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Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

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Computers, Materials & Continua 2024年第11期81卷 2395-2436页

作者： Deema Alsekait Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad saad Alqurni Applied College Department of Computer Science and Information TechnologyPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus ***,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT *** research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis ***,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and *** study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT ***,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested *** research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT ***,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world *** WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT *** summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning *** research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance *** research co

关键词： Obfuscated malware detection IoT devices Wide Residual Network(WRN) malware detection machine learning

Microbiologically influenced corrosion of 304L stainless steel caused by an alga associated bacterium Halomonas titanicae

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Journal of Materials Science & Technology 2020年第2期37卷 200-206页

作者： Yuqiao Dong Yassir Lekbach Zhong Li Dake Xu Soumya El Abed saad Ibnsouda Koraichi Fuhui Wang Shenyang National Laboratory for Materials Science Northeastern UniversityShenyang 110819China Center for Marine Materials Corrosion and Protection College of MaterialsXiamen UniversityXiamen 361005China Laboratory of Microbial Biotechnology Faculty of Science and TechnologySidi Mohamed Ben Abdellah UniversityB.P.2202FezMorocco

Algae are reported to be corrosive,while little is known about the role of the algae associated bacteria in the corrosion *** the present study,Halomonas titanicae was isolated from a culture of an alga strain,Spirulina platensis,and identified through 16 S rRNA gene *** behavior of 304L stainless steel(SS)coupons in the presence and absence of *** was characterized by using electrochemical measurements and surface *** results showed that *** significantly accelerated the corrosion rate and decreased the pitting potential of 304L SS in the biotic *** removal of the corrosion products and biofilms,severe pitting corrosion caused by *** was *** largest pit depth after 14 d reached 6.6μm,which was 5.5 times higher than that of the sterile control(1.2μm).This is the first report revealing that an alga associated bacterium can induce microbiologically influenced corrosion(MIC),and a further concern is raised that whether algae play a role in the MIC process.

关键词： Microbiologically influenced corrosion Halomonas titanicae Pitting corrosion

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

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Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

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Computers, Materials & Continua 2021年第6期67卷 3545-3562页

作者： Muhammad saad Maaz Bin Ahmad Muhammad Asif Khalid Masood Mohammad A.Al Ghamdi College of Computing and Information Sciences PAF KIETKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science Umm Al-Qura UniversityMakkah CitySaudi Arabia

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation *** technologies can help prevent outbreaks of any disease that can disperse widely and develop into a ***,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact ***,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve ***,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing *** formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation *** proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

关键词： Coronavirus disease 2019 pandemic machine-to-machine industrial internet of things social distance geo-fencing

Computer Vision-Control-Based CNN-PID for Mobile Robot

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Computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

Acaricidal activities of some essential oils and their monoterpenoidal constituents against house dust mite,Dermatophagoides pteronyssinus (Acari:Pyroglyphidae)

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2006年第12期7卷 957-962页

作者： saad El-Zemity HUSSIEN Rezk SAHER Farok AHMED Zaitoon Department of Pesticide Chemistry Faculty of Agriculture Alexandria University Alexandria 21-545 Egypt Department of Economic Entomology Faculty of Agriculture Alexandria University Alexandria 21-545 Egypt

The acaricidal activities of fourteen essential oils and fourteen of their major monoterpenoids were tested against house dust mites Dermatophagoides pteronyssinus. Five concentrations were used over two different time intervals 24 and 48 h under laboratory conditions. In general, it was noticed that the acaricidal effect based on LC50 of either essential oils or monoterpenoids against the mite was time dependant. The LCso values were decreased by increasing of exposure time. Clove, matrecary, chenopodium, rosemary, eucalyptus and caraway oils were shown to have high activity. As for the monoterpenoids, cinnamaldehyde and chlorothymol were found to be the most effective followed by citronellol. This study suggests the use of the essential oils and their major constituents as ecofriendly biodegradable agents for the control of house dust mite, D. pteronyssinus.

关键词： Natural acaricides Essential oils Monoterpenoids House dust mite Dermatophagoides pteronyssinus

A Sustainable WSN System with Heuristic Schemes in IIoT

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Computers, Materials & Continua 2022年第9期72卷 4215-4231页

作者： Wenjun Li Siyang Zhang Guangwei Wu Aldosary saad Amr Tolba Gwang-jun Kim School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityEgypt Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more ***, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and *** main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.

关键词： Industrial Internet of Things sustainable wireless sensor network system combinatorial optimization problem heuristic algorithms

Visual and refractive outcomes of posterior chamber phakic IOL in stable keratoconus

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International Journal of Ophthalmology(English edition) 2019年第5期12卷 840-843页

作者： Sherif H Emerah Moataz M Sabry Hisham A saad Waleed A Ghobashy Ophthalmology Department Research Institute of Ophthalmology Magrabi Eye Hospital Ophthalmology Department Tanta University Ophthalmology Department Suez Canal University

We evaluated the visual and refractive outcomes after phakic visian toric implantable collamer lens(ICL) insertion in stable keratoconus(KC). This retrospective study investigated toric ICL implantation in 14 eyes of 8 patients with stable KC. After 6 mo, the mean uncorrected distance visual acuity improved significantly from 0.77 to 0.15 logMAR. The mean best corrected distance visual acuity(BCDVA) improved from 0.18±0.1 to 0.15±0.1 logMAR. Fifty percent of eyes maintained their preoperative BCDVA; 42.8% gained one line. There was no statistical difference in high order or coma aberration. The mean refractive manifest spherical equivalent(MSE), mean refractive manifest spherical error, mean manifest astigmatism decreased significantly postoperatively. At 6 mo postoperatively, our achieved mean spherical equivalent was approximately 74%. No intraoperative or postoperative complications occurred. Toric ICL implantation was effective, predictable and safe to correct refractive error and improve visual acuity in patients with stable KC.

关键词： coma keratoconus lens implantation intraocular

Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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Computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI