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A Review on the Recent Trends of Image Steganography for VANET Applications

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Computers, Materials & Continua 2024年第3期78卷 2865-2892页

作者： Arshiya S.Ansari department of information technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look.Whereas vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation systems.VANETs steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection.This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss.According to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network conditions.In this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and filtering.This study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

关键词： Steganography image steganography image steganography techniques information exchange data embedding and extracting vehicular ad hoc network(VANET) transportation system

Sustainable food waste management using modified fuzzy improved analytic hierarchy process:a study of Malaysia

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Waste Disposal and Sustainable Energy 2024年第1期6卷 63-84页

作者： Chun Yuan Choo Syariza Abdul-Rahman Abdul Malek Yaakob Emy E.A-Jalil department of decision Science School of Quantitative SciencesUniversiti Utara Malaysia06010SintokKedahMalaysia Institute of Strategic Industrial decision Modeling School of Quantitative SciencesCollege of Arts and SciencesUniversiti Utara Malaysia06010SintokKedahMalaysia technology and Supply Chain Excellence Institute School of Technology Management and LogisticsUniversiti Utara Malaysia06010SintokKedahMalaysia

Food waste generation is a pressing issue that requires urgent attention and concerted efforts worldwide.The staggering amount of food wasted each year not only wastes valuable resources but also exacerbates environmental,economic,and social challenges.Food Waste Management(FWM)consists of a complex array of criteria and sub-criteria,and treatments which seems interdependent.There is a need to evaluate the FWM with the help of important criteria and sub-criteria and treatments to address this challenge.In this study,we identified four important criteria,21 sub-criteria,and four alternatives of FWM for the case of Malaysia using the integrated approach of literature review and expert opinions.Further,we employed the approach of Modified Fuzzy Improved Analytical Hierarchy Process(IAHP)to corroborate the interrelationships among the identified criteria and sub-criteria,and their associated treatments.This study undertakes linear normalization methods to transform data into comparable numerical values and the Geometric Mean method to handle uncertainty in human judgments.Moreover,the Centroid method is employed to convert fuzzy weights into crisp sets for ease of interpretation.The results indicate that environmental is the most essential criterion,followed by social,economic,and technical.In addition,air and water pollution is identified as the most critical sub-criteria.Black Soldier Fly is discovered as the most sustainable FWM treatment,since it performs the best while meeting all the criteria and sub-criteria assessed.Sensitivity analysis demonstrates that the outputs from the proposed method are robust and reliable.The finding suggests a proper and robust approach to help decision-makers select suitable FWM treatments to tackle various criteria and alternatives especially when handling inconsistent and uncertain judgments during evaluation.

关键词： Multi-criteria decision-making Sustainable food waste management Improved Analytical Hierarchy Process Fuzzy method Sensitivity analysis

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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Computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya S.Ansari department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia department of information technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

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Computer Modeling in Engineering & sciences 2024年第8期140卷 1233-1261页

作者： Yifan Liu Shancang Li Xinheng Wang Li Xu School of Computer Science and Informatics Cardiff UniversityCardiffCF244AGUK department of Mechatronics and Robotics Xi’an Jiaotong-Liverpool UniversitySuzhou215123China department of information technology&decision sciences Old Dominion UniversityNorfolk23529USA

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

关键词： Cyber security Industrial Internet of Things artificial intelligence machine learning algorithms hybrid cyber threats

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan department of information technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan department of information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Management information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan department of Electrical Engineering Balochistan University of information technology Engineeringand Management SciencesQuetta87300Pakistan department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

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Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem department of information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia department of information Systems and technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

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Computers, Materials & Continua 2024年第4期79卷 675-694页

作者： Hala AlShamlan Halah AlMazrua department of information technology College of Computer and Information SciencesKing Saud UniversityP.O.Box 145111Riyadh4545Saudi Arabia

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

关键词： Bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression grey wolf optimizer harris hawks optimization k-nearest neighbor support vector machine

Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy decision-Making Approach

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Computers, Materials & Continua 2024年第5期79卷 3211-3237页

作者： Wajdi Alhakami department of information technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in cybersecurity.KSA has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage.The widespread usage of information technology(IT)and IT Enable Services(ITES)reinforces securitymeasures.The constantly evolving cyber threats are a topic that is generating a lot of discussion.In this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region.The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online.Different competency models have been devised to address the necessary cybercrime competencies in this context.The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security.To address this research query,the present assessment uses the Fuzzy decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(F.AHP),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy.The similarities and differences between the three methods are also discussed.This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority.The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for SaudiArabia.Moreover,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

关键词： Cyber security fuzzy DMTAEL security policy cyber crime MCDM

A causal multiomics study discriminates the early immune features of Ad5-vectored Ebola vaccine recipients

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The Innovation 2024年第3期5卷 77-87页

作者： Zhe Zhang Wenjing Yang Zhengshan Chen Haoang Chi Shipo Wu Wanru Zheng Ruochun Jin Busen Wang Yudong Wang Nan Huo Jinlong Zhang Xiaohong Song Liyang Xu Jun Zhang Lihua Hou Wei Chen Institute of Biotechnology Academy of Military Medical SciencesBeijing 100071China department of Intelligent Data Science College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Intelligent Game and decision Lab Academy of Military ScienceBeijing 100091China

The vaccine-induced innate immune response is essential for the generation of an antibody response.To date,how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains unclear.Here,we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic,molecular,and cellular levels on days 0,1,3,and 7 after Ad5-EBOV vaccination.In a causal multiomics analysis,distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 antibodies.This study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

关键词： vaccine immunity alterations