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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijerathna, Udari Dissanayake, Nimavi Nimasha, Dinithi Senarathne, Udara Weerasinghe, Lokeesha Kahandawaarachchi, Chathurangika Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Abnormal formation of cells in a layer. The brain is known as the brain cancer complex, and a brain cancer detection system is needed to detect cancer in its early stages Phase. As a result, the primary goal of this initiative is to assist existing doctors in their training for the early identification o f brain cancer by utilizing magnetic resonance imaging (MRI), which is the most effective approach to identifying brain cancer. This system is done through four components, and it has made it easier to detect brain tumors. These techniques with different image processing techniques include noise removal, segmentation, classification, a nd u se o fa nalysis segments f or detection. Pre-processing techniques prepare the image for further processing by removing image noise. During segmentation, the brain tumor system determines the size of the brain tumor and estimates its size. Specialists should examine them and classify brain tumors as meningiomas, gliomas, and pituitary tumors. In analysis, this component is expected to be designed so that users or medical experts can know the future through a dashboard. We explored the methodology of using generative adversarial networks (GANs) implemented in TensorFlow to denominate brain tumor images. Analyses the outcomes of various types of established deep learning models on the brain tumor MR image datasets, such as classification methods ResNet-50, VGG 16, CNN, and Inception V3. The validation precision, AUC, recall, and validation loss for CNN are 95.3%, 99.43%, 92.1 %, and 0.360 adjustments respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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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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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Exploring the Intention to Adopt Mining-Stages VR Application Using Modified UTAUT Model: A Perspective of the Philippine Mining Sector 8

Exploring the Intention to Adopt Mining-Stages VR Applicatio...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Moyon, Elbert S. Jayoma, Jaymer M. Morales, Edsel Matt O. Information System Department Caraga Butuan City Philippines Information Technology Department Caraga Butuan City Philippines Computer Science Department Caraga Butuan City Philippines

ISBN: (纸本)9798331531492

Virtual Reality (VR) technology has gained attention in education, architecture, gaming, and healthcare industries with proven potential to enhance training and simulation experiences. In the mining sector, VR offers great potential to improve training, effectiveness, and safety;however, understanding the adaptation factors is imperative for a smooth implementation. To understand the factors driving technological acceptance and offer suggestions for effective integration in the industry, this study uses the UTAUT model to examine the adoption of VR applications in the Philippine mining sector. The findings show that adopting the Mining-Stages VR Application is primarily driven by perceived benefits and hedonic motivation, with less influence from social influence and simplicity of use. Furthermore, the behavioral intention of users turns out to be a crucial component in the actual adoption, providing crucial direction for institutions and legislators seeking to encourage the adoption of VR technology in the Philippine mining industry. © 2024 IEEE.

关键词： Mining industry

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Optimizing Connections:Applied Shortest Path Algorithms for MANETs

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computer Modeling in Engineering & Sciences 2024年第10期141卷 787-807页

作者： Ibrahim Alameri Jitka Komarkova Tawfik Al-Hadhrami Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir Ibn Hayyan Medical UniversityAlkufaNajaf54001Iraq Faculty of Economics and Administration University of PardubiceStudentskaPardubice53210Czech Republic Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of *** objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static ***,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for *** paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in *** results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse *** theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional *** adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of ***,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional *** paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

关键词： Dijkstra’s algorithm optimization complexity analysis shortest path first comparative algorithm analysis nondeterministic polynomial(NP)-complete

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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