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International Joint Conference on Neural Networks (IJCNN)

作者： Kaixiang Yang Yifan Shi Zhiwen Yu Zhijie Zhong Jichao Bi Mengzhi Wang School of Computer Science&Engineering South China University of Technology Guangzhou China College of Engineering Huaqiao University Quanzhou China Zhejiang Institute of Industry and Information Technology Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China

Consensus clustering methods provide better performance by fusing multiple clustering solutions in terms of accuracy, robustness and stability. However, most current methods suffer from different challenges: i) the high-dimensional problem; ii) limitations of single clustering method; iii) the optimal number of clusters selecting for a certain validity measure; iv) redundant clustering candidate attributes. To overcome the above limitations, we propose a hybrid clustering solutions fusion method based on gated three-way decision (HCFG) for data analysis. By integrating multiple clustering solutions and executing information fusion, HCFG enjoys four properties: (1) multiple random subspace generation strategy is utilized to generate diverse low-dimensional subspaces effectively; (2) a fusion framework that considers characteristics of both the soft clustering and hard clustering methods is designed, in which potential boundary of feature attribute sets is explored; (3) the optimal number of clusters is set by utilizing multiple clustering validity indices; (4) clustering solutions is considered as attributes and a gated three-way decision method is proposed to adaptively conduct attribute reductions. Extensive comparative experiments on 24 real-world data sets demonstrates the effectiveness and superiority of HCFG. Moreover, nonparametric tests are conducted to compare HCFG with multiple consensus clustering methods.

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors’physical activity and quality of life

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Journal of Sport and Health science 2023年第6期12卷 705-714页

作者： Zan Gao Suryeon Ryu Wanjiang Zhou Kaitlyn Adams Mohamed Hassan Rui Zhang Anne Blaes Julian Wolfson Ju Sun School of Kinesiology University of MinnesotaMinneapolisMN 55455USA Department of Kinesiology Recreationand Sport StudiesThe University of TennesseeKnoxvilleTN 37996USA Department of Pharmaceutical Care&Health Systems and Institute for Health Informatics University of MinnesotaMinneapolisMN 55455USA Department of Hematology School of MedicineUniversity of MinnesotaMinneapolisMN 55455USA School of Public Health University of MinnesotaMinneapolisMN 55455USA Department of Computer Science&Engineering University of MinnesotaMinneapolisMN 55455USA

Purpose This study aimed to examine the effects of a multi-component mobile health intervention(wearable,apps,and social media)on cancer survivors’(CS')physical activity(PA),quality of life,and PA determinants compared to exercise prescription only,social media only,and attention control *** A total of 126 CS(age=60.37±7.41 years,mean±SD)were recruited from the United *** study duration was 6 months and participants were randomly placed into 4 *** participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily ***(1)received previously established weekly personalized exercise prescriptions via email,(2)received weekly Facebook health education and interacted with one another,(3)received both Conditions 1 and 2,or(4)were part of the control condition,meaning they adopted usual *** PA daily steps,quality of life(i.e.,physical health and mental health),and PA determinants(e.g.,self-efficacy,social support)were measured at baseline,3 months,and 6 *** The final sample size included 123 *** results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention(95%confidence interval(95%CI):368–2951;p<0.05).Similarly,those in the multi-component condition had significantly greater increased physical health than the control condition(95%CI:–0.41 to–0.01;p<0.05)over *** addition,the social media condition had significantly greater increased perceived social support than the control condition(95%CI:0.01–0.93;p<0.05).No other significant differences on outcomes were *** The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical ***,offering social media intervention has the potential to improve CS perceived social support.

