Search results for: sports analytics

Authors: Priscila Vitorino, Jeeziane Rezende, Edison Pereira, Adrielly Silva, Weimar Barroso

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Body composition insight during physical activity is relevant to follow up sports income since it can be important and actuate in velocity, resistance, potency, and has an effect on force and agility. The purpose of this study was to identify anthropometric profile, evaluate and correlate body mass index and bioimpedance behavior during the days of Caminhada Ecológica de Goiás - Brasil. A longitudinal study was performed with 25 male participants, with an average age of 45.6±9.1 years. All patients were actives. Body composition was evaluated by body mass index (BMI) measurement and bioimpedance procedures. Both were collected 20 days before walking beginning (A0) and in the four days along the same (A1, A2, A3 e A4). Data were collected in the end of each walking day at athletes accommodations. Final distance during walking route was 308 km in five days, with an average of 62km/day and 7,6 km/hour, and an average temperature of 30°C. Data are represented with mean and standard deviation. ANOVA (Bonferroni pos test) was used to compare frequent measurements between the days. Pearson's correlation test was used to correlate BMI with lean mass, fat mass, and water. BMI decreased from A0 to A1, A2 and A3 (p < 0,01) and increased on A4 (p < 0,01). No changes were observed concerning fat percentage (p=0,60), lean mass (p=0,10) and body water composition (p=0,09). A positive and moderate correlation between BMI and fat percentage was observed; an inverse and moderate correlation between BMI, lean mass and body water composition occurred. Total body mass increased during high intensity and long term walking distance. However, the values of body fat, lean mass and water were maintained.

Keywords: aerobic exercise, body composition, metabolism, sports

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Authors: Farid Fahami

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This article explores the transformative role of technology in the finance, banking, and insurance sectors. It examines key technological trends such as AI, blockchain, data analytics, and digital platforms and their impact on operations, customer experiences, and business models. The article highlights the benefits of technology adoption, including improved efficiency, cost reduction, enhanced customer experiences, and expanded financial inclusion. It also addresses challenges like cybersecurity, data privacy, and the need for upskilling. Real-world case studies demonstrate successful technology integration, and recommendations for stakeholders emphasize embracing innovation and collaboration. The article concludes by emphasizing the importance of technology in shaping the future of these sectors.

Keywords: banking, finance, insurance, technology

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Authors: Mohammad Ali Ghareh, Habib Honari, Alireza Ahmadi

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The aim of this study was to investigate the relationship between strategic management and organizational culture in sport federations of Islamic Republic of Iran. Strategic management is a set of decisions and actions which define the long term performance of an organization. Organizational culture can be considered as an identity for every organization and somehow gives an identification to organization members. Organizational culture result in a certain commitments in organization members which is more valuable than individual profits and interests. The method of research was descriptive and correlational, conducted as a field study. The statistical population consisted of the employees of 10 sports federations and 170 persons were selected as sample. For data gathering, Barringer and Bluedorn’s strategic management questionnaire (1999) and Sakyn’s organizational culture questionnaire (2001) were used. The reliability of the questionnaires were 0.82 and 0.80 respectively, and the validity was approved by 8 experienced professors in sport management. To analyze data, KS (Kolmogorov–Smirnov) test and Pearson's coefficient were used. The results have shown that there is a significant meaningful relationship between strategic management and organizational culture (p < 0.05, r= 0.62). Beside this, there is a positive relationship between strategic management variables including scanning intensity, planning flexibility, locus of planning, planning horizon, strategic controls, and organizational culture (p < 0.05). Based on this research result it can be derived that strategic management planning and operation in terms of appropriate organizational culture is more applicable. By agreeing on their values and beliefs, adaptation to changes, caring about the individualities, coordination in tasks, modifying the individual and organizational goals, the federations will be able to achieve their strategic goals.

