Description |
1 online resource (xii, 663 pages) : illustrations (chiefly color) |
Series |
Lecture notes in electrical engineering ; volume 1007 |
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Lecture notes in electrical engineering ; v. 1007.
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Contents |
Intro -- Contents -- About the Editors -- Detection of Physical Impairments on Solar Panel Using YOLOv5 -- 1 Introduction -- 2 Image Pre-Processing Parameters and Algorithm -- 2.1 Collection of Specimen Images Data -- 2.2 Detection of Object Using YOLOv5 Algorithm -- 2.3 Data Augmentation for Accuracy Improvement -- 3 Training and Loss Function of Datasets -- 4 Evolution Metrics for Damage Detection -- 4.1 Object Detection Using Intersection Over Union (IoU) -- 4.2 Precision of Prediction of Damage Class -- 4.3 Recall of Total Relevant Result |
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4.4 Mean Average Precision (mAP) of Precision-Recall Curve -- 5 Training Results -- 6 Conclusion and Future Work -- References -- Image Processing Techniques on Porous Silicon to Estimate Porosity and Pore Size -- 1 Introduction -- 2 Image Processing of Porous Silicon SEM Images -- 2.1 Image De-noising and Filtration to Reduce Error -- 2.2 Morphological Operations of Images -- 2.3 Image Thresholding -- 3 Parameter Selection for Noise Filtration and Closing Operation -- 4 Results and Discussions -- 5 Conclusion -- References -- Solar PV System Fault Classification Using Machine Learning Techniques |
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1 Introduction -- 2 Simulation of the 1.3 KW PV-System for Synthetic Data Generation -- 2.1 Healthy Condition -- 2.2 Shading Faults -- 2.3 Line-To-Line Fault -- 3 Machine Learning Techniques -- 3.1 Accuracy Check and Results -- 3.2 Prediction Results -- 4 Conclusion -- References -- A Lightweight Network for Detecting Pedestrians in Hazy Weather -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments and Results -- 5 Conclusion -- References -- Hand Gesture-Based Recognition System for Human-Computer Interaction -- 1 Introduction -- 1.1 Novelty of Work -- 2 Literature Review |
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3 Methodology -- 3.1 Basic Overview -- 3.2 Step-By-Step Process -- 3.3 Tools Used -- 4 Result Analysis -- 5 Conclusion and Future Scope -- 5.1 Summary of Work -- 5.2 Conclusion -- 5.3 Future Scope -- References -- An Overview of Machine Learning Techniques Focusing on the Diagnosis of Endometriosis -- 1 Introduction -- 2 Image Datasets Used -- 3 Performance Metrics -- 3.1 Accuracy -- 3.2 Sensitivity -- 3.3 Specificity -- 3.4 Precision -- 3.5 F1-Score -- 3.6 AUC -- 3.7 AUROC -- 3.8 P-Value -- 3.9 C-Index -- 4 Techniques -- 4.1 Unsupervised Machine Learning [18] -- 4.2 Logistic Regression [19] |
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4.3 Logistic Regression + Naive Bayes [20] -- 4.4 Classification and Regression Trees [21] -- 4.5 Computer Vision [22] -- 4.6 EXtreme Gradient Boosting (XGB) [24] -- 4.7 Natural Language Processing [25] -- 4.8 Decision Tree [13] -- 4.9 Decision Tree + Generalized Linear Model [26] -- 4.10 Convolutional Neural Network [27] -- 4.11 ResNet50 Convolutional Neural Network [7] -- 4.12 VGGNet-16 Model [16] -- 4.13 Artificial Neural Network [21] -- 4.14 Deep Neural Network [14] -- 4.15 Deep Learning Along with Histopathological Subtypes [33] -- 5 Comparison of Several Machine Learning Techniques |
Summary |
This book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing |
Bibliography |
Includes bibliographical references |
Notes |
Description based on online resource; title from digital title page (viewed on June 07, 2023) |
Subject |
Artificial intelligence -- Congresses
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Computer vision -- Congresses
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Machine learning -- Congresses
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Artificial intelligence
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Computer vision
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Machine learning
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Genre/Form |
Electronic books
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proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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Form |
Electronic book
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Author |
Singh, Koushlendra Kumar, editor
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Bajpai, Manish Kumar, editor.
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Akbari, Akbar Sheikh, editor
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ISBN |
9789819901890 |
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9819901898 |
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