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An Acceptance Model of Using Mobile-Government Services(AMGS)

computer Modeling in engineering & Sciences 2022年第5期131卷 865-880页

作者： Ahmad Althunibat Mohammad Abdallah Mohammed Amin Almaiah Nour Alabwaini Thamer Ahmad Alrawashdeh Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmmanJordan Faculty of Computer Sciences and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the ***,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and ***,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s *** paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the Information System(IS)Success Factor Model and Hofstede Cultural Dimensions *** study was conducted by surveying different groups of the Jordanian *** questionnaire was used to collect data from203 *** regression analysis has been conducted to analyze the *** results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are Information Quality,Service Quality,Uncertainty Avoidance,and Indulgence ***,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

关键词： Acceptance test e-Government m-Government acceptance model IS success factor model Hofstede cultural dimensions theory

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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A Fractional Fourier Based Medical Image Authentication Approach

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computers, Materials & Continua 2022年第2期70卷 3133-3150页

作者： Fayez Alqahtani Mohammed Amoon Walid El-Shafai Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Patient medical information in all forms is crucial to keep private and secure,particularly when medical data communication occurs through insecure ***,there is a bad need for protecting and securing the color medical images against impostors and *** this paper,an optical medical image security approach is *** is based on the optical bit-plane Jigsaw Transform(JT)and Fractional Fourier Transform(FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and *** confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security ***,a comparative analysis is introduced between the suggested security approach and other conventional cryptography *** simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly *** has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

关键词： Medical image security encryption JT FFT cryptosystem

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in engineering & Sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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software Test Data Management Based on Knowledge Graph

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第16期48卷 27-36页

作者： Gao, Li Qiu, Junlin Chen, Guanhua Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an223003 China

As software development models and methods mature, large-scale software systems emerge. However, a critical challenge remains: the lack of a comprehensive software test data management model that integrates basic data management with advanced knowledge reasoning. To address this issue, we developed a software test data management model based on knowledge graphs, enabling intelligent management and reasoning of software test data. The model incorporates an entity extraction model based on a feed-forward neural network, a knowledge graph integration method based on graph databases, and a knowledge reasoning submodule based on deep learning. To validate the effectiveness of our model, we evaluated the performance of each component individually. Our deep learning-based entity extraction model achieved an accuracy of 0.92, a recall of 0.88, and an F1 score of 0.90, significantly outperforming traditional methods such as regular expressions and dictionary-based approaches. Utilizing Cypher for graph database querying, our system provides accurate answers with a response time of 0.12 seconds, outperforming SQL and SPARQL-based querying methods. Furthermore, our approach excels in knowledge-based reasoning with an accuracy of 0.89 and site coverage of 0.81, surpassing both ontology-based and graph-based reasoning methods. These results highlight the enhanced construction, querying, and reasoning capabilities of our knowledge graph-based approach for managing software testing data. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Knowledge graph

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Image-based Criticality-Aware Fire Detection

Image-based Criticality-Aware Fire Detection

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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A Novel Approach Detection for False Data Injection, and Man in the Middle Attacks in IoT and IIoT

A Novel Approach Detection for False Data Injection, and Man...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Gonen, Serkan Bariskan, Mehmet Ali Kaplan, Derya Yiltas Yilmaz, Ercan Nurcan Cetin, Aydin Istanbul Gelişim University Department of Computer and Software Engineering Istanbul Turkey Istanbul University-Cerrahpasa Department of Computer Engineering Istanbul Turkey Gazi University Faculty of Technology Ankara Turkey

ISBN: (纸本)9781728170251

Human beings have gone through stages that have made significant contributions to their lives with technological developments. One of the most important of these close to the present day is the introduction of IoTs, and IIoTs into our lives with the industry 4.0 process. From smart homes to smart grids, we are faced with a world that is getting smarter in every aspect of our lives. In this cyber environment where our data, including our personal data, is transferred to the virtual environment, the biggest threat is the attacks that can be made on this existing environment. The biggest methods used by attackers are to exploit the vulnerabilities that the existing system brings in addition to the old vulnerabilities. With the IoT process, there have been very important developments, and all operations can be carried out remotely via mobile phone or computer, such as checking the missing needs in the refrigerator, washing, drying clothes, and even in the most advanced dimension, reducing the temperature if it is too high, or increasing it if it is low, without people coming to their homes, and with these renovations, new types of threads become part of our lives. With this study we focus on one of more dangerous part of these attacks, False Data Injection (FDI), and Men in the Middle (MitM) attacks. With this study we detected MitM, and FDI attacks with success rate of %95 percent. © 2023 IEEE.

关键词： Cybersecurity

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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