Area Of Data Science Research Articles

The Role of Data Science in Predictive Analytics: Techniques and Applications

In the present era of digital transformation, data has emerged as a critical asset driving strategic decision-making across several industries. The explosion of data created by organizations, customers, and gadgets has spawned the area of data science, which includes tools and approaches for analyzing and understanding enormous amounts of data. Predictive analytics, which uses data science to estimate future events and trends, has grown in importance within this large area. This article dives into the role of data science in predictive analytics, examining key terminology, the field's evolution, importance, existing research gaps, and the study's relevance in the present situation.

What do you think caused your ALS? An analysis of the CDC national amyotrophic lateral sclerosis patient registry qualitative risk factor data using artificial intelligence and qualitative methodology

Objective Amyotrophic lateral sclerosis (ALS) is an incurable, progressive neurodegenerative disease with a significant health burden and poorly understood etiology. This analysis assessed the narrative responses from 3,061 participants in the Centers for Disease Control and Prevention’s National ALS Registry who answered the question, “What do you think caused your ALS?” Methods Data analysis used qualitative methods and artificial intelligence (AI) using natural language processing (NLP), specifically, Bidirectional Encoder Representations from Transformers (BERT) to explore responses regarding participants’ perceptions of the cause of their disease. Results Both qualitative and AI analysis methods revealed several, often aligned themes, which pointed to perceived causes including genetic, environmental, and military exposures. However, the qualitative analysis revealed detailed themes and subthemes, providing a more comprehensive understanding of participants’ perceptions. Although there were areas of alignment between AI and qualitative analysis, AI’s broader categories did not capture the nuances discovered using the more traditional, qualitative approach. The qualitative analysis also revealed that the potential causes of ALS were described within narratives that sometimes indicate self-blame and other maladaptive coping mechanisms. Conclusions This analysis highlights the diverse range of factors that individuals with ALS consider as perceived causes for their disease. Understanding these perceptions can help clinicians to better support people living with ALS (PLWALS). The analysis highlights the benefits of using traditional qualitative methods to supplement or improve upon AI-based approaches. This rapidly evolving area of data science has the potential to remove barriers to accessing the rich narratives of people with lived experience.

Computation of persistent homology on streaming data using topological data summaries

AbstractPersistent homology is a computationally intensive and yet extremely powerful tool for Topological Data Analysis. Applying the tool on potentially infinite sequence of data objects is a challenging task. For this reason, persistent homology and data stream mining have long been two important but disjoint areas of data science. The first computational model, that was recently introduced to bridge the gap between the two areas, is useful for detecting steady or gradual changes in data streams, such as certain genomic modifications during the evolution of species. However, that model is not suitable for applications that encounter abrupt changes of extremely short duration. This paper presents another model for computing persistent homology on streaming data that addresses the shortcoming of the previous work. The model is validated on the important real‐world application of network anomaly detection. It is shown that in addition to detecting the occurrence of anomalies or attacks in computer networks, the proposed model is able to visually identify several types of traffic. Moreover, the model can accurately detect abrupt changes of extremely short as well as longer duration in the network traffic. These capabilities are not achievable by the previous model or by traditional data mining techniques.

Synthetic Data and Public Policy: supporting real-world policymakers with algorithmically generated data

Good policy is best developed by drawing on a wide array of high-quality evidence. The rapid growth of data science and the emergence of big datasets has materially advanced the supply and use of quantitative evidence. However, some key constraints remain, including that available datasets are still not big enough for some analytical purposes. There are also privacy and data security risks. Synthetic data is an emerging area of data science that can potentially support policy decision making through enabling research to work faster and with fewer errors while also ensuring privacy and security.

Thematic Specialization of Institutions with Academic Programs in the Field of Data Science

Introduction: data science careers are on the rise due to the growing demand for technical skills in this area. Data science careers focus on collecting, organizing, and analyzing data to identify patterns and trends, which allows organizations to make informed decisions and develop effective solutions. Aim: to analyze the thematic specialization of institutions with academic programs in the area of data science. Methods: The Scopus database was used to conduct a bibliometric analysis aimed at examining the thematic specialization of institutions with academic programs in the field of data science. SciVal, a bibliometric analysis tool, was employed to extract the relevant data. The study period ranged from 2012 to 2021. Results: Nine higher education institutions were found to offer undergraduate or graduate degrees in the field of data science. There was no correlation found between RSI and Field-Weighted Citation Impact (r=0.05355; P=0.8912; 95% CI: -0.6331 to 0.6930). Therefore, it cannot be claimed that specialization in the subject area studied influences the greater impact of research. On the other hand, recent accreditation did not influence greater specialization (r=0.1675; P=0.6667; 95% CI: -0.5588 to 0.7484). Additionally, no differences were found regarding academic level. Conclusions: The analysis of the thematic specialization of institutions with academic programs in the field of data science shows low scientific production in this field. Moreover, more than half of the analyzed higher education institutions have thematic specialization below the global average. This suggests that there is still a long way to go for these institutions to achieve adequate specialization and compete internationally in the field of data science.