Keywords: strategic management, organizational culture, sports federations, Islamic Republic of Iran

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

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We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

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Authors: Yisong Cong, Mingming Guo, Xiaozan Wang, Yizhi Zhang, Qingqing Yuan

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This study is to test the correlation between the Physical Fitness (PF) of middle school students and their performance and sports grade of table tennis. Methods: 398 middle school students from Shanghai Tongji No.2 Middle School participated in the study, including 232 boys and 166 girls. Each participant participated in the Chinese Students’ Physical Fitness Test (CSPFT), including BMI, 50-meter running, vital capacity, sitting body flexion, standing long jump, 800-meter running, 1000-meter running, pull-ups, and sit-ups. Test scores were converted to a percentage score according to the CSPFT guidelines. In addition, each student participated in the Nation Junior Table Tennis grade test, and completed the table tennis sports grade assessment of 1-9. Results: There is a significant positive correlation between the scores of multiple PF tests and the total scores of table tennis, such as BMI (r = 0.15, p < 0.01), standing long jump (r = 0.15, p < 0.05), 800-meter run (r = 0.02, p <0.01); The scores of multiple PF are positively correlated with table tennis grade, such as vital capacity (r = 0.1, p < 0.01) and 50-meter running (r = 0.18, p < 0.05). At the same time, the sit-ups performance showed a significant negative correlation with the table tennis performance (r = -0.08, p < 0.01); There is no significant correlation between the other PF indicators and the performance and grade of table tennis. Conclusions: This study shows that there is a corresponding relationship between some PF indicators of middle school students and their table tennis performance and table tennis grade，but the specific form and reason of the relationship need to be further explored.

Keywords: middle school students, physical fitness, table tennis grade, table tennis performance

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Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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Authors: Rashmi Raghunandan, Sanjana Shankar, Rakshitha K. Bhat

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Social media contributes a vast amount of data and information about individuals to the internet. This project will greatly reduce the need for unnecessary manual analysis of large and diverse social media profiles by filtering out and combining the useful information from various social media profiles, eliminating irrelevant data. It differs from the existing social media aggregators in that it does not provide a consolidated view of various profiles. Instead, it provides consolidated INFORMATION derived from the subject’s posts and other activities. It also allows analysis over multiple profiles and analytics based on several profiles. We strive to provide a query system to provide a natural language answer to questions when a user does not wish to go through the entire profile. The information provided can be filtered according to the different use cases it is used for.

Keywords: social network, analysis, Facebook, Linkedin, git, big data

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Authors: Mahboubeh Varmaziar

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Depression is a significant public health concern affecting people of all ages and genders. Physical activity has been shown to have a positive effect on mental health, particularly in alleviating symptoms of depression. This study aims to explore the impact of continuous exercise on depression levels among young female athletes in Hamadan Province, Iran. In this randomized controlled trial, 72 women aged 20 to 35 years attending sports centers in Hamadan Province were selected through convenient sampling and randomly assigned to either the control or experimental group. The experimental group participated in a continuous exercise program consisting of 20 sessions over six weeks, with each session lasting 30 minutes. In contrast, the control group maintained their usual daily activities at the sports center. Both groups completed demographic and Beck Depression Inventory questionnaires. Data were analyzed using descriptive and inferential statistics, including two-way ANOVA. The results of the two-way ANOVA, after controlling for the pre-test effect, revealed a significant difference in the mean depression scores between the control and experimental groups (p < 0.001). This suggests that the continuous exercise program significantly reduced depression levels in the young female athletes. The findings suggest that continuous exercise is an effective non-pharmacological intervention for reducing depression in young female athletes. Incorporating regular physical activity into treatment plans may serve as a complementary therapy alongside conventional treatments, offering a low-risk and beneficial approach to managing depression.

