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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： MacHlev, R. Malka, A. Perl, M. Levron, Y. Belikov, J. Technion-Israel Institute of Technology The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Haifa3200003 Israel Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Non-Intrusive Load Monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Lately, deep learning techniques have demonstrated outstanding performance for NILM predictions. Nevertheless, a possible problem is that users and consumers may find it hard to trust the results of such algorithms if they do not fully understand the reasons for their outputs. In this light, this work presents a method that explains and justifies the outputs of NILM convolutional neural network (CNN) classifiers, using Explainable Artificial Intelligence (XAI) techniques. The method operates as follows: a CNN model for NILM is used to estimate which appliances are activated in the system based on the total power consumption. Then, a XAI technique uses this model and its outputs to explain and justify the prediction of this model. Thereby, the NILM CNN classifier outputs are both accurate and are more interpretable, allowing users to make informed and trustworthy decisions. These ideas are demonstrated on the REDD dataset using a convolutional neural network classifier and two state-of-the-art XAI techniques. © 2022 IEEE.

关键词： Convolutional neural networks

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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Improving the Performance of Firewalls through Network Traffics Logs' Classification of Firat Dataset Using Decision Tree Algorithm

Improving the Performance of Firewalls through Network Traff...

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International Conference on Web Research (ICWR)

作者： Maryam Talaeepoor Behzad Soleimani Neysiani Mohammad Davarpanah Jezi Department of Computer Engineering Faculty of Computer Engineering Naghshe Jahan Institute of Higher Education Isfahan Iran Department of Software Engineering Oil Design and Construction Company (ODCC) Isfahan Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

As long as a computer system is connected to the Internet, it is susceptible to attack as a victim. In computer networks, it becomes important to manage the network based on parameters such as network size and network data. Firewalls are devices that help network administrators in this case to establish security in the network, and can be based on the rules that the firewall is based on. It is configured to control incoming and outgoing network traffic. Firewalls can be considered the most vital components of the network in establishing security. Firat University introduced a dataset containing firewall logs with multiple classes for firewall decisions. This study uses data mining techniques to improve the validation performance of classification using various machine learning algorithms like neural networks, deep learning, and kNN. The experimental results show more than 10% improvement according to precision and recall rates among various folding scenarios used in related works with minor improvement in accuracy, too. The decision tree algorithm is fast and explainable versus other algorithms.

关键词： Deep learning Machine learning algorithms Runtime Firewalls (computing) Neural networks Telecommunication traffic Network security

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Development of a Control System for a Lower Limb Exoskeleton Rehabilitation Robot for Stroke Patients with Standing Instability

Development of a Control System for a Lower Limb Exoskeleton...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Muxin Du Qingqing Wang Liucun Zhu Zhen Liu Lei Shi Yudong Fang Yinxi Gu School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China School of Electric and Information Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China School of Information Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Qinzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower-limb exoskeleton robots can medically assist stroke patients in standing and walking, but there is little research on lower-limb exoskeleton robots that assist in standing balance. In this paper, we design the system of a lower limb exoskeleton rehabilitation robot that can assist in standing balance. We performed kinematic modeling with OpenSim and forward dynamics simulation with SCONE. Having identified the muscle groups and joints that affect standing balance more significantly. This paper focuses on the effects of the hip and iliopsoas muscles on standing balance. We have developed a control system for a lower-limb exoskeleton rehabilitation robot that realizes: when inputting a hip joint change angle signal, an opposite moment and angle are output to control the lower limb exoskeleton rehabilitation robot to assist the patient to achieve standing balance. The robotic system was simulated and analyzed using Matlab and the results showed the effectiveness of the system to keep the patient in standing balance.

关键词： Legged locomotion Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Control systems MATLAB Robots Hip

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Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System

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IEEE Transactions on Network Science and engineering 2023年第5期10卷 2710-2718页

作者： Mahmood, Khalid Obaidat, Mohammad S. Ghaffar, Zahid Alzahrani, Bander A. Shamshad, Salman Saleem, Muhammad Asad Hussain, Shafiq Lahore Campus Lahore55150 Pakistan University of Padua Department of Mathematics Padua35131 Italy University of Jordan KASIT Amman11942 Jordan University of Science and Technology Beijing100083 China Barani Institute of Sciences Department of Computer Science Sahiwal57000 Pakistan King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia The University of Lahore Department of Software Engineering Lahore54590 Pakistan COMSATS University Islamabad Department of Computer Science Sahiwal Campus Sahiwal57000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan

Security and privacy are the leading solicitude in cloud computing since users have restricted privilege on the data maintained by distinct service providers at remote locations. The situation becomes more strenuous when a huge amount of data is sensed and produced by wearable devices as it is usually considered more sensitive and heterogeneous. The majority of existing authenticated solutions for such a challenging environment either suffer due to high computation and communication costs or due to the fact that they are susceptible to known cyber threats making them impractical for utilization. Therefore we introduced a cost-effective and secure authenticated scheme for the cloud-assisted remote wearable health monitoring system. Informal security proof is discussed to showcase the security strength of the proposed scheme against known security threats. After that a detailed performance analysis of the proposed scheme with related protocols reveals the fact that it can reduce the computation and communication costs by more than 37% and 40%, respectively. Moreover, our scheme offers some added security features like physical and desynchronization attacks prevention. © 2013 IEEE.

关键词： Authentication

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A Transfer Learning Based Detection and Grading of Cataract using Fundus Images 25

A Transfer Learning Based Detection and Grading of Cataract ...

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25th International Multi Topic Conference, INMIC 2023

作者： Abbas, Ahsan Alzahrani, Abdulkareem Imran, Azhar Almuhaimeed, Abdullah Khan, Ali Haider Air University Department of Creative Technologies Islamabad Pakistan Al-Baha University Faculty of Computer Science & It Saudi Arabia Digital Health Institute King Abdulaziz for Science and Technology Riyadh Saudi Arabia Lahore Garrison University Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350317701

Among the most prominent causes for visual impairment, especially among older adults, is cataract. As per the World Health Organisation (WHO), there are 2.2 billion individuals globally are estimated to be blind or possess an eyesight problem. One of the most identified and important causes of this is cataracts. Cataracts should be identified and treated as soon as possible to avoid blindness. Ophthalmologists use an expensive slit lamp to diagnose cataracts in regions with few medical facilities. Consequently, the issue is that a lack of skilled ophthalmologists may delay the identification of cataracts, for which medical treatment is unavoidable. Medical image analysis based on artificial intelligence provides a rapid and precise diagnosis in modern healthcare. We utilized deep learning models based on transfer learning, namely VGG19, and ResNet-50, to diagnose cataracts using fundus images and enhance classification accuracy. The performance metric for the model is accuracy;the highest achieved accuracy was 98%. © 2023 IEEE.

关键词： Diagnosis

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

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

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Advancements in Computing (ICAC), International Conference on

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

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

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.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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Advancements in Computing (ICAC), International Conference on

作者： K.N. Dilshan H.A.C.K. Rathnayake L.P.T.L. Pathirana D.H.S.V. Dhanapala Harinda Fernando Pradeep K.W Abeygunawardhana Faculty of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

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Properties of Nonlinear Power System Models that are Symmetrically Configured in the Narrow Sense

Properties of Nonlinear Power System Models that are Symmetr...

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IEEE General Meeting Power& Energy Society

作者： Elior Segev Ron Ofir Yoash Levron Juri Belikov Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia

Symmetry is a fundamental property of three-phase circuits and is widely used in the analysis of large-scale power systems. However, when the system is nonlinear or time-varying, classical symmetry generally does not provide sufficient information. Therefore, in this paper, we explore properties of three-phase circuits with dynamic models that are “simple” in some stronger sense. We define a circuit to be simple if its state and input matrices in the dq0 domain are constant, that is, independent of the reference angle, and show that this property leads to dynamic models which are linear and time-invariant, or at least suitable for low-complexity numerical analysis. We formally define circuits with this property to be Symmetrically Configured in the Narrow Sense (SCNS), and explore their properties. We show, for instance, that SCNS circuits must be symmetrically configured and that their state-space models have several interesting properties and structures. The overall aim is to develop a small toolbox, which may help identify and analyze SCNS circuits in the context of large-scale power systems. We believe this may contribute to the understanding of dynamic phenomena in large-scale systems which are too complex for direct simulation.

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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