As preservers of history and the objects that others leave behind, it is easy to get distracted by a desire for those objects to be “perfect.” We worry about every little tear, stain or blemish, and we want nothing more than to return objects to their "original state." However, so many times those rough spots are part of the item’s history. It is important to understand the difference between the types of condition issues on objects that cause structural issues versus those that are purely cosmetic. Knowing and identifying these differences can help when determining whether or not an item requires conservation or deciding simple factors like the best way to store or exhibit an item.

Facebook“卖流量”新招

Film industries all over the world are producing several hundred movies rapidly and grabbing the attraction of people of all ages. Every movie producer is of keen interest in knowing which movies are either likely to hit or flop in the box office. So, the early prediction of the popularity of a movie is of the utmost importance to the film industry. In this study, we examine factors inside the hidden patterns which become movie popular. In past studies, machine learning techniques were implemented on blog articles, social networking, and social media to predict the success of a movie. Their works focused on which algorithms are better at predicting the success of a movie but less focused on data and attributes related to an ongoing movie and in various directions. In this paper, we inspect this perspective that might be related to the prediction of the results. Data collected from the publicly available Internet Movie Database (IMDb). We implemented five machine learning algorithms, i.e., Generalized Linear Model (GLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT) using Root Mean Squared Error (RMSE) as a performance metric and got the accuracy performances of GLM: 47.9%, DL: 51.1%, DT: 54.5%, RF: 50.0%, and GBT: 49.5%, respectively. We found that GLM is the high achieving accuracy regression classifier due to the lower value of RMSE, which is considered to be better.

Popularity prediction of movies: from statistical modeling to machine learning techniques

Film Industry is not only a industry or a centre of entertainment, rather it is now a centre of global business. All over the world is now excited about a movie's box office success, popularity etc. A huge data is available online about these movies success or popularity. We have used hollywood movie list from Wikipedia and their rating from IMDb movie rating website to create our data set. Then machine learning classification algorithms are applied of the data set. Lastly an efficient model is developed to predict a movie's IMDb rating. The model gives good classification measures with the data set.

Predicting IMDb Rating of Movies by Machine Learning Techniques

Identifying the key success factors of movie projects in crowdfunding

Movies have become a significant part of today's generation. In this chapter, the authors worked on data mining and ML techniques like random forest regression, decision tree regression, support vector regression, and predict the success of the movies on the basis of ratings from IMDb and data retrieved from comments on social media platforms. Based on ML techniques, the chapter develops a model that will predict movie success before the release of the movie and thereby decrease the risk. Twitter sentimental analysis is used to retrieve data from Twitter, and polarity and subjectivity of the movie is calculated based on the user reviews, and those retrieved data machine learning algorithms are used to predict the IMDb rating. A predictive model is developed by using three algorithms, decision tree regression, SVR, and random forest regression. The chapter compared the results using three different techniques to get the movie success prediction at a reasonable accuracy. 

Prediction of Movie Success Using Sentimental Analysis and Data Mining

The film industry has always been a very important sector in the global market. Therefore, it is very important to maximize the profit by predicting the movie success before its release. Although several studies have been done in this field, it is still needed to improve the prediction performance and collect more data. This study aims to explore the use of Factorization Machines approach in order to predict movie success by predicting IMDb ratings for newly released movies using social media data and compare it to current studies. Also, a framework has been developed in order to gather the movie data from different sources including social media. Comparison of the Factorization Machines to the current models shows that there are promising results.

Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media

The usability and health of a person's voice has dire impact on the person's quality of life. Pathological issues that may exist on a person's voice often cannot be detected by a regular listener. Medical attention from a professional may be necessary to detect vocal pathologies. Analysis of the patients complaints and a perceptual evaluation performed by a doctor is one of the most common ways to diagnose a vocal condition. This method can be invasive, time consuming and expensive. Features of the voice can be extracted and utilized in a computer environment to make the same diagnosis which may increase the speed and accuracy of the diagnosis and decrease the cost. In this paper, a cloud application which collects vocal data in a database is proposed. With data mining and machine learning methods, a new tool has been developed to detect and diagnose vocal anomalies in patients. The effectiveness of the suggested platform has been demonstrated with a pathological detection and recognition application running in the server.

Beyza Cizmeci

Papers

Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media

Voice cloud platform for medical applications