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The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

In the last years, the use of free-online instructional videos has gained popularity among educators and students. Its success is mainly based on the provision of fast and inexpensive access to educational contents which can be consulted at the own convenience of students, all over the world. Free-online platforms as YouTube offer access to more than ten million instructional videos. The purpose of this paper is to assess and rank the educational quality of free-online instructional videos from a multidimensional perspective.

In this paper, the authors propose a MCDM approach based on a compromise ranking method, VIKOR. The approach integrates a normalization process which is especially suitable for situations where the nature of the different decision-making criteria is such that it does not allow homogeneous aggregation.

With the proposed normalization approach, the initial valuations of the alternatives with respect to the criteria are transformed in order to reflect their similarity with a given reference point (ideal solution). The normalized data are then integrated in a VIKOR-based framework in order to obtain those mathematical videos closer to the ideal video from the instructors’ perspective.

The ranking of instructional videos based on their quality from an educational multidimensional perspective is a good example of a real decision-making problem where the nature of the criteria, qualitative and quantitative, implies heterogeneous data. The proposed IS-VIKOR approach overcomes some of the problems inherent to this real decision-making problem.

We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories).

We provide a survey of existing techniques that have been proposed, either directly or indirectly, towards achieving intelligent video categorization. We also compare the performance of two popular ANNs: Kohonen's self‐organizing map (SOM) and fuzzy adaptive resonance theory (Fuzzy ART). In particular, the ANNs are trained offline to form the necessary knowledge base prior to online categorization.

Experimental results show that accurate categorization can be achieved near instantaneously.

The main limitation of this research is the need for a finite set of predefined categories. Further research should focus on generalization of such techniques.

Machine understanding of video footage has tremendous potential for three reasons. First, it enables interactive broadcast of video. Second, it allows unequal error protection for different video shots/segments during transmission to make better use of limited channel resources. Third, it provides intuitive indexing and retrieval for video‐on‐demand applications.

Creating a Bibliographic Workstation for Scientists and Engineers. A library workstation, in its simplest form, is a computer that provides access to bibliographic information transparently in spite of its myriad forms. The conceptual kernel of this workstation is, more or less, a machine designed by, and for the use of, librarians and their staff, obstensibly to care and feed a bibliographic database. Suppose we expand our ideas on this particular kind of computer in order to invent a special species that will meet the demands of a critical clientele, for specialized literature.

The aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.

By comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.

To determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).

This work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

The main purpose of this paper is to overview and summarize all articles published in the UNLV Gaming Research & Review Journal (GRRJ) from 1994 to 2008, in order to understand the gaming research trend.

A content analysis is performed on a total of 129 articles in 24 issues of GRRJ.

The paper suggests thematic content analysis and the trend of the research in order to better understand the scope of gaming research area.

The overview of GRRJ helps identify research gaps to suggest future research.

This paper is the first review of the GRRJ. It provides an overview of the research that is available to managers and researchers interested in the gaming business.

In recent years there has been a significant acceleration in the market growth of social impact investing. Policy makers, regulatory bodies and national decision-makers should base their decision-making processes on multiple criteria. These criteria are, by nature, imprecise, ambiguous and uncertain. The purpose of this paper is to provide decision-makers with a mathematical tool which aids them in their decision-making processes identifying the degree of appropriateness of less developed countries in terms of potential success of investment in vaccination campaigns.

In this work, the authors have developed a decision-making tool within the framework of multiple criteria decision making and Fuzzy Logic, which aims to aid decision-makers for vaccinations campaigns in less developed countries. In particular, the authors have proposed a Technique for Order Preference by Similarity to Ideal Solution-based method which is able to work in fuzzy environment in order to assess and rank countries based on their fuzzy degree of appropriateness for impact investing in vaccines.

The impact investing market provides capital from private sources to address many pressing global challenges such as access to basic services as health. Governments have, therefore, an essential role in supporting the development of this market by improving the risk/return profile of investments through access to credit facilities, tax credits or subsidies or defining the regulation of the supply of investments, provision of technical assistance to investing private companies and co-financing. The proposed framework permits funding decision making taking into account the degree of preparedness and adequacy for impact investing in vaccines of the selected countries.

Impact investing can play a key role in the reduction of immunization gap offering suitable strategies for both, governments and private investors for the achievement of United Nations Sustainable Development Goals (SDGs). However, in order to make good financial decisions managers should take into account not only health, income, education and other social criteria but also the degree of basic preparedness of the countries in order to ensure the success of the immunization campaigns which means taking into account availability of basic infrastructures, access to electricity, political stability among other criteria.

However, in order to make good financial decisions managers should take into account not only health, income, education and other social criteria but also the degree of basic preparedness of the countries in order to ensure the success of the immunization campaigns which means taking into account availability of basic infrastructures, access to electricity, political stability among other criteria.

The proposed model will allow public and private decision makers to make better investment decisions in terms of effectiveness as the provided ranking of countries candidates for the investments is more realistic and takes into account more decision dimensions.

Sex differences in circumscribed interests (CI) may delay diagnosis for females with autism spectrum disorder (ASD); therefore, it is important to characterize sex differences in CI to determine if differential approaches to diagnostic assessment are warranted for females with ASD. The purpose of this paper is to examine sex differences in parent-reported quantity, content and functional impairment of children’s interests.

Parent responses to the Interests Scale were analyzed using descriptive statistics and ANOVAs to determine diagnostic (ASD vs typical development (TD)) and sex differences between four groups of children ages six to ten years: ASD males, ASD females, TD males and TD females.

Groups were comparable on the quantity of interests reported on the Interests Scale. Children with ASD demonstrated significantly more nonsocial interests and had greater functional impairment associated with their interests than TD children. A significant diagnosis×sex effect was found for the number of interests in folk psychology. Descriptively, males with ASD were more likely to have a primary interest in the traditionally male category of physics than females with ASD whose primary interest mainly fell into the categories of TV or the more traditionally female category of psychology.

These findings strengthen the results of Turner-Brown et al. (2011) by replicating their findings that children with ASD have more nonsocial interests and greater functional impairments related to their interests compared to TD children in a sample that is balanced on biological sex. However, there are distinctions between males and females with ASD in their primary interests that have implications for diagnostic assessment.