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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Shivam Batra Varun Kamath Priyadarshini R Prasham Titiya Manigandan Ramadasan Ronit Naik Software Engineer 1 JPMorgan Chase & Co Bengaluru India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A critical aspect of medical imaging for accurate diagnosis and treatment of gastrointestinal disorders. This study addresses the importance of this topic and aims to fill the existing research gap. By employing four distinct models, including CNN, UNet, LinkNet, and SegNet, we achieved exceptional results with $99 \%$ accuracy and an IoU score of 0.95. Our findings significantly contribute to the field of image analysis and segmentation, improving the accuracy and efficiency of gastrointestinal endoscopy image segmentation. This research has the potential to improve the uses of medical imaging, leading to more precise diagnosis and better patient outcomes. Overall, this work contributes to the progress of gastrointestinal endoscopy image segmentation, with potential applications to the medical imaging domain.

关键词： Deep learning Image segmentation Accuracy Image resolution Image analysis Endoscopes Gastrointestinal tract

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Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

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computer Systems Science & engineering 2023年第7期46卷 671-686页

作者： Xiaorui Zhang Xianglong Zeng Wei Sun Yongjun Ren Tong Xu Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China University of Southern California Los AngelesCaliforniaUSA

Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language *** researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition,but the networks are affected by much interference information in the input data and thus fit to some unimportant *** this paper,we proposed a novel method for encoding spatio-temporal information,which can enhance the key features required for gesture recognition,such as shape,structure,contour,position and hand motion of gestures,thereby improving the accuracy of gesture *** encoding method can encode arbitrarily multiple frames of gesture data into a single frame of the spatio-temporal feature map and use the spatio-temporal feature map as the input to the neural *** can guide the model to fit important features while avoiding the use of complex recurrent network structures to extract temporal *** addition,we designed two sub-networks and trained the model using a sub-network pre-training strategy that trains the sub-networks first and then the entire network,so as to avoid the subnetworks focusing too much on the information of a single category feature and being overly influenced by each other’s *** results on two public gesture datasets show that the proposed spatio-temporal information encoding method achieves advanced accuracy.

关键词： Dynamic gesture recognition spatio-temporal information encoding multimodal input pre-training score fusion

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN 16

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Zhou, Lei Yu, Cheng Liu, Hu Li, Xin Li, Fenfen Zhang, Haiyan National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology huai'An Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Jiangsu China Nanjing University of Technology Nanjing College of Computer Science and Technology Jiangsu211816 China Huaiyin Institute of Technology huai'An Faculty of Chemical Engineering Jiangsu China

ISBN: (纸本)9781665405539

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved. © 2021 IEEE.

关键词： Fault detection

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

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Frontiers of Information technology & Electronic engineering 2023年第10期24卷 1375-1389页

作者： Zhen LIANG Taoran WU Wanwei LIU Bai XUE Wenjing YANG Ji WANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of Software Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Key Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical *** of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given ***-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this ***,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation *** alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great *** is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

关键词： Robust neural networks Training method Drawdown risk Global robustness training Monotonically decreasing robustness

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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A Multi-Focal Image Fusion Network for Implantation Outcome Prediction of Blastocyst 7

A Multi-Focal Image Fusion Network for Implantation Outcome ...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Cheng, Yi Chen, Tingting Hu, Yaojun Meng, Xiangqian Liu, Zuozhu Chen, Danny Z. Wu, Jian Ying, Haochao School of Software Technology Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China Sichuan Jinxin Xinan Women & Children Hospital Chengdu China ZJU-UIUC Institute Zhejiang University Haining China Department of Computer Science and Engineering University of Notre Dame Notre Dame United States Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China School of Public Health Zhejiang University Hangzhou China

Accurately predicting implantation outcomes based on blastocyst developmental potential is valuable in in-vitro fertilization (IVF). Clinically, embryologists analyze multiple focal-plane images (FP-images) to comprehensively assess embryo grades, which is extremely cumbersome and easily prone to inconsistency. Developing automatic computer-aided methods for analyzing embryo images is highly desirable. However, effectively fusing multiple FP-images for prediction remains a largely under-explored issue. To this end, we propose a novel Multiple Focal-plane Image Fusion Network, called MFIF-Net, to predict implantation outcomes of blastocyst. Specifically, our MFIF-Net consists of two sub-networks: a Core Image Generation Network (CI-Gen) and a Key Feature Fusion Network (KFFNet). In CI-Gen, we fuse multiple FP-images to generate a core image by pixel-wise weighting since different FP-images can have different focus positions. To further capture key features in each FP-image, we propose KFFNet to extract key information from the FP-images again and fuse them with the core image. In KFFNet, a Fusion Module is designed to capture key information of each FP-image, for which Squeeze Multi-Headed Attention is developed to exchange features and mitigate computationally intensive issue in attention. Comprehensive experiments validate the superiority and the rationality of our MFIF-Net approach over state-of-the-art methods in various metrics. Ablation studies also confirm the positive impact of each component in our MFIF-Net. The code will be publicly available upon acceptance. Model implementation details are available on https://***/Ch3ngY1/MFIF-Net. © 2024 CC-BY 4.0, Y. Cheng, T. Chen, Y. Hu, X. Meng, Z. Liu, D.Z. Chen, J. Wu & H. Ying.

关键词： Image fusion

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Pattern recognition in programming to support the evaluation of Computational Thinking

Pattern recognition in programming to support the evaluation...

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IEEE World engineering Education Conference (EDUNINE)

作者： Steven Pacheco-Portuguez Antonio Gonzalez-Torres Lilliana Sancho-Chavarria Ignacio Trejos-Zelaya Jorge Monge-Fallas School of Computing Costa Rica Institute of Technology Cartago Costa Rica School of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica School of Software Engineering & School of Computing Universidad CENFOTEC & Costa Rica Institute of Technology San José & Cartago Costa Rica School of Mathematics Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Computational thinking is a high-level skill that involves both critical and creative thinking. This research proposes an advanced method for analyzing source code to assess computational thinking skills in elementary school students. We present a large-scale approach that examines source code from various programming exercises using a generalized abstract syntax tree, enabling language-independent analysis. Clustering techniques are applied to identify different levels of computational thinking development in areas such as parallelism, data representation, abstraction and decomposition, control flow, and programming structure. The results of the automated assessment provide insights into students' computational thinking abilities across diverse populations, highlighting opportunities for improving the educational framework.

关键词： Codes Source coding Data visualization Syntactics Parallel processing Pattern recognition software measurement engineering education Programming profession

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems Science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Conjugate Surrogate for Expensive Multiobjective Optimization

Conjugate Surrogate for Expensive Multiobjective Optimizatio...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Yang, Qi-Te Luo, Liu-Yue Xu, Xin-Xin Chen, Chun-Hua Wang, Hua Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Artificial Intelligence Nankai University Tianjin300350 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC 8001 Australia Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665430654

The Kriging surrogate (KS) has been widely used in surrogate-assisted multiobjective evolutionary algorithms (SAMOEAs) for solving expensive multiobjective optimization problems (EMOPs). Typically, when tackling an M-objective EMOP, a KS consists of M Kriging models, in which each model is used to approximate one objective function to replace the expensive fitness evaluation. Since such a KS is only efficient in solving low-dimensional EMOPs, the dimension reduction method has been adopted to construct the reduction surrogate (RS) to reduce training costs. However, both KS and RS can only approximate the mapping from variables to different objectives (i.e., objective function) but ignore the potential relationship between objectives. For practical applications, it is necessary to take into account the mapping between different objectives for the reliability of the surrogate. Therefore, we for the first time propose the concept of the conjugate surrogate (CS) and construct a simple CS to realize the approximated mapping from objectives to objectives. Different from KS or RS, all models in CS are conjugate symbiosis. In collaboration with RS, CS can not only benefit the light training cost, but also improve the convergence speed. Compared with five state-of-the-art SAMOEAs, the CS-assisted algorithm shows its effectiveness and competitiveness in solving EMOPs. © 2023 IEEE.

关键词： Evolutionary Computation Expensive Multiobjective Optimization Kriging Surrogate-assisted

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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