关键词： Daily steps Exercise app Fitbit Health wearable Physical activity determinantsTagedAPTARAEnd

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MA-VoxelMorph:Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images

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Journal of Innovative Optical Health sciences 2025年第1期18卷 135-151页

作者： Qing Huang Lei Ren Tingwei Quan Minglei Yang Hongmei Yuan Kai Cao School of Computer Science&Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan Hubei 430074P.R.China Beijing Wandong Medical Technology Co. Ltd.Beijing 100015P.R.China Changhai Hospital of Shanghai Shanghai 200433 P.R.China

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization ***,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is *** deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration *** alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels *** further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding *** method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local *** proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine *** also proved to be fast in 3D image registration,using about 1.5 s,and faster than most *** and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

关键词： Thoracoabdominal CT image registration large deformation fine structure multi-scale attention mechanism

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Robust Event-Triggered Control of Nonlinear Systems Under a Global Sector-Bound Condition

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Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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Efficient parallelization of SPH algorithm on modern multi-core CPUs and massively parallel GPUs

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第6期12卷 102-128页

作者： Pravin Jagtap Rupesh Nasre VSSanapala B.S.V.Patnaik Department of Applied Mechanics Indian Institute of Technology Madras Chennai 600036 India Department of Computer Science&Engineering Indian Institute of Technology Madras Chennai 600036 India Indira Gandhi Centre for Atomic Research Homi Bhabha National Institute Kalpakkam 603102 India

Smoothed Particle Hydrodynamics (SPH) is fast emerging as a practically usefulcomputational simulation tool for a wide variety of engineering problems. SPH isalso gaining popularity as the back bone for fast and realistic animations in graphicsand video games. The Lagrangian and mesh-free nature of the method facilitates fastand accurate simulation of material deformation, interface capture, etc. Typically,particle-based methods would necessitate particle search and locate algorithms tobe implemented efficiently, as continuous creation of neighbor particle lists is acomputationally expensive step. Hence, it is advantageous to implement SPH, on modernmulti-core platforms with the help of High-Performance Computing (HPC) tools. Inthis work, the computational performance of an SPH algorithm is assessed on multicore Central Processing Unit (CPU) as well as massively parallel General PurposeGraphical Processing Units (GP-GPU). Parallelizing SPH faces several challenges suchas, scalability of the neighbor search process, force calculations, minimizing threaddivergence, achieving coalesced memory access patterns, balancing workload, ensuringoptimum use of computational resources, etc. While addressing some of these challenges,detailed analysis of performance metrics such as speedup, global load efficiency, globalstore efficiency, warp execution efficiency, occupancy, etc. is evaluated. The OpenMP andCompute Unified Device Architecture (CUDA) parallel programming models have beenused for parallel computing on Intel Xeon(R) E5-2630 multi-core CPU and NVIDIAQuadro M4000 and NVIDIA Tesla p100 massively parallel GPU architectures. Standardbenchmark problems from the Computational Fluid Dynamics (CFD) literature are chosen for the validation. The key concern of how to identify a suitable architecturefor mesh-less methods which essentially require heavy workload of neighbor search andevaluation of local force fields from neighbor interactions is addressed.

关键词： SPH GPU CUDA OpenMP HPC CFD particle based methods

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Intention to Use Mobile Augmented Reality in the Tourism Sector

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computer Systems science & engineering 2021年第5期37卷 187-202页

作者： Malik Khlaif Gharaibeh Natheer Khlaif Gharaibeh Mohammad Ayoub Khan Waleed Abdel karim Abu-ain Musab Kasim Alqudah Faculty of Business Jadara UniversityIrbid21110Jordan College of Computer Science&Engineering at Yanbu Taibah UniversityYanbu41911Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia College of Community Taibah UniversityBadr46354Saudi Arabia

This article examines the main variables that influence the intention to use Augmented Reality(AR)applications in the tourism sector in *** study model has been constructed based on the unified theory of acceptance and the use of technology2(UTAUT2),by incorporating a new construct(aesthetics)to explore the usage intention of Mobile Augmented Reality in Tourism(MART).A questionnaire was used and distributed to a sample of 450 *** were analyzed using the Smart PLS version *** testing 12 hypotheses.29 measurement items were carefully reviewed based on previous studies that were selected to assess the research *** findings revealed that the proposed model elucidates 35.7%of the variance in the users’intention to use *** results also showed that both performance expectancy and aesthetics were found to be the most significant factors at level(0.001).Four variables,respectively,were at level(0.01)which consisted of social influence,facilitating conditions,hedonic motivation,and price *** weakest effect was effort expectancy at level(0.05).As the use of AR has become important for tourists,this study establishes a research base that can be built upon for future *** developers can benefit from the results of this research to design and deliver this service successfully and to ensure that its adoption by users is achieved.

