Search results for: football analytics

Authors: S. A. N. Rashani, S. Pigera, P. N. J. Fernando, S. Jayawickema, M. A. Niriella, A. P. De Silva

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Dietary supplement (DS) consumption is drastically trending among the young athlete generation in developing countries. Many athletes try to fulfill their nutrition requirements using dietary supplements without knowing their effects on health and performance. This study aimed to assess the DS usage patterns of university athletes in Sri Lanka. A self-administered questionnaire was employed to collect data from state university students representing a university team, and a sample of 200 respondents was selected based on a stratified random sampling technique. Incomplete questionnaires were omitted from the analysis. The data were analyzed using IBM SPSS statistics for Windows version 25. The level of significance was set at p<0.05 in the data analysis. The prevalence of DS was 48.2% (n= 94), with no significant association between gender and DS intake. Protein (15.9%), vitamin (14.9%), sports drinks (12.8%), and creatine (8.2%) were the most consumed DS by students. Weightlifting (85.0%), football (62.5%), rugby (57.7%), and wrestling (40.9%) players showed higher DS usage among other sports. Coaches were reported as the most frequent person who was advised to use DS (43.0%). Students who won interuniversity games showed significantly low DS intake (p = 0.002) compared to others. Interestingly, DS use was significantly affected by the season of use (p = 0.000), pointing out that during competition and training seasons (62.4%) was the most frequent use. The pharmacy (27.0%) was the commonest place to buy DS. Students who used nutrient-dense meal plans during the training and competition period still showed a 61.0% tendency to consume DS. Most claimed reason to use DS was to increase energy and strength (29.0%). A majority reported that they used DS for less than one month (35.5%), while the second-highest duration was over three years (17.2%). Considering body mass index (BMI), healthy weight students showed 71.0% DS prevalence. DS prevalence was moderate among Sri Lankan university students, highlighting that the highest DS use was during competition and training seasons. Moreover, it emphasizes the need for nutrition and anti-doping counseling in the Sri Lankan university system.

Keywords: athlete, dietary, supplements, university

Procedia PDF Downloads 175

Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

Procedia PDF Downloads 51

Authors: Ridwan Dewayanto Rusli

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We show how prisoner's dilemma-type competition problems can be mitigated through rapid platform diversification and ecosystem expansion. We analyze a ride-hailing company in Southeast Asia, Gojek, whose network grew to more than 170 million users comprising consumers, partner drivers, merchants, and complementors within a few years and has already achieved higher contribution margins than ride-hailing peers Uber and Lyft. Its ecosystem integrates ride-hailing, food delivery and logistics, merchant solutions, e-commerce, marketplace and advertising, payments, and fintech offerings. The company continues growing its network of complementors and App developers, expanding content and gaining critical mass in consumer data analytics and advertising. We compare the company's growth and diversification trajectory with those of its main international rivals and peers. The company's rapid growth and future potential are analyzed using Cusumano's (2012) Staying Power and Six Principles, Hax and Wilde's (2003) and Hax's (2010) The Delta Model as well as Santos' (2016) home-market advantages frameworks. The recently announced multi-billion-dollar merger with one of Southeast Asia's largest e-commerce majors lends additional support to the above arguments.

Keywords: ride-hailing, prisoner's dilemma, platform and ecosystem strategy, digital applications, diversification, home market advantages, e-commerce

Procedia PDF Downloads 73

Authors: Kyung Bae Park, Sung Ho Ha

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Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: latent dirichlet allocation, R program, text mining, topic model, user generated contents, visualization

