Narrow Results

In the past decades we have witnessed a paradigm-shift from scarcity of data to abundance of data. Big data and data analytics have fundamentally reshaped many areas including operations research. In this paper, we discuss how to integrate data with the model-based analysis in a controlled way. Specifically, we consider techniques to quantify input uncertainty and the decision making under input uncertainty. Numerical experiments demonstrate that different ways in decision making may lead to significantly different outcomes in a maintenance problem.

SINGAPORE – Biofourmis to Run New Study on How Singapore’s Lifestyle Affects Heart Health SingHEART Vitals Study with Support from IMDA.

SINGAPORE – Smart Nation Boost: Singapore Engineers Develop Remote Vital Signs Monitoring System.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Routine Blood Test Predicts How Long Cancer Patients Will Survive.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Cancer Costs Leaving Patients in Debt.

ESMO ASIA 2016 CONGRESS, SINGAPORE – Asian Head and Neck Cancer Patients Live Longer with Immunotherapy than Mixed Race Group.

JAPAN – Avastin® Received Orphan Drug Designation for the Treatment of Malignant Pleural Mesothelioma.

UNITED STATES – Recovery from Brain Injury and Better Sleep Go Hand in Hand.

UNITED STATES – Erasing the Line between Imaging and Analysing.

For the month of January 2021, APBN features a cover story on innovations in home-based faecal calprotectin test for self-management of inflammatory bowel disease. Also, in the Features section, we bring you highlights of the new technologies and trends in cell-line research and development Taking a dive into healthcare technology, the Spotlights section covers interviews from two different experts on data analytics in healthcare as well as mitigating cybersecurity threats in healthcare systems. Keeping to this same umbrella of health technology, the Columns section the articles cover on the use of artificial intelligence in healthcare.

This study investigates the volatility of the Euro-to-US Dollar exchange rate, specifically focusing on identifying long-memory characteristics. Through the analysis of daily data spanning from January 1, 2018, to January 10, 2023, the study uncovers a robust long-memory feature. Supporting this exploration, the study endorses the use of sophisticated models such as Fractionally Integrated Generalized Autoregressive Conditionally Heteroskedastic (FIGARCH) and Hyperbolic Generalized Autoregressive Conditionally Heteroskedastic (HYGARCH), incorporating both student and skewed student innovation distributions. The results underscore the superior performance of FIGARCH and HYGARCH models, particularly when coupled with a skewed student distribution. This collaborative approach enhances the predictability of crucial financial metrics, including Value at Risk (VaR) and Expected Shortfall (ESF), for both long and short trading positions. Significantly, the FIGARCH model, when utilizing a skewed student distribution, demonstrates exceptional predictive power. This outcome challenges the efficient market hypothesis and suggests the potential for generating outstanding returns. In light of these findings, this research contributes valuable insights for comprehending and navigating the intricacies of the Euro-to-US Dollar exchange rate, providing a forward-looking perspective for financial practitioners and researchers alike.

The emerging data explosion in sports field has created new opportunities to practice data science and analytics for deeper and larger scale analysis of games. With collaborating and competing 22 players on the field, soccer is often considered as a complex system. More specifically, each game is usually modeled as a network with players as nodes and passes between them as the edges. The number of passes usually define the weight of each edge, and these weights are employed to identify the key players using network modeling theory. However, the number of passes metric considers each pass the same and cannot differentiate players who are making ordinary passes, usually in their own pitch to a close teammate, from those who make key passes that start or improve an attack. As a solution, in this paper, we present a descriptive model to quantify the effectiveness of passes in soccer to differentiate between key passes and regular passes with not much contribution to the play of a team. Our model captures the perception of domain experts with a careful combination of risk and gain assessments. We have implemented our model in a soccer data analytics software. We performed a user study with domain experts, and the results show that our model captures domain expert evaluations of a number of example scenarios with 94% accuracy. The proposed model is not computationally demanding which allows real-time pass assessment during games on commodity hardware as demonstrated by our software prototype.

Information visualisation plays an important role for executives in order to drive business effectively and efficiently. Dashboard design, which is one of the best tools of visualization to meet the information needs at adequate levels, is becoming more important parallel to advances in information processing technologies. This study aims to provide a conceptual framework for developing a dashboard for executives. Healthcare management was chosen to demonstrate the methodology of research design, data gathering and visualisation examples. Results show that the study is applicable to other areas as well to meet the information requirements of top and mid-level managers.

Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.