Keywords: depression, exercise, female athletes, yong women

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Authors: Leticia Lengler, Jefferson Oliveira, Vania Estivalete, Jordana Marques Kneipp

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The context of social, economic and environmental transformations has driven innumerable changes in the organizational environment, influencing the social interactions that occur in this scenario. In this sense, social entrepreneurship emerges as a unique opportunity to challenge, question, rethink certain concepts and traditional theories widely discussed in relation to entrepreneurship. Therefore, the interest in studying personal values has been based on the idea that they might be predictors of the behavior of individuals. As an attempt to relate personal values with the characteristics of social entrepreneurs, this study aims to investigate the salient values and the social entrepreneurship perceptions that occur in two social projects responsible for developing sports skills among the students. For purposes of analysis, it is intended to consider: (i) a description of both Social Projects and their respective institutions, considering their history and relevance in the context; (ii) analysis of the personal values of the idealizers and teachers responsible for the projects, (iii) identification of the characteristics of social entrepreneurship manifested in the two projects, and (iv) discussion of similarities and disparities of the categories identified among the participants of the projects. Therefore, this study will carry a qualitative analysis from the interviews with 10 participants of each social project (named Projeto Remar/ASENA and Projeto Mãos Dadas/JUDÔ SANTA MARIA): 2 projects coordinators, 2 students, 2 parents of students, 2 physical education internships and 2 businessmen who stablished a partnership with each project. The data collection will be done through semi-structured interviews that are going to last around 30 minutes each, being recorded, transcribed and later analyzed, through the categorical analysis. The option for categorical analysis is supported by the fact that it is the best alternative when one wants to study values, opinions, attitudes and beliefs, through qualitative ones. In the present research, the pre-analysis phase consisted of an organization of the material collected during the research with Remar and Mãos Dadas Project, and a dynamic reading of this material, seeking to identify the characteristics of social entrepreneurship and values addressed in the study. In the analytical description phase, a more in-depth analysis of the material collected in the research will be carried out. The third phase, referred to as referential interpretation or treatment of results obtained will allow to verify the homogeneity and the heterogeneity among the participants' perceptions of the projects. Some preliminary results coming from the first interviews revealed the projects are guided by values such as cooperation, respect, well-being and nature preservation. These values are linked to the social entrepreneurship perception of the projects managers, who established their activities in behalf of the local community.

Keywords: personal values, social entrepreneurship, social projects, sports participants

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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Authors: Ejeh Benjamin Ijuo

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The purpose of this study was to investigate the influence of social, economic, political, and legal environment of sport organizations on sport development in zone ten (10) national zonal sport offices in Nigeria (Plateau, Nasarawa, Benue and F.C.T Abuja). To achieve this purpose, a structured 26 item questionnaire (ISEPLESOQ) designed by the researcher was used for this study. Related literature to this study was reviewed. 311 copies of questionnaire were administered to randomly selected respondents. Out of this number, 306 was dully completed and returned representing 98.4%. The respondents included: Athletes, games masters/ mistresses, coaches in state sport councils, zonal sport coordinators, team managers, directors of state sports council. Four research questions were answered using the mean and standard deviation, while the inferential statistics of chi-square(x2) test of goodness of fit was used to test the four hypotheses at 0.05 alpha levels. The findings of this study revealed that the social, economic, political and legal environment of sport organizations significantly influenced sport development in zone ten (10) national zonal sport offices in Nigeria. It was also established that the general environment of sport organizations influences people’s participation in sport, funding and sponsorship of sports, sitting of equipment and facilities at different locations, selection of athletes. It was therefore, recommended among other things that government should privatize and commercialized sport programmes to enable corporate organizations and individuals participation. Lt was further suggested that the federal government should harness her social, economic, political and legal environment to improve sport development in Nigeria.

Keywords: sport organization, sport development, sport environment, zonal sport offices

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Authors: Rachael Olubukola Afolabi

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Immersive technologies known as X Reality or Cross Reality that include virtual reality augmented reality, and mixed reality have pervaded into the education system at different levels from elementary school to adult learning. Instructors, instructional designers, and learning experience specialists continue to find new ways to engage students in the learning process using technology. While the progression of web technologies has enhanced digital learning experiences, analytics on learning outcomes continue to be explored to determine the relevance of these technologies in learning. Digital learning has evolved from web 1.0 (static) to 4.0 (dynamic and interactive), and this evolution of technologies has also advanced teaching methods and approaches. This paper explores how these technologies are being utilized in learning and the results that educators and learners have identified as effective learning opportunities and approaches.