Book Review

The Encyclopedia of Data Science and Machine Learning (EDSML) examines current, state-of-the-art research in the areas of data science, ML, data mining (DM), optimization, artificial intelligence (AI), statistics, and the interactions, linkages, and applications of knowledge-based business with information systems. It provides an international forum for practitioners, educators, and researchers to advance the knowledge and practice of all facets of BDML, emphasizing emerging theories, principles, models, processes, and applications to inspire and circulate cutting-edge findings into research, business, and communities. This encyclopedia contains a collection of 187 high-quality chapters, which were written by an international team of more than 370 experts representing leading scientists and talented young scholars from more than 46 countries and regions.

Stochastic dynamic programming solution to transmission scheduling: Multi sensor-multi process with wireless noisy channel

We investigate sensor scheduling for remote estimation when multiple smart sensors monitor multiple stochastic dynamical systems. The sensors transmit their measurements to a remote estimator through a noisy wireless communication channel. Such a remote estimator can receive multiple packets simultaneously sent by local sensors. Sensors transmit their measurements if their Signal Interference and Noise Ratio (SINR) is above a threshold. We compute the optimal policy for sensor scheduling by minimizing expected error covariance subject to total signal transmissions from all sensors. We model this problem as Markov Decision Process (MDP) with discounted cost per stage in the finite time horizon framework, then we employ stochastic Dynamic Programming as the optimization method. A novel algorithm based on sampling and machine learning techniques is proposed as the approximation. At each phase of the DP algorithm, samples are collected using a uniform probability distribution. The data is used to feed Neural Network (NN) and Random Forest (RF) models for cost function and policy approximation. The results of the proposed framework are supported by simulation examples comparing RF and NN as Approximate DP (ADP). Note that this idea builds a bridge among the recent advances in the area of data science, Machine Learning, and the ADP.

When black box algorithms are (not) appropriate

In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. In this paper, we introduce the term “outcome reasoning” to refer to this form of reasoning. Though outcome reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, outcome reasoning is the primary way we reason about whether “black box” algorithms are performing well. In this paper we analyze outcome reasoning’s most common form (i.e., as “the common task framework”) and its limitations. We discuss why a large class of prediction-problems are inappropriate for outcome reasoning. As an example, we find the common task framework does not provide a foundation for the deployment of an algorithm in a real world situation. Building off of its core features, we identify a class of problems where this new form of reasoning can be used in deployment. We purposefully develop a novel framework so both technical and non-technical people can discuss and identify key features of their prediction problem and whether or not it is suitable for outcome reasoning.

Survey steganography applications

There are many and many ways that play an important role in terms of information security, including the most common method known as cryptography, which is changing/hiding the basic data according to a certain method to make it unreadable. Steganography is another art that aims to completely hide data to communicate between two parties unobtrusively to a third party, and this is known as steganography. Only the relevant persons are aware of this contact. The practice of steganography includes concealing a message inside a suitable carrier, such as a picture or an audio file, for example. Steganography is also known as cryptography. The carrier may then be delivered to the intended recipient with no one except the intended recipient being aware that it conceals a message. For instance, civil rights organizations in repressive countries may utilize this strategy to get their message out to the globe without raising suspicion at home. This is due to the fact that it is possible to use this strategy in private. In this piece, we've attempted to explain the many steps involved in using a "multimedia" file to create steganography (text, static image, audio, and video). Stainless is an emerging area of data science that tries to detect steganographic masks and, if possible, retrieve hidden communications. Stainless is an emerging industry. It's extremely similar to cryptanalysis, which is used in the field of cryptography. The technique has been around for quite some time, but it has only recently gained widespread notice because of its application to the secure transmission of digital information. The primary objective is to conceal the existence of the communication, as well as to prevent it from being read.

A Hybrid System For Pandemic Evolution Prediction

The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures.
 
