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Smart Computing and Application (ICSCA), International Conference on

作者： Sarra Guefrachi Marwa Ben Jabra Naif A. Alsharabi Mohamed Tahar Ben Othman Yasser O. Alharabi Abdulsalam Alkholidi Habib Hammam Faculty of Engineering Université de Moncton) Moncton NB Canada Spectrum of Knowledge Production & Skills Development Sfax Tunisia Computer Engineering University of Ha'il Computer Science Amran University Yemen Ha'il KSA Department of Computer Science College of Computer) Qassim University Buraydah Saudi Arabia line Computer Science and Information College of computer sc. & engneering Ha'il University Ha'il KSA College Department of Software Engineering Canadian Institute of Technology Tirana Canada International Institute of Technology and Management Commune d'Akanda Libreville BP Ga-bon Dept of Electrical and Electronic Eng. Science School of Electrical Engineering University of Johannes-burg Tirana Canada

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%.

关键词： Deep learning Deepfakes computer vision Social networking (online) Computational modeling Neural networks Transfer learning

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Diagnosing Diabetes Disease Based on Machine Learning Techniques

Diagnosing Diabetes Disease Based on Machine Learning Techni...

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2024 1st International Conference on Logistics (ICL)

作者： Salama A. Mostafa Wisam Subhi Al-Dayyeni Mokhalad Abdulameer Kadhim Alsaeedi Mustafa Hamid Hassan Mustafa Musa Jaber Umar Farooq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan Department of Computer Eng. Tech. Bilad Alrafidain University College Ba'aqubah Diyala Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq School of Information Technology UNITAR International University Kelana Jaya Petaling Jaya Selangor Malaysia

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

Machine learning has been popular in medicine for offering methods and assessing disease data. Gradually, machine learning algorithms are becoming critical in attaining early disease diagnosis. Standard datasets of different types of diseases have been utilized in medical research and applications, including liver disease, diabetes, chronic kidney disease, heart disease, breast cancer, brain tumors, and many more. This research reviewed numerous machine learning algorithms for diabetes illness prediction. Subsequently, four machine learning classification algorithms of k-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forest (RF), and XgBoost (XB) are selected to find the best model that performs the diabetes diagnosis. The Pima Indians Diabetes Database (PIDD) dataset is used for testing the models, which includes 768 instances and 10 features, and the class has labels of 0 that indicate negative diabetes or 1 that indicates positive diabetes. Precision, accuracy, and recall criteria are used to evaluate the performance of the algorithms. The experimental results show that the XB is the most suitable algorithm for building a diabetes diagnostic model. The XB model has the highest accuracy of 83.72% in the data split of 70% training and 30% testing. It has achieved an average accuracy of 81.20% as compared with the k-NN (74.02%), DT (77.71%), and RF (77.16%).

关键词： Radio frequency Training Machine learning algorithms Accuracy Diabetes Medical diagnostic imaging Standards Random forests Diseases Testing

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Feasibility study of using electronics wireless electricity detector with python software

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AIP Conference Proceedings 2024年第1期2898卷

作者： H. A. Fadhil M. N. Junita F. N. Hasoon T. A. Fadil R. A. Rahim Electrical and Computer Dep. Faculty of Eng. Sohar University Sohar-Oman Faculty of Electronics Engineering Technology Universiti Malaysia Perlis 02600 Perlis Malaysia Electrical & communication Eng. National University of Science and Technology Muscat-Oman Department of Computer Eng. Techniques Dijlah University Baghdad IRAQ

In this paper, an electronic instrument was developed that can detect electrical energy without using the conventional probes. The proposed system can detect the AC and DC voltage via wireless detector. The circuit design is based on the theory of an electronic circuit consisting of Darlington transistors that have a high gain, and a copper strip (or wire) is used as a probe. It is primarily concerned with the development of an electronic circuit for the safe handling of hazardous electrical, radiation and chemical substances. Furthermore, the proposed system offers multipurpose services such as short circuit prevention and troubleshooting as well as cable disconnection prevention. It also utilizes hazardous sensor services for any leakage voltages and currents since only DC voltages are used in the circuit design. The design is considered "green innovation" due to the fact that a DC voltage source is used rather than AC or high voltage. Intelligently designed and selection practices make efficient use of resources and minimize energy consumption by creating efficient environments. System analysis has been conducted using Python software since this language offers flexibility, and is simple to use.