Keywords: immersive technologoes, virtual reality, augmented reality, technology in learning

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Authors: Shizra Waris, Saleem Akhtar

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Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems.

Keywords: computer, it community, industry, big data

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Authors: Kien Vu, Jien Min Soh, Mohamed Jahangir Abubacker, Piyawut Pattamanon, Soojin Lee, Suvro Banerjee

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Recent advances in Generative Artificial Intelligence (GenAI), in particular Large Language Models (LLMs) have shown promise to disrupt multiple industries at scale. However, LLMs also present unique challenges, notably, these so-called "hallucination" which is the generation of outputs that are not grounded in the input data that hinders its adoption into production. Common practice to mitigate hallucination problem is utilizing Retrieval Agmented Generation (RAG) system to ground LLMs'response to ground truth. RAG converts the grounding documents into embeddings, retrieve the relevant parts with vector similarity between user's query and documents, then generates a response that is not only based on its pre-trained knowledge but also on the specific information from the retrieved documents. However, the RAG system is not suitable for tabular data and subsequent data analysis tasks due to multiple reasons such as information loss, data format, and retrieval mechanism. In this study, we have explored a novel methodology that combines planning-and-execution and code generation agents to enhance LLMs' data analysis capabilities. The approach enables LLMs to autonomously dissect a complex analytical task into simpler sub-tasks and requirements, then convert them into executable segments of code. In the final step, it generates the complete response from output of the executed code. When deployed beta version on DataSense, the property insight tool of PropertyGuru, the approach yielded promising results, as it was able to provide market insights and data visualization needs with high accuracy and extensive coverage by abstracting the complexities for real-estate agents and developers from non-programming background. In essence, the methodology not only refines the analytical process but also serves as a strategic tool for real estate professionals, aiding in market understanding and enhancement without the need for programming skills. The implication extends beyond immediate analytics, paving the way for a new era in the real estate industry characterized by efficiency and advanced data utilization.

Keywords: large language model, reasoning, planning and execution, code generation, natural language processing, prompt engineering, data analysis, real estate, data sense, PropertyGuru

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Authors: Miyako Oulevey, Kaori Tsutsui, David Lavallee, Naohiko Kohtake

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The Japan Sports Agency has made efforts to unify several career support programs for Olympic athletes prior to the 2020 Tokyo Olympics. One of the programs, the Japan Olympic Committee Career Academy (JCA) was established in 2008 for Olympic athletes at their retirement. Research focusing on the service content of sport career support programs can help athletes experience a more positive transition. This study was designed to investigate the service content of the JCA program in relation to athletes’ career transition needs, including any differences of the reasons for retirement between Summer/Winter and Male/Female Olympic athletes, and to suggest the directions of how to unify the career support programs in Japan after hosting the Olympic Games using sport career transition models. Semi-structured interviews were conducted and analyzed the JCA director who started and managed the program since its inception, and a total of 15 conceptual categories were generated by the analysis. Four conceptual categories were in the result of “JCA situation”, 4 conceptual categories were in the result of “Athletes using JCA”, and 7 conceptual categories were in the result of “JCA current difficulties”. Through the analysis it was revealed that: the JCA had occupational supports for both current and retired Olympic athletes; other supports such as psychological support were unclear due to the lack of psychological professionals in JCA and the difficulties collaborating with other sports organizations; and there are differences in tendencies of visiting JCA, financial situations, and career choices depending on Summer/Winter and Male/Female athletes.

