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Personalized Federated Learning for Heterogeneous Residential Load Forecasting

Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

关键词： load forecasting personalized federated learning differential privacy

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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Speech Emotion Recognition Using yet Another Mobile Network Tool

Speech Emotion Recognition Using yet Another Mobile Network ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Oyediran, Mayowa Oyedepo Ojo, Olufemi Samuel Bharany, Salil Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Farhaoui, Yousef Awotunde, Joseph Bamidele Department of Computer Science Ajayi Crowther University Oyo Oyo Nigeria CET Department GNDU Amritsar India Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria

ISBN: (纸本)9783031262531

Recognizing and reacting to the emotions of others is a key necessity for successful human engagement since emotions are such an important and vital component of being human. Speaking is the most intuitive method by which individuals ex-press themselves. It follows that this communication medium should be extended to computer programs. Affective computing focuses on the automated perception, understanding, and synthesizing of emotional responses, consequently, speech emotion recognition seeks to recognize a speaker's core emotional state derived from the voice signal. The paralinguistic information conveyed by speech expressions has been identified to be beneficial in speech processing in a variety of ways, most notably as a significant component of computer emotional intelligence and contributing to Human – computer Interaction. In this regard, this study uses a spoken emotion recognition technology to deliver "Yet Another Mobile Net-work Tool" where 1295 primary dataset acquired from Bells University of Technology. The dataset consists of five emotions (anger, fear, neutral, happy, and sad) that were used for the experiment. The experimental results show that the study obtained a recognition accuracy of 85.5% and a rejection rate of 10.5%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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Deep Learning Approaches for Malware Detection in the Industrial Internet of Things: A Comprehensive Analysis 25

Deep Learning Approaches for Malware Detection in the Indust...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Samara, Ghassan Fadda, Mohammad Abu Al-Mohtaseb, Abeer Alazaidah, Raed Alrefai Mohamed, N. Odeh, Mahmoud Aljaidi, Mohammad Shuaib, Mohammed Mahmod Kanan, Mohammad Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Science Department of Mathematics Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This survey aims to identify hazards associated with the Internet of Things (IoT), focusing on malware that can infiltrate various devices, applications, and systems within the Industrial Internet of Things (IIoT). Such malware enables malicious actors to manipulate these systems. This research utilizes deep learning as a method for detecting malicious programs. Additionally, it provides hypotheses and background information on deep learning and the IIoT, along with an overview of the types of malware that can compromise IoT applications. Finally, the study presents findings related to the research hypotheses and relevant topics. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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An eBPF-Based Trace-Driven Emulation Method for Satellite Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第3期6卷 188-192页

作者： Tian, Weibiao Li, Ye Zhao, Jinwei Wu, Sheng Pan, Jianping Nantong University School of Information Science and Technology Nantong226019 China Nantong Research Institute for Advanced Communication Technologies Nantong226019 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Victoria Faculty of Engineering and Computer Science VictoriaBCV8P 5C2 Canada

System-level performance evaluation over satellite networks often requires a simulated or emulated environment for reproducibility and low cost. However, the existing tools may not meet the needs for scenarios such as the low-earth orbit (LEO) satellite networks. To address the problem, this letter proposes and implements a trace-driven emulation method based on Linux's eBPF technology. Building a Starlink traces collection system, we demonstrate that the method can effectively and efficiently emulate the connection conditions, and therefore provides a means for evaluating applications on local hosts. © 2019 IEEE.

关键词： Satellite communication systems

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Design of a Detection Device for Low Resistance in Electrical Circuits of EMUs 4

Design of a Detection Device for Low Resistance in Electrica...

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4th International Conference on Intelligent Power and Systems, ICIPS 2024

作者： Bing, Chenyang Geng, Tianyu Zhao, Siao Li, Liwei CRRC Qingdao Sifang Locomotive & Rolling Stock Co. Ltd Qingdao China Faculty of Electronic and Information Engineering Liaoning University of Science and Technology Anshan China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9798331532765

With the development of high-speed rail technology, the electrical system of the rolling stock, as a core component, is crucial for its normal operation. Once the electrical system fails, it may lead to safety accidents and property losses. Therefore, it is of great significance to study the identification and judgment of the false connection of the electrical circuit of the moving train set. The low resistance of the circuit is measured by injecting high current and voltammetry to establish the correlation between the resistance value and the false connection, so as to realize the accurate identification of the false connection abnormality. Aiming at the characteristics of vehicle electrical circuits with many complex structures, the magnetic retention relay array is used to realize multi-channel matrix testing, which improves the efficiency of circuit resistance testing. At the same time, the data is transmitted to the system platform through wireless communication. In the end, the development of "electrical connection reliability testing device"with the functions of high-current low-resistance testing, grounding resistance testing, automatic circuit testing, etc., to provide reliability assurance for the electrical system of the rolling stock. © 2024 IEEE.

关键词： Railroad transportation

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AMBO:All Members-Based Optimizer for Solving Optimization Problems

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computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate *** optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： Algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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Optimization technique based on cluster head selection algorithm for 5G-enabled IoMT smart healthcare framework for industry

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Paladyn 2022年第1期13卷 99-109页

作者： Jaaz, Zahraa A. Ansari, Mohd Dilshad Josephng, P.S. Gheni, Hassan Muwafaq Computer Department College of Science AlNahrain University Baghdad Iraq Selangor Malaysia CMR College of Engineering Technology Hyderabad India Faculty of Data Science Information Technology INTI International University Persiaran Perdana BBN Negeri Sembilan71800 Malaysia Computer Techniques Engineering Department Al-Mustaqbal University College Hillah51001 Iraq

Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS;however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS. © 2022 Zahraa A. Jaaz et al., published by De Gruyter.

关键词： Energy utilization

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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