Search results for: machine learning in soccer

Authors: Amiel Satvedi, Richard Pyne

Abstract:

Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

Procedia PDF Downloads 144

Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

Abstract:

Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

Procedia PDF Downloads 403

Authors: Saeb Farhan Al Ganideh

Abstract:

We draw on insights from theories of group threat and identity to explore how soccer rivalries can decode the relationship between ethnic minorities and local societies. How ethnic minorities used soccer, in the Arab countries of the Middle East, to express their racial-ethnic heritage is the main question that this paper grapples with at its most general level. The rhetoric around soccer and minorities in the Middle East show that ethnic minorities’ soccer clubs have faced varying degrees of discrimination. The paper relies on an analysis of 4 ethnic minorities’ soccer clubs, namely, Circassians in Jordan, Kurds in Syria, Sahrawis in Morocco, and Amazighs in Algeria, focusing on previous and current performance of these clubs. Ethnic minorities’ soccer clubs were the pinnacle in the Middle East region a few decades ago. Nonetheless, these soccer clubs, currently, fighting for not only their place in their countries’ local competitions but also for their existence as soccer clubs. Minorities’ soccer clubs have been plagued with challenges related to the change in political and social contexts in these countries.

Keywords: minorities, rivalries, soccer, middle east

Procedia PDF Downloads 489

Authors: Ajayi Olusola Olajide, Alonge Olaide Moses

Abstract:

Predicting the outcome of soccer matches poses an interesting challenge for which it is realistically impossible to successfully do so for every match. Despite this, there are lots of resources that are being expended on the correct prediction of soccer matches weekly, and all over the world. Soccer Match Result Prediction System Model (SMRPSM) is a system that is proposed whereby the results of matches between two soccer teams are auto-generated, with the added excitement of giving users a chance to test their predictive abilities. Soccer teams from different league football are loaded by the application, with each team’s corresponding manager and other information like team location, team logo and nickname. The user is also allowed to interact with the system by selecting the match to be predicted and viewing of the results of completed matches after registering/logging in.

Keywords: predicting, soccer match, outcome, soccer, matches, result prediction, system, model

Procedia PDF Downloads 461

Authors: J. O. Alvarez, L. Y. Rivera

Abstract:

Physical activity can reduce symptomatology in children with autism spectrum disorder (ASD). The sport of soccer has been widely applied as a therapeutic method for ASD children. However, empirical results from soccer programs are not widely known. This paper presents the case study of Golitos, the only dedicated soccer program for children on the island of Puerto Rico, and analyzes its results in the reduction of ASD social and emotional symptoms. The paper shows evidence that suggests that program approach, content, and community-based elements can make a notable difference in the effectiveness of using soccer or sports as therapeutic methods for children with ASD.

Keywords: autism, soccer, program, community

Procedia PDF Downloads 97

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

Abstract:

Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 207

Authors: S. Jagadeesh Kumar

Abstract:

This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

Procedia PDF Downloads 412

Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

Abstract:

In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

Procedia PDF Downloads 38

Authors: Jennifer Leach, Umashanger Thayasivam

Abstract:

The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

Procedia PDF Downloads 156

Authors: Jan Stodt, Christoph Reich

Abstract:

The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

Procedia PDF Downloads 236

Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

Abstract:

Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

Procedia PDF Downloads 133

Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

Abstract:

Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

Procedia PDF Downloads 180

Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

Abstract:

Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

Procedia PDF Downloads 17

Authors: Christodoulou Christos, Politis Anastasios

Abstract:

A large majority of users favoured to wireless LAN connection since it was so simple to use. A wireless network can be the target of numerous attacks. Class hijacking is a well-known attack that is fairly simple to execute and has significant repercussions on users. The statistical flow analysis based on machine learning (ML) techniques is a promising categorization methodology. In a given dataset, which in the context of this paper is a collection of components representing frames belonging to various flows, machine learning (ML) can offer a technique for identifying and characterizing structural patterns. It is possible to classify individual packets using these patterns. It is possible to identify fraudulent conduct, such as class hijacking, and take necessary action as a result. In this study, we explore a way to use machine learning approaches to thwart this attack.