Keywords: career support programs, causes of career termination, Olympic athlete, Olympic committee

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Authors: Agboka Komi Mensah, John Odindi, Elfatih M. Abdel-Rahman, Onisimo Mutanga, Henri Ez Tonnang

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Ecosystems are networks of organisms and populations that form a community of various species interacting within their habitats. Such habitats are defined by abiotic and biotic conditions that establish the initial limits to a population's growth, development, and reproduction. The habitat’s conditions explain the context in which species interact to access resources such as food, water, space, shelter, and mates, allowing for feeding, dispersal, and reproduction. Dispersal is an essential life-history strategy that affects gene flow, resource competition, population dynamics, and species distributions. Despite the importance of dispersal in population dynamics and survival, understanding the mechanism underpinning the dispersal of organisms remains challenging. For instance, when an organism moves into an ecosystem for survival and resource competition, its progression is highly influenced by extrinsic factors such as its physiological state, climatic variables and ability to evade predation. Therefore, greater spatial detail is necessary to understand organism dispersal dynamics. Understanding organisms dispersal can be addressed using empirical and mechanistic modelling approaches, with the adopted approach depending on the study's purpose Cellular automata (CA) is an example of these approaches that have been successfully used in biological studies to analyze the dispersal of living organisms. Cellular automata can be briefly described as occupied cells by an individual that evolves based on proper decisions based on a set of neighbours' rules. However, in the ambit of modelling individual organisms dispersal at the landscape scale, we lack user friendly tools that do not require expertise in mathematical models and computing ability; such as a visual analytics framework for tracking and forecasting the dispersal behaviour of organisms. The term "visual analytics" (VA) describes a semiautomated approach to electronic data processing that is guided by users who can interact with data via an interface. Essentially, VA converts large amounts of quantitative or qualitative data into graphical formats that can be customized based on the operator's needs. Additionally, this approach can be used to enhance the ability of users from various backgrounds to understand data, communicate results, and disseminate information across a wide range of disciplines. To support effective analysis of the dispersal of organisms at the landscape scale, we therefore designed Pydisp which is a free visual data analytics tool for spatiotemporal dispersal modeling built in Python. Its user interface allows users to perform a quick and interactive spatiotemporal analysis of species dispersal using bioecological and climatic data. Pydisp enables reuse and upgrade through the use of simple principles such as Fuzzy cellular automata algorithms. The potential of dispersal modeling is demonstrated in a case study by predicting the dispersal of Fopius arisanus (Sonan), endoparasitoids to control Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) in Kenya. The results obtained from our example clearly illustrate the parasitoid's dispersal process at the landscape level and confirm that dynamic processes in an agroecosystem are better understood when designed using mechanistic modelling approaches. Furthermore, as demonstrated in the example, the built software is highly effective in portraying the dispersal of organisms despite the unavailability of detailed data on the species dispersal mechanisms.

Keywords: cellular automata, fuzzy logic, landscape, spatiotemporal

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Authors: Subhadeep Mandal, Sujoy Bhattacharya, Pabitra Mitra, Diya Guha Roy, Seema Bhattacharya

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E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews.

Keywords: e-commerce, online shopping, customer reviews, customer behaviour, text analytics, n-grams classification

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Authors: Keyvan Molanorouzi, Selina Khoo, Tony Morris

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In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to whether PA motives measured by the Physical Activity and Leisure Motivation Scale PALMS predicted actual amount of PA at a later time to provide evidence for the construct validity of the PALMS. A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.Methods: A quantitative, cross-sectional descriptive research design was employed. The Demographic Form, PALMS, and International Physical Activity Questionnaire Short form (IPAQ-S) questionnaires were used to assess motives and amount for physical activity in adults on two occasions. A sample of 640 (489 male, 151 female) undergraduate students aged 18 to 25 years (mean ±SD; 22.30±8.13 years) took part in the study. Male participants were divided into three types of activities, namely exercise, racquet sport, and team sports and female participants only took part in one type of activity, namely team sports. After 14 weeks, all 640 undergraduate students who had filled in the initial questionnaire (Occasion 1) received the questionnaire via email (Occasion 2). Of the 640 students, 493 (77%; 378 males, 115 females) emailed back the completed questionnaire. Results: The results showed that not only were pertinent sub-scales of PALMS positively related to amount of physical activity, but separate regression analyses showed the positive predictive effect of PALMS motives for amount of physical activity for each type of physical activity among participants. This study supported the construct validity of the PALMS by showing that the motives measured by PALMS did predict amount of PA. Conclusion: This information can be obtained to match people with specific sport or activity which in turn could potentially promote longer adherence to the specific activity.

