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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

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

Learning Dual-Layer User Representation for Enhanced Item Recommendation

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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

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

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

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

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

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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Computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： mohammed Maray Haya Mesfer alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

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Computers, Materials & Continua 2022年第3期70卷 5871-5887页

作者： Mesfer Al Duhayyim Haya Mesfer alshahrani Fahd NAl-Wesabi mohammed Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza 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

An exponential growth in advanced technologies has resulted in the exploration of Ocean *** has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine *** Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental *** this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water *** models have been developed earlier for fish ***,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different *** this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN *** presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded ***-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish ***,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is *** model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate ***,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier *** performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under differe

关键词： Aquaculture background subtraction deep learning fish detection marine surveillance underwater sensor networks