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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Teaching Tip Rethinking How We Teach Git: Pedagogical Recommendations and Practical Strategies for the information systems Curriculum

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Journal of information systems Education 2025年第1期36卷 1-12页

作者： Wagner, Gerit Thurner, Laureen Faculty Information Systems and Applied Computer Science Otto-Friedrich-Universität Bamberg Bamberg Germany

Git, as the leading version-control system, is frequently employed by software developers, digital product managers, and knowledge workers. information systems (IS) students aspiring to fill software engineering, management, or research positions would therefore benefit from familiarity with Git. However, teaching Git effectively can be challenging, as students in IS and other disciplines report themselves overwhelmed by the plethora of Git’s detailed commands and options, including those involved in local setup and secure shell (SSH) connections. From our view, such technical considerations distract students, and even prevent them from developing a deeper understanding of the Git model and its underlying concepts. Ideally, teaching efforts should convey a solid understanding of the Git model and thereby enable students to ask the right questions and look up the relevant commands. With this teaching tip, we therefore challenge the common approach to organizing Git teaching materials. In particular, we draw on established pedagogical theory to propose a novel approach that employs a new, macro-level ordering of contents beginning with the concept of branches and then proceeding to committing and collaboration. We present several practical strategies that make this approach feasible. In addition, we recommend that teachers clearly separate conceptual from applied learning and present the more challenging transfer questions at the end of the course. Our hope is to stimulate reflection on the most effective ways to teach Git to future professionals. © (2025), (ISCAP- information systems and Computing Academic Professionals). All rights reserved.

关键词： information use

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Revolutionizing online shopping with FITMI: a realistic virtual try-on solution

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Neural Computing and Applications 2025年第8期37卷 6125-6144页

作者： Samy, Tassneam M. Asham, Beshoy I. Slim, Salwa O. Abohany, Amr A. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Damanhour University Damanhour Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

In today’s digital age, consumers increasingly rely on online shopping for convenience and accessibility. However, a significant drawback of online shopping is the inability to physically try on clothing before purchasing. This limitation often leads to uncertainty regarding fit and style, resulting in customer post-purchase dissatisfaction and higher return rates. Research indicates that online items are three times more likely to be returned than in-store ones, especially during the pandemic. To address this challenge, we propose a virtual try-on method called FITMI, an enhanced Latent Diffusion Textual Inversion model for virtual try-on purposes. The proposed architecture aims to bridge the gap between traditional in-store try-ons and online shopping by offering users a realistic and interactive virtual try-on experience. Although virtual try-on solutions already exist, recent advancements in artificial intelligence have significantly enhanced their capabilities, enabling more sophisticated and realistic virtual try-on experiences than ever before. Building on these advancements, FITMI surpasses ordinary virtual try-ons relying on generative adversarial networks, often producing unrealistic outputs. Instead, FITMI utilizes latent diffusion models to generate high-quality images with detailed textures. As a web application, FITMI facilitates virtual try-ons by seamlessly integrating images of users with garments from catalogs, providing a true-to-life representation of how the items would look. This approach differentiates us from competitors. FITMI is validated using two widely recognized benchmarks: the Dress-Code and Viton-HD datasets. Additionally, FITMI acts as a trusted style advisor, enhancing the shopping experience by recommending complementary items to elevate the chosen garment and suggesting similar options based on user preferences. © The Author(s) 2025.

关键词： Generative adversarial networks

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Fuzzy-HLSTM(Hierarchical Long Short-Term Memory)for Agricultural Based information Mining

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computers, Materials & Continua 2023年第2期74卷 2397-2413页

作者： Ahmed Abdu Alattab Mohammed Eid Ibrahim Reyazur Rashid Irshad Anwar Ali Yahya Amin A.Al-Awady Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajranSaudi Arabia Department of Computer Science College of Computer Science&Information SystemsNajran UniversityNajranSaudi Arabia Computer Skills Department Deanship of Preparatory YearNajran UniversityNajranSaudi Arabia

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop *** case-based reasoning systems,case representation is critical,and thus,researchers have thoroughly investigated textual,attribute-value pair,and ontological *** big databases result in slow case retrieval,this research suggests a fast case retrieval strategy based on an associated representation,so that,cases are interrelated in both either similar or dissimilar *** soon as a new case is recorded,it is compared to prior data to find a relative *** proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional *** Long Short-Term Memory(HLSTM)is used to evaluate the efficiency,similarity of the models,and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the *** on the results,the proposed approaches allows for rapid case retrieval with high accuracy.

关键词： Machine learning agriculture IoT HLSTM fuzzy rules

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Deepfake detection using convolutional vision transformers and convolutional neural networks

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Neural Computing and Applications 2024年第31期36卷 19759-19775页

作者： Soudy, Ahmed Hatem Sayed, Omnia Tag-Elser, Hala Ragab, Rewaa Mohsen, Sohaila Mostafa, Tarek Abohany, Amr A. Slim, Salwa O. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. © The Author(s) 2024.

关键词： Face recognition

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in Engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Dynamic Resource Utilization Prediction Model for Cloud Datacenter

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 307-331页

作者： Bliedy, Doaa Khafagy, Mohamed H. Badry, Rasha M. Department of Information Systems Faculty of Computers and Artificial Intelligence Fayoum University Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University Egypt

Much research has been done on predicting resource utilization in the cloud to avoid over- and under-provisioning resources. Most existing systems focus on estimating the utilization of one or two resources at most, including memory, CPU, storage, network, or servers dedicated to cloud applications;they do not consider the correlation between resources. A maximum of one or two machine learning algorithms is employed for prediction purposes. Additionally, traditional prediction methods in the cloud provide a one-dimensional output. Most current solutions predict resources, such as memory and CPU utilization, as a single output. Unfortunately, a one-dimensional output in resource provision and usage cannot capture the relationship between the application requirements of different resources, such as CPU, memory, CPU cores, disk, and network;this results in incomplete and inaccurate information and prediction results. Efficient resource allocation and management require predicting several parameters using multivariate state variables. This study presents a multi-resource utilization prediction model that uses several machine learning approaches, such as support vector regression, random forests, MLP regression, neural networks (NN) using Adam and SGD optimizers, and decision tree regression. The prediction model is based on univariate and multivariate time series. Google cluster trace data is used to evaluate the work. Four experiments are executed on the dataset, seeking to predict the resources for different time series interval periods. The various algorithms’ mean absolute errors (MAE), root mean square error, mean absolute percentage error and R-squared are compared to determine which technique achieves the lowest error rate. The experimental results have shown that the prediction model yields better accuracy than previous research. © (2024), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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