The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.

Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective forecasts with minimum time duration. The BiGRNN is an enhanced version of Gated Recurrent Unit (GRU) that follows the process of passing input via forward and backward neural network and the outputs are linked to the identical output layer. The BiGRNN technique includes several hyper-parameters and hence, the hyperparameter optimization process takes place using Bird Mating Optimizer (BMO). The design of BMO algorithm for hyperparameter optimization of the BiGRNN, particularly for weather forecast shows the novelty of the work. The BMO algorithm is used to set hyperparameters such as momentum, learning rate, batch size and weight decay. The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively.

This paper discusses detection of change in land usage in Davangere (Karnataka State, India) between the years 2016 and 2021. After the place has been declared as one of the smart cities identified by the Govt. of India in 2014 and subsequent to the international price crash for sugar, there were noticeable changes in land utilization in terms of urbanization and shift in traditional cropping pattern. The objective of this research work is to capture this change using remote sensing, the images from MSI Sentinel-2 were collected at two points of time and processed for LULC with the help of supervised machine learning classifiers such as Minimum Distance, Mahalanobis Distance and Maximum Likelihood to ascertain the accurate one. It was found that Maximum Likelihood classifier ensures highest accuracy of 95.2%. It was also found that during the study period, there was a significant change in the land use with respect to Built-up area and Area under cultivation of Paddy.

English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest model is constructed using a decision trees selection method, which focuses on 19 attributes of the English teaching model. Results show, to begin with, that the indigenous teaching plan and environment fail to adapt to the pace of knowledge iteration in the era of big data. Moreover, interactions between teachers and students appear to be shallow with little constructive interaction, causing a decline in the relationship between teachers and students. Last, there is still no signs of any legitimate construction in terms of in-person English teaching, relativity attribute, corpus and platform. Therefore, this paper has proposed a new English teaching model to adapt to the current college English teaching environment. The experimental results show that the method is effective and feasible for the current college English teaching.