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Author: Jay Bhatt
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this study, a comparison is made between Artificial Neural Network Algorithm and Machine Learning Algorithm of Support Vector Machine by considering pre-release and pre plus post release features of a movie for predicting a movie to a particular success class. These success classes includes Flop/Disaster, Average, Hit, Super-Hit and Block-Buster of a movie. Our best performing model from Artificial Neural Network and Support Vector Machine is able to give better performance in order to predict any particular movie to specific success class of a movie for both the cases of prerelease as well as pre plus post release features of a movie.
Author: Jay Bhatt
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this study, a comparison is made between Artificial Neural Network Algorithm and Machine Learning Algorithm of Support Vector Machine by considering pre-release and pre plus post release features of a movie for predicting a movie to a particular success class. These success classes includes Flop/Disaster, Average, Hit, Super-Hit and Block-Buster of a movie. Our best performing model from Artificial Neural Network and Support Vector Machine is able to give better performance in order to predict any particular movie to specific success class of a movie for both the cases of prerelease as well as pre plus post release features of a movie.
Author: Cengiz Kahraman
Publisher: Springer
ISBN: 3030237567
Category : Technology & Engineering
Languages : en
Pages : 1392
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Book Description
This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
Author: Petra Perner
Publisher: Springer
ISBN: 3642397123
Category : Computers
Languages : en
Pages : 671
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
Author: Preston Soepranoto
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Predicting a movies profitability or return on investment (ROI) is a complex problem, especially for investors looking to make a sizable investment with the prospect of getting even greater dividends. In order to predict the ROI of a movie, movie studios would have to select the right mix of factors that would accrue to the success of the movie. Some factors involved in a movie production include and not limited to the production budget, actors, directors, genre and rating. This research integrates the classic factors tied to movies, and developed new factors that were deemed to be crucial to a movies success to predict the ROI. Factors in this study are divided into four groups, star feature, integrated feature, descriptive feature and time-based feature. This thesis aims to build a predictive model by utilizing historical film data from a paid resource, nashinformationservices.com, and to predict a films success by looking at its ROI. Only movies released between 2007 and 2016 were used. An ROI of a film was more indicative of investors goals as opposed to its box office revenue. The predictive model will be based upon machine learning techniques, specifically supervised learning. We approached this problem by classifying movies into different classes of ROI, ranging from low ROI to very good ROI, and the goal is for our model to accurately place a film in the right class. Attempting to achieve this feat would require us to first complete the data preparation stage. Data then was used to train the model using various learning algorithms and the model was tested on how well it did using a 10-fold cross validation. Applying this methodology, we were able to see which algorithm would perform the best for our model. We further went one step ahead and tried to predict the numerical value of a movies ROI through a regression analysis. The prediction accuracy (based on ROI) was used as a benchmarking measure. The model with the highest overall accuracy, area under the receiving operating curve (AUC), precision and recall was chosen for the final application. Based on these metrics, we found that out of all of the multiple learning algorithms, the Random Forest performed the best for our model. This analysis also gave a clear indication on the impact of the features selected and how they correlate with the ROI of a film. The correlation between the features and the ROI of the movie was measured using the Gini Index. According to the Gini Index, the attributes Total ROI for Supporting Actor, Total ROI for Director, and Total ROI for Actor make up the top 3 on the list of features starting from the first to the third.
Author: Jian Chen
Publisher: Springer
ISBN: 9811069891
Category : Computers
Languages : en
Pages : 285
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Book Description
This book constitutes the refereed proceedings of the 18th International Symposium, KSS 2017, held in Bangkok, Thailand, in November 2017. The 21 revised full papers presented were carefully reviewed and selected from 63 submissions. This year KSS 2017 provides opportunities for presenting interesting new research results, facilitating interdisciplinary discussions, and leading to knowledge transfer under the theme of "Artificial Intelligence and Information Systems for Knowledge, Technology and Service Management".
