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Titlebook: Sustainable Development through Machine Learning, AI and IoT; First International Pawan Whig,Nuno Silva,Pavika Sharma Conference proceedin

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21#
發(fā)表于 2025-3-25 05:12:14 | 只看該作者
1865-0929 uly 15–16, 2023..The book comprises 31 full papers that were selected from a total of 129 submissions. It provides insights into the latest research and advancements in sustainable development through the integration of machine learning, artificial intelligence, and IoT technologies. It serves as a
22#
發(fā)表于 2025-3-25 11:01:20 | 只看該作者
Deep Learning Model for Computer Vision: Sustainable Image Classification Using Machine Learningion that using deep learning is preferable to using machine learning. Additionally, we have also presented a comparative study of various CNN models to observe how they could potentially be employed?as pre-trained models for the image classification job.
23#
發(fā)表于 2025-3-25 14:21:45 | 只看該作者
Predicting Brain Stroke Using IoT-Enabled Deep Learning and Machine Learning: Advancing Sustainable tree for stroke detection. The results obtained show that Deep Learning models outperformed the Machine Learning models, moreover the DenseNet-121 provided the best results for brain stroke prediction with an accuracy of 96%. The ResNet and VGG-16 obtained an accuracy of 92% and 81% respectively.
24#
發(fā)表于 2025-3-25 15:53:54 | 只看該作者
Achieving Peak Energy Efficiency in Smart Grids Using AI and IOTcase studies and research initiatives that have successfully implemented AI and IoT solutions for optimizing energy consumption, demand response, renewable energy integration, and load forecasting. The findings of this study highlight the significant role of AI and IoT in achieving energy efficiency goals in smart grids.
25#
發(fā)表于 2025-3-25 21:51:39 | 只看該作者
26#
發(fā)表于 2025-3-26 01:21:50 | 只看該作者
27#
發(fā)表于 2025-3-26 07:13:23 | 只看該作者
Predicting Crop Yield in Smart Agriculture Using IoT and Machine Learning for Sustainable Developmens, thereby aiding farmers in making informed decisions regarding crop management and optimizing agricultural productivity. The findings of this research hold significant implications for the agricultural industry, providing valuable insights into crop yield estimation and supporting sustainable farming practices.
28#
發(fā)表于 2025-3-26 12:18:09 | 只看該作者
978-3-031-47054-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
29#
發(fā)表于 2025-3-26 14:15:46 | 只看該作者
Sustainable Development through Machine Learning, AI and IoT978-3-031-47055-4Series ISSN 1865-0929 Series E-ISSN 1865-0937
30#
發(fā)表于 2025-3-26 20:34:51 | 只看該作者
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