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A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

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Journal of Computer science & Technology 2024年第2期39卷 320-335页

作者： Younhyun Jung Jim Kong 盛斌 Jinman Kim School of Computing Gachon UniversitySeongnam 13120Korea School of Computer Science The University of SydneySydney 2006Australia Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its *** DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of *** generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and *** at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain *** these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the *** these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the *** this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge *** of pre-designed TFs,our knowledge database contains volumes with SOI *** an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the *** approach largely reduces manual TF navigation and fine *** our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the *** the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature *** the intensity profile,we extend the dynamic time warping technique to address subtle alignment

关键词： data retrieval direct volume rendering ray analysis transfer function

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Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

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Computer Systems science & engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of Computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： Brain tumor machine learning segmentation computer-aided diagnosis skull stripping

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Hybrid model with improved score level fusion for heart disease classification

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Multimedia Tools and Applications 2024年第18期83卷 54951-54987页

作者： Maithani, Ankit Verma, Garima Department of Computer Science and Engineering School of Computing DIT University Makka Wala Uttarakhand Dehradun248009 India School of Computing DIT University Uttarakhand Dehradun248009 India

The heart, an essential organ of the body, pumps blood filled with oxygen to every area of the body through a complex network of veins and arteries. Any condition affecting the heart is referred to as heart disease. It is essential to start the right treatments early on in order to reduce the death toll. Predicting cardiac disease before heart attacks or strokes is therefore a significant challenge for the healthcare industry. This article introduces a methodology for predicting heart disease which consists of the three steps: (1) pre-processing, (2) feature extraction, and (3) disease prediction. Initially, the input data is pre-processed using improved data normalization. Next, the features are extracted from the normalized data;they are higher-order statistical features, information gain-based features, mutual information-based features, and improved entropy-based features. The results are then decided utilising an improved score-level fusion procedure, which is used to the suggested hybrid classification model that incorporates models like Deep Maxout and QNN algorithms. By adjusting the Deep Maxout and QNN classifiers' ideal weights, a self-improved Beluga whale optimisation is developed for optimal training to increase the performance of the hybrid model. When using dataset 1, the Hybrid + SI-BWO produced a precision of 94.25%, while CNN, DBN, Bi-LSTM, SVM, RNN, DL, and RF all produced precisions of 75.04%, 77.38%, 80.17%, and 86.52%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Forecasting

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CENTRAL LIMIT THEOREM FOR TEMPORAL AVERAGE OF BACKWARD EULER-MARUYAMA METHOD

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Journal of Computational Mathematics 2025年第3期43卷 588-614页

作者： Diancong Jin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific ComputingHuazhong University of Science and TechnologyWuhan 430074China

This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem(CLT)of the temporal average of the BEM method,which characterizes its asymptotics in *** the deviation order is smaller than the optimal strong order,we directly derive the CLT of the temporal average through that of original equations and the uniform strong order of the BEM *** the case that the deviation order equals to the optimal strong order,the CLT is established via the Poisson equation associated with the generator of original *** experiments are performed to illustrate the theoretical *** main contribution of this work is to generalize the existing CLT of the temporal average of numerical methods to that for SODEs with super-linearly growing drift coefficients.

关键词： Central limit theorem Temporal average Ergodicity Backward Euler-Maruyama method

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Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 785-787页

作者： Yao Cui Pei Cheng Xiaohua Ge the School of Mathematic Science Anhui UniversityHefei 230601China IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

Dear Editor,This letter addresses the synchronization problem of a class of delayed stochastic complex dynamical networks consisting of multiple drive and response *** aim is to achieve mean square exponential synchronization for the drive-response nodes despite the simultaneous presence of time delays and stochastic noises in node dynamics.