Author: Duoqian Miao
Publisher: Springer
ISBN: 3319117408
Category : Computers
Languages : en
Pages : 877
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Book Description
This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.
Author: Daowen Qiu
Publisher: Springer Nature
ISBN: 9464630302
Category : Business & Economics
Languages : en
Pages : 1730
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Book Description
This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. These endeavors have made new contribution to globalization and creating a community of shared future. With the rapid development of modern economic society, in the process of economic management, informatization has become the mainstream of economic development in the future. At the same time, with the emergence of advanced management technologies such as blockchain technology and big data technology, real market information can be quickly obtained in the process of economic management, which greatly reduces the operating costs of the market economy and effectively enhances the management level of operators, thus contributing to the sustained, rapid and healthy development of the market economy. Under the new situation, the innovative application of economic management research is of great practical significance. 2022 International Conference on Bigdata, Blockchain and Economic Management (ICBBEM 2022) will be held on March 25–27, 2022 in Wuhan, China. ICBBEM 2022 will focus on the latest fields of Bigdata, Blockchain and Economic Management to provide an international platform for experts, professors, scholars and engineers from universities, scientific institutes, enterprises and government-affiliated institutions at home and abroad to share experiences, to expand professional fields, to exchange new ideas face to face, to present research results, and to discuss the key challenging issues and research directions facing the development of this field, with a view to promoting the development and application of theories and technologies in universities and enterprises.
Author: IEEE Staff
Publisher:
ISBN: 9781538663745
Category :
Languages : en
Pages :
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Book Description
It includes Object oriented analysis, continuous process modelling and improvement, cost estimation and project planning and effectiveness of agile lean practices, Web Engineering, Quality Management, Managing Software Projects, Multimedia and Visual Software Engineering, Software Maintenance and Testing, Languages and Formal Methods, Web based Education Systems and Learning Applications, Software Engineering Decision Making This track focuses on Natural language processing, artificial intelligence, and computational linguistics with respect to the interactions between computers and human languages It includes Genetic Algorithms, Grammatical Evolution, Differential Evolution, Probabilistic Meta heuristic, Swarm Intelligence, Ant Colony Algorithms, Artificial Immune Systems, High Performance Computing and Computational Intelligence, Fuzzy Logic, Bayesian statistical methods, Neural Networks, Multi Agent Systems, Stochastic Optimization It also focuses on focuses on Cloud and its servi
Author: Anupam Shukla
Publisher: Springer Nature
ISBN: 9811973466
Category : Technology & Engineering
Languages : en
Pages : 818
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Book Description
The book constitutes the peer-reviewed proceedings of the 2nd International Conference on Information Technology (InCITe-2022): The Next Generation Technology Summit. The theme of the conference is Computational Intelligence: Automate your World. The volume is a conglomeration of research papers covering interdisciplinary research and in-depth applications of computational intelligence, deep learning, machine learning, artificial intelligence, data science, enabling technologies for IoT, blockchain, and other futuristic computational technologies. The volume covers various topics that span cutting-edge, collaborative technologies and areas of computation. The content would serve as a rich knowledge repository on information & communication technologies, neural networks, fuzzy systems, natural language processing, data mining & warehousing, big data analytics, cloud computing, security, social networks and intelligence, decision making, and modeling, information systems, and IT architectures. The book will be useful to researchers, practitioners, and policymakers working in information technology.
Author: José C. Principe
Publisher: John Wiley & Sons
ISBN:
Category : Computers
Languages : en
Pages : 680
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Book Description
Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text The text and CD combine to become an interactive learning tool. Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations. Each key concept is followed by an interactive example. Over 200 fully functional simulations of adaptive systems are included. The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines. Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts. The CD-ROM Contains: A complete, electronic version of the text in hypertext format NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator A tutorial on how to use NeuroSolutions Additional data files to use with the simulator "An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight." —James Zeidler, University of California, San Diego
