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Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amol C. Goje,Alfred M. Bruckstein Conference proceedings 2024 T

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發(fā)表于 2025-3-21 19:39:40 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Management, Analytics and Innovation
副標(biāo)題Proceedings of ICDMA
編輯Neha Sharma,Amol C. Goje,Alfred M. Bruckstein
視頻videohttp://file.papertrans.cn/285/284444/284444.mp4
概述Presents research works in the field of data management, analytics and innovation.Addresses state-of-the-art topics and discusses challenges and solutions for future development.Serves as a reference
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Data Management, Analytics and Innovation; Proceedings of ICDMA Neha Sharma,Amol C. Goje,Alfred M. Bruckstein Conference proceedings 2024 T
描述.This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes..
出版日期Conference proceedings 2024
關(guān)鍵詞Algorithms and Models; Feature Engineering; Supervised Learning - Classification, Regression; Unsupervi
版次1
doihttps://doi.org/10.1007/978-981-97-3245-6
isbn_softcover978-981-97-3244-9
isbn_ebook978-981-97-3245-6Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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esearchers because of poor visual clarity. Further, in densely populated areas, the low contrast of the built-up areas causes huge difficulty in the detection of roof-tops individually. In view of such challenges, we develop a Mask-RCNN model for segmentation of probable built-up areas in low-resolu
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o test the performance of our proposed system, we used several machine learning algorithms like Support Vector Machines (SVMs) and Random Forest (RF) algorithms. Here, these algorithms are mainly used for diagnosing mental health disorders on the basis of given input (i.e. verified dataset of sympto
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two main stages: feature extraction and speech recognition. The feature extraction stage involves the use of the VGGish model to process the raw audio signals and create a feature representation that is both concise and comprehensive. The resulting feature vectors capture various acoustic attribute
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g pothole detection model, built upon YOLO V8, has been proposed. Additionally, road quality and PHR mapping via sensory data (accelerometer and gyrometer), which undergoes post-processing to provide quality observations for various vehicles and classify them, has been introduced. Leveraging this da
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learning-based 3D facial reconstruction algorithm with motion capture data to produce high-quality facial animation. We evaluate our method by comparing it to traditional animation methods and show that our approach produces more realistic and natural facial expressions. Our results demonstrate the
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,An revolution?re Traditionen anknüpfen,inopathy (NPDR), or proliferative diabetic retinopathy (PDR), from a healthy eyeball to a proliferative diabetic retinopathy presence. Remarkably, our approach attains an impressive 93.5% accuracy, bolstering the model‘s reliability in diabetic retinopathy diagnosis from fundus images. With potentia
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