scholarly journals Data-driven method for mobile game publishing revenue forecast

2021 ◽  
Author(s):  
Yanhui Su ◽  
Per Backlund ◽  
Henrik Engström
Keyword(s):  
Prediction Models ◽  
Time Series Prediction ◽  
Research Work ◽  
Data Driven ◽  
Future Research ◽  
Business Decisions ◽  
Mobile Game ◽  
Revenue Forecast ◽  
The Right ◽  
Game Developers

AbstractGames as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.

scholarly journals Experimental Data of Fluid Phase Equilibria- Correlation and Prediction Models: A Review

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 277 ◽  
Author(s):  
Urszula Domańska
Keyword(s):  
Phase Equilibria ◽  
Binary Systems ◽  
Prediction Models ◽  
Research Work ◽  
Ternary Mixtures ◽  
Excess Gibbs Free Energy ◽  
Future Research ◽  
Published Data ◽  
Gas Liquid Chromatography ◽  
Excess Molar Enthalpy

The examples of phase equilibria in binary systems, solid/liquid (SLE), liquid/liquid (LLE), vapor/liquid (VLE), as well as liquid/liquid equilibria in ternary systems mainly containing ionic liquids (ILs), or the infragrance materials, or pharmaceuticals with molecular organic solvents, such as an alcohol, or water, or hydrocarbons, are presented. The most popular correlation methods of the experimental phase equilibrium data are presented, related to the excess Gibbs free energy models such as Wilson, universal-quasichemical, UNIQUAC and non-random two-liquid model, NRTL as well as several popular theories for the modeling of the phase equilibria and excess molar enthalpy, HE in binary or ternary mixtures are presented: the group contribution method (Mod. UNIFAC) and modified UNIFAC model for pharmaceuticals and lattice theory based on non-random hydrogen bonding (NRHB). The SLE, LLE, or VLE and HE of these systems may be described by the Perturbed-Chain Polar Statistical Associating Fluid Theory (PC-SAFT), or a Conductor-like Screening Model for Real Solvents (COSMO-RS). The examples of the application of ILs as extractants for the separation of aromatic hydrocarbons from alkanes, sulfur compounds from alkanes, alkenes from alkanes, ethylbenzene from styrene, butan-1-ol from water phase, or 2-phenylethanol (PEA) from water are discussed on the basis of previously published data. The first information about the selectivity of extrahent for separation can be obtained from the measurements of the limiting activity coefficient measurements by the gas–liquid chromatography technique. This review outlines the main research work carried out over the last few years on direct measurements of phase equilibria, or HE and limiting activity coefficients, the possibility of thermodynamic modeling with emphasis on recent research achievements and potential for future research.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

6. The Communicative Pragmatics of Data-Use for Equity: A Theoretical and Methodological Framework

2020 ◽  
Vol 2 (1) ◽  
pp. 117-149
Author(s):  
Mary B. Ziskin
Keyword(s):  
Critical Race Theory ◽  
Communicative Action ◽  
Educational Equity ◽  
Data Use ◽  
College Faculty ◽  
Postsecondary Institutions ◽  
Data Driven ◽  
Future Research ◽  
Education Data ◽  
Critical Qualitative Research

<?page nr="117"?>Abstract Calls for higher education institutions to implement improvements guided by “data-driven” processes are prevalent and widespread. Despite the pervasiveness of this turn toward data, research on how data-use works on the ground in postsecondary institutions—that is, how individuals within institutions make sense of education data and use it to inform practice—is still developing.Drawing on Habermas’ Theory of Communicative Action (TCA), critical-race theory, and methodological guidance on critical-qualitative research methods, this paper synthesizes methodological and substantive insights from P–12 data-use research, with an eye to applying these insights to critical questions on postsecondary educational equity. The result of the review and analysis is a theoretical framework and a set of methodological recommendations for future research on the perceptions and experiences of college faculty, administrators, and practitioners, regarding their data-use and its implications for equity.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Transferrin-Conjugated Nanocarriers as Active-Targeted Drug Delivery Platforms for Cancer Therapy

2017 ◽  
Vol 23 (3) ◽  
pp. 454-466 ◽  
Author(s):  
Daniele R. Nogueira-Librelotto ◽  
Cristiane F. Codevilla ◽  
Ammad Farooqi ◽  
Clarice M. B. Rolim
Keyword(s):  
Drug Delivery ◽  
Cancer Therapy ◽  
Tumor Cells ◽  
Therapeutic Benefit ◽  
Active Targeting ◽  
Future Research ◽  
Passive Targeting ◽  
The Right ◽  
Review Current ◽  
Target Effects

A lot of effort has been devoted to achieving active targeting for cancer therapy in order to reach the right cells. Hence, increasingly it is being realized that active-targeted nanocarriers notably reduce off-target effects, mainly because of targeted localization in tumors and active cellular uptake. In this context, by taking advantage of the overexpression of transferrin receptors on the surface of tumor cells, transferrin-conjugated nanodevices have been designed, in hope that the biomarker grafting would help to maximize the therapeutic benefit and to minimize the side effects. Notably, active targeting nanoparticles have shown improved therapeutic performances in different tumor models as compared to their passive targeting counterparts. In this review, current development of nano-based devices conjugated with transferrin for active tumor-targeting drug delivery are highlighted and discussed. The main objective of this review is to provide a summary of the vast types of nanomaterials that have been used to deliver different chemotherapeutics into tumor cells, and to ultimately evaluate the progression on the strategies for cancer therapy in view of the future research.

