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Learning Dual-Layer User Representation for Enhanced Item Recommendation

Computers, Materials & Continua 2024年第7期80卷 949-971页

作者： Fuxi Zhu Jin Xie mohammed alshahrani Applied Research Center of Artificial Intelligence Wuhan CollegeWuhan430212China College of Computer Science South-Central MINZU UniversityWuhan430074China Unmanned.Company Riyadh11564Saudi Arabia

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured ***-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user ***,these technologies only extract common features in historical records and cannot represent changes in user ***,sequential feature can express the user’s interests and intentions that change time by *** the sequential recommendation results based on the user representation of the item lack the interpretability of preference *** address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer ***,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for *** sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s ***,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of *** extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation *** addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

关键词： User representation latent semantic sequential feature interpretability

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Droid-IoT:Detect Android IoT Malicious Applications Using ML and Blockchain

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Computers, Materials & Continua 2022年第1期70卷 739-766页

作者： Hani mohammed alshahrani College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

One of the most rapidly growing areas in the last few years is the Internet of Things(IoT),which has been used in widespread fields such as healthcare,smart homes,and *** is one of the most popular operating systems(OS)used by IoT devices for communication and data *** OS captured more than 70 percent of the market share in *** of the popularity of the Android OS,it has been targeted by cybercriminals who have introduced a number of issues,such as stealing private *** reported by one of the recent studies Androidmalware are developed almost every 10 ***,due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT *** paper introduces Droid-IoT,a collaborative framework to detect Android IoT malicious applications by using the blockchain ***-IoT consists of four main engines:(i)collaborative reporting engine,(ii)static analysis engine,(iii)detection engine,and(iv)blockchain *** engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious *** features are extracted automatically fromthe inspected applications to be classified by the machine learning model and store the results into the *** performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art ***-IoT achieved a detection rate of 97.74%with a low false positive rate by using an extreme gradient boosting(XGBoost)classifier.

关键词： Android blockchain analysis malware

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A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

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Computer Systems Science & Engineering 2023年第7期46卷 551-562页

作者： Ali Alqazzaz Mohammad Tabrez Quasim mohammed Mujib alshahrani Ibrahim Alrashdi Mohammad Ayoub Khan College of Computing and Information Technology University of Bisha67714BishaSaudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72388Saudi Arabia

Facebook,Twitter,Instagram,and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts,posts,comments,images,and videos that express moods,sentiments,and *** this,it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and ***,the analysis of depression based on social media has gained widespread acceptance worldwide,other verticals still have yet to be *** depression analysis uses Twitter data from a publicly available web source in this *** assess the accuracy of depression detection,long-short-term memory(LSTM)and convolution neural network(CNN)techniques were *** method is both efficient and *** simulation results have shown an accuracy of 86.23%,which is reasonable compared to existing methods.

关键词： Emotions depression social media

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Secure Multifactor Remote Access User Authentication Framework for IoT Networks

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Computers, Materials & Continua 2021年第9期68卷 3235-3254页

作者： mohammed Mujib alshahrani College of Computing and Information Technology The University of BishaSaudi Arabia

The term IoT refers to the interconnection and exchange of data among devices/*** devices are often small,low cost,and have limited *** IoT issues and challenges are growing *** and privacy issues are among the most important concerns in IoT applications,such as smart *** cybersecurity attacks are the attacks which do not require physical access to the IoT networks,where the attacker can remotely access and communicate with the IoT devices through a wireless communication ***,remote cybersecurity attacks are a significant *** applications in smart environments such as smart buildings require remote access for both users and *** the user/building communication channel is insecure,a lightweight and secure authentication protocol is *** this paper,we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building *** protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous,unlinkable,and untraceable *** protocol also avoids clock synchronization problem and can resist quantum computing *** security of the protocol is evaluated using two different methods:(1)informal analysis;(2)model check using the automated validation of internet security protocols and applications(AVISPA)*** communication overhead and computational cost of the proposed are *** security and performance analysis show that our protocol is secure and efficient.

关键词： Internet of things threats smart building attacks remote access authentication smart buildings

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani alshahrani mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Perceptions of emergency medicine residents on the quality of residency training in the United States and Saudi Arabia

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World Journal of Emergency Medicine 2018年第1期9卷 5-12页

作者： Ahmad Aalam Mark Zocchi Khalid Alyami Abdullah Shalabi Abdullah Bakhsh Asaad Alsufyani Abdulrahman Sabbagh mohammed alshahrani Jesse M.Pines Department of Emergency Medicine King Abdulaziz University Department of Emergency Medicine The George Washington University Center for Healthcare Innovation and Policy Research The George Washington University Department of Pediatrics University of Dammam Department of Emergency Medicine King Abdulaziz Medical City National Guard Hospital Department of Emergency Medicine University of Toledo Department of Emergency Medicine King Fahad Medical City

BACKGROUND: We compare educational environments(i.e. physical, emotional and intellectual experiences) of emergency medicine(EM) residents training in the United States of America(USA) and Saudi Arabia(SA).METHODS: A cross-sectional survey study was conducted using an adapted version of the validated Postgraduate Hospital Educational Environment Measure(PHEEM) survey instrument from April 2015 through June 2016 to compare educational environments in all emergency medicine residency programs in SA and three selected programs in the USA with a history of training Saudi physicians. Overall scores were compared across programs, and for subscales(autonomy, teaching, and social Support), using chi-squared, t-tests, and analysis of ***: A total of 219 surveys were returned for 260 residents across six programs(3 SA, 3 USA), with a response rate of 84%. Program-specific response rates varied from 79%–100%. All six residencies were qualitatively rated as "more positive than negative but room for improvement". Quantitative PHEEM scores for the USA programs were significantly higher: 118.7 compared to 109.9 for SA, P=0.001. In subscales, perceptions of social support were not different between the two countries(P=0.243); however, role autonomy(P<0.001) and teaching(P=0.005) were better in USA programs. There were no significant differences by post-graduate training ***: EM residents in all three emergency medicine residency programs in SA and the three USA programs studied perceive their training as high quality in general, but with room for improvements. USA residency programs scored higher in overall quality. This was driven by more favorable perceptions of role autonomy and teaching. Understanding how residents perceive their programs may help drive targeted quality improvement efforts.

关键词： Emergency medicine residents Residency training United States of America Saudi Arabia

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跨国公司社会责任的国际法律规制

跨国公司社会责任的国际法律规制

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作者： mohammed N.A.alshahrani 中国政法大学

学位级别：硕士

在经济全球化背景下,国际经济活动的主体日渐丰富,除传统的个人和国家外,跨国公司日益成为国际经济活动的重要主体。由于跨国公司的本质属性是在全球范围内取得利润最大化,必然与东道国、母国的经济、法律、政治等社会利益发生冲突,加... 详细信息

在经济全球化背景下,国际经济活动的主体日渐丰富,除传统的个人和国家外,跨国公司日益成为国际经济活动的重要主体。由于跨国公司的本质属性是在全球范围内取得利润最大化,必然与东道国、母国的经济、法律、政治等社会利益发生冲突,加之当地法律制度不健全无法进行有效监管等因素,跨国公司往往是以侵犯雇佣员工人权、破坏当地环境、阻碍经济自由发展、逃避税收等对社会发展整体利益的牺牲来实现资本收益的最大化。从上个世纪开始,尤其是在发展中国家,跨国公司可谓劣迹斑斑,人们必须正视跨国公司的社会责任问题。本文从跨国公司的角度出发,论证强调社会责任是解决全球化过程中跨国公司产生的社会问题的一个重要途径,并就在国际法律层面构建跨国公司的社会责任制度提出建议,着力展示国际法律制度因应跨国公司社会责任的要求进行制度调整,并指出制度构建中面临的新课题。本文共分三章。第一章是跨国公司社会责任国际法律规制的背景。在这一章里,主要探讨了公司社会责任的提出与跨国公司的负面影响;欧美关于公司社会责任的国内立法;国际经济法立法一体化趋势与跨国公司社会责任的关系;以及跨国公司社会责任国际法律规制的动力。第二章重点分析了现行跨国公司社会责任的国际法律规制。首先探讨了联合国、经济合作与发展组织以及欧盟在间接规范跨国公司社会责任方面所作的国际立法努力;其次,阐述了联合国、多边投资担保机构以及国际金融公司在直接规范跨国公司社会责任方面所作的国际立法尝试;再次,介绍了非政府间国际组织在跨国公司社会责任方面所制定的一些社会标准;最后,作者对现行跨国公司社会责任国际规范做了一个总体评价,指出了现行跨国公司社会责任国际规范的特征,并且分析了制定跨国公司社会责任国际法律制度之所以困难的深层次原因。第三章展望了制定规范跨国公司社会责任国际法律制度的前景。作者认为,在目前无法根本改变国际经济立法供给不足现状的情况下,可以通过新型的立法方式,适度加强目前跨国公司社会责任国际规范领域软法的“硬度”,并与全球治理模式下的多层次规范体系配合,达到较为良好的效果。

关键词：跨国公司社会责任国际规范

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YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

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Computers, Materials & Continua 2023年第12期77卷 3697-3722页

作者： Fares Alharbi Reem alshahrani mohammed Zakariah Amjad Aldweesh Abdulrahman Abdullah Alghamdi Department of Computer Science College of Computing and ITShaqra UniversityShaqra15526Saudi Arabia Department of Computer Science College of Computers and ITTaif UniversityP.O.Box 11099Taif21944Saudi Arabia College of Computer and Information Science King Saud UniversityRiyadhSaudi Arabia

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain *** Internet of Things(IoT)is a network of connected,interconnected *** issues occasionally arise due to the amount of data ***,they have been primarily addressed by blockchain and smart contract *** there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT *** study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license *** YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research ***-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source *** method works reasonably well for identifying any license *** Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various ***,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 ***,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR ***,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting *** proposed model,however,produced beneficial outcomes.

关键词： Intelligent transportation system blockchain technology license plate recognition privacy YOLO deep learning technique ALPR

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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

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Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： Mesfer Al Duhayyim Haya Mesfer alshahrani Fahd NAl-Wesabi mohammed Alamgeer Anwer Mustafa Hilal mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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