An Elo score is a known measure of past performance in chess and other games. This paper investigates the impact of Elo ratings on chess game results and whether this measure can be used to predict future performance in matches. To achieve the aim, various machine learning classification techniques have been evaluated using chess data sourced from an online chess server. We examine how much influence the Elo score has on the prediction power of these techniques based on classifiers they derive. The prime objective of this experiment is to accurately predict the winner of a chess game from attributes that are available before the game starts. We are particularly interested in how large an impact the Elo score has on the prediction when compared with other features. Empirical results reported that classifiers derived by artificial neural network (Multilayer Perceptron), Decision Tree (J48/C4.5), Rule Induction (JRip/RIPPER) and Probabilistic (Naïve Bayes) showed how useful the Elo is at predicting chess results, at least on the dataset considered, improving classifiers’ performance with respect to accuracy, precision, recall and area under curve, among others.

Social media platforms have become ubiquitous and allow users to share information in real-time. Our study uses data analytics as an approach to explore non-communicable diseases on social media platforms and to identify trends and patterns of related disease symptoms. Exploring epidemiological patterns of non-communicable diseases is vital given that they have become prevalent in low-income communities, accounting for about 38 million deaths worldwide. We collected data related to multiple disease conditions from the Twitter microblogging platform and zoomed into symptoms related to heart diseases. As part of our analyses, we focussed on the mechanism and trends of disease occurrences. Our results show that specific symptoms may be attributed to multiple disease conditions and it is viable to identify trends and patterns of their occurrences using a structured analytics approach. This can then act as a supplementary tool to support epidemiological initiatives that monitor non-communicable diseases. Based on the study’s results, we identify that non-communicable disease surveillance approach using social media analytics could support the design of effective health intervention strategies.

In today’s data-driven world, the volume of information produced daily is staggering. Without a robust data engineering strategy, companies face the risk of prolonged delays, decreased productivity, dissatisfied customers, and strained business relationships. Effective data management and data modelling are critical for transforming this vast amount of information into valuable insights that drive business growth and provide a competitive edge. By gathering and analysing data through these methods, businesses can make informed decisions that significantly impact their growth and success. Data modelling and data management are both critical components of working with data, but they focus on different aspects of handling and utilising data within an organisation. Understanding the distinction between these two areas is crucial for effectively managing data within an organisation and ensuring that data systems are well-designed and properly maintained. By delving into the specifics of data modelling and data management, this paper aims to provide a comprehensive understanding of how these practices can be leveraged to enhance organisational efficiency, productivity, and decision-making. We provide a comprehensive and insightful exploration of data modelling and data management while highlighting their critical roles in modern business environments.

Recent shifts in technology have created a data-rich environment and made it necessary for firms to develop new capabilities to cope with these changes. To address this challenge, this study introduces marketing-enabled data analytics capability, a specific type of information technology (IT) capability that enables firms to better understand customer needs and achieve a competitive advantage in the digital era. Using empirical results collected via online survey, we argue that marketing-enabled data analytics capability, which comprises data analytics infrastructure, marketing-oriented analytics expertise, and IT–marketing social capital, positively influences a firm’s organisational agility and innovation success. Moreover, the results show that organisational agility partially mediates the relationship between marketing-enabled data analytics capability and innovation success. By developing the construct of marketing-enabled data analytics capability, this paper lays a foundation for future research on this new type of IT capability, which is critical in the digitization process.

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

This paper discusses about the latest and efficient financial tools and techniques which optimize the financial cost of the organization and predict the financial situation of the organization to flourish the business in the efficient way. Financial management basically means to accumulate funding for the enterprise at a low cost and to expend this collected funding for earning extreme profits. Thus, financial management means to plan and control the finance of the company. Financial management is usually concerned with the flow and control of money within an organization and be it either reserved or open sector. So, these tools if functional in the better way then business will reach in the utmost altitude. Recently some computational tools have been developed by the computer scientist for the efficient management and prediction for the business which is very useful for forecast and prediction in the era of digital world. The intention of this paper is to review and discuss the most significant applications of Business Intelligence in financial management and forecasting and prediction domain as well as point to new technology trends that will affect financial development.

In this paper, we discuss about platform-oriented business approach which is a prerequisite of the frontrunners for any noble organizations to keep customers active, alive and compete the business in the digital age of information and platform focussed. We focussed the study on how to concentrate the strategic revolution in financial sector with various econometric tools.