 
 

A Comparative Study and Impact Analysis of Different Oversampling Techniques for CIP

In the area of data science, machine learning based classification techniques are the first choice for an accurate analysis of a huge amount of data. The first requirement while developing such classification techniques is the robustness that it will imply for an accurate and efficient classification. However, it is seen that in many times these algorithms suffer from “class-imbalance problem”, shortly CIP. Due to CIP, many difficulties arise during the learning process, which as a whole results a poor classification process. Resampling the data set is one common technique for dealing with CIP where in general oversampling the size of the rare class is made. We have different oversampling techniques available in the literature like SMOTE, ADYSN, and Random Oversample are the noted ones. In this paper, an effort is made to compare these different techniques as well as their impact on classification performance.

How Anheuser-Busch InBev is tackling the top challenges of Category Management for modern offline retail channel

In this paper we present a practical approach that AB InBev’s Global Capabilities Centre (“GCC or ABI or Ab InBev”) has developed to solve the challenges of Category Management for Retailers. The approach brings technical rigor from the areas of data science, econometrics, and measurement methodologies very close to business context. This has allowed us to create a solution which is highly contextual and relatable to our business stakeholders. The strength of the presented solution lies in it being a semi-automated framework that allows a wide array of disparate data to be modelled and captures the nuances of different markets - such as socio-demographic profiles, consumption behaviours, local preferences towards beer styles. We also present the ABI created 4C framework to arrive at the optimal assortment recommendation for a Retailer.

Analysis of publications on natural language processing indexed in RSCI

Natural Language Рrocessing (NLP), defined, as the general direction of artificial intelligence and mathematical linguistics, is an important tool for understanding and processing a gigantic amount of unstructured data.Representing theoretical and practical basis for solving a large number of problems, NLP is being actively studied by scientists around the world, since it is one of the most popular areas of data science and is used in many fields of human activity. The purpose of this bibliometric analysis is to identify the main research centers, research trends, the rating of leading Russian scientists in the field of NLP, as well as to show the picture of thematic fields’ distribution of publications. The results of the analysis will allow us to determine the dynamics of NLP field development in the domestic science and will provide scientists and specialists working in the field of NLP with up-to-date information on various aspects of the considered research area.Russian Science Citation Index (RSCI) – the main scientometric database in our country was used as the data source for scientific publications in the field of NLP search. The RSCI platform contains information on current research areas, allows you to assess the productivity of scientists, scientific communities, etc.The presented work will be interesting to researchers in the field of NLP, since it contains actual data on the development dynamics and structure of the documentary and information flow in the field of NLP in Russia, its distribution by branches of knowledge. Besides, the conducted bibliometric analysis of documents allows us to gain information about the authors most productively working in the studied area and their affiliations, as well as about the most cited articles – this is the review of the 30 most cited publications of Russian scientists and rating of TOP-10 scientific organizations of the Russian Federation by the number of publications reflecting the main problems and scientific achievements in the field of NLP.

Towards an ethical framework about Big Data era: metaethical, normative ethical and hermeneutical approaches

The main ethical challenges that arise for Information Sciences (with the daily use in different areas of Big Data applications) are not about the reliability of its professionals to carry out tasks in the organization area in an impartial way or about the obligation to train themselves technologically in the area of Data Science. The most important problems are related to the concept of moral responsibility, especially from a metaethical perspective, in line with the reflection of the implementation of technology with respect to human autonomy. In this paper it is stated that the challenges of Big Data go beyond the individual spectrum of responsibility of a professional in Information Sciences (specifically, due to the negative social consequences), so that the changes brought about by massive data sets are essentially problems of a group ethics, so they require approaches from the theoretical postulates of these disciplines. In addition to this, the moral challenges in dealing with Big Data are usually approached from applied ethics (such as information ethics), but in this article it will be approached as a problem of metaethics and normative ethics (as a foundation for its application in professional codes), and also from some ideas of digital hermeneutics and the philosophy of technology.

Improved KNN Imputation for Missing Values in Gene Expression Data

The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is the first study that applies the concept of ordered weighted averaging (OWA) operator to such a problem context. In particular, two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models. Using different ratios of missing values from 1%–20% and a set of six published gene expression datasets, the experimental results suggest that new methods usually provide more accurate estimates than those compared methods. Specific to the missing rates of 5% and 20%, the best NRMSE scores as averages across datasets is 0.65 and 0.69, while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84, respectively.

