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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Abbas, Ghulam Ali, Arshad Zubair, Muhammad Mushtaq, Zohaib Farooq, Umar Khan, Irfan A. Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan IEECE University of the Punjab Lahore Pakistan Lab Texas A&M University GalvestonTX77553-1675 United States

ISBN: (纸本)9798331504861

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures—Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)—are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology. ©2024 IEEE.

关键词： Windmill

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Enhancing Medical Support in the Arabic Language Through Personalized ChatGPT Assistance

arXiv

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arXiv 2024年

作者： Issa, Mohamed Abdelwahed, Ahmed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Faculty of Computer Science and Information Technology Egypt-Japan University for Science and Technology Egypt

This Paper discusses the growing popularity of online medical diagnosis as an alternative to traditional doctor visits. It highlights the limitations of existing tools and emphasizes the advantages of using ChatGPT, which provides real-time, personalized medical diagnosis at no cost. The paragraph summarizes a research study that evaluated the performance of ChatGPT in Arabic medical diagnosis. The study involved compiling a dataset of disease information and generating multiple messages for each disease using different prompting techniques. ChatGPT's performance was assessed by measuring the similarity between its responses and the actual diseases. The results showed promising performance, with average scores of around 76% for similarity measures. Various prompting techniques were used, and chain prompting demonstrated a relative advantage. The study also recorded an average response time of 6.12 seconds for the ChatGPT API, which is considered acceptable but has room for improvement. While ChatGPT cannot replace human doctors entirely, the findings suggest its potential in emergency cases and addressing general medical inquiries. Overall, the study highlights ChatGPT's viability as a valuable tool in the medical field. © 2024, CC BY.

关键词： Diagnosis

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Model to improve an ERP implementation based on agile best practice: A Delphi study.

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Procedia computer Science 2023年 219卷 1785-1792页

作者： Werner H. Salas Postgraduate Unit of the Faculty of Systems Engineering and Computer Science National University of San Marcos Lima Perú

Enterprise Resource Planning (ERP) is a business system that supports most of the critical processes of a company. It helps maintain a unified and reliable repository of information for decision-making. Implementing an ERP is a complex project, which implies a high level of effort and investment. Although the methodologies provided by the leading ERP providers are beneficial, there is still a high failure rate in the implementation. Many authors have analyzed the factors and causes of these implementation failures. There is also research to propose new implementation models that replace the existing ones. However, there is little research on existing methodologies incorporating new concepts from other disciplines to improve them. In this research, we propose a model that complements the current methodologies and uses the best practices in project management and software engineering directly related to the issues found in the literature. We submitted it to a group of experts on the subject to validate the model based on the Delphi method.

关键词： ERP Enterprise Resource Planning Project Management Agile Best Practices

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QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

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computers, Materials & Continua 2022年第1期70卷 1127-1140页

作者： Nadia Tabassum Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Syeda Binish Zahra Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer science University of veterinary and animal sciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network,and users can use them at any *** assessing and choosing cloud providers,however,customers confront the variety and difficulty of quality of service(QoS).To increase customer retention and engagement success rates,it is critical to research and develops an accurate and objective evaluation *** is the emerging environment for distributed services at various *** to the benefits of this environment,globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure,accessible,and manageable services to all stakeholders but Cloud computing has security challenges,including vulnerability for clients and association acknowledgment,that delay the rapid adoption of computing *** of resources in the Cloud is difficult because resources provide numerous measures of quality of *** this paper,the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset,task completion time,task completion ratio,and resource *** article is focused on the cloud service’s security,cloud reliability,and could *** this paper,the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy,trust *** findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.

关键词： Cloud computing performance quality of service machine learning neuro-fuzzy

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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The Development of EthoshuntTM to Transform Teaching and Learning Practices of Counseling Ethics Education 30

The Development of EthoshuntTM to Transform Teaching and Lea...

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30th International Conference on computers in Education Conference, ICCE 2022

作者： Zakaria, Noor Syamilah Subarimaniam, Neerushah Saripan, M. Iqbal Ismail, Alyani Department of Counselor Education and Counseling Psychology Faculty of Educational Studies Universiti Putra Malaysia Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia

ISBN: (纸本)9786269689002

Ethics incorporate the sources of human standards and values. However, there is a consistent struggle to locate both concepts of social condition and human values;and the question of what comprises counseling ethics education is complex and intriguing. Therefore, there is an urgency to transform teaching and learning practices of counseling ethics education by integrating educational gamification to complement the conservative classroom teaching and learning methods. A prototype called EthoshuntTM is developed from gamification ideation as an educational gamification-based app that can be utilized in teaching and learning of counseling ethics education. The elements of game dynamics, game mechanics, functionality, and learning flow of EthoshuntTM emerged as unique aspects which enable the students engrossed with learning objectives and charted with learning outcomes, digitally. The researchers advance that EthoshuntTM may bring a new gamut of talent for educators in developing technology-mediated learning to augment teaching and learning activities in counseling ethics education. EthoshuntTM holds the potential to inspire students while making counseling ethics education learning more fun and enjoyable. This offers possibilities to revolutionize existing counseling ethics education teaching and learning practices in line with the needs of the 4th Industrial Revolution. © ICCE *** rights reserved.

关键词： Students

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HSCM: A New Framework For Handwritten Signature Verification Using ConvMixer

HSCM: A New Framework For Handwritten Signature Verification...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Mona Alaa Fathy Amr E. Mohamed Sameh A. Salem Computer & Systems Engineering Dept Faculty of Engineering Helwan University Cairo Egypt Computer & Systems Engineering Dept Faculty of Engineering Helwan University Cairo Egypt Egyptian Computer Emergency Readiness Team (EG-CERT) NTRA Egypt

As nations attempt to recover from the coronavirus disease (COVID-19) epidemic, digital solutions facilitate economic change and put economies on the path to green growth. Therefore, identifying an individual based on biometric characteristics has rapidly become an urgent and essential subject. Banks, intelligence agencies, and high-profile institutions are frequently use handwritten signature verification to authenticate the identification of an individual. This paper introduces an innovative model (HSCM) for the authentication of online handwritten signatures based on the ConvMixer Network. The main idea of the proposed model is to divide the signature into several patches known as strokes, which are then processed using ConvMixer to generate signature features. These features are processed using Feed-Forward network. Experimental results on the real-world dataset show that the proposed model achieved 11.6% equal error rate (EER) compared with other competitors. This vindicates the efficacy and reliability of the proposed model in signature verification).

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The Next Wave of IoT - Extreme IoT

The Next Wave of IoT - Extreme IoT

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IFIP Networking Conference

作者： Ranga Rao Venkatesha Prasad Faculty of Engineering Mathematics and Computer Science Networked Systems Delft University of Technology Delft the Netherlands

ISBN: (数字)9783903176638

ISBN: (纸本)9798350390605

In this interactive and thought-provoking session, we will embark on a captivating exploration of the remarkable world of systems that have some extreme characteristics. These systems hold immense potential to benefit us even in applications that confront extreme limits. While the Internet of Things (IoT) has already revolutionized our interaction with technology by connecting everyday objects to the digital world, this lecture takes us beyond conventional boundaries. We will dive deeper into the realm of extreme IoT, where we push the limits and discover how IoT can thrive and excel in environments with traditional constraints. We will explore various aspects, including crucial factors such as battery life, longevity, delay, and the challenging environmental conditions in which these systems must operate. By challenging the status quo, we uncover novel solutions that overcome these hurdles and unleash the true potential of sensors and radios. Prepare to be inspired as we present real-world examples and showcase research that demonstrates the transformative power of this field. Through this interactive session, we will, together, engage, ponder, and envision the future possibilities that lie within the realm of extreme IoT.

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