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Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah md.Siraj Bander Ali Saleh Al-rimy Sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

Heat and Mass Transfer on Non-Newtonian Peristaltic Flow in a Channel in Presence of Magnetic Field: Analytical and Numerical Analysis

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Applied Mathematics 2024年第8期15卷 568-583页

作者： md. Maruf Hasan md. Enamul Karim md. Abdus Samad Department of Mathematics Comilla University Comilla Bangladesh Department of Applied Mathematics University of Dhaka Dhaka Bangladesh

A study has been arranged to investigate the flow of non-Newtonian fluid in a vertical asymmetrical channel using peristalsis. The porous medium allows the electrically conductive fluid to flow in the channel, while a uniform magnetic field is applied perpendicular to the flow direction. The analysis takes into account the combined influence of heat and mass transfer, including the effects of Soret and Dufour. The flow’s non-Newtonian behavior is characterized using a Casson rheological model. The fluid flow equations are examined within a wave frame of reference that has a wave velocity. The analytic solution is examined using long wavelengths and a small Reynolds number assumption. The stream function, temperature, concentration and heat transfer coefficient expressions are derived. The bvp4c function from MATLAB has been used to numerically solve the transformed equations. The flow characteristics have been analyzed using graphs to demonstrate the impacts of different parameters.

关键词： Stream Function Mass Transfer Vertical Channel Casson Fluid

Structure Design of Underwater Micro Robot

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Modern Mechanical Engineering 2024年第1期14卷 12-24页

作者： md Shahriar Sujan Joy Howlader md Jahangir Alam md Al Imran Tapu College of Harbor Coastal & Offshore Engineering Hohai University Nanjing China Department of Computer Science and Information Technology College of Computer and Information Hohai University Nanjing China College of Civil and Transportation Engineering Hohai University Nanjing China

A submergible robot model has been presented, and for 3D printing measures, their parts have been modified enough. It has been shown in our design that using printable connectors—a few engines and weight arrangements can be carried out, permitting distinctive moving prospects. After presenting our configuration and delineating a bunch of potential structures, a helpful model dependent on open-source equipment and programming arrangements has been presented conditionally. The model can be effectively tried in a few makes-a plunge streams and lakes throughout the planet. The unwavering quality of the printed models can be strained distinctly in generally shallow waters. Nonetheless, we accept that their accessibility will inspire the overall population to construct and test submerged robots, subsequently accelerating the improvement of imaginative arrangements and applications.

关键词： Submergible Robot Thruster Adapter Raspberry Pi 3D Printer Open Source Software

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

A Comparative Study of 3D Virtual Pattern and Traditional Pattern Making

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Journal of Textile Science and Technology 2024年第1期10卷 1-24页

作者： md. Ahshan Habib md. Shamsul Alam Department of Fashion Design & Technology Sonargaon University Dhaka Bangladesh Department of Apparel Manufacture & Technology Sonargaon University Dhaka Bangladesh

Pattern making plays a key role in the aspect of fashion design and garment production, as it serves as the transformative process that turns a simple drawing into a consistent accumulation of garments. The process of creating conventional or manual patterns requires a significant amount of time and a specialized skill set in various areas such as grading, marker planning, and fabric utilization. This study examines the potential of 3D technology and virtual fashion designing software in optimizing the efficiency and cost-effectiveness of pattern production processes. The proposed methodology is characterized by a higher level of comprehensiveness and reliability, resulting in time efficiency and providing a diverse range of design options. The user is not expected to possess comprehensive knowledge of traditional pattern creation procedures prior to engaging in the task. The software offers a range of capabilities including draping, 3D-to-2D and 2D-to-3D unfolding, fabric drivability analysis, ease allowance calculation, add-fullness manipulation, style development, grading, and virtual garment try-on. The strategy will cause a shift in the viewpoints and methodologies of business professionals when it comes to the use of 3D fashion design software. Upon recognizing the potential time, financial, and resource-saving benefits associated with the integration of 3D technology into their design development process, individuals will be motivated to select for its utilization over conventional pattern making methods. Individuals will possess the capacity to transfer their cognitive processes and engage in introspection regarding their professional endeavors and current activities through the utilization of 3D virtual pattern-making and fashion design technologies. To enhance the efficacy and ecological sustainability of designs, designers have the potential to integrate 3D technology with virtual fashion software, thereby compliant advantages for both commercial enterpri

关键词： Pattern-Making 3D Pattern Making Virtual Fashion 3D-to-2D Unfolding Sustainable Designs

Design and Analysis of Power and Transmission System of Downhole Pure Electric Command Vehicle

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Journal of Transportation Technologies 2024年第1期14卷 31-52页

