information Engineering Taiwan Institut Teknologi Kalimantan Department of Informatics Indonesia National Cheng Kung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728163277

Contrastive learning has advantages for non-parallel voice conversion, but the previous conversion results could be better and more preserved. In previous techniques, negative samples were randomly selected in the features vector from different locations. A positive example could not be effectively pushed toward the query examples. We present contrastive learning in non-parallel voice conversion to solve this problem using hard negative examples. We named it CNEG-VC. Specifically, we teach the generator to generate negative examples. Our proposed generator has specific features. First, Instance-wise negative examples are generated based on voice input. Second, when taught with an adversarial loss, it can produce hard negative examples. The generator significantly improves non-parallel voice conversion performance. Our CNEG-VC achieved state-of-the-art results by outperforming previous techniques. © 2023 IEEE.

关键词： contrastive learning generative adversarial networks hard negative example non-parallel data Voice conversion

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Evaluating a Game-based and a Web-based User Interface Using Skin Conductivity Signals and Gamers and Non-Gamers Preferences 8

Evaluating a Game-based and a Web-based User Interface Using...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Mallas, Andreas Samanta, Maria Xenos, Michalis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9781665491068

There is a growing research interest in how video games influence individuals, with many studies highlighting the positive aspects of gaming to players, with multiple benefits, including task switching, working memory, visual short-Term memory, mental rotation, etc. In our work, we examine if the benefits found in previous research influence how users interact with computers and in particular user interfaces (UIs). To this end, we designed two interfaces for the same car renting application, one incorporating game UI elements-That are specifically used in video games-and the other as a conventional version of the same web application that follows the UI design of similar websites. A lab experiment was conducted where the participants (n=51) used the UIs to book a car. The experiment examined the differences between gamers and non-gamers concerning efficiency (measured by time to complete a task), stress events during the use of the interfaces (measured by a sensor indicating skin conductance response), users' evaluation of the UIs, and user preference on which interface was more satisfying to use. Results indicated that there were no significant differences between gamers and non-gamers in efficiency, the users' evaluation of the UIs, and the number of stress events for both UIs tested, but there was a significant difference in the user preference regarding which UI was more satisfying to use, with gamers selecting the web-based UI while non-gamers the game-based one. © 2022 IEEE.

关键词： User interfaces

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Detection of Dead Victims at Volcanic Disaster Location based on Drone and LoRa

Detection of Dead Victims at Volcanic Disaster Location base...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Hadi, Moch. Zen Samsono Dafa, Achmad Abie Kristalina, Prima Department of Informatics and Computer Engineering Surabaya East Java Indonesia

ISBN: (纸本)9781665476508

When a natural disaster occurs, the process of evacuating victims after a disaster must be carried out immediately to reduce the risk due to the late evacuation process. In this evacuation process, the Search and Rescue (SAR) team played a big role in addition to focusing on the safety of victims and also paying attention to their own safety. Meanwhile, due to the large area of the disaster the search took quite a long time. In this paper, research was carried out to facilitate the evacuation process at the location of volcanic disasters by using drones carrying electronic devices to find the whereabouts of victims on the surface. This process of detecting and classifying is based on the Convolutional Neural Network (CNN) method with the MobileNetV2 model. We train the data set of disaster victims using batch sizes 16 and 32 with epochs of 40, 80 and 100. The resulting model has the greatest accuracy of 0.81 and f1-score of 0.86. © 2022 IEEE.