Keywords: physical activity, motivation, level of physical activity, type of physical activity

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Authors: Biju Lukose

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The objective of the study was to identify the isolated and combined effect of multi-media computer assisted coaching and traditional coaching on selected motor ability component and physiological variables among sports school basketball players. Forty male basketball players aged between 14 to 18 years were selected randomly. They were divided into four groups of three experimental and one control. Isolated multi-media computer assisted coaching, isolated traditional coaching and combined coaching (multimedia computer assisted coaching and traditional coaching) are the three experimental groups. All the three experimental groups were given coaching for 24 weeks and control group were not allowed to participate in any coaching programme. The subjects were tested dependent variables such as speed and cardio vascular endurance; at the beginning (pre-test) in middle 12 week (mid-test) and after the coaching 24 week (post-test). The coaching schedule was for a period of 24 weeks. The data were collected two days before and after the coaching schedule and mid test after the 12 weeks of the coaching schedule. The data were analysed by applying ANCOVA and Scheffe’s Post hoc test. The result showed that there were significant changes in dependent variables such as speed and cardio vascular endurance. The results of the study showed that combined coaching (multimedia computer assisted coaching and traditional coaching) is more superior to traditional coaching and multimedia computer assisted coaching groups and no significant change in speed in the case of isolated multimedia computer assisted coaching group.

Keywords: computer, computer-assisted coaching, multimedia coaching, traditional coaching

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Authors: Sukhdev Singh, Baljinder Singh Bal, Amandeep Singh, Kanchan Thappa

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A nation's future almost certainly rests on the future of its children, and a nation's wellbeing can be greatly improved by providing for the right upbringing of its children. Participating in physical education and sports programmes is crucial for reaching one's full potential. As we are all aware, sports have recently become incredibly popular on a global scale. Sports are continually becoming more and more popular, and this positive trend is probably going to last for some time to come. Motor abilities will provide more accurate information on the developmental process of children. Motor fitness is a component of physical fitness that includes strength, speed, flexibility, and agility, and is related to enhanced performance and the development of motor skills. In recent years, there has been increased interest in the differences in child growth between urban and rural environments. Differences in student growth, body dimensions, body composition, and fitness levels due to urban and rural environmental disparities have come into focus in recent years. The main aim of this study is to know the differentials of motor fitness components among the school children of rural and urban areas of the Jammu region. Material and Methods: In total, sixty male subjects (mean ± SD; age, 16.475 ± 1.0124 yrs.; height, 172.8 ± 2.0153 cm; Weight, 59.75 ± 3.628 kg) from the Jammu region took part in the study. A minimum sample size of 40 subjects was obtained and was derived from Rural (N1=20) and Urban (N2=20) school-going children. Statistical Applications: The Statistical Package for the Social Sciences (SPSS) version 14.0 was used for all analyses. The differences in the mean of each group for the selected variable were tested for the significance of difference by an independent samples t-test. For testing the hypotheses, the level of significance was set at 0.05. Results: Results revealed that there were significant differences of leg explosive strength (p=0.0040*), dynamic balance (p=0.0056*), and Agility (p=0.0176*) among the School Children of the rural and urban areas of the Jammu region. However, Results further revealed that there were not significant differences of cardio respiratory endurance (p=0.8612), speed (p=0.2231), Low Back/Hamstring Flexibility (p=0.6478), and Two Hand Coordination. (p= 0.0953) among the School Children of the rural and urban areas of the Jammu region. Conclusion: The results of study showed that there is significance difference between Rural and Urban School children of the Jammu region with regards to a variable," leg explosive strength, dynamic balance, Agility” and the there is no significance difference between Rural and Urban School children of the Jammu region with regards variable “cardio-respiratory endurance, speed, Low Back/Hamstring Flexibility, Two Hand Coordination”.