Procedia PDF Downloads 164

Authors: Martin Elstner

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Processing of information by means of handling chemicals is a ubiquitous phenomenon in nature. Technical implementations of chemical information processing lack of low integration densities compared to electronic devices. Stimuli responsive hydrogels are promising candidates for materials with information processing capabilities. These hydrogels are sensitive toward chemical stimuli like metal ions or amino acids. The binding of an analyte molecule induces conformational changes inside the polymer network and subsequently the water content and volume of the hydrogel varies. This volume change can control material flows, and concurrently information flows, in microfluidic devices. The combination of this technology with powerful chemical logic gates yields in a platform for highly integrated chemical circuits. The manufacturing process of such devices is very challenging and rapid prototyping is a key technology used in the study. 3D printing allows generating three-dimensional defined structures of high complexity in a single and fast process step. This thermoplastic master is molded into PDMS and the master is removed by dissolution in an organic solvent. A variety of hydrogel materials is prepared by dispenser printing of pre-polymer solutions. By a variation of functional groups or cross-linking units, the functionality of the hole circuit can be programmed. Finally, applications in the field of bio-molecular analytics were demonstrated with an autonomously operating microfluidic chip.

Keywords: bioanalytics, hydrogels, information processing, microvalve

Procedia PDF Downloads 284

Authors: Aqsa Mehmood, Muhammad Ali Tahir, Hafiz Syed Hamid Arshad, Salman Atif, Ejaz Hussain, Gavin McArdle, Michela Bertolotto

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The evolution of smart cities in recent years has been developing dramatically. As urbanization increases, the demand for big data analytics and digital technology-based solutions for cities has also increased. Many cities around the world have now planned to focus on smart cities. To obtain a systematic overview of smart city models, we carried out a bibliometric analysis in the context of seven regions of the world to understand the main dimensions that characterize smart cities. This paper analyses articles published between 2017 and 2021 that were captured from Web of Science and Scopus. Specifically, we investigated publication trends to highlight the research gaps and current developments in smart cities research. Our survey provides helpful insights into the geographical distribution of smart city publications with respect to regions of the world and explores the current key topics relevant to smart cities and the co-occurrences of keywords used in these publications. A systematic literature review and keyword analysis were performed. The results have focused on identifying future directions in smart city development, including smart citizens, ISO standards, Open Geospatial Consortium and the sustainability factor of smart cities. This article will assist researchers and urban planners in understanding the latest trends in research and highlight the aspects which need further attention.

Keywords: smart cities, sustainability, regions, urban development, VOS viewer, research trends

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Authors: Pietro D'Ambrosio, Roberta D'Ambrosio

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The upgrading of the architectural and urban heritage of the urban historic centers almost always involves the planning for the reuse and refunctionalization of the structures. Such interventions have complexities linked to the need to take into account the urban and social context in which the structure and its intrinsic characteristics such as historical and artistic value are inserted. To these, of course, we have to add the need to make a preliminary estimate of recovery costs and more generally to assess the economic and financial sustainability of the whole project of re-socialization. Particular difficulties are encountered during the pre-assessment of costs since it is often impossible to perform analytical surveys and structural tests for both structural conditions and obvious cost and time constraints. The methodology proposed in this work, based on a multidisciplinary and data-driven approach, is aimed at obtaining, at very low cost, reasonably priced economic evaluations of the interventions to be carried out. In addition, the specific features of the approach used, derived from the predictive analysis techniques typically applied in complex IT domains (big data analytics), allow to obtain as a result indirectly the evaluation process of a shared database that can be used on a generalized basis to estimate such other projects. This makes the methodology particularly indicated in those cases where it is expected to intervene massively across entire areas of historical city centers. The methodology has been partially tested during a study aimed at assessing the feasibility of a project for the reuse of the monumental complex of San Massimo, located in the historic center of Salerno, and is being further investigated.

