Business analytics in sport talent acquisition: methods, experiences, and open research opportunities

Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts' opinions but without systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

KB-WOT Fisheries Research; programme for 2008

LNV programme 406 covers the execution of statutory tasks (WOT) in fisheries carried out by DLO. Part of the KB programme, presented in this report, contains resources earmarked to maintain and develop the expertise needed to carry out the WOT programme. As well as maintaining expertise, innovation is an important part of the programme. The programme is also part of the Wageningen UR Kennisbasis and comes under the theme KB01: “Groene en blauwe ruimte”. This report describes the allocation and utilisation of the Kennisbasis budget in 2008. The available budget in 2008 is €621 000. The money is spent through projects, each of which is described here. The projects are split up into 4 research priority areas: A) Influence of changes in the environment on marine ecosystems, B) impact of fisheries on ecosystems, C) changing fishery management, D) maintenance and international exchange of key WOT expertise. All of these areas fall under the wider WUR "kennisbasis" theme

The Development, Implementation, and Evaluation of a Campus-Based Culinary Nutrition Program for College Students

College students, on average, do not consume enough fruits and vegetables. Contributing to poor eating habits is an overall decline in young adults’ cooking skills as compared to previous decades, with today’s college students often relying on ubiquitous convenience food options. The detriments associated with these food choices are linked to a number of chronic diseases, including obesity. Though programming for college students which incorporates both nutrition education and hands-on cooking opportunities is rare, programs which have been implemented have had positive outcomes associated with increased self-efficacy with cooking and eating healthfully, and improved eating behaviors. This research utilized a mixed methods approach driven by the social cognitive theory to develop, implement, and assess the impact of a culinary nutrition education program, The College CHEF. The first phase of the research included conducting a PRECEDE-PROCEED model-driven primary and secondary needs assessment to develop programming. The second phase consisted of program implementation and evaluation. The program was evaluated through a Qualtrics survey to determine participants’ changes pre- to –post- with cooking and eating attitudes, behaviors, self-efficacy and knowledge. Pre- and –post- measures consisted primarily of Likert-type scales, in addition to demographic questions. Research participants were students living on University of Kentucky’s campus who were part of particular Living Learning Programs (LLPs), through which they lived, socialized, and often took classes together. Participants from two intervention groups (N = 15) attended four weekly 2-hour sessions, completing the measures online before and after the program. An inclusion criteria to be included in the study was that participants must have attended at least three of the four sessions. Control group participants (N = 17) did not partake in programming, but completed both pre- and -post- surveys at the same time as the intervention groups. The study\u27s results indicated that campus-based, hands-on culinary nutrition education programming was impactful in improving college students\u27: 1) self-efficacy for using fruits, vegetables, and seasonings (p = .015); 2) fruit and vegetable consumption (p = 0.03); and 3) knowledge of cooking terms and techniques (p = .000). Given the limited research studying the impact of culinary nutrition education programming on college students, especially as it applies to those living in the same environment and reciprocally influencing one another, this study provides a unique perspective to the field of health promotion. Its findings can support campus-based, culinary nutrition programming for the college population in an effort to improve eating and cooking attitudes, behaviors, self-efficacy, and knowledge, and subsequently, overall health

DATA MINING ANALYTICS FUNDAMENTALS AND THEIR APPLICATION IN LOGISTICS

The article describes several basic data mining fundamentals and their application in logistics and it consists of two sections. The first one is a description of different parts of data mining process: preparing the input data, completing the missing data, classification method using k-nearest neighbours algorithm with theoretical examples of usage conducted in open-source software called R and Weka. The second section of the article focuses on theoretical application of data mining methods in logistics, mainly in solving transportation problems and enhancing customer’s satisfaction. This section was strongly influenced by data provided by DHL enterprise report on Big Data. The data used in theoretical examples is of own elaboration.The article describes several basic data mining fundamentals and their application in logistics and it consists of two sections. The first one is a description of different parts of data mining process: preparing the input data, completing the missing data, classification method using k-nearest neighbours algorithm with theoretical examples of usage conducted in open-source software called R and Weka. The second section of the article focuses on theoretical application of data mining methods in logistics, mainly in solving transportation problems and enhancing customer’s satisfaction. This section was strongly influenced by data provided by DHL enterprise report on Big Data. The data used in theoretical examples is of own elaboration

Promoting Health Literacy in Pregnant Women using a Lifestyle Intervention

Pregnancy is a unique time during which a woman is responsible for both her own health and that of her unborn child. Lifestyle choices during pregnancy such as physical activity, diet, alcohol consumption and smoking affect a woman's health, which in turn affects the health and development of the child. It is thus crucial for pregnant women to develop and maintain healthy behaviors that promote good health. One approach that can help is improving health literacy. Increased health literacy has a positive impact on understanding and applying health information and thus can improve health behaviors. However, current research is lacking in terms of investigations into health literacy in pregnant women. This dissertation fills this gap using four dissertation projects (DPs). A lifestyle intervention was developed to promote health literacy in pregnant women as part of prenatal care. The results of the DPs offer discussion approaches for integrating health literacy-sensitive counseling into prenatal care. DP 1 presents the current state of research on health literacy in pregnancy using a systematic review. Here, the level of health literacy and interventions to improve it in pregnant women are presented. DP 2 describes the development of a lifestyle intervention (GeMuKi) and how health literacy is addressed within this intervention. DP 3 addresses the development and evaluation of a knowledge-based questionnaire to assess pregnant women's knowledge on lifestyle topics. This forms part of the objective health literacy assessment in the lifestyle intervention. DP 4 evaluates the effectiveness of the lifestyle intervention (measured both objectively and subjectively) on improving pregnant women's health literacy. The results of DP 1 suggest that research in health literacy among pregnant women is scarce and requires more attention. There is a lack of studies that measure health literacy and efforts to improve it through interventions. The intervention developed in DP 2 includes a comprehensive lifestyle counseling and is integrated into the regular antenatal appointments. As part of the intervention, pregnant women are actively involved in the lifestyle counseling to positively influence health literacy. In DP 3, the developed questionnaire with a total of eight items is used to assess pregnant women's knowledge on lifestyle topics. The results show gaps on certain lifestyle topics. This concerns topics such as breastfeeding and recommended weight gain during pregnancy, which need special attention in counseling. In DP 4, the evaluation of the lifestyle intervention regarding health literacy shows that approximately 66% of the study participants had adequate health literacy. The intervention was not able to improve subjectively measured health literacy, while a significant, positive effect was achieved in improving objectively measured health literacy

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities