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A Deep Learning Approach for Crowd Counting in Highly Congested Scene

computers, Materials & Continua 2022年第12期73卷 5825-5844页

作者： Akbar Khan Kushsairy Abdul Kadir Jawad Ali Shah Waleed Albattah Muhammad Saeed Haidawati Nasir Megat Norulazmi Megat Mohamed Noor Muhammad Haris Kaka Khel Electrical Section Universiti Kuala Lumpur British Malaysian InstituteSelangor53100Malaysia Department of Electrical Engineering International Islamic UniversityIslamabad44000Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesIslamabad44000Pakistan Computer Engineering Technology Section Universiti Kuala Lumpur Malaysian Institute of Information TechnologyKuala Lumpur50250Malaysia

With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the *** congested scene monitoring,a variety of crowd density estimating approaches has been *** understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high *** deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original *** dilated convolution neural network is mainly chosen to expand the kernel size without changing other ***,several loss functions have been applied to strengthen the evaluation accuracy of the ***,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_*** model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.

关键词： Deep learning congested scene crowd counting fully convolutional neural network dilated convolution

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

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computer systems Science & engineering 2023年第6期45卷 3135-3150页

作者： Mohammed Alonazi Mrim M.Alnfiai Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia

Human-computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for computer *** has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive *** is an aspect of HCI dedicated to guar-anteeing that human–computer communication is,amongst other things,efficient,effective,and sustaining for the ***,Human activity recognition(HAR)aim is to identify actions from a sequence of observations on the activities of subjects and the environmental *** vision-based HAR study is the basis of several applications involving health care,HCI,and video *** article develops a Fire Hawk Optimizer with Deep Learning Enabled Activ-ity Recognition(FHODL-AR)on HCI driven *** the presented FHODL-AR technique,the input images are investigated for the identification of different human *** feature extraction,a modified SqueezeNet model is intro-duced by the inclusion of few bypass connections to the SqueezeNet among Fire ***,the FHO algorithm is utilized as a hyperparameter optimization algorithm,which in turn boosts the classification *** detect and cate-gorize different kinds of activities,probabilistic neural network(PNN)classifier is *** experimental validation of the FHODL-AR technique is tested using benchmark datasets,and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.

关键词： Activity recognition fire hawks optimizer deep learning usability human computer interaction

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Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

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computer systems Science & engineering 2023年第4期45卷 955-969页

作者： Anwer Mustafa Hilal Jaber S.Alzahrani Dalia H.Elkamchouchi Majdy M.Eltahir Ahmed S.Almasoud Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,*** UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple *** this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV *** proposed GTOADL-SCS model encompasses a two stage process namely clustering and *** the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize ***,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV *** classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)*** ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

关键词： Unmanned aerial vehicles energy efficiency smart communication system deep learning

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

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computer systems Science & engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

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Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

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computers, Materials & Continua 2023年第6期75卷 5597-5613页

作者： V.Banupriya S.Anusuya Department of Computer Science and Business Systems M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSIMATSChennai602117Tamil NaduIndia

In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to *** retinopathy(DR)can be well treated with early *** fundus images of humans are used to screen for lesions in the ***,detecting DR in the early stages is challenging due to the minimal ***,the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the ***,the resources required for manually identifying the lesions are ***,training for Convolutional Neural Networks(CNN)is more *** proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model(EDLM)for timely DR identification that is potentially more accurate than existing CNN-based *** proposed model will detect various lesions from retinal images in the early ***,characteristics are retrieved from the retinal fundus picture and put into the EDLM for *** dimensionality reduction,EDLM is ***,the classification and feature extraction processes are optimized using the stochastic gradient descent(SGD)*** EDLM’s effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images,and results are compared over VGG16,VGG19,RESNET18,RESNET34,and *** results show that the EDLM achieves higher average sensitivity by 8.28%for VGG16,by 7.03%for VGG19,by 5.58%for ResNet18,by 4.26%for ResNet 34,and by 2.04%for ResNet 50,respectively.

关键词： Diabetic retinopathy deep learning classification retinal fundus

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New Perspectives on Data Exfiltration Detection for Advanced Persistent Threats Based on Ensemble Deep Learning Tree 19

New Perspectives on Data Exfiltration Detection for Advanced...

