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Titlebook: Second International Conference on Image Processing and Capsule Networks; ICIPCN 2021 Joy Iong-Zong Chen,João Manuel R. S. Tavares,Ke-Li Co

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An Evaluation of Multiclass Leaf Classification Using Transfer Learning Techniques,ification with better accuracy. Experiments are conducted on both Swedish and flavia datasets and the result indicates that Densenet201 gives better performances of maximum accuracy of 98.10% in Swedish dataset compared to 96.34% obtained on Flavia dataset.
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Machine Learning Approaches for Image Quality Improvement,various types of noise reduction approaches in-detail. The study also includes brief elaboration of magnetic resonance imaging. Machine learning that is foremost used field for complex problems is also discussed with pros and cons of existing techniques with performance parameters to measure the noise in MR images.
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Deep Convolutional Neural Networks (CNNs) to Detect Abnormality in Musculoskeletal Radiographs,ighest accuracy. In this paper, found that model (Densenet169) achieved training accuracy of 87.88% and validation accuracy of 79.20%. In case of training loss and validation loss, the model achieved 0.3304 and 0.4887 respectively.
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,Antıspoofing Detection System for Online Classes and Online Meeting,or meeting. The state-of-the-art development in the face recognition techniques help to identify the spoofing person. If any spoofing activity is detected; it alerts the teacher or organizer that the attendee is not physically present and/or not belonging to the class.
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Optimal Sizing and Placement of Multiple DGs in Distribution Network to Reduce Total Loss Using Cucwith the help of MATPOWER/MATLAB and the comparison of obtained results with DG using cuckoo search algorithm is presented related to the base case results. At last, it can be concluded that cuckoo search is an efficient methodology for optimal size and placement of DGs.
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