Keywords: motor fitness, rural areas, school children, urban areas

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Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Jamuna Loganath

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Education is the key to the development of any individual’s personality. Today’s students will be tomorrow’s citizens of the global society. The education of the student is the edifice on which his/her future will be built. Schools therefore should provide an all-round development of students so as to foster a healthy society. The behaviors and the attitude of the students in school play an essential role for the success of the education process. Frequent reports of misbehaviors such as clowning, harassing classmates, verbal insults are becoming common in schools today. If this issue is left unattended, it may develop a negative attitude and increase the delinquent behavior. So, the need of the hour is to find a solution to this problem. To solve this issue, it is important to monitor the students’ behaviors in school and give necessary feedback and mentor them to develop a positive attitude and help them to become a successful grownup. Nevertheless, measuring students’ behavior and attitude is extremely challenging. None of the present technology has proven to be effective in this measurement process because actions, reactions, interactions, response of the students are rarely used in the course of the data due to complexity. The purpose of this proposal is to recommend an effective supervising system after carrying out a feasibility study by measuring the behavior of the Students. This can be achieved by equipping schools with CCTV cameras. These CCTV cameras installed in various schools of the world capture the facial expressions and interactions of the students inside and outside their classroom. The real time raw videos captured from the CCTV can be uploaded to the cloud with the help of a network. The video feeds get scooped into various nodes in the same rack or on the different racks in the same cluster in Hadoop HDFS. The video feeds are converted into small frames and analyzed using various Pattern recognition algorithms and MapReduce algorithm. Then, the video frames are compared with the bench marking database (good behavior). When misbehavior is detected, an alert message can be sent to the counseling department which helps them in mentoring the students. This will help in improving the effectiveness of the education process. As Video feeds come from multiple geographical areas (schools from different parts of the world), BIG DATA helps in real time analysis as it analyzes computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It also analyzes data that can’t be analyzed by traditional software applications such as RDBMS, OODBMS. It has also proven successful in handling human reactions with ease. Therefore, BIG DATA could certainly play a vital role in handling this issue. Thus, effectiveness of the education process can be enhanced with the help of video analytics using the latest BIG DATA technology.

Keywords: big data, cloud, CCTV, education process

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

Abstract:

This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: Anuj Pathare, Aleksandra Birn-Jeffery

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Background: The Anterior Cruciate Ligament(ACL) helps stabilize the knee joint minimizing tibial anterior translation. Anterior Cruciate Ligament (ACL) injury is common in racquet sports and often occurs due to sudden acceleration, deceleration or changes of direction. This mechanism in badminton most commonly occurs during landing after an overhead stroke. Knee biomechanics during dynamic movements such as walking, running and stair negotiation, do not return to normal for more than a year after an ACL reconstruction. This change in the biomechanics may lead to re-injury whilst performing non-steady movements during sports, where these injuries are most prevalent. Aims: To compare if the knee kinetics and kinematics in ACL injury recovered athletes return to the same level as those from an uninjured cohort during standard movements used for clinical assessment and badminton shots. Objectives: The objectives of the study were to determine: Knee valgus during the single leg squat, vertical drop jump, net shot and drop shot; Degree of internal or external rotation during the single leg squat, vertical drop jump, net shot and drop shot; Maximum knee flexion during the single leg squat, vertical drop jump and net shot. Methods: This case-control study included 14 participants with three ACL injury recovered athletes and 11 uninjured participants. The participants performed various functional tasks including vertical drop jump, single leg squat; the forehand net shot and the forehand drop shot. The data was analysed using the two-way ANOVA test, and the reliability of the data was evaluated using the Intra Class Coefficient. Results: The data showed a significant decrease in the range of knee rotation in ACL injured participants as compared to the uninjured cohort (F₇,₅₅₆=2.37; p=0.021). There was also a decrease in the maximum knee flexion angles and an increase in knee valgus angles in ACL injured participants although they were not statistically significant. Conclusion: There was a significant decrease in the knee rotation angles in the ACL injured participants which could be a potential cause for re-injury in these athletes in the future. Although the results for decrease in maximum knee flexion angles and increase in knee valgus angles were not significant, this may be due to a limited sample of ACL injured participants; there is potential for it to be identified as a variable of interest in the rehabilitation of ACL injuries. These changes in the knee biomechanics could be vital in the rehabilitation of ACL injured athletes in the future, and an inclusion of sports based tasks, e.g., Net shot along with standard protocol movements for ACL assessment would provide a better measure of the rehabilitation of the athlete.

