Field Of Data Science Research Articles

Data Science Education in <i>DS<sup>4</sup></i> Program at Osaka University

In the context of the human resource development program for independent data scientists led by Osaka University, this report provides a roadmap for human resource development in the field of data science by providing detailed information on the type of human resources to be developed, an outline of the main subjects, and administrative methods for a portion of the subjects.

Swarm Intelligence in Data Science: Challenges, Opportunities and Applications

The Swarm Intelligence (SI) algorithms have been useful in solving multifaceted optimization problems. SI Algorithms as the name suggests work on the simulation principle of behaviour of biological swarms. Today, there is lot of advancement in the field of Data Science; which comprises of large amount of Big Data that needs to managed as well as analysed. Traditional methods may only be applied to differentiable and continuous functions. For population-based approaches, Swarm Intelligence proves to be effective and efficient. In order to have better insight into applications of SI Algorithms in Data Science, and to accumulate a further reference for the future researchers in this field, this paper discusses scope of Swarm Intelligence in Data Science. On the basis of existing work; the following paper also focusses on the Challenges and Opportunities of SI in Data Science and also encourage to design more efficient algorithms in future that is capable to solve Data Science related problems in real world.

The importance of machine learning in autonomous actions for surgical decision making

Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path.

DATA SCIENCE FOR SMALL AND MEDIUM-SIZED ENTERPRISES: A STRUCTURED LITERATURE REVIEW

Small and medium-sized enterprises (SMEs) are faced with the challenge of implementing Industry 4.0 (I4.0) technologies to keep up with larger industry players, and to pave the way for sustainable business development and digital transformation. Data science (DS) specifically has the potential to provide SMEs with the ability to elevate their decision-making processes by making informed data-driven decisions. A vast amount of literature relates to the field of DS and its implementation; however, the literature related to the implementation of DS in the context of SMEs is limited. Consequently, this article presents a structured literature review with regard to the implementation of DS to support sustainable business operations in SMEs, and particularly focusing on SMEs in developing countries. Keywords Data science; Big data; SMEs; Sustainable development; Industry 4.0; Digital transformation

On the anti-missile interception technique of unpowered phase based on data-driven theory

Abstract. The anti-missile interception technique of unpowered phase is of much importance in the military field, which depends on the prediction of the missile trajectory and the establishment of the missile model. With rapid development of data science field and large amounts of available data observed, there are more and more powerful data-driven methods proposed recently in discovering governing equations of complex systems. In this work, we introduce an anti-missile interception technique via a data-driven method based on Koopman operator theory. More specifically, we describe the dynamical model of the missile established by classical mechanics to generate the trajectorial data. Then we perform the data-driven method based on Koopman operator to identify the governing equations for the position and velocity of the missile. Numerical experiments show that the trajectories of the learned model agree well with the ones of the true model. The effectiveness and accuracy of this technique suggest that it will be realized in practical applications of anti-missile interception.

An Interview with Dr. Shipeng Yu, Winner of ACM SIGKDD 2021 Service Award

Shipeng Yu, Ph.D. is the recipient of the 2021 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Yu was honored for his years of service and many accomplishments as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. Dr. Yu is Director of AI Engineering, Head of the Growth AI team at LinkedIn, the world's largest professional network. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 2006, the benefits and drawbacks of virtual conferences, his work at LinkedIn, and KDD's place in the field of machine learning, data science and artificial intelligence.

Embedding algorithm of spined cube into grid structure and its wirelength computation

In the field of data science, graph embedding is a vital tool to simulate, visualize and design a parallel architecture. The binary cube is one of the most desirable and reliable network-based architecture with high performance, yet many variations have been proposed on the links of the cube to enhance its computational power. The spined cube is a new efficient variant of the binary cube with a smaller diameter and preserves the advantageous properties of the original cube. Grid embedding is accustomed to study simulation capacity of a parallel and distributed computer system and conjointly designs its VLSI layout. In this paper, we propose an algorithm to embed the spined cube into a grid and compute the minimum wirelength over all possible embeddings using the edge congestion technique.

