The movie is one of the integral components of our everyday entertainment.The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy.A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie.This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.

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Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.

Over the last few decades, there has been an increase in the probability of occurrence of wildfires. Also known as bushfires, the catastrophe can be attributed to climate changes and extreme weather conditions. Australia’s dry and warm climate makes it prone to wildfires, which risks the ecosystem and decreases the forest area. Hence it is necessary to reduce bushfire risk by monitoring their intensity. The availability of remotely sensed data enables us to analyse wildfires, explore and discover patterns, and help provide real-time warnings. This paper examines the forest fire data from 2018-2020, considering parameters like Brightness (Prediction) and Fire Radiative Power (Classification). The analysis is conducted using several machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (kNN), eXtreme Gradient Boosting (xGB), Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), etc. The prediction models are evaluated using Mean squared error (MSE), Root Mean Square Error (RMSE), R squared (R2), and Mean Absolute Error (MAE). In contrast, the classification models are evaluated using accuracy, precision, recall, and F-1 score. Our study shows that the RF model is the best prediction model, and the ANN model is the best classification model compared to the baseline models.