Keywords: ACL, biomechanics, knee injury, racquet sport

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Authors: Trevor Toy, Josef Langerman

Abstract:

Around a quarter of the world’s data is generated by financial with an estimated 708.5 billion global non-cash transactions reached between 2018 and. And with Open Banking still a rapidly developing concept within the financial industry, there is an opportunity to create a secure mechanism for connecting its stakeholders to openly, legitimately and consensually share the data required to enable it. Integration and data sharing of anonymised transactional data are still operated in silos and centralised between the large corporate entities in the ecosystem that have the resources to do so. Smaller fintechs generating data and businesses looking to consume data are largely excluded from the process. Therefore there is a growing demand for accessible transactional data for analytical purposes and also to support the rapid global adoption of Open Banking. The following research has provided a solution framework that aims to provide a secure decentralised marketplace for 1.) data providers to list their transactional data, 2.) data consumers to find and access that data, and 3.) data subjects (the individuals making the transactions that generate the data) to manage and sell the data that relates to themselves. The platform also provides an integrated system for downstream transactional-related data from merchants, enriching the data product available to build a comprehensive view of a data subject’s spending habits. A robust and sustainable data market can be developed by providing a more accessible mechanism for data producers to monetise their data investments and encouraging data subjects to share their data through the same financial incentives. At the centre of the platform is the market mechanism that connects the data providers and their data subjects to the data consumers. This core component of the platform is developed on a decentralised blockchain contract with a market layer that manages transaction, user, pricing, payment, tagging, contract, control, and lineage features that pertain to the user interactions on the platform. One of the platform’s key features is enabling the participation and management of personal data by the individuals from whom the data is being generated. This framework developed a proof-of-concept on the Etheruem blockchain base where an individual can securely manage access to their own personal data and that individual’s identifiable relationship to the card-based transaction data provided by financial institutions. This gives data consumers access to a complete view of transactional spending behaviour in correlation to key demographic information. This platform solution can ultimately support the growth, prosperity, and development of economies, businesses, communities, and individuals by providing accessible and relevant transactional data for big data analytics and open banking.

Keywords: big data markets, open banking, blockchain, personal data management

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Authors: Jerry Q. Cheng

Abstract:

Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

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Authors: Rania A. Aboalela, Javed I. Khan

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This research presents a learning assessment theory of Cognitive Skill in Concept Space (CS2) to measure the assessed knowledge in terms of cognitive skill levels of the concepts. The cognitive skill levels refer to levels such as if a student has acquired the state at the level of understanding, or applying, or analyzing, etc. The theory is comprised of three constructions: Graph paradigm of a semantic/ ontological scheme, the concept states of the theory and the assessment analytics which is the process to estimate the sets of concept state at a certain skill level. Concept state means if a student has already learned, or is ready to learn, or is not ready to learn a certain skill level. The experiment is conducted to prove the validation of the theory CS2.

Keywords: cognitive skill levels, concept states, concept space, knowledge assessment theory

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Authors: Julie Delany

Abstract:

To thrive in today’s digital climate, it is vital that organisations adopt new technology and prepare for rising digital trends. This proves more difficult in government where, traditionally, people lack change readiness. While individuals may have a desire to work smarter, this does not necessarily mean embracing technology. This paper discusses the rollout of an application into a small department of highly experienced nurses. The goal was to both streamline the department's workflow and provide a platform for gathering essential business metrics. The biggest challenges were adoption and motivating the nurses to change their routines and learn new computer skills. Two-thirds struggled with the change, and as a result, some jeopardised the validity of the business metrics. In conclusion, there are lessons learned and recommendations for similar projects.

Keywords: change ready, information technology, end-user, iterative method, rollout plan, data analytics

Procedia PDF Downloads 137