Automation seems widespread today, yet it is not implemented in daily life. However, most home automation systems are expensive, object-dependent, and lacking in crucial features. The Internet of Things was enabled by this paper’s low-cost home automation system. For development of the IoT, the system used Node-RED, an open-source platform that uses nodes to visualize tasks. This innovation could operate home devices, including plugs, from anywhere. Wireless sensor network (WSN) technology would record and upload data to the web server from each room. Using the publish-and-subscribe Message Queuing Telemetry Transport (MQTT) protocol, these WSN technologies would communicate. The third feature can modify notifications. In situations of doubt, the house member would be notified by email. This proposal promotes home automation through the IoT.

This paper proposes a method that classifies the emotion status of a human being based on one's interactions with the smart phone. Due to one or the other practical limitations, the existing set of emotion recognition methods are difficult to use on daily basis (most of the known methods cause inconvenience to user since they require devices like wearable sensors, camera, or answering a questionnaire). The essence of this paper is to analyze the textual content of the message and user typing behavior to build a classifier that efficiently classifies the future instances. Each observation in the data set consists of 14 features. A machine learning technique called Naive Bayes classifier is applied to construct the classifier. Method proposed is capable of classifying emotions in one of the seven classes (anger, disgust, happy, sad, neutral, surprised, and fear). Experimental result has shown 72% accuracy in classification.

Plant identification is a significant activity in agriculture, botany, and environmental protection for the precise classification of plants into species to ensure an efficient control of the crops, measurement of diversity, and ecosystems control. Conventional processes of plant classification based on manual identification by specialists are resource-intensive, inefficient, and error-prone. To address these limitations, this research explores Deep Learning (DL)-based approaches for automatic plant classification using chilli weeds as an example. This research evaluated and compared the performance of three Convolutional Neural Network (CNN) architectures—MobileNetV2, ResNet50, and VGG16—trained on a self-collected image dataset from the College of Horticultural Engineering and Food Technology (DSLD CHEFT), Devihosur, Haveri, comprising four plant species. Among the models, MobileNetV2 achieved the highest classification accuracy of 96.6%, outperforming ResNet50 with a 95.0% accuracy, and VGG16 with an 88.0% accuracy. MobileNetV2's lightweight design offers a distinct advantage, which enables efficient inference and makes the model highly suitable for future deployment on edge devices with limited computational resources. This study highlights the potential of CNN-based systems for practical applications in agriculture, such as automated weed detection and precision farming.

Blockchain is an emerging platform for developing decentralized applications and data storage, over and beyond its role as a platform for cryptocurrencies. This reference text provides a comprehensive discussion on blockchain technology from research and application perspective. Discusses different approaches for building distributed applications (DAPPS). Provides detailed listing and discussion of blockchain technology applications in solving real life problems. Covers proof of work (PoW) based blockchain consensus, and proof of stake (PoS) based blockchain consensus. Discusses blockchain algorithms including practical byzantine fault tolerance (PBFT) and simplified byzantine fault tolerance (SBFT). It comprehensively covers important topics including blockchain consensus algorithms, Ethereum, Hyperledger, blockchain scalability, smart contracts with solidity, ERC20 standards, building DApp with Golang, building DApp using Hyperledger, building PoCs with Hyperledger fabric, blockchain as a server, blockchain security and privacy. The text will serve as a useful text for senior undergraduate and graduate students in interdisciplinary areas including electronics and communications engineering, electrical engineering, computer science, and information technology.