Field Of Data Science Research Articles

MODELING COVID-19 DAILY CASES IN EGYPT

The present COVID-19 virus outbreak has started in the Wuhan area of China and is expanding worldwide. This lethal epidemic had an immense impact on millions of citizens and provided some political and social response. The outcome of any research investigation in this area remains to be decided. However, it is essential to assess the epidemic dynamics to correctly predict the quarantine operations and safeguard individuals at least at an appropriate stage. In big data science and other relevant fields, it is essential to have the best possible overview of data. To accomplish this, we researched the COVID-19 pandemic dynamics in Egypt to include a structure for effective quarantines. Guidelines for studying pandemic processes have been clarified and can be used to arrive at hypotheses. The statistical model is a significant development in describing the COVID-19 daily cases in Egypt.

Improved salp swarm algorithm based on the levy flight for feature selection

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA.

Spatial data science for data driven urban planning: The youth economic discomfort index for Rome

Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Copulae: An overview and recent developments

AbstractOver the decades that have passed since they were introduced, copulae still remain a very powerful tool for modeling and estimating multivariate distributions. This work gives an overview of copula theory and it also summarizes the latest results. This article recalls the basic definition, the most important cases of bivariate copulae, and it then proceeds to a sketch of how multivariate copulae are developed both from bivariate copulae and from scratch. Regarding higher dimensions, the focus is on hierarchical Archimedean, vine, and factor copulae, which are the most often used and most flexible ways to introduce copulae to multivariate distributions. We also provide an overview of how copulae can be used in various fields of data science, including recent results. These fields include but are not limited to time series and machine learning. Finally, we describe estimation and testing methods for copulae in general, their application to the presented copula structures, and we give some specific testing and estimation procedures for those specific copulae.This article is categorized under: Statistical Models > Multivariate Models Statistical Models > Semiparametric Models Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

Data science in cell imaging.

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.

Machine seismic: an automatic approach for the identification of subsurface structural models

Subsurface structure identification and modeling in three-dimensional dataset is a very complex problem for the geoscientist. Manual dealing of seismic data involves different complexities and requires more time and effort in order to address the errors and mistakes associated with the quality of seismic data. It is sometimes not straightforward to classify the features of interest especially when the data are of poor quality or associated with complex micro-tectonic regime. This brings the need of automatic working engine which can handle such issues and can find the target-oriented features with the more accuracy. Therefore, advance machine learning approach has been considered as a powerful tool which extensively used in different fields of data science to enhance the quality of data and fast processing. In this study, a new technology has been introduced in the seismic processing and development to make processes in automated routine. This method is based on deep neural network (DNN) that directly translates the raw seismic data into final fault models. A test example of deep semi-supervised learning method was performed for seismic faults and reflector identification without prior information and without going into complete dataset for labeling. This example explains the challenges to the issue and gives the solution to the problem related to the structure and tectonic modeling to explain the performance gain. In this approach of semi-supervised learning, only one of any reflectors and one or two of the faults has been introduced in the learning phase to identify such features in the complete 3D cube. This is an example of deep learning neural network for machine seismic in real case study and allows exploration geoscientists to easier mark the hot spots of their interest before mining or drilling.

Undergraduate data science degrees emphasize computer science and statistics but fall short in ethics training and domain-specific context.

The interdisciplinary field of data science, which applies techniques from computer science and statistics to address questions across domains, has enjoyed recent considerable growth and interest. This emergence also extends to undergraduate education, whereby a growing number of institutions now offer degree programs in data science. However, there is considerable variation in what the field actually entails and, by extension, differences in how undergraduate programs prepare students for data-intensive careers. We used two seminal frameworks for data science education to evaluate undergraduate data science programs at a subset of 4-year institutions in the United States; developing and applying a rubric, we assessed how well each program met the guidelines of each of the frameworks. Most programs scored high in statistics and computer science and low in domain-specific education, ethics, and areas of communication. Moreover, the academic unit administering the degree program significantly influenced the course-load distribution of computer science and statistics/mathematics courses. We conclude that current data science undergraduate programs provide solid grounding in computational and statistical approaches, yet may not deliver sufficient context in terms of domain knowledge and ethical considerations necessary for appropriate data science applications. Additional refinement of the expectations for undergraduate data science education is warranted.

Establishing a Data Science Unit in an Academic Medical Center: An Illustrative Model.