A Case Study: Analysis of Patents of Coronaviruses and Covid-19 for Technology Assesment and Future Research

2020 ◽  
Vol 26 ◽  
Author(s):  
Pankaj Musyuni ◽  
Geeta Aggarwal ◽  
Manju Nagpal ◽  
Ramesh K. Goyal
Keyword(s):  
Disease Management ◽  
Research And Development ◽  
Technological Development ◽  
Research Work ◽  
Large Family ◽  
Future Research ◽  
Biological Drugs ◽  
Ongoing Research ◽  
Patent Literature ◽  
Patent Documents

Background: Protecting intellectual property rights are important and particularly pertinent for inventions which are an outcome of rigorous research and development. While the grant of patents is subject to establishing novelty and inventive step, it further indicates the technological development and helpful for researchers working in the same technical domain. The aim of the present research work is to map the existing work through analysis of patent literature, in the field of Coronaviruses (CoV), particularly COVID-19 (2019-nCoV). CoV is a large family of viruses known to cause illness in human and animals, particularly known for causing respiratory infections as evidenced in earlier times such as in MERS i.e. Middle East Respiratory Syndrome; SRS i.e. Severe Acute Respiratory Syndrome. A recently identified novel-coronavirus has known as COVID-19 which has currently caused pandemic situation across the globe. Objective: To expand analysis of patents related to CoV and 2019-nCoV. Evaluation has been conducted by patenting trends of particular strains of identified CoV diseases by present legal status, main concerned countries via earliest priority years and its assignee types and inventors of identified relevant patents. We analyzed the global patent documents to check the scope of claims along with focuses and trends of the published patent documents for the entire CoV family including 2019- nCoV through the present landscape. Methods: To extract the results, Derwent Innovation database is used by a combination of different key-strings. Approximately 3800 patents were obtained and further scrutinized and analyzed. The present write-up also discusses the recent progress of patent applications in a period of the year 2010 to 2020 (present) along with the recent developments in India for the treatment options for CoV and 2019-nCoV. Results: Present analysis showed that key areas of the inventions have been focused on vaccines and diagnostic kits apart from the composition for treatment of CoV. We also observed that no specific vaccine treatments is available for treatment of 2019-nCov, however, developing novel chemical or biological drugs and kits for early diagnosis, prevention and disease management is the primarily governing topic among the patented inventions. The present study also indicates potential research opportunities for the future, particularly to combat 2019-nCoV. Conclusion: The present paper analyzes the existing patents in the field of Coronaviruses and 2019-nCoV and suggests a way forward for the effective contribution in this upcoming research area. From the trend analysis, it was observed an increase in filing of the overall trend of patent families for a period of 2010 to the current year. This multifaceted analysis of identified patent literature provides an understanding of the focuses on present ongoing research and grey area in terms of the trends of technological innovations in disease management in patients with CoV and 2019-nCoV. Further, the findings and outcome of the present study offer insights for the proposed research and innovation opportunities and provide actionable information in order to facilitate policymakers, academia, research driven institutes and also investors to make better decisions regarding programmed steps for research and development for the diagnosis, treatment and taking preventive measures for CoV and 2019-nCoV. The present article also emphasizes on the need for future development and the role of academia and collaboration with industry for speedy research with a rationale.

ANTECEDENTS AND CONSEQUENCES OF ORGANIZATIONAL JUSTICE

2016 ◽  
Vol 12 (02) ◽  
Author(s):  
Reeta Yadav
Keyword(s):  
Job Satisfaction ◽  
Organizational Justice ◽  
Organizational Citizenship ◽  
Organizational Citizenship Behavior ◽  
Turnover Intentions ◽  
Research Work ◽  
Citizenship Behavior ◽  
Future Research ◽  
Antecedents And Consequences ◽  
And Performance

Employee’s perception regarding fairness in the organization is termed as organizational justice. The objective of this paper is to study the antecedents and consequences of organizational justice on the basis of earlier relevant studies from the period ranging from 1964 to 2015. Previous research identified employee participation, communication, justice climate as the antecedents and trust, job satisfaction, commitment, turnover intentions, organizational citizenship behavior and performance as the consequences of organizational justice. Finding reveals the gaps existing in the literature and gives suggestions for future research work.

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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