Choudhary A., Agrawal A.P., Logeswaran R., Unhelkar B. (Eds.) Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020

Springer, 2021. — 725 p. — (Lecture Notes in Electrical Engineering 778). — ISBN: 978-981-16-3066-8.Covers research in the areas of artificial intelligence, machine learning, and deep learning applications.The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.Machine Learning (ML), Deep Learning (DL), and neural networks are the three fundamental concepts of Artificial Intelligence (AI). While AI and Machine Learning are sometimes considered interchangeable terms, AI covers a broad domain with the rest of the terms as a subset of it. Machine Learning is a part of AI that enables machines to learn without explicitly programming them to perform a task.Internet of Things (IoT) Based Automated Light Intensity Model Using NodeMcu ESP 8266 Microcontroller: Abstract The Internet of Things (IoT) is a ubiquitous technology for connecting anything from anywhere impacting life drastically by expanding its reach in economic, commercial, and social areas. In this paper, the authors used NodeMcu ESP 8266 microcontroller for modeling an automated system by embedding a Wi-Fi module. The light intensity is continuously captured and transmitted over a cloud network. The captured data is then used for analyzing voltage fluctuations using Python. The automated system works according to the intensity of light, if the light intensity falling on light dependent resistor (Ldr) is low then light emitting diode will switch on and if the light intensity falling on light dependent resistor (Ldr) is high then the light emitting diode will remain off making system energy efficient which works automatically according to the light intensity.Face Mask Detection Using Deep Learning: Our goal is to develop a better way to detect face masks. In this paper, we propose a comparison between all available networks, which is an efficient one-stage face mask detector. The detection scheme follows preprocessing, feature extraction, and classification. The mask detector has been built using deep learning, specifically ResNetV2, as the base pre-trained model upon which we have our own CNN. We use OpenCV’s ImageNet to extract faces from video frames and our trained model to classify if the person is wearing a mask or not. We also propose an object removal algorithm to reject predictions below absolute confidence and accept only predictions above it. For training purposes, we are using the face mask dataset, which consists of 680 images with masks and 686 images without masks. The results show mask detector has an accuracy of 99.9%.