Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

Agriculture is the primary profession in India which relies on several climatic factors such as rainfall, temperature, humidity, etc., for the successful growth of crops. Weather and drought forecasting may help to take preventive measures in an unusual situation due to crop failure. Most of the existing work attempts to forecast the weather or analyse the reason for the occurrence of drought and its effects in the past. This paper adopts the machine learning model called the long short-term memory (LSTM) neural network to forecast the long-term rainfall and standardised precipitation index for drought estimation. The predicted annual rainfall from LSTM is taken as an input to forecast the drought conditions of India for the upcoming years, which is a unique approach and objective, and based on this work appropriate decisions can be made for future actions. The past 117 years of rainfall and drought conditions are compared with the recent 50 years by analysing the data in several ways in order to predict the future scenario. The forecasted results are compared with actual observations to demonstrate the effectiveness of the LSTM model to produce adequate results. The error and network loss of the model is 0.059 and 0.0036, which is minimal, and the forecasted rainfall level is almost equal to actual level specifically accuracy is 99.46% for the previous year, 2021. It was found that there is a rainfall decline of 0.04% every year. Apart from the prediction for the country, a clear picture of the region regarding drought forecasts is presented in this work. The real-time drought level is mild and moderate for most of the regions in the country which matches with the drought level determined using forecasted rainfall.

Crop selection is the meticulous process of choosing the most suitable crops for a specific geographical region, taking into consideration a range of factors. This research work delves into the transformative role of advanced technology by focusing on the application of IoT sensors and machine learning for crop selection in agriculture. The problem at hand is the demand for precise crop recommendations, with the existing system relying on static data collection and a single ML model for predictions. The proposed solution is a crop recommendation system that integrates rainfall forecasts, drought predictions, and soil-related parameters to enhance recommendations. An innovative ensemble approach, utilizing 12 weak classifiers including logistic regression and artificial neural networks is implemented for better prediction accuracy. This research involves the creation of a compound ensemble model and the use of a distance-based similarity index for improved recommendations. The methodology includes an IoT sensor for data collection and ML model integration. The ensemble approach and similarity index enhance adaptability to regional variations, benefiting the agricultural sector. An accuracy of 99.8% is observed for the models, indicating precision and robustness. Eventually, this paper exploits advanced technology to transform crop selection, fostering agricultural productivity and global food security. This research serves as a technical proof-of-concept for regional precision agriculture, offering a scalable framework to mitigate the impact of climate variability on crop productivity in semi-arid zones.

The publishing industry is highly competitive, and predicting the popularity of books before their publication can significantly benefit publishers and authors The publishing industry is highly competitive, and predicting the popularity of books before their publication can significantly benefit publishers and authors

The rapid growth of digital typography has led to an increasing diversity of font styles, creating the need for efficient and accurate methods of font style and attribute detection. In this work, we propose Font Sense Network (FSnet), a deep learning framework based on convolutional neural networks (CNNs), designed to identify font styles along with attributes such as boldness, italicization, height, width, and aspect ratio. To enhance feature extraction, the model integrates advanced image processing techniques, including contouring and segmentation, which enable a more robust representation of font characteristics. A comprehensive synthetic dataset, generated using publicly available TrueType fonts, was employed to train and evaluate FSnet. Experimental results demonstrate that the proposed approach achieves strong predictive performance, with high agreement between actual and predicted values, outperforming traditional methods in both accuracy and efficiency. This proposed work establishes FSnet as a scalable and adaptable solution for font style and attribute detection, with potential extensions toward multilingual and handwritten scripts in future work.

A recommendation system is the building block of massive enormous systems that use specific recommender algorithms to recommend a single item or a group of item A recommendation system is the building block of massive enormous systems that use specific recommender algorithms to recommend a single item or a group of item