关键词： dynamics stochastic letter

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Fibrillar adhesives with unprecedented adhesion strength,switchability and scalability

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National science Review 2024年第10期11卷 20-33页

作者： Changhong Linghu Yangchengyi Liu Xudong Yang Dong Li Yee Yuan Tan Mohamed Haziq Bin Mohamed Hafiz Mohammad Fadhli Bin Rohani Zihao Du Jiangtao Su Yan Li Yucheng Huo Hanyan Xu Xiufeng Wang Yifan Wang Jing Yu Huajian Gao K.Jimmy Hsia School of Mechanical and Aerospace Engineering Nanyang Technological University School of Materials Science and Engineering Xiangtan University Department of Engineering Mechanics Zhejiang University School of Materials Science and Engineering Nanyang Technological University Institute of High-Performance Computing A*STAR Mechano-X Institute Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Chemistry Chemical Engineering and BiotechnologyNanyang Technological University

Bio-inspired fibrillar adhesives have received worldwide attention but their potentials have been limited by a trade-off between adhesion strength and adhesion switchability, and a size scale effect that restricts the fibrils to micro/nanoscales. Here, we report a class of adhesive fibrils that achieve unprecedented adhesion strength（～2 MPa）, switchability（～2000）, and scalability（up to millimeter-scale at the single fibril level）,by leveraging the rubber-to-glass（R2G） transition in shape memory polymers（SMPs）. Moreover, R2G SMP fibrillar adhesive arrays exhibit a switchability of >1000（with the aid of controlled buckling） and an adhesion efficiency of 57.8%, with apparent contact area scalable to 1000 mm2, outperforming existing fibrillar adhesives. We further demonstrate that the SMP fibrillar adhesives can be used as soft grippers and reusable superglue devices that are capable of holding and releasing heavy objects >2000 times of their own weight. These findings represent significant advances in smart fibrillar adhesives for numerous applications,especially those involving high-payload scenarios.

关键词： JKR-DMT transition rubber-to-glass (R2G) transition R2G fibrillar adhesives shape memory polymers adhesion switchability and scalability

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 167-177页

作者： Xiuchun Xiao Chengze Jiang Qixiang Mei Yudong Zhang School of Electronics and Information Engineering Guangdong Ocean UniversityZhanjiangChina School of Cyber Science and Engineering Southeast UniversityNanjingChina School of Computing and Mathematical Sciences University of LeicesterLeicesterUK

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering *** the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence ***,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed ***,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed ***,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem *** with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

关键词： adaptive intelligent systems neural network real-time systems

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Human Drawing-Driven Automated Evaluation for Parkinson's Disease Detection

Human Drawing-Driven Automated Evaluation for Parkinson's Di...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Pareek, Keshav Jain, Garima Jain, Sanat Bhargav, Vaibhav Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vit Bhopal University School of Computing Science and Engineering Sehore India

ISBN: (纸本)9798331529833

Globally, Parkinson's Disease is the second most prevalent neurodegenerative disorder, which is identified by distinct symptoms like tremors, bradykinesia, rigidity, and postural instability. Early signs may include reduced facial expression, impaired hand-eye coordination, and decreased muscle-brain coordination. As PD progresses, symptoms worsen, potentially leading to severe disability. Although there is no cure, early detection and intervention can improve patient outcomes. Traditional PD diagnosis methods, involving clinical assessments and neurological tests, are often time-consuming and subjective. Machine Learning (ML) provides a promising alternative for automating PD detection using data-driven algorithms. This paper explores various ML methods, including Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis, Random Forest, Gaussian Naive Bayes, Decision Tree, and Support Vector Classifier (SVC), to predict early-stage PD symptoms. After experiments, the Gaussian Naive Bayes model scored the highest accuracy of 78%, showcasing the potential of ML to improve early detection. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Design and Development of Defected Ground Structure Fractal Antenna based on Osgood Curve for C-band Modern Wireless Communications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 23-29页

作者： Singh, Gurmeet Kumar, Ashish School of Computing Science and Engineering Galgotias University Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Multifunctional behavioral antennas are one of the significant features of the modern wireless communication systems. This is due to the fact that the miniaturized sizes of the devices demand compact and low-profile antennas that cover limited space and provide multi-frequency functionalities over a wide range of the spectrum. In view of these prerequisites, this paper presents the two design approaches utilizing Osgood Curve Fractal structure as the antenna element with different feeding techniques. The proposed antennas resonate at multiple frequencies covering S-and C-band with excellent impedance matching. The designs have been optimized in terms of scaling factor and position of microstrip feed line with peak gain of 4.5 dBi. Design 1 has significant bandwidth for required applications;however, design 2 possesses low bandwidth with small frequency ratio. Both the proposed designs are fabricated, and results are quite promising. The proposed designs can be utilized for various sub-6 GHz 5G automotive applications as it can easily integrated in the various parts of the modern passenger car. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Bandwidth

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