Comparative study of regressor and classifier with decision tree using modern tools

Machine Learning is one of the importantareas for modeling the data and itcan be saidthat this is the core part of the field of Data Science. Supervised Machine Learning (SML)has many algorithms to train the machine. The decision tree as the classifier is used to trainthe model based onthe categorical label and the Decision Tree as Regressor is used to trainthe model based ona non-categorical label. There are two kinds of algorithms like Classification and Regression. In this paper, we focus on the Decision Tree as a Regressor and Classifier and compare the metrics. This paper describes the decision tree with the analysis as well as a comparison with the most efficient algorithm based on the different datasets using python programming. Results show the accuracy and comparison of the decision tree as a Regressor and Classifier. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Confusion Matrix are performance parametersused to analysis of decision treesand also using different python libraries to analyze and visualize the result.In this paper, we used a shopping mall dataset for classification as a case study from UCI (Machine Learning Repository) to predict that users purchase an item or not. This dataset contains 400records. Decision Tree as a Regressor is used as the dataset from the Kaggle repository for analysis and visualization of results and show comparison. In this paper, the Accuracy score is the most important measure to compare decision treesbased on regression and classification but Mean Squared Error (MSE) is also an important factor to decide and split the node into two or more nodes.

Artificial Intelligence in Vaccine and Drug Design.

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

Recent Books and Journals Articles in Public Opinion, Survey Methods, Survey Statistics, Big Data, Data Science, and User Experience Research. 2020 Update

Welcome to the 13th edition of this column on recent books and journal articles in the field of public opinion, survey methods, survey statistics, Big Data, data science and user experience research. After a hiatus due to the pandemic which affected my productivity, I am publishing this 2020 update. Special issues of journals have a space in this article because, in my view, they are like edited books. I also added review papers from the journal series of Annual Reviews because these papers are seminal state of the art writeups, a mini book, if you wish on a specific subject.

Recent Books and Journals Articles in Public Opinion, Survey Methods, Survey Statistics, Big Data, Data Science, and User Experience Research. 2019 Update

Welcome to the 12th edition of this column on recent books and journal articles in the field of public opinion, survey methods, survey statistics, Big Data, data science and user experience research. After a hiatus due to the pandemic which affected my productivity, I am publishing this 2019 update, and shortly, I will publish the 2020 update. Special issues of journals have a space in this article because, in my view, they are like edited books. I also added review papers from the journal series of Annual Reviews because these papers are seminal state of the art writeups, a mini book, if you wish on a specific subject.

Experimental interpretation of adequate weight-metric combination for dynamic user-based collaborative filtering

Recommender systems include a broad scope of applications and are associated with subjective preferences, indicating variations in recommendations. As a field of data science and machine learning, recommender systems require both statistical perspectives and sufficient performance monitoring. In this paper, we propose diversified similarity measurements by observing recommendation performance using generic metrics. Considering user-based collaborative filtering, the probability of an item being preferred by any user is measured. Having examined the best neighbor counts, we verified the test item bias phenomenon for similarity equations. Because of the statistical parameters used for computing in a global scope, there is implicit information in the literature, whether those parameters comprise the focal point user data statically. Regarding each dynamic prediction, user-wise parameters are expected to be generated at runtime by excluding the item of interest. This yields reliable results and is more compatible with real-time systems. Furthermore, we underline the effect of significance weighting by examining the similarities between a user of interest and its neighbors. Overall, this study uniquely combines significance weighting and test-item bias mitigation by inspecting the fine-tuned neighborhood. Consequently, the results reveal adequate similarity weight and performance metric combinations. The source code of our architecture is available at https://codeocean.com/capsule/1427708/tree/v1.

Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

Empirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria

The rapid rate as well as the volume in amount of data churned out on daily basis has necessitated the need for data mining process. Advanced by the field of data science with machine learning approaches as new paradigm and platform, it has become imperative to provide beneficial support in constructing models that can effectively assist domain experts/practitioners – to make comprehensive decisions regarding potential cases. The study uses deep learning prognosis to effectively respond to problematic cases of cancer in Nigeria. We use the fuzzy rule-based memetic model to predict potential problematic cases of cancer – predicting results from data samples collected from the Epidemiology laboratory at Federal Medical Center Asaba, Nigeria. Dataset is split into training (85%) and testing (15%) to aid model validation. Results indicate that age, obesity, environmental conditions and family relations (to the first and second degree) are critical factors to be watched for benign and malignant cancer types. Constructed model result shows high predictive capability strength compared to other models presented on similar studies.

