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Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

computers, Materials & Continua 2022年第11期73卷 3685-3703页

作者： Mohamed K.Hussein Mohamed H.Mousa Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt Department of Information Technology College of Computer Science at AlKamilUniversity of JeddahJeddahSaudi Arabia

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent *** exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and *** this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC *** proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge *** addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)*** experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

关键词： High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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User Experience Evaluation Framework for Malaysian E-government Mobile Applications: A Participatory Design-infused approach 24

User Experience Evaluation Framework for Malaysian E-governm...

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18th Biennial Participatory Design Conference - Reaching Out: Connecting Beyond Participation, PDC 2024

作者： Hossain, Emmy Faculty of Computer Science and Information Technology Universiti Putra Malaysia Malaysia

ISBN: (纸本)9798400706554

This proposal outlines a research on user experience evaluation frameworks, specifically for the Malaysian e-government mobile applications. The primary objective is to propose a comprehensive framework for user experience evaluation, taking into consideration the cultural aspects. In order to achieve this, the research will employ a mix of both qualitative and quantitative methods, with participatory design infused in the research design. The evaluation framework and recommendations from this research can be used by those who are involved in producing e-government mobile applications. © 2024 Owner/Author.

关键词： E-governments

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Autism Detection in Children by Features Extraction and Classification Using a Deep Learning Model

Autism Detection in Children by Features Extraction and Clas...

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2024 International Conference on Horizons of information technology and Engineering, HITE 2024

作者： Ahmed, Daniyal Hassan, Muhammad Ammar Zubair, Muhammad University of Central Faculty of Information Technology & Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Autism Spectrum Disorder (ASD) is a very complex nervous growth syndrome that can manifest in different forms. Timely diagnosis and suitable medical intervention can significantly enhance the daily functioning of children with ASD and their families. This study investigates the potential of utilizing static facial characteristics extracted from photos of children suffering from ASD in comparison to normally developing children as a biomarker to distinguish between them. To achieve this, we utilized a publicly accessible dataset containing facial images of both ASD and non-ASD controls, which were classified into autistic and non-autistic categories. Our study used binary image classification to differentiate between ASD and non-ASD pictures. The ResNet50 is used as a pre-trained model for feature extraction, and the last few layers are modified to fit the binary classification task. The performance of the model has produced exceptional results on the testing set. © 2024 IEEE.

关键词： Diseases

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

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

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Development of Robot to Improve Learning of Programming Skills among Students

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第3期4卷 1-11页

作者： Hashim, Ahmad Sobri Aminu Muazu, Aminu Yusof, Mohammad Ateff Md. Arshad, Noreen Izza Computer & Information Sciences Department Faculty of Science and Information Technology Universiti Teknologi PETRONAS Malaysia Computer Sciences Department Faculty of Natural and Applied Science Umaru Musa Yar’adua University Katsina Nigeria

The Fourth Industrial Revolution (IR4.0) has shifted the mindsets of engineering students on the importance of IT skills for current and future engineering related jobs. Nowadays, programming is the most fundamental skill that needs to be learnt by the students using mBot technology. The mBot technology is consider as programmable educational robot designed for beginners to learn basic programming concepts which can be assessed and evaluated via bloom's taxonomy framework. It can be a daunting task to learn programming, especially to new students who do not have any prior experience in coding. Average and low performing students are lacking algorithmic skills, where they could not visualize how the programming concepts work. Therefore, this paper presents the effectiveness of using robot to improve students’ learning of the programming concepts. In designing the learning modules, bloom’s taxonomy model and problem-based learning are adopted using mBot. Moreover, a low-cost and pre-programmed line follower robot has been used to demonstrate the outputs from the programs written in a more interactive manner. For the evaluation, pre-test and post-test of Quasi experimental design have been applied involving 40 students who were identified and categorized as average and low performing groups in the course. The findings show that a significant improvement has been observed from students’ performance and motivation. As such, the students’ performance is measured based on two phases of experiments. Whereas the students’ motivation is measured based on four motivation attributes: self-efficacy, active learning strategy, programming learning value and performance goal. © 2023 College of Education, Al-Iraqia University. All Rights Reserved.

关键词： Students

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A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

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computers, Materials & Continua 2024年第2期78卷 2087-2110页

作者： Shahriar Md Arman Tao Yang Shahadat Shahed Alanoud AlMazroa Afraa Attiah Linda Mohaisen School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation *** credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various *** paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric *** proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing *** review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye *** survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research *** aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.

关键词： Biometric modalities biometric recognition biometric security privacy-preserving security threats

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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computer Modeling in Engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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Priority Detector and Classifier Techniques Based on ML for the IoMT

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computers, Materials & Continua 2023年第8期76卷 1853-1870页

作者： Rayan A.Alsemmeari Mohamed Yehia Dahab Badraddin Alturki Abdulaziz A.Alsulami Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing *** importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and *** major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric ***,real-time monitoring of employees’stress in the workplace is *** levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated ***,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational *** study aims to reduce fog computation by proposing machine learning(ML)models with two *** first phase of theMLmodel assesses the priority of the situation based on the stress *** the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a *** approach reduced the computation cost for the fog node,as only high-priority records were transferred to the ***-priority records were forwarded to the *** MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

关键词： Machine learning priority detector Internet of Medical Things IoMT fog computing cloud computing

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