The field of data science has great potential to address critical questions relevant for academic medical centers. Data science initiatives are consequently being established within academic medicine. At the cornerstone of such initiatives are scientists who practice data science. These scientists include biostatisticians, clinical informaticians, database and software developers, computational scientists, and computational biologists. Too often, however, those involved in the practice of data science are viewed by academic leadership as providing a noncomplex service to facilitate research and further the careers of other academic faculty. The authors contend that the success of data science initiatives relies heavily on the understanding that the practice of data science is a critical intellectual contribution to the overall science conducted at an academic medical center. Further, careful thought by academic leadership is needed for allocation of resources devoted to the practice of data science. At the Stanford University School of Medicine, the authors have developed an innovative model for a data science collaboratory based on 4 fundamental elements: an emphasis on collaboration over consultation, a subscription-based funding mechanism that reflects commitment by key partners, an investment in the career development of faculty who practice data science, and a strong educational component for data science members in team science and for clinical and translational investigators in data science. As data science becomes increasingly essential to learning health systems, centers that specialize in the practice of data science are a critical component of the research infrastructure and intellectual environment of academic medical centers.

Preface

The international conference on Applied Scientific Computational Intelligence using Data Science (ASCI-2020) has been grappled its importance to discuss the research challenges in the field of Data Science by applying scientific techniques in terms of computational intelligence. The initiative has been taken by Department of Computer Applications, Manipal University Jaipur, Rajasthan, India. Manipal University Jaipur (MUJ) was launched in 2011 on an invitation from the Government of Rajasthan, as a self-financed State University. MUJ has redefined academic excellence in the region, with the Manipal way of learning; one that inspires students of all disciplines to learn and innovate through hands on practical experience. In line with Manipal University’s legacy of providing quality education to its students, the campus uses the latest in technology to impart education. Thus, ASCI-2020 drives this legacy of MUJ to join forces and showcase exorbitant research and industry inventiveness, and to recognize and hear from experts in the field of Data Science. Data science is a huge diverted field. Different kind of algorithms, scientific techniques, processes and systems are used in data science to pull out knowledge and insights from Big Data i.e. structured and unstructured data. It is a concept to data analysis; unify statistics, machine learning and their related methods. This conference aims to reveal into advanced methodologies, prototypes, systems, tools, and techniques of data science from academia, industry and government agencies scientists and practitioners. Whether it is information technology or hardware, banking to healthcare, automation and innovation are revolutionary in almost everything. Cities and infrastructure are becoming smarter, health care is being integrated and education is becoming super-focused. The conference will bring together all topics of interest to those who are inclined towards computing and data science using intelligence techniques.ASCI-2020 proceeding has tried to fetch innovative facts and information from academician, research scholars and scientists in terms of their research results and key findings from all the aspects of data science and computational intelligence. The manuscripts of ASCI-2020 has been called for three tracks of data science and computational intelligence. The first track focused on Big Data Management. Then, second track emphasized the Computational Intelligence Techniques, and third track concentrated on Data Science Applications. Subsequently, these tracks has been formulated on the sub themes of Data Science and Computational Intelligence evolved with emerging fields in this present scenario. The sub themes of first track described Heuristic and Nature Inspired Search, Fuzzy and Rough Sets, Reinforcement Learning, ANN and Deep Neural Networks, Auto Encoder, GAN, Transfer Learning, Data Optimization, Data and Network Outsourcing Services. In addition, the second track includes the topics related to Algorithms and Models, Cognitive Computing Development, Business Intelligence and Strategies, Machine Learning and Statistics, Machine Learning Tools and Techniques, Fielded Applications, Generalization as Search, Machine Translation, Data Communication and Intelligence and Natural Language Processing. Finally, track three has been intended on interdisciplinary topics such as Predictive and Statistical Analysis, Application in Computer Vision, Natural Language Processing, Time Series Data, Computational Mathematics, Drug Discovery and Genomic Sequencing, Data Analysis for Improving Defence Security, Cyber Security Analytics, Data Recommendation in Social Networks, Data Analysis of Customer Need in E-commerce, Search Keyword Analysis, Web and Digital Media and Business Analytics in Agriculture.This conference received 235 papers from across the world such as United States, United Kingdom, United Arab Emirates, Norway, Russia, Saudi Arabia, Turkey, Ukraine, Ethiopia, Canada, Morocco, Poland, Uzbekistan, Ghana, Ecuador and Bangladesh etc. Out of 235 manuscripts 91 high quality papers are selected with 38 % accepting ratio. As a whole, 35 international authors submitted their papers, and 350 authors submitted their manuscript from the country. All the manuscripts have been discussed the new findings in the field of data science and computational intelligence. Authors of the proceedings focused on latest trends such as different data models for classification and prediction in various applications. Nevertheless, this proceeding also bring together different theories and paradigm of neural network, fuzzy systems for computational intelligence.List of Editors are available in the pdf.