Keywords: evaluation, methodology, restoration, reuse

Procedia PDF Downloads 152

Authors: Cheryl Marie Cordeiro

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Although fisheries and aquaculture studies might seem marginal to international business (IB) studies in general, fisheries and aquaculture IB (FAIB) management is currently facing increasing pressure to meet global demand and consumption for fish in the next coming decades. In part address to this challenge, the purpose of this systematic review of literature (SLR) study is to investigate the use of the term ‘market access’ in its context of use in the generic literature and business sector discourse, in comparison to the more specific literature and discourse in fisheries, aquaculture and seafood. This SLR aims to uncover the knowledge/interest gaps between the academic subject discourses and business sector practices. Corpus driven in methodology and using a triangulation method of three different text analysis software including AntConc, VOSviewer and Web of Science (WoS) analytics, the SLR results indicate a gap in conceptual knowledge and business practices in how ‘market access’ is conceived and used in the context of the pharmaceutical healthcare industry and FAIB research and practice. While it is acknowledged that the product orientation of different business sectors might differ, this SLR study works with the assumption that both business sectors are global in orientation. These business sectors are complex in their operations from product to market. This SLR suggests a conceptual model in understanding the challenges, the potential barriers as well as avenues for solutions to developing market access for FAIB.

Keywords: market access, fisheries and aquaculture, international business, systematic literature review

Procedia PDF Downloads 125

Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

Procedia PDF Downloads 131

Authors: B. McGovern, J. F. Luck, A. Gade, I. V. Lake, D. O’Connell, H. C. Cutcliffe, K. P. Shah, E. E. Ginalis, C. M. Lambert, N. Christian, J. R. Kait, A. W. Yu, C. P. Eckersley, C. R. Bass

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Mild traumatic brain injury (mTBI) in the form of post-concussive injuries and attention deficit / hyperactivity disorder (ADHD) share similar cognitive impairments, including impaired working memory and executive function. The memory-guided oculomotor task separates working memory and inhibitory components to provide further information on the nature of these deficits in each pathology. Eleven subjects with ADHD, fifteen control subjects, and ten subjects with recent concussive injury were matched on age, gender, and education (all high school-age males). Eye movements were recorded during memory-guided oculomotor tasks with varying delays using EyeLink 1000 (SR Research). The percentage of premature saccades and the latency of correct response are the analyzed measures for response inhibition and working memory, respectively. No significant differences were found in latencies between controls subjects and subjects with ADHD or post-concussive injuries, in accordance with previous studies. Subjects with ADHD and post-concussive injuries both demonstrated a trend of increased percentages of premature saccades compared to control subjects in the same oculomotor task. This trend reached statistical significance between the post-concussive and control groups (p < 0.05). These findings support the primary nature of the executive function deficits in response inhibition in ADHD and mTBI. The interpretation of results is limited by the small sample size and the exploratory nature of the study. Further investigation into oculomotor performance differences in mTBI and ADHD may help in differentiating these pathologies in consequent diagnoses and provide insight into the interaction of these deficits in mTBI.

Keywords: attention deficit / hyperactivity disorder (ADHD), concussion, diagnosis, oculomotor, pediatrics

Procedia PDF Downloads 280

Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

Procedia PDF Downloads 124

Authors: Sergio Zabala-Vargas, Miguel Jiménez-Barrera, Luz VArgas-Sánchez

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In recent years, information related to project management in organizations has been increasing exponentially. Performance data, management statistics, indicator results have forced the collection, analysis, traceability, and dissemination of project managers to be essential. In this sense, there are current trends to facilitate efficient decision-making in emerging technology projects, such as: Machine Learning, Data Analytics, Data Mining, and Big Data. The latter is the most interesting in this project. This research is part of the thematic line Construction methods and project management. Many authors present the relevance that the use of emerging technologies, such as Big Data, has taken in recent years in project management in the construction sector. The main focus is the optimization of time, scope, budget, and in general mitigating risks. This research was developed in the northeastern region of Colombia-South America. The first phase was aimed at diagnosing the use of emerging technologies (Big-Data) in the construction sector. In Colombia, the construction sector represents more than 50% of the productive system, and more than 2 million people participate in this economic segment. The quantitative approach was used. A survey was applied to a sample of 91 companies in the construction sector. Preliminary results indicate that the use of Big Data and other emerging technologies is very low and also that there is interest in modernizing project management. There is evidence of a correlation between the interest in using new data management technologies and the incorporation of Building Information Modeling BIM. The next phase of the research will allow the generation of guidelines and strategies for the incorporation of technological tools in the construction sector in Colombia.

