Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. But with the discovery of nonlinearity in the nature of weather data, the focus has shifted towards the nonlinear prediction of the weather data. Although, there are many literatures in nonlinear statistics for the weather forecasting, most of them required that the nonlinear model be specified before the estimation is done. But since the weather data is nonlinear and follows a very irregular trend, Artificial Neural Network (ANN) has evolved out to be a better technique to bring out the structural relationship between the various entities. The paper examines the applicability of ANN approach by developing effective and reliable nonlinear predictive models for weather analysis also compare and evaluate the performance of the developed models using different transfer functions, hidden layers and neurons to forecast maximum, temperature for 365 days of the year

The multilayered artificial neural network with learning by back-propagation algorithm configuration is the most common in use, due to of its ease in training. It is estimated that over 80% of all the neural network projects in development use back-propagation. In back-propagation algorithm, there are two phases in its learning cycle, one to propagate the input patterns through the network and other to adapt the output by changing the weights in the network. The back-propagation-feed forward neural network can be used in many applications such as character recognition, weather and financial prediction, face detection etc. The paper implements one of these applications by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. In the present research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study are different in the number of hidden neurons.

The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

Real‐time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance‐Net. It is possible to classify the experiment into six distinct stages. Champion‐Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet‐18, and VGG‐19). This stage helps choose the optimal model. In the second step, Champion‐Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion‐Net performance. The next stage extracts green channel data. In the fourth step, Champion‐Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance‐performance. The next stage compares the Enhance‐Net findings to the lightness order error (LoE). In Enhance‐Net models, the current study combines image enhancement and green channel with Champion‐Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real‐time medical image prediction. The study effort uses the musculoskeletal radiograph‐bone classification (MURA‐BC) dataset. Classification accuracy of Enhance‐Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real‐time testing with the unseen dataset.