作者： md Jahangir Alam md Shahriar Sujan md. Al Imran Tapu Joy Howlader College of Civil and Transportation Engineering Hohai University Nanjing China College of Harbor Coastal & Offshore Engineering Hohai University Nanjing China Department of Computer Science and Technology College of Computer and Information Hohai University Nanjing China

Electric vehicles use electric motors, which turn electrical energy into mechanical energy. As electric motors are conventionally used in all the industry, it is an established development site. It’s a mature technology with ideal power and torque curves for vehicular operation. Conventional vehicles use oil and gas as fuel or energy storage. Although they also have an excellent economic impact, the continuous use of oil and gas threatened the world’s reservation of total oil and gas. Also, they emit carbon dioxide and some toxic ingredients through the vehicle’s tailpipe, which causes the greenhouse effect and seriously impacts the environment. So, as an alternative, electric car refers to a green technology of decarbonization with zero emission of greenhouse gases through the tailpipe. So, they can remove the problem of greenhouse gas emissions and solve the world’s remaining non-renewable energy storage problem. Pure electric vehicles (PEV) can be applied in all spheres, but their special implementation can only be seen in downhole operations. They are used for low noise and less pollution in the downhole process. In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The checking calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

关键词： Pure Electric Vehicles Transmission Gears Electronic Differential Control Algorithms

Activation of cerebral Ras-related C3 botulinum toxin substrate(Rac) 1 promotes post-ischemic stroke functional recovery in aged mice

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Neural Regeneration Research 2024年第4期19卷 881-886页

作者： Fan Bu Jia-Wei Min md Abdur Razzaque Ahmad El Hamamy Anthony Patrizz Li Qi Akihiko Urayama Jun Li Department of Neurology University of Texas Health Science Center Department of Neurology&Psychology The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine

Brain functional impairment after stroke is common;however,the molecular mechanisms of post-stroke recovery remain *** is well-recognized that age is the most important independent predictor of poor outcomes after stroke as older patients show poorer functional outcomes following *** evidence suggests that axonal regeneration and angiogenesis,the major forms of brain plasticity responsible for post-stroke recovery,diminished with advanced *** studies suggest that Ras-related C3 botulinum toxin substrate(Rac)1 enhances stroke recovery as activation of Rac1 improved behavior recovery in a young mice stroke ***,we investigated the role of Rac1 signaling in long-term functional recovery and brain plasticity in an aged(male,18 to 22 months old C57BL/6J)brain after ischemic *** found that as mice aged,Rac1 expression declined in the *** overexpression of Rac1,using lentivirus encoding Rac1 injected day 1 after ischemic stroke,promoted cognitive(assessed using novel object recognition test)and sensorimotor(assessed using adhesive removal tests)recovery on days 14–*** was accompanied by the increase of neurite and proliferative endothelial cells in the periinfarct zone assessed by *** a reverse approach,pharmacological inhibition of Rac1 by intraperitoneal injection of Rac1 inhibitor NSC23766 for 14 successive days after ischemic stroke worsened the outcome with the reduction of neurite and proliferative endothelial ***,Rac1 inhibition reduced the activation of p21-activated kinase 1,the protein level of brain-derived neurotrophic factor,and increased the protein level of glial fibrillary acidic protein in the ischemic brain on day 28 after *** work provided insight into the mechanisms behind the diminished plasticity after cerebral ischemia in aged brains and identified Rac1 as a potential therapeutic target for improving functional recovery in the older adults after stroke.

关键词： aging angiogenesis brain-derived neurotrophic factor(BDNF) cerebral ischemia cognitive recovery neurite Pak1 Rac1 sensorimotor recovery

YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

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Computers, Materials & Continua 2024年第9期80卷 4973-4995页

作者： Yuanyuan Wang Jialong Huang md Sharid Kayes Dipu Hu Zhao Shangbing Gao Haiyan Zhang Pinrong Lv School of Computer and Software Engineering Huaiyin Institute of TechnologyHuai’an223003China Huaiyin Institute of Technology Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu ProvinceHuai’an223001China Jiangsu Kesheng Xuanyi Technology Co. Ltd.Huai’an223300China

Printed circuit boards(PCBs)provide stable connections between electronic ***,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable ***,it is crucial to detect defective printed circuit boards during the generation *** detection methods have low accuracy in detecting subtle defects in complex background *** order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization *** neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep *** the aspect ratio of the bounding box to alleviate the issue of specificity *** training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of *** comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

关键词： Deep learning PCB defect detection large kernel noise filtering weighted fusion YOLO

Insights into Manipulation: Unveiling Tampered Images Using Modified ELA, Deep Learning, and Explainable AI

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Journal of Computer and Communications 2024年第6期12卷 135-151页

作者： md. Mehedi Hasan md. Masud Rana Abu Sayed md. Mostafizur Rahaman Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.

关键词： IFD DIF ELA CNN FNN XAI SHAP CASIA2.0