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Implementation of problem-based learning in process safety and risk management unit 6

Implementation of problem-based learning in process safety a...

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6th International Conference on e-Learning, ICEL 2020

作者： Chin, Bridgid Lai Fui Juwono, Filbert H. Yong, Kelvin Sheng.Chek Chemical Engineering Department Curtin University Malaysia Sarawak Malaysia Dept of Electrical and Computer Eng. Curtin University Malaysia Sarawak Malaysia Faculty of Eng Comput and Science Swinburne University of Technology Sarawak Malaysia

ISBN: (纸本)9781665415507

Problem-based learning (PBL) has been identified by numerous educational professionals as superior to the traditional teaching system, which may promote higher-level learning. In particular, the Active Learning Approach should be used as a catalyst to promote the values and ideals of student learning. This method has been introduced in one of the chemical engineering units, i.e. CHEN4001 Process Safety and Risk Management for the undergraduate program at Curtin University Malaysia. This unit covers the fundamental risk and process safety principles and reviews of the historical accident scenarios to help students develop an understanding for the complexities involved in managing the risks in the process industries. Hence, this paper demonstrates the application of PBL approach in the CHEN4001 Process Safety and Risk Management unit. Furthermore, the student learning and unit evaluation for CHEN4001 with PBL implementation are discussed. © 2020 IEEE.

关键词： Students

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Sensitivity Analysis for Uncertain Linear Systems: A Set-Membership Approach

SSRN

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SSRN 2023年

作者： Silvestre, Daniel Rosa, Paulo Hespanha, João P. Silvestre, Carlos Caparica2829-516 Portugal COPELABS The Lusófona University Portugal The Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal Deimos Engenharia Lisbon Portugal The Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China The Dept. of Electrical and Computer Eng. University of California Santa BarbaraCA93106-9560 United States

This paper deals with the problem of deciding which inputs or parameters in a dynamic model have the greatest impact on the state or output of a linear system. In over-parameterized models or Multi-Agent Systems (MAS), a sensitivity analysis tool would allow for reduced representations of the same system or to decide which agents should be monitored. The proposed approach consists in constructing the exact reachable set for a linear system (uncertain in the case of parameter sensitivity) and projecting onto the desired coordinates associated with the state or output in question. Given the exact representation, under mild assumptions, sensitivity values are optimal for the proposed functions, although others can be specified using the same sets. Illustrative examples are presented in order to provide insights on the proposed method. © 2023, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Comparative Study of Efficiency Enhancement Technologies in 5G Networks - A survey

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Procedia computer science 2021年 182卷 150-158页

作者： Ibrahim Salah M. Mourad Mabrook Aziza I. Hussein Kamel Hussein Rahouma CCE department faculty of Engineering Nahda University Beni-Suef Egypt. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt Electrical & Computer Eng. Dept. Effat University Jeddah KSA Department of Electrical Engineering Faculty of Engineering Minia University Minia Egypt

Mobile communication technologies are tremendously grown in the last few years. With the advent of the ﬁfth generation of wireless networks, and with millions of base stations and billions of connected devices, the need for spectral and energy-efﬁcient system design will be more convincing. In this paper, the evolution of mobile communication networks starting from first-generation to the fifth generation with comparative studies are first introduced. Then, summarizing the recent research initiatives towards the next generation, 5G, evolution. The main requirements of 5G networks and emerging technologies are highlighted. Furthermore, an overview of several technologies that might be used to achieve the 5G requirements including Massive-MIMO, Millimetre-waves, beamforming, full-duplex, and Small-Cells are explained. Finally, a comparative analysis survey of Spectral Efficiency (SE) and Energy-Efﬁciency (EE) based Massive MIMO techniques is introduced as a key contribution of this review article. Good trade-off conditions between EE and SE technologies based on various algorithms are explained with comparative analysis.

关键词： 5G networks Massive-MIMO 5G Technologies Spectral efficiency Energy efficiency

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Performance Optimization for Standalone Virtual Reality Headsets

Performance Optimization for Standalone Virtual Reality Head...

