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Improved Power Transfer Efficiency Using a Dual Transmitter Receiver Design With Integrated Decoupling Coils for IPT EV Charging

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IEEE Access 2025年 13卷 91362-91373页

作者： Bilal Alam Saad Mekhilef Marizan Mubin Kok Soon Tey Addy Wahyudie Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory University of Malaya Kuala Lumpur Malaysia School of Engineering Swinburne University of Technology Melbourne VIC Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Electrical and Communication Engineering United Arab Emirates University (UAE-U) Al Ain United Arab Emirates

In high-power inductive power transfer (IPT) systems, multiple coils are used to enhance efficiency, but interactions between coils on the same side can reduce overall performance. Traditionally, decoupling coils are added to minimize these interactions, however they don’t contribute to power transfer and may even hinder system efficiency. This article introduces a new IPT system and coil structure that integrates decoupling coils, which not only improve mutual interaction between same-side coils but also actively contribute to power transfer. The coils are optimized for high mutual inductance, strong coupling coefficient, reduced self-inductance, and minimized side-by-side mutual inductance, performing well even with X-axis, Y-axis, and angular misalignments. Using 3-D Finite Element Analysis (ANSYS-Maxwell), the magnetic coupler’s performance is thoroughly evaluated, and a downsized experimental setup demonstrates the system’s feasibility. The proposed system achieves a maximum dc-dc efficiency of 94.27%, successfully delivering 1 kW across a 120-mm air gap with zero-voltage switching, presenting a significant advancement for multi-transmitter and multi-receiver systems.

关键词： Coils Receivers Transmitters Mathematical models Couplings Inductance Costs Voltage Rectifiers Integrated circuit modeling

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Covert Channel Detection and Generation Techniques: A Survey

Covert Channel Detection and Generation Techniques: A Survey

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Nesmah A. AL-Khulaidi Ammar T. Zahary Muneer A.S Hazaa Adel A. Nasser Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen Department of Information Systems and Computer Science Faculty of Sciences Sa'adah University Sa'adah Yemen

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment.

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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computer systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator

Analyzing Code Injection Attacks on Applications of Android ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Adhitia Erfina Rahmat Hidayat Rafli Radya Rizaldi Puput Riko Maulana Falentino Sembiring Departement of Infomation System Faculty of Engineering Computing and Design Nusa Putra University Sukabumi Indonesia Master of Computer Science Faculty of Information Technology Budi Luhur University Jakarta Indonesia

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application’s source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion.

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Information technology Challenges for Integrated Governance, Risk and Compliance (GRC) 1

Information Technology Challenges for Integrated Governance,...

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1st International Conference on Smart technology, Applied Informatics, and Engineering, APICS 2022

作者： Mahendra, Irfan Meyliana Prabowo, Harjanto Hidayanto, Achmad Nizar Bina Nusantara University Computer Science Program Jakarta11480 Indonesia Nusa Mandiri University Technology Information Faculty Jakarta11450 Indonesia Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Management Departement Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

Integrated GRC is an area of concern for organizations today. Integrated GRC provides benefits for increasing competitiveness and organizational excellence, realizing effectiveness, efficiency, and optimization, as well as supporting the prevention of corruption and fraud. Information technology has a very important role in determining the success of implementing an integrated GRC in the organization. However, based on the research conducted, adequate research on the application of information technology in the implementation of integrated GRC is still lacking. In this paper, a study will be conducted on trends and research gaps related to the application of information technology in the current integrated GRC implementation. The conclusion of this study states that there is a great opportunity for wider research on the opportunities and challenges of implementing the latest information technology such as blockchain, artificial intelligence, big data, and cloud computing to support the effectiveness and success of integrated GRC implementation. © 2022 IEEE.

关键词： Crime

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Traffic Congestion Control with Emergency Awareness and Optimized Communication Infrastructure using Reinforcement Learning and Non-Dominated Sorting Genetic Algorithm

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IEEE Access 2025年

作者： Al-Heety, Othman S. Zakaria, Zahriladha Abu-Khadrah, Ahmed Ismail, Mahamod Shakir, Mohammed Mudhafar Alani, Sameer Alsariera, Hussein Faculty of Electronic and Computer Engineering Durian Tunggal Malaysia Al-Balqa Applied University College of Engineering Technology Department of Electrical Engineering Amman Jordan Universiti Kebangsaan Malaysia UKM Bangi Department of Electrical Electronic and System Engineering Selangor Malaysia Faculty of Engineering and Electronic Engineering Batu Pahat Malaysia University of Anbar Computer Center Anbar5543 Iraq

Urban centers are grappling with increasing traffic congestion, which hampers mobility and reduces the effectiveness of emergency responses. Current traffic management systems struggle to dynamically prioritize emergency vehicles during peak congestion, highlighting the need for an advanced solution that improves both everyday traffic flow and emergency response efficiency. Traditional traffic light systems typically follow either fixed schedules ("Fixed"approach) or make real-time adjustments without achieving long-term efficiency ("Greedy"approach), often failing to effectively balance regular traffic dynamics with emergency needs. We propose the "Avg Log "method, an adaptive Q-learning strategy that enhances conventional traffic management systems by incorporating an emergency-aware module. This module uses real-time data to prioritize emergency vehicles and adjust traffic signals accordingly. Additionally, our approach employs a multi-objective optimization algorithm to strengthen communication infrastructure, optimizing latency, throughput, and reliability. Through simulations in a multi-intersection urban setting, we compared the performance of our method against the standard "Greedy"and "Fixed"approaches. Key metrics assessed included average waiting times, system throughput, and the efficiency of emergency vehicle passage. The "Avg Log"method outperformed traditional methods, showing superior long-term adaptability and learning. It demonstrated lower average waiting times and effectively managed emergency situations, significantly reducing delays for emergency vehicles compared to both control methods. This method provides an efficient balance between overall traffic flow and emergency prioritization, suggesting a scalable solution for complex urban environments. © 2025 IEEE.

关键词： Efficiency

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K-Banhatti Sombor Invariants of Certain computer Networks

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computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method

A Comparative Analysis on Three Duplication Elements in Copy...

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International Conference on Software Engineering and computer systems (ICSECS)

作者： Nur Izzati Nor Azaimi Nor Bakiah Abd Warif Nor-Syahidatul N Ismail Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science and Information Technology Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Johor Malaysia Department of Computer System and Networking Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets.

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