An Assessment of Football Through the Lens of Data Science

The rise of Data Science and related fields of Big Data, Machine Learning, and Deep Learning has transformed the industrial landscape. The areas of sports and sports analytics are no exception. While to the layman, its influence may not be evident, but they have changed the way various sports are played up to different degrees. Hence, in recent times, sports institutions and clubs have given increased importance to such research that will ultimately help them have a competitive edge over rivals. The effects of these institutions incorporating these researches into their ways of competing have had impacts on and off the playing field. These effects aren’t only in terms of physiological enhancements of the athletes, but also socio-political and economic impacts as well. Out of the various sports implementing these techniques, we will focus on the effects mentioned above of Data Science on Football (“Soccer” in the USA). The following is a detailed review of the concepts as mentioned earlier.

Data science in organizations: Conceptualizing its breakthroughs and blind spots

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds: state-of-the-practice and challenges

PurposeIn recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists.Design/methodology/approachAn exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received.FindingsThe results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight.Research limitations/implicationsThe collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location.Practical implicationsThe current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background.Originality/valueThe conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.

A Social Media Campaign (#datasaveslives) to Promote the Benefits of Using Health Data for Research Purposes: Mixed Methods Analysis.

BackgroundSocial media provides the potential to engage a wide audience about scientific research, including the public. However, little empirical research exists to guide health scientists regarding what works and how to optimize impact. We examined the social media campaign #datasaveslives established in 2014 to highlight positive examples of the use and reuse of health data in research.ObjectiveThis study aims to examine how the #datasaveslives hashtag was used on social media, how often, and by whom; thus, we aim to provide insights into the impact of a major social media campaign in the UK health informatics research community and further afield.MethodsWe analyzed all publicly available posts (tweets) that included the hashtag #datasaveslives (N=13,895) on the microblogging platform Twitter between September 1, 2016, and August 31, 2017. Using a combination of qualitative and quantitative analyses, we determined the frequency and purpose of tweets. Social network analysis was used to analyze and visualize tweet sharing (retweet) networks among hashtag users.ResultsOverall, we found 4175 original posts and 9720 retweets featuring #datasaveslives by 3649 unique Twitter users. In total, 66.01% (2756/4175) of the original posts were retweeted at least once. Higher frequencies of tweets were observed during the weeks of prominent policy publications, popular conferences, and public engagement events. Cluster analysis based on retweet relationships revealed an interconnected series of groups of #datasaveslives users in academia, health services and policy, and charities and patient networks. Thematic analysis of tweets showed that #datasaveslives was used for a broader range of purposes than indexing information, including event reporting, encouraging participation and action, and showing personal support for data sharing.ConclusionsThis study shows that a hashtag-based social media campaign was effective in encouraging a wide audience of stakeholders to disseminate positive examples of health research. Furthermore, the findings suggest that the campaign supported community building and bridging practices within and between the interdisciplinary sectors related to the field of health data science and encouraged individuals to demonstrate personal support for sharing health data.

Bitcoin economic behavior analysis and policy implications by leveraging deep learning and high-frequency data

The recent surge in Bitcoin price performance has attracted significant attention from both the market and academic researchers. This paper constitutes the first principled attempt to determine market risk own-funds requirements for Bitcoin. To this end, we examine price microstructure of the USD per bitcoin, and compare to other financial variables, as a proxy toward classifying Bitcoin into the appropriate risk-class. Using the outcomes of this analysis, we classify and quantify the entailed risk from a market risk minimum capital requirements perspective. To perform the prescribed analysis, we introduce a novel methodological paradigm, which adopts bleeding-edge concepts from the field of Data Science and Machine Learning.

Statistics Practicum: Placing 'Practice' at the Center of Data Science Education

Much of the current postsecondary training in core data science fields treats ‘practice’ as something to be relegated to capstone projects or other final preparations before students leave their programs. Here we argue for a paradigm shift, placing a so-called practicum course at the center of a data science program, intentionally organized as a hybrid between an educational classroom and an industry-like environment. As a case study, we detail our experience of the past 5 years developing the Statistics Practicum in Boston University’s M.S. in Statistical Practice (MSSP) program. We describe the motivation, organization, and logistics of our practicum, as well as both successes and challenges we have faced. In particular, the challenge of fairly and effectively assessing student achievement and program impact in this novel setting is discussed.