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IEEE Conference on Graphics and media (GAME)

作者： Youssef Samir Sadek Hosny Mohammed A.-M Salem Ahmed Wahby Computer Science and Engineering Faculty of Media Eng. and Technology German University in Cairo Egypt Faculty of Media Eng. and Technology German University in Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Applied Sciences and Arts German University in Cairo Egypt

ISBN: (数字)9781728192444

ISBN: (纸本)9781728192451

This paper focuses on the performance optimization process for standalone virtual reality headsets. With the release of the Oculus Quest on May 2019, and its wide adoption, developing a VR application is not as simple as it used to be on regular PCVR headsets, which run on VR ready desktops powered by dedicated processors and graphics cards. The mobile processor of standalone headsets is much weaker than the ones on a dedicated VR computer. Therefore, heavy performance optimization is needed in order to run full scale applications on the Oculus Quest without facing application slow downs and performance drops. PlayBricks; A VR application running on the Oculus Quest using Unreal eng.ne 4 and C++, is developed to simulate real life LEGOs for architectural design and educational applications. The hardest problem faced in development is the performance problem, where the number of blocks the user places before the application starts slowing down is not enough to build anything meaningful. Through the optimization of the CPU, GPU and draw call threads, the paper reveals how the number of building blocks can be increased from 100 blocks to reach 12,000 blocks.

关键词： Games Optimization eng.nes Headphones Tools Graphics processing units Education

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Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

arXiv

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arXiv 2022年

作者： Jameel, Samer Kais Aydin, Sezgin Ghaeb, Nebras H. Majidpour, Jafar Rashid, Tarik A. Salih, Sinan Q. JosephNg, P.S. Computer Science Department University of Raparin Rania46012 Iraq Department of Natural and Mathematical Sciences Engineer Faculty Tarsus University Tarsus33402 Turkey Biomedical Engineering Department Al-Khawarezmi Eng. College University of Baghdad Baghdad1001 Iraq Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil44001 Iraq Department of Communication Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad10011 Iraq Faculty of Data Science & Information Technology INTI International University Persiaran Perdana BBN Negeri Sembilan Nilai71800 Malaysia

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients. © 2022, CC BY.

关键词： Generative adversarial networks

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Performance Monitoring and Controlling of Multicore Shared-Memory Parallel Processing Systems

Performance Monitoring and Controlling of Multicore Shared-M...

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Information technology To Enhance e-learning and Other Application (IT-ELA)

作者： Lailan M Haji Subhi R. M. Zeebaree Zainab Salih Ageed Omar M. Ahmed Mohammed A. M. Sadeeq Hanan M. Shukur Computer Science Dep. Faculty of Science University of Zakho Duhok Iraq Energy Eng. Dept. Technical College of Engineering Duhok Polytechnic University Duhok Iraq Computer Science Dept. College of Science Nawroz University Duhok Iraq Information Technology Dept. Zakho Technical Institute Duhok Polytechnic University Duhok Iraq ITM Dept. Technical College of Administration Duhok Polytechnic University Duhok Iraq Computer Engineering Dept. Al-Kitab University Kirkuk Iraq

The field of research known as “parallel processing” examines architectural and algorithmic methods for increasing the efficiency and other desirable qualities (such as cost-effectiveness and reliability) of computers by exploiting many forms of concurrency. The development of multi-core CPUs has opened the door to novel strategies for boosting system performance via parallelism. Increasing the number of concurrent threads greatly affects the time cost. While increased parallelism might reduce calculation time, the associated synchronization costs would likely be prohibitive for most applications. In this article, we'll go through the first half of the process, which involves gathering requirements and designing a prototype; the second half, which involves monitoring and managing a program that can run on a wide range of multicore system architectures, including those with (2, 4, and 8 CPUs). A shared-memory parallel processing method was proposed and built to show off the system's full potential. The implications of multi-core systems on overall program execution time and parallel processing in shared-memory systems provide the backbone of this investigation.

关键词： Costs Multicore processing System performance Systems architecture Prototypes Parallel processing Central Processing Unit

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Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

Research Square

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Research Square 2021年

作者： Salah, Ibrahim Mabrook, M. Mourad Rahouma, Kamel Hussein Hussein, Aziza I. CCE department faculty of Engineering Nahda University Beni-Suef Egypt Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia Egypt Faculty of Computer science Nahda University Beni-Suef Egypt Electrical & Computer Eng. Dept. Effat University Jeddah Saudi Arabia

Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process. © 2021, CC BY.

关键词： 5G mobile communication systems

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