Technology Focus: EOR Modeling (January 2022)

No one will be surprised to learn of the reduction in the produced literature on enhanced oil recovery (EOR) this past year measured in terms of papers produced through various SPE forums. The constraints created by the COVID-19 pandemic have been unprecedented. More encouraging and noteworthy is the fact that, even in such hard times, the wheels of knowledge have kept turning. Statistics are important in knowing where the sharp minds of SPE members are investing their energies. Typically, this is also in proportion to what those members need. Hence, it is noteworthy that more than a fifth of the papers were about CO2-based EOR, indicating not only renewed interest but also the motivation for carbon capture, use, and storage. You will find CO2-based EOR represented in the recommended-reading papers by a paper that describes use of simple techniques to predict fieldwide behavior using the Pulser method. What’s more, old is new again. In an enhanced shale gas recovery technique, as summarized in this issue, you will recognize repurposing of a much-discussed coalbed-methane method from a couple of decades ago. CO2-EOR literature is closely followed by continued momentum in the area of data science (DS) and machine learning but with a perceptible degree of maturity in the types of application. As opposed to the “everything goes in the Wild West” approach of a couple of years ago, more and more researchers are realizing that a practical application of DS must respect the underlying fundamentals. No wonder, then, that we are seeing a gradual return of physics, though now strengthened with the power of DS. A good examples of this is found in a paper summarized here that presents data-driven approaches for waterfloods. The other two inclusions presented in this feature combine learnings from other areas to extend and strengthen the frontiers of EOR modeling—namely, a coupled geomechanical and wellbore model, and an elegant manner of simulating phase appearance/disappearance in thermal simulations in the recommended-reading papers. I hope this brief primer will not only bring you up to date with the trends in the EOR literature but also equip you with a good sense of emerging tools and their applications. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200390 - Pulser Model: Updated Framework and Lessons Learned From Applications in Forecasting CO2 Flood Performance by Deepanshu Kumar, Occidental Petroleum, et al. SPE 203912 - Development of a Thermal Stability Method for Phase Appearance and Disappearance Handling in Thermal Compositional Simulators by Mohammad Reza Heidari, Schlumberger, et al.

Comprehensive Examination of Network Intrusion Detection Models on Data Science

The increased requirement of data science in recent times has given rise to the concept of data security, which has become a major issue; thus, the amalgamation of data science methodology with intrusion detection systems as a field of research has acquired a lot of prominence. The level of access to the information system and its visibility to user pursuit was required to operate securely. Intrusion detection has been gaining popularity in the area of data science to incorporate the overall information security infrastructure, where regular operations depend upon shared use of information. The problems are to build an intrusion detection system efficient enough for detecting attacks and to reduce the false positives with a high detection rate. In this paper, the authors analyse various techniques of intrusion detection combined with data science, which will help in understanding the best fit technique under different circumstances.

Leading Research by Institutions and Authors: A Modern Research Analysis

Purpose: The article concludes on the importance of scientometric analysis in the process of finding partners for scientific cooperation. Design/Methodology/Approach: Scientometric analysis. Findings: The paper quantitatively characterizes the literature thematically related to the area of data science. The scientometric method was used and the Scopus database was the source of data for analysis. The study made it possible to determine when the first publications on data science appeared and how the dynamics of the growth of the literature in this range developed in years 2006-2021. The authors identified leading researchers and their area of interest, along with having reported statistics that describe top countries, institutions, subject areas, and founding sponsors. Practical Implications: Executing scientific analysis can help to identify institutions and researchers to build cooperation partneship. Originality/value: Our study has shown developing areas of data science methodology to identify leading institutions and researchers.

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective.

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Development of an innovative index to assess worker’s health risk: the WHRI applied to an oil industry in Bahia, Brazil

Purpose: The objective of this study was to assess the worker’s health (WH) risk, focused on sustainable development in a work context and based on the development and application of the Worker’s Health Risk Index (WHRI) in the oil extraction and production industry in Bahia, Brazil.Design/methodology/approach: The sample, obtained by quota sampling, comprised 965 participants. The development stage integrated a group of 10 specialists, including physicians, nurses, nutritionists, dentists and physical educators, all specialists in the WH area, as well as 3 experts in the area of data science. Three risk ranges were defined: "Low", "Moderate" and "High".Findings: The WHRI validation revealed reliability and reproducibility, as well as the ability to identify differences among the population studied according to sex, age group and education level. The results indicate that the WH risk is higher in men, aged above 50 years old and with a low level of education (p < 0.001). 74% of the participants are in the "Low", 21% in the "Moderate" and 5% in the "High" risk ranges. High-risk workers are also those with diabetes mellitus, altered triglyceride or glycemia levels, hypertension, poor oral hygiene and periodontal conditions, tobacco use, low levels of physical activity (all with p < 0.05), or alcohol abuse.Originality/Value: The WHRI’s major contribution is to make available a useful tool for the identification of WH risk, helping to define clearer health promotion, prevention and intervention policies in the context of WH.