关键词： Drones

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An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection 13

An Enhanced Grey Wolf Optimizer with Opposition, Mutation, a...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Sirmayanti Prastyo, Pulung Hendro Mahyati Politeknik Negeri Ujung Pandang Department of Electrical Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Informatics and Computer Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Chemical Engineering Makassar Indonesia

ISBN: (纸本)9798350368086

Feature selection is a pivotal process in machine learning and data analysis, aimed at improving model performance by identifying and utilizing the most relevant features while eliminating redundant and irrelevant ones. The Grey Wolf Optimizer (GWO), a metaheuristic algorithm based on the social hierarchy and hunting behavior of grey wolves, has proven to be an effective method for feature selection. However, it is also easy to get trapped in the local optima. Therefore, this study presents an enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy (GWO-OMLSS) to boost exploration and exploitation of GWO in order to avoid the algorithm falling into local optima. The proposed method was validated using ten becnhmark data sets from the UCI machine learning repository and compared with state-of-the-art metaheuristic algorithm-based feature selections, such as GWO, PSO, ESSA, and CS. The Experimental results demonstrate that GWO-OMLSS outperforms the standard of GWO and other metaheuristic algorithms on all data sets except Ionosphere and Wine. The proposed method achieves the best an accuracy of 99.7% and 99.8% on the Exactly and M-of-n data sets, respectively, while also significantly reducing the number of features. It indicates that the proposed method not only obtains high accuracy, but also reduces the dimensionality of features effectively. It means that the proposed method can be considered as a promising algorithm for feature selection. © 2024 IEEE.

关键词： Heuristic algorithms

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WiCHORD: A Chord Protocol Application on P2P LoRa Wireless Sensor Networks 13

WiCHORD: A Chord Protocol Application on P2P LoRa Wireless S...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Balatsouras, Christos-Panagiotis Karras, Aristeidis Karras, Christos Tsolis, Dimitrios Sioutas, Spyros Group Computer Engineering and Informatics Department Patras26504 Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

On the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for reliable wireless long range communications. LoRa is an emerging technology for effective long range communications which can be directly applied to IoT applications. Wireless sensor networks (WSNs) are by far an efficient infrastructure where sensors act as nodes and exchange information among themselves. Distributed applications such as Peer-to-Peer (P2P) networks are inextricably linked with Distributed Hash Tables (DHTs) whereabouts DHTs offer effective and speedy data indexing. A Distributed Hash Table structure known as the Chord algorithm enables the lookup operation of nodes which is a major algorithmic function of P2P networks. In the context of this paper, the inner workings of Chord protocol are highlighted along with an introduced modified version of it for WSNs. Additionally, we adapt the proposed method on LoRa networks where sensors function as nodes. The outcomes of the proposed method are encouraging as per complexity and usability and future directions of this work include the deployment of the proposed method in a large scale environment, security enhancements and distributed join, leave and lookup operations. © 2022 IEEE.

关键词： Internet of things

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Cascaded PFLANN Model for Intelligent Health informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 124-131页

作者： Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda Electronics and Communications Engineering C V Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering Institute of Aeronautical EngineeringHyderabadIndia School of Computing and Informatics University of LouisianaLafayetteLAUSA

Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.

关键词： AI-ML ASR FLANN health informatics neural network PFLANN

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Federated Learning Models for Intrusion Detection in Industrial IoT Networks 7

Federated Learning Models for Intrusion Detection in Industr...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prasad, Sanjana Sharma, Ishu Rajendraprasad, Deepashree HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Boston University Department of Health Informatics BostonMA United States

ISBN: (纸本)9798350372816

The Industrial Internet of Things has emerged as an essential tool for building Industry 4.0 and Industry 5.0 where timely information can be retrieved from different scenarios. These devices are highly vulnerable to cyberattacks as heterogeneous types of devices can be used in the infrastructure that may or may not be equipped with standardized security protocols. Artificial intelligence-based methodologies can effectively identify these types of attacks on a prior basis for taking mitigation action. This method raises concerns about data privacy as building a machine learning-based method requires the sharing of network data that can reveal the actual information of industries. The proposed Federated Learning based framework handles this concern by preserving each device's critical data by utilizing the benefits of the decentralized model aggregation. This research work presents the comparison of the proposed framework on the CICIoT2023 dataset with federated averaging and federated proximal techniques for achieving a global model. The performance evaluation of these two aggregation techniques is performed based on metrics of accuracy, loss, precision and recall. The results prove that the federated proximal method achieves higher accuracy in comparison to the federated averaging method. © 2024 IEEE.

关键词： Network intrusion

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