Keywords: big data, building information modeling, tecnology, project manamegent

Procedia PDF Downloads 106

Authors: Tanuj Joshi

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Smart transportation is fast emerging as a solution to modern cities’ approach mobility issues, delayed emergency response rate and high congestion on streets. Present day scenario with Google Maps, Waze, Yelp etc. demonstrates how information and communications technologies controls the intelligent transportation system. This intangible and invisible infrastructure is largely guided by the big data analytics. On the other side, the exponential increase in Indian urban population has intensified the demand for better services and infrastructure to satisfy the transportation needs of its citizens. No doubt, India’s huge internet usage is looked as an important resource to guide to achieve this. However, with a projected number of over 40 billion objects connected to the Internet by 2025, the need for systems to handle massive volume of data (big data) also arises. This research paper attempts to identify the ways of exploiting the big data variables which will aid commuters on Indian tracks. This study explores real life inputs by conducting survey and interviews to identify which gaps need to be targeted to better satisfy the customers. Several experts at Mumbai Metropolitan Region Development Authority (MMRDA), Mumbai Metro and Brihanmumbai Electric Supply and Transport (BEST) were interviewed regarding the Information Technology (IT) systems currently in use. The interviews give relevant insights and requirements into the workings of public transportation systems whereas the survey investigates the macro situation.

Keywords: smart transportation, mobility issue, Mumbai transportation, big data, data analysis

Procedia PDF Downloads 152

Authors: Babajide Olufemi Diyaolu

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The opportunity provided by advances in technology as regards sports betting is so numerous that even at one's comfort, with the use of a phone, Nigerian youth are found engaging in all kinds of betting. Today it is more difficult to differentiate a true fan as there are quite a number of them that became fans as a result of betting on live games. This study investigated the influence of online sports events on betting among Nigerian youth. A descriptive survey research design was used, and the population consists of all Nigerian youth that engages in betting and live within the southwest zone of Nigeria. A simple random sampling technique was used to pick three states from the southwest zone of Nigeria. Two thousand five hundred respondents comprising males and female were sampled from the three states. A structured questionnaire on online sports event contribution to sports betting (OSECSB) was used. The Instrument consists of three sections. Section A seeks information on the demographic data of the respondents. Section B seeks information on online sports events, while section C is used to extract information on sports betting. The modified instrument, which consists of 14 items, has a reliability coefficient of 0.74. The hypothesis was tested at 0.05 significance level. The completed questionnaire was collated, coded, and analyzed using descriptive statistics of frequency counts, percentage and pie chart, and inferential statistics of multiple regressions. The findings of this study revealed that online sports betting is a significant predictor of an increase in sports betting among Nigerian youth. The media and television, as well as globalization and the internet coupled with social media and various online platforms, have all contributed to the immense increase in sports betting. The increase in the advertisement of the betting platform during live matches, especially football, is becoming more alarming. In most organized international events, the media attention, as well as sponsorship right, are now been given to one or two betting platforms. There is a need for all stakeholders to put in place school-based intervention programs to reorientate our youth about the consequences of addiction to betting. Such programs must include meta-analyses and emotional control towards sports betting.

Keywords: betting platform, Nigerian fans, Nigerian youth, sports betting

Procedia PDF Downloads 52

Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

Procedia PDF Downloads 209

Authors: Shivaram P. Arunachalam, Mustafa Y. Sir, Andy Boggust, David M. Nestler, Thomas R. Hellmich, Kalyan S. Pasupathy

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Emergency departments (EDs) provide urgent care to patients throughout the day in a complex and chaotic environment. Real-time location systems (RTLS) are increasingly being utilized in healthcare settings, and have shown to improve safety, reduce cost, and increase patient satisfaction. Radio Frequency Identification Device (RFID) data in an ED has been shown to compute variables such as patient-provider contact time, which is associated with patient outcomes such as 30-day hospitalization. These variables can provide avenues for improving ED operational efficiency. A major challenge with ED financial operations is under-coding of critical care services due to physicians’ difficulty reporting accurate times for critical care provided under Current Procedural Terminology (CPT) codes 99291 and 99292. In this work, the authors propose a framework to optimize ED critical care billing using RFID data. RFID estimated physician-patient contact times could accurately quantify direct critical care services which will help model a data-driven approach for ED critical care billing. This paper will describe the framework and provide insights into opportunities to prevent under coding as well as over coding to avoid insurance audits. Future work will focus on data analytics to demonstrate the feasibility of the framework described.

