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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bashir, Muhammad Tahir, Sidra Almufareh, Maram Fahaad Hamid, Bushra Qamar, Farkhanda PMAS ARID Agriculture University Rawalpindi Punjab Pakistan Jouf University College of Computer and Information Sciences Department of Information Systems AL-Jouf Sakakah Saudi Arabia Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

MANET is a wireless medium, with no infrastructure, for nodes to communicate within a network. Movement of nodes is a lot more in this network than other networks due to lack of infrastructure (Wireless nature). In a mobile ad hoc network, each node functions as a router and finds the best path forwarding a packet via origin to destination. This network is important in places where building a framework is impossible, like in a military setting. A mobile ad-hoc network (MANET) is a temporary network created by unrestricted nodes that can move around and communicate without a centralized network. In a black hole attack, several or even more attacker node use a tunnel-like private channel to find a wormhole attack. Wormhole tunnel will then start collecting the data packets and transferring them to another location. The proposed work encouraged secure and effective routing in MANET. At that point, we selected 20 important attributes to create a dataset that was labelled with the aid of a unique node address. Apply two well-known machine learning classifiers that categorise data into two groups, specifically normal and malicious, to test sample data in this manner. For feature selection and SVM (Support Vector Machine) classification, respectively, we employ genetic algorithms. The demonstration of the technology was assessed on many quantifiable characteristics and compared to contemporary techniques. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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computer systems science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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Offloading Decision Making in Mobile Edge Computing: A Survey 2

Offloading Decision Making in Mobile Edge Computing: A Surve...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Abu-Taleb, Najlaa Abdulrahman Hasan Abdulrazzak, Fuad Zahary, Ammar Thabit Al-Mqdashi, Abdulmajid M. Thamar University Faculty of Computer Science and Information Systems Thamar Yemen University of Science and Technology Faculty of Computing and IT Sana'a Yemen

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Mobile Edge Computing (MEC) is considered as a promising solution to augment the computational capabilities of mobile devices. Because of frequently changing locations, mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Mobile users can offload computation-intensive tasks to edge clouds collocated with base stations. Task offloading in MEC is a solution to handle such computational-intensive applications. However, making high-quality offloading decisions at runtime is difficult in mobile environments. This paper presents a survey for offloading decisions in MEC. It shows the most critical issues and challenges that affect offloading decisions. In addition, the survey discusses the existing offloading decision-making approaches and models which have been categorized according to their schemes' optimization. Furthermore, a literature review of offloading decisions for various studies is investigated and analyzed from the perspectives of objectives, infrastructures, and optimization approach. Finally, the survey highlights the open research challenges and trends in the field of offloading decisions in MEC. © 2022 IEEE.

关键词： Mobile edge computing

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Constructing Varied and Attractive Shortlists from Databases: A Group Decision Approach 7th

Constructing Varied and Attractive Shortlists from Databas...

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7th Seminar on Quantitative Methods of Group Decision Making, 2021

作者： Ramsey, David M. Mariański, Aleksander Department of Computer Science and Systems Engineering Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland Department of Applied Computer Science Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783662665961

Thanks to the Internet, basic information can be found about a very large number of offers at very little cost. Many sites give basic information about real estate offers, including price, size, location and number of rooms. information of this kind is sufficient to assess whether an offer is potentially attractive or not, but does not suffice in making an ultimate decision. The number of real estate offers in a city may be very large. In such a case, it can be beneficial to use an automatic procedure to first eliminate offers that do not satisfy the basic criteria of an individual or family and then construct a shortlist of varied and potentially attractive offers. This article recalls an algorithm that derives such a shortlist for an individual searcher. The novelty of this article lies in adapting this algorithm to scenarios in which a group decision is made. A practical example based on real estate offers in Warsaw is used to illustrate the algorithm. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Software engineering

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An Ontology-Based Customer Relationship Management Model for Educational Institutions Using Service-Oriented Architecture 3rd

An Ontology-Based Customer Relationship Management Model for...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Samih, Islam Abdelsalam, Mohammed Moawad, Ibrahim Fathy Business Information Systems Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Abbassia Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura City Egypt

ISBN: (纸本)9789819947638

Increasing satisfaction of customers is the most important goal for any institution. Therefore, customer relationship management (CRM) is founded for this purpose to keep track of the interaction between the organization and their customers to satisfy them and to guarantee their loyalty. In this paper, we introduce a layered architecture model for customer relationship management in Education domain. This model integrates educational domain ontology and service-oriented architecture to improve the business processes and hence to increase the customer satisfaction in the educational institutions. The model design is presented in detail for a huge Egyptian educational institution called the Education City. The paper presents a workflow design for customer relationship management describing the interaction among stakeholders to achieve the different business processes in the Education City. As well as, a detailed ontology design for the Education City is presented to describe and to visualize the different scenarios for the services provided by the Education City. To achieve flexibility and reusability, the model was built based on service-oriented architecture for the possibility of reusing the model in more than one field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Reusability

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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