Platform is a business stage where people and technology share the information and make the business ecosystem through the network effect. It is a state-of-the-art technology for business. Platform business is an interaction of the network effects which creates profit in multidimensional way. It encourages business growth and efficiency, which is both exponentially and asymptotically. A platform is a creation which helps and empowers other products and amenities. Digital platforms occur at many levels in internet-based businesses and business models with blockchain technology. They array from high-level platforms to enable a platform business model to a low-level platform which provides an assembly line of business and technology dimensions that other products or services consume to deliver their own business capabilities. This paper deliberates the analysis of platform learning business approaches and secure business transactions like blockchain technology. Platform business along with secure business intelligence (BI) is now a prerequisite of the frontrunners for noble organizations to keep customers active, alive and compete the business in the digital age. Blockchain technology plays a vital role in this issue. Presently, businesses implement attract data-driven, network-driven, cloud-driven and blockchain technology platform-intensive approaches. Artificial intelligence, deep learning and machine learning are playing a great role in analyzing the data and classifying customer patterns and retaining customers in the business principles of market. Currently, we are competent enough to analyze online and offline data using these intelligent business tools. These business tools are very useful to identify customers’ marketing trends and their buying patterns. Securities in the business are more demanding and need professional ventures. Business people are now copious, focused and vigilant for secure transactions and demand blockchain technology-based platforms. Blockchain technology is nowadays becoming a boon for industrialists to keep their transactions more secure. As businesses and industries grow, they are now using blockchain technology to secure and track the items in supply chain management using digital platforms and BI. This concept is very relevant in the current scenario and state-of-the-art business platforms. We have exasperated the study in this direction, that is, how to concentrate on strategic revolution in financial sector with various innovative econometrics and simulator tools. This paper also addresses the recent research gap in the secure digital platform and analyzes the state-of-the-art methodology for the optimized solutions for the industry.

Big Data is a very popular term today. Everywhere you turn companies and organizations are talking about their Big Data solutions and Analytic applications. The source of the data used in these applications varies. However, one type of data is of great interest to most organizations, Social Media Data. Social Media applications are used by a large percentage of the world’s population. The ability to instantly connect and reach other people and companies over distributed distances is an important part of today’s society. Social Media applications allow users to share comments, opinions, ideas, and media with friends, family, businesses, and organizations. The data contained in these comments, ideas, and media are valuable to many types of organizations. Through Data Mining and Analysis, it is possible to predict specific behavior in users of the applications. Currently, several technologies aid in collecting, analyzing, and displaying this data. These technologies allow users to apply this data to solve different problems, in different organizations, including the finance, medicine, environmental, education, and advertising industries. This paper aims to highlight the current technologies used in Data Mining and Analyzing Social Media data, the industries using this data, as well as the future of this field.

Global climate change has already had observable effects on the environment. Glaciers have shrunk, ice on rivers and lakes is breaking up earlier, plant and animal ranges have shifted and trees are flowering sooner. Under these conditions, air pollution is likely to reach levels that create undesirable living conditions. Anthropogenic activities, such as industry, release large amounts of greenhouse gases into the atmosphere, increasing the atmospheric concentrations of these gases, thus significantly enhancing the greenhouse effect, which has the effect of increasing air heat and thus the speedup of climate change. The use of sophisticated data analysis methods to identify the causes of extreme pollutant values, the correlation of these values with the general climatic conditions and the general malfunctions that can be caused by prolonged air pollution can give a clear picture of current and future climate change. This paper presents a thorough study of preprocessing steps of data analytics and the appropriate big data architectures that are appropriate for the research study of Climate Change and Atmospheric Science.

With the advent of big data technologies such as cloud computing, internet of things, and artificial intelligence, data-driven business models have become a new unit of analysis in business model research. Tracing back to different roots in scholarly business model literature, researchers have begun to create a new body of work while trying to capture the essence of data-driven businesses. This article provides an overview of this emerging field, combining a systematic literature review with a qualitative desk research of recently founded data-driven and data-enhanced start-ups. To contribute to the discussion, we propose a definition and a taxonomy of data-driven business models. We then proceed to use the taxonomy in order to analyze the business models of 75 Berlin-based start-ups, focusing on four economic sectors.

Voice recordings constitute information that enriches previous studies on mood disorders in which sensor data that measure physical activity are used. The analysis of all these data is applied to mental health to prevent crises due to depression or mania mainly. This chapter analyzes voice records and the application of machine learning for the first classification of emotions (sadness, happiness, fear, etc.). Subsequently, the emotions detected will be associated with mood states (mania, depression, mixed, euthymia, etc.). The usefulness of voice recordings to improve diagnostic prediction in the classification of mood disorders is concluded.

Two case studies reveal that in most application domains neither the classical metrics are feasible to measure distance nor are the components of multidimensional feature spaces similar or numeric. Therefore, it is argued to design suitable distance functions, which do not need to be metrics, quite individually on the basis of domain knowledge.