Keywords: critical care billing, CPT codes, emergency department, RFID

Procedia PDF Downloads 107

Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Rahul Thakur, Onkar Chandel

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Red Hat is the world leader in providing open source software and solutions. A global enterprise, like Red Hat, has unique issues of connecting employees with content because of distributed offices, multiple teams spread across geographies, multiple languages, and different cultures. Employees, of a global company, create content that is distributed across departments, teams, regions, and countries. This makes finding the best content difficult since owners keep iterating on the existing content. When employees are unable to find the content, they end up creating it once again and in the process duplicating existing material and effort. Also, employees may not find the relevant content and spend time reviewing obsolete duplicate, or irrelevant content. On an average, a person spends 15 minutes/day in failed searches that might result in missed business opportunities, employee frustration, and substandard deliverables. Red Hat Knowledge Management Office (KMO) applied 'open source strategy' to solve the above problems. Under the Open Source Strategy, decisions are taken collectively. The strategy aims at accomplishing common goals with the help of communities. The objectives of this initiative were to save employees' time, get them authentic content, improve their content search experience, avoid duplicate content creation, provide context based search, improve analytics, improve content management workflows, automate content classification, and automate content upload. This session will describe open source strategy, its applicability in content management, challenges, recommended solutions, and outcome.

Keywords: content classification, content management, knowledge management, open source

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Authors: Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Kasivajjala

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Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Most of the data in the field are well-structured and available in numerical or categorical formats which can be used for experiments directly. But on the opposite end of the spectrum, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature which can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written narrative format and neither have any relational model nor any standard grammatical structure. An important step in the utilization of these texts for such studies is to transform and process the data to retrieve structured information from the haystack of irrelevant data using information retrieval and data mining techniques. To address this problem, the authors present Q-Map in this paper, which is a simple yet robust system that can sift through massive datasets with unregulated formats to retrieve structured information aggressively and efficiently. It is backed by an effective mining technique which is based on a string matching algorithm that is indexed on curated knowledge sources, that is both fast and configurable. The authors also briefly examine its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval and present the advantages the former displays over the latter.

Keywords: information retrieval, unified medical language system, syntax based analysis, natural language processing, medical informatics

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Authors: Bongs Lainjo

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Educational institutions and authorities that are mandated to run education systems in various countries need to implement a curriculum that considers the possibility and existence of elementary school dropouts. This research focuses on elementary school dropout rates and the ability to replicate various predictive models carried out globally on selected Elementary Schools. The study was carried out by comparing the classical case studies in Africa, North America, South America, Asia and Europe. Some of the reasons put forward for children dropping out include the notion of being successful in life without necessarily going through the education process. Such mentality is coupled with a tough curriculum that does not take care of all students. The system has completely led to poor school attendance - truancy which continuously leads to dropouts. In this study, the focus is on developing a model that can systematically be implemented by school administrations to prevent possible dropout scenarios. At the elementary level, especially the lower grades, a child's perception of education can be easily changed so that they focus on the better future that their parents desire. To deal effectively with the elementary school dropout problem, strategies that are put in place need to be studied and predictive models are installed in every educational system with a view to helping prevent an imminent school dropout just before it happens. In a competency-based curriculum that most advanced nations are trying to implement, the education systems have wholesome ideas of learning that reduce the rate of dropout.

