scholarly journals Movie Success Prediction using Historical and Current Data Mining

2019 ◽  
Vol 178 (47) ◽  
pp. 1-5
Author(s):  
Partha Chakraborty ◽  
Md. Zahidur ◽  
Saifur Rahman
Keyword(s):  
Data Mining ◽  
Current Data ◽  
Success Prediction ◽  
Movie Success

scholarly journals Movie Success Prediction Using Data Mining

2021 ◽  
Vol 4 (2) ◽  
pp. 22-30
Author(s):  
Olubukola D.A. ◽  
Stephen O.M. ◽  
Funmilayo A.K. ◽  
Ayokunle O. ◽  
Oyebola A. ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Success Prediction ◽  
Movie Industry ◽  
Release Date ◽  
Data Mining Techniques ◽  
Box Office ◽  
Public Consumption ◽  
Using Data ◽  
Movie Success

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

Movie Success Prediction Using ML

2020 ◽  
Author(s):  
Narayana Darapaneni ◽  
Christopher Bellarmine ◽  
Anwesh Reddy Paduri ◽  
Sujana Entoori ◽  
Abir Kumar ◽  
...  
Keyword(s):  
Success Prediction ◽  
Movie Success

Incremental Discovery of Fuzzy Functional Dependencies

2011 ◽  
pp. 615-633 ◽  
Author(s):  
Shyue-Liang Wang ◽  
Ju-Wen Shen ◽  
Tuzng-Pei Hong
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Search Algorithm ◽  
Current Data ◽  
Research Interest ◽  
Functional Dependencies ◽  
Incremental Search ◽  
Data Mining Techniques ◽  
Analysis Technique ◽  
Mining Algorithms

Mining functional dependencies (FDs) from databases has been identified as an important database analysis technique. It has received considerable research interest in recent years. However, most current data mining techniques for determining functional dependencies deal only with crisp databases. Although various forms of fuzzy functional dependencies (FFDs) have been proposed for fuzzy databases, they emphasized conceptual viewpoints and only a few mining algorithms are given. In this research, we propose methods to validate and incrementally search for FFDs from similarity-based fuzzy relational databases. For a given pair of attributes, the validation of FFDs is based on fuzzy projection and fuzzy selection operations. In addition, the property that FFDs are monotonic in the sense that r1 ? r2 implies FDa(r1) ? FDa(r2) is shown. An incremental search algorithm for FFDs based on this property is then presented. Experimental results showing the behavior of the search algorithm are discussed.

The Development of Data Mining System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2280-2283
Author(s):  
Qiong Ren
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Current Data ◽  
Mining System ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Cloud Computing Platform ◽  
Data Mining System ◽  
Process Cost ◽  
Mining Algorithms

With the increasing of input data size, process cost will be very long, for the explosive growth of the Internet data even reached the point of single machine can handle. This article mainly introduces the architecture of the concept of cloud computing and, the mainstream of the analysis of the current data mining algorithms, based on cloud computing to develop the data mining system, providing the operation feasibility of data mining in cloud computing platform, having strong guiding significance.

scholarly journals Use of data mining at the Food and Drug Administration

2015 ◽  
Vol 23 (2) ◽  
pp. 428-434 ◽  
Author(s):  
Hesha J Duggirala ◽  
Joseph M Tonning ◽  
Ella Smith ◽  
Roselie A Bright ◽  
John D Baker ◽  
...  
Keyword(s):  
Data Mining ◽  
Drug Administration ◽  
Food And Drug Administration ◽  
The United States ◽  
Current Data ◽  
Mining Activities ◽  
Use Of Data ◽  
Address Data ◽  
United States Food ◽  
Veterinary Products

Abstract Objectives This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). Target audience We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. Scope Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

Photovoltaics Informatics: Harnessing Energy Science via Data-driven Approaches

2012 ◽  
Vol 1425 ◽  
Author(s):  
Changwon Suh ◽  
Kristin Munch ◽  
David Biagioni ◽  
Stephen Glynn ◽  
John Scharf ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Scientific Data ◽  
Current Data ◽  
High Rate ◽  
Rapid Screening ◽  
Data Management System ◽  
Process Conditions ◽  
Structure Property ◽  
Test Bed

ABSTRACTWe discuss our current research focus on photovoltaic (PV) informatics, which is dedicated to functionality enhancement of solar materials through data management and data mining-aided, integrated computational materials engineering (ICME) for rapid screening and identification of multi-scale processing/structure/property/performance relationships. Our current PV informatics research ranges from transparent conducting oxides (TCO) to solar absorber materials. As a test bed, we report on examples of our current data management system for PV research and advanced data mining to improve the performance of solar cells such as CuInxGa1-xSe2 (CIGS) aiming at low-cost and high-rate processes. For the PV data management, we show recent developments of a strategy for data modeling, collection and aggregation methods, and construction of data interfaces, which enable proper archiving and data handling for data mining. For scientific data mining, the value of high-dimensional visualizations and non-linear dimensionality reduction is demonstrated to quantitatively assess how process conditions or properties are interconnected in the context of the development of Al-doped ZnO (AZO) thin films as the TCO layers for CIGS devices. Such relationships between processing and property of TCOs lead to optimal process design toward enhanced performance of CIGS cells/devices.

scholarly journals Predicting Bankruptcy After The Sarbanes-Oxley Act Using The Most Current Data Mining Approaches

2012 ◽  
Vol 10 (4) ◽  
pp. 233 ◽  
Author(s):  
Wikil Kwak ◽  
Yong Shi ◽  
Gang Kou
Keyword(s):  
Data Mining ◽  
Financial Reporting ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Current Data ◽  
Corporate Fraud ◽  
Sarbanes Oxley ◽  
Net Method ◽  
Better Than

Our study proposes several current data mining methods to predict bankruptcy after the Sarbanes-Oxley Act (2002) using 2007-2008 U.S. data. The Sarbanes-Oxley Act (SOX) of 2002 was introduced to improve the quality of financial reporting and minimize corporate fraud in the U.S. Because of this SOX implementation, a companys financial statements are assumed to provide higher quality financial information for investors and other stakeholders. The results of our data mining approaches in our bankruptcy prediction study show that Bayesian Net method performs the best (85% overall prediction rate with 94% in AUC), followed by J48 (85% with 82% AUC), Decision Table (83.52%), and Decision Tree (82%) methods using financial and other data from the 10-K report and Compustat. These results are better than previous bankruptcy prediction studies before the SOX implementation using most current data mining approaches.

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