Decision Rules in Cost-Utility Analysis of Health Technologies

This review identified 4 distinct decision rules used in the cost-utility analysis of health technologies: threshold incremental cost-effectiveness ratio (ICER), league table, decision making (DM) plane, and linear programming. The threshold ICER is currently the most widely-used approach. However, it comes with certain disadvantages: unrealistic assumptions (perfect divisibility and constant return to scale), arbitrarily-set threshold, and ignorance of opportunity cost. League table involves comparison of several health technologies simultaneously. The issue related to comparability between evaluation of each health technology limits its application. DM plane ensures improvement in health and sufficient resources mostly by disinvestment of currently funded programs. Its major disadvantages are difficulties in identification and disinvestment of such programs. Linear programming is, in theory, the best approach. Ideally, it requires sufficient data from all currently-funded or potential health technologies in conducting the analysis. Hence, the approach is somewhat impractical. Ongoing development in the field of data science and increasing availability of big data might enable its application in the near future. Given the mentioned shortcomings, pragmatic applications of league table and DM plane use them to evaluate competitive treatment programs for single health conditions. Using linear programming in prioritizing health programs was proven possible at the district level.

Моделі управління проектом створення ІТ компанії у галузі Data Science

The basis for the emergence of projects to create IT companies in the field of Data Science is considered. The relevance of such projects is substantiated. The feasibility of their study using models and methods of project management is proven. Literary sources in three directions are analyzed. Such areas include classical project management, flexible approaches to project management, value-oriented management. Insufficient research of the described subject is proved. Groups of models and methods are described that describe the relevant areas of project management knowledge that will be necessary and minimally sufficient to develop the scientific basis of the IT company creation project in the field of Data Science. Namely: project team management, project value management, project concept development, project content management, project communications management, flexible project management tools. In the analysis of each area, the relevant models and methods are highlighted. Their applicability to the researched project is analyzed. A model for selecting a set of models and methods within the identified key knowledge areas for the research project in the form of a convolution of criteria is proposed. SWOT-analysis of the proposed approach was performed. The strengths, weaknesses, opportunities and threats associated with the proposed approach are highlighted. Conclusions to the study are formulated. Prospects for further research in the chosen direction are outlined.

A Two-Step Approach for Improving Sentiment Classification Accuracy

Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is of deep interest for researchers in the field of data science. The proposed article is an effort to find a model which can improve the classification accuracy of sentiment data. In this paper, a complete model is introduced to improve the accuracy of binary-class sentiment data. The model is reliable as it has been validated on three distinctive datasets with various sample sizes. In this regard, Amazon reviews, Yelp reviews, and IMDB reviews are taken into account. This model is based on completely referenced datasets and considerable improvement in accuracy is seen as compared to individual classifiers. Various heterogeneous classifiers are trained on the above-mentioned datasets at the base level. These base-level classifiers ought to be chosen from various groups of classifiers. At that point, the output of these base-level classifiers is consolidated to create a new dataset that acts as input for meta-level classifiers. At the meta-level, the base-level classifier with the best outcomes is selected with the new dataset framed. The model accordingly framed can be used to predict any sample query.

ОСОБЛИВОСТІ ВИКОРИСТАННЯ DATA SCIENCE У СФЕРІ БІЗНЕСУ

The article considers the peculiarities of companies development in the conditions of innovations active introduction and users and business reorientation to the digital environment. The peculiarities of collecting, accumulating and processing large arrays of structured, semi-structured and unstructured information due to the use of cloud services are studied. The preconditions for the application of Data Science approaches for business needs and a significant increase in the demand for specialists with relevant skills and abilities in the near future are presented. The main approaches to collecting information in the digital environment are revealed, first of all it is about encouraging users to provide personal data in exchange for certain benefits (interesting and useful thematic content, discounts, free access to paid resources for a certain period of time, etc.). The expediency of using machine learning methods in the process of optimizing the company's activities and developing certain products is proved. Current examples of Data Science algorithms use that increase the efficiency of companies in modern conditions are given. The field of algorithms and approaches, including machine learning and artificial intelligence in the field of Data Science is very wide and includes a large number of methods. The use of data and the use of machine learning methods makes it possible to understand the specific needs of customers within individual groups, using classification approaches. Classification methods allow to build recommendation systems, which on the basis of complex information and appropriate algorithms show users relevant products. The use of sophisticated machine learning algorithms makes it possible to identify suspicious transactions and fraudulent activities in various areas of economic activity. Modern machine learning methods make it possible to implement very complex predictive models, including neural networks with different architectures. Thanks to the anticipation of the company get the opportunity to build effective strategies for the long term with a high probability of achieving the outlined targets.