关键词： Intention to use UTAUT2 aesthetics performance expectancy augmented reality social influence effort expectancy MART AR

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Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

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computers, Materials & Continua 2021年第7期68卷 1117-1128页

作者： Shankaranarayanan Nalini Nagappan Krishnaraj Thangaiyan Jayasankar Kalimuthu Vinothkumar Antony Sagai Francis Britto Kamalraj Subramaniam Chokkaligam Bharatiraja Department of Computer Science&Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Electronics&Communication Engineering SSM Institute of Engineering and TechnologyDindigul624002India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of ECE Karpagam Academy of Higher EducationCoimbatore641021India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the *** diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming *** of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image *** errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning *** DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification *** leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering *** was then applied to eliminate regions not indicative of ***,a set of features were extracted from the previously isolated diseased ***,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.

关键词： Leaf classification paddy leaf deep learning metaheuristics optimization crow search algorithm

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Real-Time Control System Adopted to Energy Storage for Smart Grid Low Wind Applications:A Part of Distributed Renewables in Smart City

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Journal of Electronic science and Technology 2021年第4期19卷 379-389页

作者： MD Shahrukh Adnan Khan Yoong Choon Chang Md Ashraful Kabir Muhammad Izuan Fahmi Bin Romli Chockalingam Aravind Vaithilingam Jakir Hosain with the Department of Electrical and Electronic Engineering Canadian University of BangladeshDhaka 1212 with the Department of Electrical&Electronic Engineering Universiti Tunku Abdul RahmanKamper 31900 with the Department of Computer Science&Engineering Canadian University of BangladeshDhaka 31900 with the Department of Electrical and Electronic Engineering University Malaysia PerlisPerlis 01000 with the Department of Electrical and Electronic Engineering Taylor’s UniversityKuala Lumpur 47500 with the School of Science&Engineering Canadian University of BangladeshDhaka 1212

In this paper,extensive efforts have been undertaken to design and develop a control system,which is incorporated with an energy storage device that can store energy from low-voltage renewable *** developed device acts as a storage element,which can be used to charge small-scale batteries,cellular devices,and other applications in remote places where the grid connection is not *** circuit is developed using a case-by-case *** order to solve the low output voltage problem,a bipolar junction transistor-metal oxide semiconductor field-effect transistor(BJT-MOSFET)based switch control technology with the Arduino microcontroller has been *** developed control system is extremely efficient in charging batteries through a supercapacitor for low-voltage *** this research,a small-scale 200-W portable vertical axis wind turbine is used at a wind speed of 3 m/*** result shows the efficiency of the proposed system as compared with the conventional *** proposed system can be an important tool of the latest distributed energy generation technology which is an important part of a smart ***,the limitations and future scopes of the development of the control device are discussed for the future *** important future scope identified is to integrate the Internet of Things based mobile interface for remote monitoring for any kind of pandemic situation like ***,it is high time to get our smart city concept aligned with the post COVID pandemic situation and get us prepared smartly for similar future occurrences.

关键词： Energy storage low-voltage application novel control system post pandemic smart city

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Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

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computers, Materials & Continua 2021年第5期67卷 1881-1897页

作者： Thavavel Vaiyapuri Sachi Nandan Mohanty M.Sivaram Irina V.Pustokhina Denis A.Pustokhin K.Shankar College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science&Engineering IcfaiTechICFAI Foundation for Higher EducationHyderabadIndia Assistant professor Research Research CenterLebanese French UniversityErbil44001Iraq Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the *** VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing *** current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN *** presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and *** Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in *** SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN *** HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge *** experimentation outcome verified that the presented method was better under several *** projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

关键词： Deep learning license plate recognition intelligent transportation segmentation

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