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00000cam a2200000 i 4500 |
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on1396065461 |
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OCoLC |
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20241006213017.0 |
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m o d |
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cr cnu---unuuu |
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230909s2023 si o 100 0 eng d |
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|a EBLCP
|b eng
|e rda
|c EBLCP
|d GW5XE
|d YDX
|d WSU
|d OCLCO
|d OCLCF
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| 019 |
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|a 1395945806
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|a 9789819958474
|q electronic book
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|a 9819958474
|q electronic book
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|z 9819958466
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|z 9789819958467
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| 024 |
7 |
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|a 10.1007/978-981-99-5847-4
|2 doi
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| 035 |
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|a (OCoLC)1396065461
|z (OCoLC)1395945806
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| 050 |
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4 |
|a QA76.87
|b .N33 2023
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|a HCDD
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2 |
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|a NCAA (Conference)
|n (4th :
|d 2023 :
|c Hefei Shi, China ; Online)
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| 245 |
1 |
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|a International Conference on Neural Computing for Advanced Applications :
|b 4th International Conference, NCAA 2023, Hefei, China, July 7-9, 2023, Proceedings.
|n Part II /
|c Haijun Zhang, Yinggen Ke, Zhou Wu, Tianyong Hao, Zhao Zhang, Weizhi Meng, Yuanyuan Mu, editors.
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3 |
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|a NCAA 2023
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| 264 |
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1 |
|a Singapore :
|b Springer,
|c [2023]
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| 300 |
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|a 1 online resource (627 p.).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Communications in Computer and Information Science ;
|v 1870
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| 505 |
0 |
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|a Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Deep Learning-Driven Pattern Recognition, Computer Vision and Its Industrial Applications -- Improved YOLOv5s Based Steel Leaf Spring Identification -- 1 Introduction -- 2 YOLOv5 Structure and Method Flow -- 2.1 Steel Leaf Spring Visual Identification Process -- 2.2 YOLOv5s Network Structure -- 3 YOLOv5 Recognition Algorithm Improvement -- 3.1 YOLOv5 Steel Leaf Spring Recognition Based On Migration Learning -- 3.2 CBAM Convolutional Attention Mechanism -- 3.3 Network Model Lightweighting
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| 505 |
8 |
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|a 4 Experimental Results and Analysis. -- 4.1 Ablation Experiments -- 4.2 Comprehensive Comparison Experiments of Different Target Detection Models -- 5 Summary -- References -- A Bughole Detection Approach for Fair-Faced Concrete Based on Improved YOLOv5 -- 1 Introduction -- 2 Model Design -- 2.1 The Network Structure of YOLOv5 -- 2.2 Network Structure Improvement -- 3 Experimental Settings and Results -- 3.1 The Experiment Platform -- 3.2 Data Acquisition and Dataset -- 3.3 Evaluation Metrics -- 3.4 Experimental Results and Analysis -- 4 Conclusion -- References
|
| 505 |
8 |
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|a UWYOLOX: An Underwater Object Detection Framework Based on Image Enhancement and Semi-supervised Learning -- 1 Introduction -- 2 UWYOLOX -- 2.1 Joint Learning-Based Image Enhancement Module (JLUIE) -- 2.2 Improved Semi-supervised Learning Method for Underwater Object Detection (USTAC) -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Experiment Results -- 4 Discussion and Conclusion -- References -- A Lightweight Sensor Fusion for Neural Visual Inertial Odometry -- 1 Introduction -- 2 Relate Work -- 2.1 VO -- 2.2 Traditional VIO Methods -- 2.3 Deep Learning-Based VIO -- 3 Method
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| 505 |
8 |
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|a 3.1 Attention Mechanism for the Visual Branch -- 3.2 Lightweight Pose Estimation Module -- 3.3 Loss Function -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Setup and Details -- 4.3 Main Result -- 5 Conclusion -- References -- A Two-Stage Framework for Kidney Segmentation in Ultrasound Images -- 1 Introdution -- 2 Relate Works -- 2.1 Automated Kidney Ultrasound Segmentation -- 2.2 Level-Set Function -- 2.3 Self-correction -- 3 Method -- 3.1 Overview -- 3.2 Shape Aware Dual-Task Multi-scale Fusion Network -- 3.3 Self-correction Part -- 4 Experiments -- 4.1 Dataset and Implementation Details
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| 505 |
8 |
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|a 4.2 Experiment Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Applicability Method for Identification of Power Inspection Evidence in Multiple Business Scenarios -- 1 Introduction -- 2 Constructing a Sample Library for Identifying Power Inspection Supporting Materials -- 3 Text Recognition Based on YOLOv3 Network -- 4 Network Compression with Structure Design and Knowledge Distillation -- 5 Experiment and Analysis -- 5.1 Training Sample Augmentation Quality Assessment -- 5.2 Model Recognition Results and Analysis
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| 500 |
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|a 5.3 Application Effect of Intelligent Verification in Power Inspection
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| 520 |
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|a The two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023. The 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. The papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Machine learning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, Machine Learning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and Their Engineering Applications; and Other Neural computing-related topics.
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| 588 |
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|a Description based on online resource; title from digital title page (viewed on October 23, 2023).
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| 650 |
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0 |
|a Neural computers
|v Congresses.
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| 650 |
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7 |
|a Neural computers.
|2 fast
|0 (OCoLC)fst01036251
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 fast
|0 (OCoLC)fst01423772
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Zhang, Haijun
|c (Professor of computer science)
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| 700 |
1 |
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|a Ke, Yinggen.
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| 700 |
1 |
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|a Wu, Zhou
|c (Researcher on optimization and artificial intelligence)
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| 700 |
1 |
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|a Hao, Tianyong.
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| 700 |
1 |
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|a Zhang, Zhao
|c (Computer scientist)
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| 700 |
1 |
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|a Meng, Weizhi.
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| 700 |
1 |
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|a Mu, Yuanyuan.
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| 776 |
0 |
8 |
|i Print version:
|a Zhang, Haijun
|t International Conference on Neural Computing for Advanced Applications
|d Singapore : Springer,c2023
|z 9789819958467
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| 830 |
|
0 |
|a Communications in computer and information science ;
|v 1870.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-99-5847-4
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
|