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Titlebook: Advanced Computational Paradigms and Hybrid Intelligent Computing; Proceedings of ICACC Tapan Kumar Gandhi,Debanjan Konar,Kalpana Sharma Co

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11#
發(fā)表于 2025-3-23 11:40:56 | 只看該作者
Mapping and Monitoring of Water Bodies Using Sentinel 1A Images,tzlichen Rente oder Pension nicht mehr aus. Neben der zus?tzlichen privaten Vorsorge des Arbeitnehmers spielt die . als dritte S?ule zur Altersabsicherung eine wichtige Rolle. Unter einer betrieblichen Altersversorgung versteht man alle Leistungen, die der Arbeitgeber seinem Arbeitnehmer zur Altersv
12#
發(fā)表于 2025-3-23 14:27:25 | 只看該作者
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發(fā)表于 2025-3-23 20:11:12 | 只看該作者
14#
發(fā)表于 2025-3-24 01:51:15 | 只看該作者
Advanced Computational Paradigms and Hybrid Intelligent Computing978-981-16-4369-9Series ISSN 2194-5357 Series E-ISSN 2194-5365
15#
發(fā)表于 2025-3-24 03:53:58 | 只看該作者
16#
發(fā)表于 2025-3-24 07:47:14 | 只看該作者
Zielsetzung und Vorgehensweise,elopment of the country. Each year many crops go waste due to a lack of optimal climatic conditions to support the growth of crops. Losses of around 11 billion dollars are reported in India each year alone. The farmers in India suffer a lot due to the low productivity. The only way out to this probl
17#
發(fā)表于 2025-3-24 10:59:53 | 只看該作者
Zielsetzung und Vorgehensweise,ge of the network, placed near the field of data generation. It has significantly reduced some of IoT’s significant challenges, such as scalability, availability, reliability, and resiliency. In this domain, researchers are further incorporating advanced technology such as Software-Defined Network (
18#
發(fā)表于 2025-3-24 18:47:45 | 只看該作者
https://doi.org/10.1007/978-3-642-81787-8 appropriate at conveying the emotions present behind the text. The systems can speak out the text monotonically lacking expressiveness. In this paper, an Expressive Text-to-Speech Synthesis System (ETSSS) is proposed which considers the dominant emotions in the text provided. ETSSS works in two par
19#
發(fā)表于 2025-3-24 19:00:48 | 只看該作者
20#
發(fā)表于 2025-3-25 01:16:16 | 只看該作者
https://doi.org/10.1007/978-3-663-04731-5s. This study proposes 16 Convolutional Neural Network (CNN) models with different hyperparameters for heart sound classification. The Mel frequency cepstral coefficients (MFCC) is a well-known method used for feature extraction of sound data, and this method is used in this study. The objective of
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