Against generalisation: Data-driven decisions need context to be human-compatible

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

DataStory\u2122: an interactive sequential art approach for data science and artificial intelligence learning experiences

Technical training in the fields of data science and artificial intelligence has recently become a highly desirable skill for industry positions as well as a focus of STEM education programs in higher education. However, most of the educational training and courses in data science and artificial intelligence are abstract and highly technical which is not appropriate for all audiences. In this paper, we propose a sequential art approach that uses visual storytelling with integrated coding learning experiences to teach data science concepts. A scoping literature review was conducted to answer the following question: does sufficient evidence exist in the literature to support a sequential art approach to data science and A.I. education? The learning science, sequential art, and dual coding literature bases were then interrogated to answer that question. With knowledge gained from this review, an initial DataStory™ prototype was constructed, using a technical platform capable of delivering an engaging and interactive sequential art learning experience. And finally, findings from a focus group study using the DataStory™ prototype are discussed in which participant feedback to this new learning experience is reported.

Use of machine-learning for monitoring solar thermal plants

A machine-learning algorithm (MLA) was developed to assess the operational state of solar thermal plants, based on the data of only one temperature sensor, and the irradiance and ambient temperature data from the nearest weather station. A detailed requirements analysis of the situation results in the classification of a multivariate time series problem. Neural networks used in the field of data science are ideally suited for problems of this type. Data from the operational monitoring system, which runs a rule-based algorithm, were used to train the neural network using the software framework TensorFlow. It was shown that the chosen MLA can detect malfunctions such as heat loss due to gravity-driven circulation during night. However, further development towards a practical tool requires not only far more data for training and validation. It became clear that corresponding pressure data are needed to classify temperature transients and to attribute these classes to certain malfunctions.

Forecasting the action of CAR-T cells against SARS-corona virus-II infection with branching process.

The CAR-T cells are the genetically engineered T cells, designed to work specifically for the virus antigens (or other antigens, such as tumour specific antigens). The CAR-T cells work as the living drug and thus provides an adoptive immunotherapy strategy. The novel corona virus treatment and control designs are still under clinical trials. One of such techniques is the injection of CAR-T cells to fight against the COVID-19 infection. In this manuscript, the hypothesis is based on the CAR-T cells, that are suitably engineered towards SARS-2 viral antigen, by the N protein. The N protein binds to the SARS-2 viral RNA and is found in abundance in this virus, thus for the engineered cell research, this protein sequence is chosen as a potential target. The use of the sub-population of T-reg cells is also outlined. Mathematical modeling of such complex line of action can help to understand the dynamics. The modeling approach is inspired from the probabilistic rules, including the branching process, the Moran process and kinetic models. The Moran processes are well recognized in the fields of artificial intelligence and data science. The model depicts the infectious axis “virus—CAR-T cells—memory cells”. The theoretical analysis provides a positive therapeutic action; the delay in viral production may have a significant impact on the early stages of infection. Although it is necessary to carefully evaluate the possible side effects of therapy. This work introduces the possibility of hypothesizing an antiviral use by CAR-T cells.

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management.

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world’s population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, “omics” techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

MIDST: an enhanced development environment that improves the maintainability of a data science analysis

With the increasing ability to generate actionable insight from data, the field of data science has seen significant growth. As more teams develop data science solutions, the analytical code they develop will need to be enhanced in the future, by an existing or a new team member. Thus, the importance of being able to easily maintain and enhance the code required for an analysis will increase. However, to date, there has been minimal research on the maintainability of an analysis done by a data science team. To help address this gap, data science maintainability was explored by (1) creating a data science maintainability model, (2) creating a new tool, called MIDST (Modular Interactive Data Science Tool), that aims to improve data science maintainability, and then (3) conducting a mixed method experiment to evaluate MIDST. The new tool aims to improve the ability of a team member to update and rerun an existing data science analysis by providing a visual data flow view of the analysis within an integrated code and computational environment. Via an analysis of the quantitative and qualitative survey results, the experiment found that MIDST does help improve the maintainability of an analysis. Thus, this research demonstrates the importance of enhanced tools tohelp improve the maintainability of data science projects.