Keywords: elementary school, predictive models, machine learning, risk factors, data mining, classifiers, dropout rates, education system, competency-based curriculum

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Mirza Adil Ahmad Mughal

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Transcendental analytics, in the critique of pure reason, combines space and time as conditions of the possibility of the phenomenon from the transcendental aesthetic with the pure magnitude-intuition notion. The property of continuity as a qualitative result of the additive magnitude brings the possibility of connecting with experience, even though only as a potential because of the a priori necessity from assumption, as syntheticity of the a priori task of a scientific method of philosophy given by Kant, which precludes the application of categories to something not empirically reducible to the content of such a category's corresponding and possible object. This continuity as the qualitative result of a priori constructed notion of magnitude lies as a fundamental assumption and property of, what in Microeconomic theory is called as, 'choice rules' which combine the potentially-empirical and practical budget-price pairs with preference relations. This latter result is the purest qualitative side of the choice rules', otherwise autonomously, quantitative nature. The theoretical, barring the empirical, nature of this qualitative result is a synthetic a priori truth, which, if at all, it should be, if the axiomatic structure of the economic theory is held to be correct. It has a potentially verifiable content as its possible object in the form of quantitative price-budget pairs. Yet, the object that serves the respective Kantian category is qualitative itself, which is utility. This article explores the validity of Kantian qualifications for this application of 'categories' to the economic structure of society.

Keywords: categories of understanding, continuity, convexity, psyche, revealed preferences, synthetic a priori

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Authors: Enido Fabiano de Ramos, Ieda Kanashiro Makiya, Francisco I. Giocondo Cesar

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This study presents an analysis of the Digital Twin concept applied to the academic environment, focusing on the development of a Digital Twin Smart Campus Framework. Using bibliometric analysis methodologies and literature review, the research investigates the evolution and applications of the Digital Twin in educational contexts, comparing these findings with the advances of Industry 4.0. It was identified gaps in the existing literature and highlighted the need to adapt Digital Twin principles to meet the specific demands of a smart campus. By integrating Industry 4.0 concepts such as automation, Internet of Things, and real-time data analytics, we propose an innovative framework for the successful implementation of the Digital Twin in academic settings. The results of this study provide valuable insights for university campus managers, allowing for a better understanding of the potential applications of the Digital Twin for operations, security, and user experience optimization. In addition, our framework offers practical guidance for transitioning from a digital campus to a digital twin smart campus, promoting innovation and efficiency in the educational environment. This work contributes to the growing literature on Digital Twins and Industry 4.0, while offering a specific and tailored approach to transforming university campuses into smart and connected spaces, high demanded by Society 5.0 trends. It is hoped that this framework will serve as a basis for future research and practical implementations in the field of higher education and educational technology.

Keywords: smart campus, digital twin, industry 4.0, education trends, society 5.0

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

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Authors: Lefteris Tsipis, Demosthenes Vouyioukas, George Loumos, Antonis Kargas, Dimitris Varoutas

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The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artefacts. This utilization is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a digital repository that functions as Software as a Service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials, enhancing accessibility, and fostering a deeper understanding and appreciation of cultural heritage. Moreover, the proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. Moreover, the creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, that are provided by the platform.

Keywords: cultural technologies, gaming technologies, web sharing, digital repository

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Authors: Florin G. Borcan, Ramona C. Albulescu, Adela Chirita-Emandi

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pH-responsive drug delivery systems are gaining more importance because these systems deliver the drug at a specific time in regards to pathophysiological necessity, resulting in improved patient therapeutic efficacy and compliance. Polyurethane materials are well-known for industrial applications (elastomers and foams used in different insulations and automotive), but they are versatile biocompatible materials with many applications in medicine, as artificial skin for the premature neonate, membrane in the hybrid artificial pancreas, prosthetic heart valves, etc. This study aimed to obtain the physico-chemical characterization of a drug delivery system based on polyurethane microparticles. The synthesis is based on a polyaddition reaction between an aqueous phase (mixture of polyethylene-glycol M=200, 1,4-butanediol and Tween® 20) and an organic phase (lysin-diisocyanate in acetone) combined with simultaneous emulsification. Different active agents (omeprazole, amoxicillin, metoclopramide) were used to verify the release profile of the macromolecular particles in different pH mediums. Zetasizer measurements were performed using an instrument based on two modules: a Vasco size analyzer and a Wallis Zeta potential analyzer (Cordouan Technol., France) in samples that were kept in various solutions with different pH and the maximum absorbance in UV-Vis spectra were collected on a UVi Line 9,400 Spectrophotometer (SI Analytics, Germany). The results of this investigation have revealed that these particles are proper for a prolonged release in gastric medium where they can assure an almost constant concentration of the active agents for 1-2 weeks, while they can be disassembled faster in a medium with neutral pHs, such as the intestinal fluid.