Keywords: wireless lan, quality of service, machine learning, class hijacking, EDCA remapping

Procedia PDF Downloads 16

Authors: Hamza Zidoum

Abstract:

This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

Procedia PDF Downloads 346

Authors: V. Antevska, B. Dejanova, L. Todorovska, J. Pluncevic, E. Sivevska, S. Petrovska, S. Mancevska, I. Karagjozova

Abstract:

Introduction: The aim of this study was to optimise the laboratory oxidative stress (OS) markers in soccer players. Material and methods: In a number of 37 soccer players (21±3 years old) and 25 control subjects (sedenters), plasma samples were taken for d-ROMs (reactive oxygen metabolites) and NO (nitric oxide) determination. The d-ROMs test was performed by measurement of hydroperoxide levels (Diacron, Italy). For NO determination the method of nitrate enzyme reduction with the Greiss reagent was used (OXIS, USA). The parameters were taken after the training of the soccer players and were compared with the control group. Training was considered as maximal exercise treadmill test. The criteria of maximum loading for each subject was established as >95% maximal heart rate. Results: The level of d-ROMs was found to be increased in the soccer players vs. control group but no significant difference was noticed. After the training d-ROMs in soccer players showed increased value of 299±44 UCarr (p<0.05). NO showed increased level in all soccer players vs. controls but significant difference was found after the training 102±29 μmol (p<0.05). Conclusion: Due to these results we may suggest that the measuring these OS markers in sport medicine may be useful for better estimation and evaluation of the training program. More oxidative stress should be used to clarify optimization of the training intensity program.

Keywords: oxidative stress markers, soccer players, training, sport

Procedia PDF Downloads 414

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

Procedia PDF Downloads 159

Authors: Abdolrasoul Daneshjoo

Abstract:

The aim of this study was studying the effect of acute creatine supplementation on physiological variables of continuous and intermittent soccer activities of men soccer players. 32 soccer players from Tarbiat Moalem University aged (22/3+-1/6) volunteered for this research and were divided into two groups randomly. Both experimental and control groups after 6 days taking supplementation were tested. For measuring height and weight meter and balance were used. Questionnaire for health background, lactate electro, heart beat measuring polar electro, continuous and intermittent training program and time recorder were used for data collection. For data analysis descriptive statistical techniques, two-way ANOVA and F test were used. The result of this study showed increased significantly in heart rate in control group. For control group heart beat was (71/6 +- 3/5) and for experimental group it was (75/3 +- 4/9). No significant differences were observed in players weight after taking creatine.

Keywords: heartbeat, lactate Blood, creatine, soccer players of Tarbiat Moalem University

Procedia PDF Downloads 349

Authors: Sadia Nasrin Tisha, Mushfika Sharmin Rahman, Javier Orduz

Abstract:

Machine learning is applied in a variety of fields throughout the world. The healthcare sector has benefited enormously from it. One of the most effective approaches for predicting human heart diseases is to use machine learning applications to classify data and predict the outcome as a classification. However, with the rapid advancement of quantum technology, quantum computing has emerged as a potential game-changer for many applications. Quantum algorithms have the potential to execute substantially faster than their classical equivalents, which can lead to significant improvements in computational performance and efficiency. In this study, we applied quantum machine learning concepts to predict coronary heart diseases from text data. We experimented thrice with three different features; and three feature sets. The data set consisted of 100 data points. We pursue to do a comparative analysis of the two approaches, highlighting the potential benefits of quantum machine learning for predicting heart diseases.

Keywords: quantum machine learning, SVM, QSVM, matrix product state

Procedia PDF Downloads 55

Authors: Azene Zenebe

Abstract:

Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

Procedia PDF Downloads 119

Authors: Songul Cinaroglu

Abstract:

Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures.

Keywords: machine learning, lasso regression, random forest regression, support vector regression, hyperparameter tuning, health expenditure

Procedia PDF Downloads 187

Authors: Sam Khozama, Ali M. Mayya

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 129

Authors: Avinash Kharel

Abstract:

Recreational soccer (RS) as a medium of small-sided soccer game (SSG) has an immense positive effect on physical health, mental health and wellbeing. The RS has reflected both acute responses and long-term effects of training on sedentary, trained and clinical population on any age, gender or health status. The enjoyable mode of training elicits greater adherence by optimising intrinsic motivation while offering health benefits that match those achieved by treadmill and cycle ergometer programmes both as continuous and interval forms of training. Additionally, recreational soccer is effective and efficient regimens with highlighted social, motivational and competitive components overcoming the barriers such as cost-efficiency, time-efficiency, assess to facilities and intrinsic motivation. Further, it can be applied as an effective broad-spectrum non-pharmacological treatment of lifestyle diseases producing a positive physiological response in healthy subjects, patients and elderly people regardless of age, gender or training experience.