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19th International Conference on Web information systems and Technologies, WEBIST 2023

作者： Cai, Xiaojuan Koide, Hiroshi Department of Information Science and Technology Information Science and Electrical Engineering Kyushu University Fukuoka Japan Section of Cyber Security for Information Systems Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9789897586729

Data exfiltration of Advanced Persistent Threats (APTs) is a critical concern for high-value entities such as governments, large enterprises, and critical infrastructures, as attackers deploy increasingly sophisticated and stealthy tactics. Although extensive research has focused on methods to detect and halt APTs at the onset of an attack (e.g., examining data exfiltration over Domain Name System tunnels), there has been a lack of attention towards detecting sensitive data exfiltration once an APT has gained a foothold in the victim system. To address this gap, this paper analyzes data exfiltration detection from two new perspectives: exfiltration over a command-and-control channel and limitations on exfiltration transfer size, assuming that APT attackers have established a presence in the victim system. We introduce two detection mechanisms (Transfer Lifetime Volatility & Transfer Speed Volatility) and propose an ensemble deep learning tree model, EDeepXGB, based on eXtreme Gradient Boosting, to analyze data exfiltration from these perspectives. By comparing our approach with eight deep learning models (including four deep neural networks and four convolutional neural networks) and four traditional machine learning models (Naive Bayes, Quadratic Discriminant Analysis, Random Forest, and AdaBoost), our approach demonstrates competitive performance on the latest public real-world dataset (Unraveled-2023), with Precision of 91.89%, Recall of 93.19%, and F1-Score of 92.49%. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Sensitive data

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Security model to identify block withholding attack in blockchain

Security model to identify block withholding attack in block...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Keshta, Ismail Reegu, Faheem Ahmad Ahmad, Adeel Saxena, Archana Chandan, Radha Raman Mahalakshmi, V. Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Applications Invertis University UP Bareilly India Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology College Jhalwa Prayagraj India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781032669663

Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper briefly introduces the mining mechanism and the working principle of block interception attack, summarizes several models of block interception attack, studies the attack methods and benefits of the existing block interception attack models, and analyses their attack effects. A block interception attack model is constructed to increase the rate of return while increasing the rate of return. The completed model is verified through simulated mining experiments. The typical and made models are compared and analyzed based on the rate of return. Finally, the applicable environments of different attack models are given based on the experimental results. © 2024 The Author(s).

关键词： Blockchain

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Deep Radiomics: A Picture's Worth a Thousand Words - A Review 1

Deep Radiomics: A Picture's Worth a Thousand Words - A Revie...

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1st IEEE Karachi section Humanitarian technology Conference, Khi-HTC 2024

作者： Meraj, Syeda Shah, Asadullah Ismail, Ahsiah Sembokb, Tengku Mt Shadab, Syed Aftab, Syed Nageena, Syeda Khan, Nusratullah Nusratullah, Kajal Kulliyyah of Information and Communications Technology Department of Information Systems Selangor Malaysia Kulliyyah of Information and Communications Technology Department of Computer Science Selangor Malaysia Al Musanna College of Technology City Department of Information Technology Oman Higher Colleges of Technology Department of Aeronautical Engineering Technology Al-Ain United Arab Emirates Department of Health and Family Welfare Karnataka Koppal India Yanbu Industrial College Royal Commission Computer Science and Engineering Department Yanbu Saudi Arabia

ISBN: (纸本)9798350373042

In recent decades, the prevalence of diseases has witnessed a notable surge. The increasing health challenges have led to a greater use of advanced diagnostic machines for detecting and understanding these disease conditions. The diagnostic modalities, such as X-rays, CT scans, MRI scans, and nuclear medicine imaging, generate a wealth of interpretative data through medical images. Deep Radiomics aims to improve the efficiency and accuracy of image interpretation by automating the process of feature extraction, allowing for more comprehensive insights for advancing personalized medicine and improving outcomes in medical imaging. Enhance disease detection, diagnosis, and subsequent treatment strategies. © 2024 IEEE.

关键词： computer aided diagnosis

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