Keywords: lysin-diisocyanate, nanostructures, polyurethane, Zetasizer

Procedia PDF Downloads 163

Authors: Shutchapol Chopvitayakun

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Internship program is a compulsory course of many undergraduate programs in Thailand. It gives opportunities to a lot of senior students as interns to practice their working skills in the real organizations and also gives chances for interns to face real-world working problems. Interns also learn how to solve those problems by direct and indirect experiences. This program in many schools is a well-structured course with a contract or agreement made with real business organizations. Moreover, this program also offers opportunities for interns to get jobs after completing it from where the internship program takes place. Interns also learn how to work as a team and how to associate with other colleagues, trainers, and superiors of each organization in term of social hierarchy, self-responsibility, and self-disciplinary. This research focuses on senior students of Suan Sunandha Rajabhat University, Thailand whose studying major is information technology program. They practiced their working skills or took internship programs in the real business sector or real operating organizations in 2015-2016. Interns are categorized in to two types: normal program and special program. For special program, students study in weekday evening from Monday to Friday or Weekend and most of them work full-time or part-time job. For normal program, students study in weekday working hours and most of them do not work. The differences of these characters and the outcomes of internship performance were studied and analyzed in this research. This work applied some statistical analytics to find out whether the internship performance of each intern type has different performances statistically or not.

Keywords: internship, intern, senior student, information technology program

Procedia PDF Downloads 241

Authors: Jolanta Uszko, Tomasz Wloch, Aneta Pirowska, Roman Nowobilski

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Introduction: Phantom phenomena are often reported by subjects who have undergone limb amputation. Mostly, patients feel the amputated part of the limb as if it was still attached to the body. Two types of phantom phenomena: painless (phantom sensation) and painful (phantom pain) were described. Triggers of phantom sensations and phantom pain, as well as fully effective treatment, have not been clearly described yet. Purpose: To assess the influence of psychosocial factors and some clinical conditions on the occurrence of phantom phenomena in amputee athletes. Subjects: 21 men (age: 31 years, SD = 7.5 years) after lower or upper extremity amputation, who regularly performed high-intensity sports (Amp Football Team Players) were included to the study. Method and equipment: In the research, the following method and tools were used: Questionnaire [Pirowska] adapted for athletes with disabilities, Numerical Rating Scale (NRS) - for phantom pain assessment, McGill Pain Assessment Questionnaire (short version), Beck's Depression Inventory (BDI), State Trait Anxiety Inventory (STAI): X-1 and X-2, shortened version of The World Health Organization Quality of Life (WHOQOLBREFF). Results: In the study group, the lower leg amputations with traumatic etiology were predominant. Phantom sensations were present in all subjects. Half of the respondents claimed to experience phantom sensations at least once a day, paroxysmally. There was a prevalence of phantom sensations characterized as incomplete, immobile limb. Phantom pain was reported by over 85% of respondents. The nature of phantom pain was frequently described as stabbing, squeezing, shooting, pulsing, tiring. There was a significant correlation between phantom pain intensity and anxiety, quality of life, depressive tendencies, perception of phantom pain as the obstacle in daily functioning and intensity of the limb pain before amputation. Conclusions: The etiology of phantom phenomena is complex. Psychological factors seem to have a significant influence on the intensity of the phantom pain. Particular attention should be paid to patients who complain about persistent limb pain before the amputation. These are patients with an increased risk of the phantom pain of relatively high intensity.

