Titlebook: Intelligent Systems; Proceedings of 3rd I Siba K. Udgata,Srinivas Sethi,Xiao-Zhi Gao Conference proceedings 2024 The Editor(s) (if applicab

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Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare,nt organs in the human body. Diabetes can cause a variety of slow bad consequences if not detected and left without given medical care. The emergence of machine learning approaches, on the other hand, solves this crucial issue. The purpose and objectives of this work is to build a prototypical model

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Solar Powered Smart Home Automation and Smart Health Monitoring with IoT,e automation system. The sensors are spread all across the entrance Gate, corridor, room and kitchen. This (IOT) design prototype has LCD transistor which keep on provides the information. We have also use Wi-Fi technology for online control and monitoring. we also have an LCD which keeps us providi

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Seasonal-Wise Occupational Accident Analysis Using Deep Learning Paradigms,d for automating the safety precautions for employees in the industrial sectors such as mining, metals, construction, chemical, and electrical sections. However, the automation cannot be accurate as the data analysis is based on real-life data. Since the real-life data are imbalanced and uncertain,

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2367-3370 a reference resource for researchers and practitioners in a.This book features best selected research papers presented at the Third International Conference on Machine Learning, Internet of Things and Big Data (ICMIB 2023) held at Indira Gandhi Institute of Technology, Sarang, India, during March 1

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MLFP: Machine Learning Approaches for Flood Prediction in Odisha State,rning models. Before this process, the data is cleaned and pre-processed, and the dataset for training is split into a train set and a test set in an 80:20 ratio. Then the accuracy of each model is compared and the confusion matrix parameters are taken to evaluate and analyze. In the end, the best model is chosen by comparing the accuracy.

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Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf,lytics, a proper confusion matrix for support vector machines driven by CNN was created. Along with k-mean clustering, fuzzy logic with accurate feature extraction, and color moment definition, we also compared our results with these techniques. The findings indicate a higher effectiveness of up to 95% in correctly predicting grapes leaf disease.

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Conference proceedings 2024in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, Internet security, pattern recognition, predictive analytics applications in health care, sensor networks and social sensing, and statistical analysis of search techniques..