Keywords: recreational soccer, health benefits, diseases prevention, physiology

Procedia PDF Downloads 56

Authors: Tarik Ozmen

Abstract:

The soccer players need good dynamic balance and jumping performance for dribbling, crossing rival, and to be effective in high balls during soccer game. The quadriceps angle (Q-angle) is used to assess biomechanics of the patellofemoral joint in the musculoskeletal medicine. The Q angle is formed by the intersection of two lines drawing from the anterior superior iliac spine to the centre of the patella and to the midline of the tibia tuberosity. Studies have shown that the Q angle is inversely associated with quadriceps femoris strength. The purpose of this study was to investigate relationship between dynamic balance, jumping performance and Q-angle in soccer players. Thirty male soccer players (mean ± SD: age, 15.23 ± 0.56 years, height, 170 ± 8.37 cm, weight, 61.36 ± 6.04 kg) participated as volunteer in this study. Dynamic balance of the participants were evaluated at directions of anterior (A), posteromedial (PM) and posterolateral (PL) with Star Excursion Balance Test (SEBT). Each participant was instructed to reach as far as with the non-dominant leg in each of the 3 directions while maintaining dominant leg stance. Leg length was used to normalize excursion distances by dividing the distance reached by leg length and then multiplying the result by 100. The jumping performance was evaluated by squat jump using a contact mat. A universal (standard) goniometer was used to measure the Q angle in standing position. The Q angle was not correlated with directions of SEBT (A: p = 0.32, PM: p = 0.06, PL: p = 0.37). The squat jump height was not correlated with Q-angle (p = 0.21). The findings of this study suggest that there are no significant relationships between dynamic balance, jumping performance and Q-angle in soccer players. Further studies should investigate relationship between balance ability, athletic performance and Q-angle with larger sample size in soccer players.

Keywords: balance, jump height, Q angle, soccer

Procedia PDF Downloads 429

Authors: Vincent Liu

Abstract:

Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

Procedia PDF Downloads 20

Authors: Bezuglov Eduard, Ryland Morgans, Talibov Oleg, Kalinin Evgeny, Butovsky Mikhail, Savin Evgeny, Tzgoev Eduard, Artemii Lazarev, Bekzhan Pirmakhanov, Anthony C. Hackney

Abstract:

Currently, there is conflicting evidence regarding the efficacy of citrulline for physical performance and post-exercise recovery. Moreover, the vast majority of studies conducted used physically active volunteers from the general population and heterogeneous exercise protocols that are not specific to most sports. A single use of citrulline, regardless of the dose, will not have a significant effect on physical performance and post-exercise recovery in highly trained soccer players performing sport-specific exercises at maximum intensity. To evaluate the effectiveness of a single administration of citrulline at various doses in adult male professional soccer players performing sport-specific exercise at maximum intensity. A randomized, double-blind, placebo-controlled study analyzing eighteen soccer players from the top divisions of several European countries. The participants were randomized into three groups of six and performed a field-based soccer-specific test at 115% VO2max for 18-minutes. Comparative analysis of the cardiovascular system, physical activity, subjective perceived fatigue and post-exercise recovery was conducted. There were no statistically significant differences in more than one analyzed parameter. A single application of 3 to 6 grams of citrulline does not affect physical performance, subjective feeling of fatigue and post-exercise recovery in adult professional soccer players who have performed a sport-specific test. Currently, citrulline cannot be recommended for use as a supplement in adult professional soccer players

Keywords: citrulline, performance, recovery, soccer players

Procedia PDF Downloads 69

Authors: Rodrigo Aguiar, Adelino Ferreira

Abstract:

Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

Procedia PDF Downloads 44

Authors: Steffanie Kiourkas

Abstract:

This study sought to examine fans’ responses to San Diego Loyal player Collin Martin being called a homophobic slur by Phoenix Rising player Junior Flemmings. Fans’ responses were gathered from Reddit comments on a thread posted immediately after the incident. Fans specifically centered their conversations on the homophobic slur, the coaches’ reactions, and the use of homophobia in sports in general. This study identified five themes present in the conversations between soccer fans. The themes generally highlighted critiques of the Phoenix Rising team and head coach, critiques of the use of homophobia in soccer, and support for the San Diego Loyal and hopefulness about the state of society moving forward.

Keywords: homophobia, LGBTQ+, United Soccer League, Reddit

Procedia PDF Downloads 57

Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

Abstract:

As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: apartment housing, machine learning, multi-objective optimization, performance prediction

Procedia PDF Downloads 442

Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

Abstract:

Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

Procedia PDF Downloads 129