Keywords: amputation, phantom pain, phantom sensations, adaptive sports

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Authors: Jana Atteah, Wid Jan, Yara AlHibshi, Rahaf AlRougi

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Anyword is an AI copywriting tool that helps marketers create effective campaigns for specific audiences. It offers a wide range of templates for various platforms, brand voice guidelines, and valuable analytics insights. Anyword is used by top global companies and has been recognized as one of the "Fastest Growing Products" in the 2023 software awards. A recent study examined the utilization and impact of AI-powered writing tools, specifically focusing on the adoption of AI in writing pursuits and the use of the Anyword platform. The results indicate that a majority of respondents (52.17%) had not previously used Anyword, but those who had were generally satisfied with the platform. Notable productivity improvements were observed among 13% of the participants, while an additional 34.8% reported a slight increase in productivity. A majority (47.8%) maintained a neutral stance, suggesting that their productivity remained unaffected. Only a minimal percentage (4.3%) claimed that their productivity did not improve with the usage of Anyword AI. In terms of the quality of written content generated, the participants responded positively. Approximately 91% of participants gave Anyword AI a score of 5 or higher, with roughly 17% giving it a perfect score. A small percentage (approximately 9%) gave a low score between 0-2. The mode result was a score of 7, indicating a generally positive perception of the quality of content generated using Anyword AI. These findings suggest that AI can contribute to increased productivity and positively influence the quality of written content. Further research and exploration of AI tools in writing pursuits are warranted to fully understand their potential and limitations.

Keywords: artificial intelligence, marketing platforms, productivity, user interface

Procedia PDF Downloads 33

Authors: Mohamed Shafi Bin Mahboob Ali

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Introduction: Synchronous tumor is defined as the presence of more than one primary malignant lesion in the same patient at the indexed diagnosis. It is a rare occurrence, especially in the spectrum of colorectal cancer, which accounts for less than 4%. The underlying pathology of a synchronous tumor is thought to be due to a genomic factor, which is microsatellite instability (MIS) with the involvement of BRAF, KRAS, and the GSRM1 gene. There are no specific sites of occurrence for the synchronous colorectal tumor, but many studies have shown that a synchronous tumor has about 43% predominance in the ascending colon with rarity in the sigmoid colon. Case Report: We reported a case of a young lady in the middle of her 30's with no family history of colorectal cancer that was diagnosed with a synchronous adenocarcinoma at the descending colon and rectosigmoid region. The lady's presentation was quite perplexing as she presented to the district hospital initially with simple, uncomplicated hemorrhoids and constipation. She was then referred to our center for further management as she developed a 'football' sized right gluteal swelling with a complete intestinal obstruction and bilateral lower-limb paralysis. We performed a CT scan and biopsy of the lesion, which found that the tumor engulfed the sacrococcygeal region with more than one primary lesion in the colon as well as secondaries in the liver. The patient was operated on after a multidisciplinary meeting was held. Pelvic exenteration with tumor debulking and anterior resection were performed. Postoperatively, she was referred to the oncology team for chemotherapy. She had a tremendous recovery in eight months' time with a partial regain of her lower limb power. The patient is still under our follow-up with an improved quality of life post-intervention. Discussion: Synchronous colon cancer is rare, with an incidence of 2.4% to 12.4%. It has male predominance and is pathologically more advanced compared to a single colon lesion. Down staging the disease by means of chemoradiotherapy has shown to be effective in managing this tumor. It is seen commonly on the right colon, but in our case, we found it on the left colon and the rectosigmoid. Conclusion: Managing a synchronous colon tumor could be challenging to surgeons, especially in deciding the extent of resection and postoperative functional outcomes of the bowel; thus, individual treatment strategies are needed to tackle this pathology.

Keywords: synchronous, colon, tumor, adenocarcinoma

Procedia PDF Downloads 86