Indika Wickramasinghe

The development of large language models (LLMs) is making waves across various fields, bringing numerous benefits and innovations. At the same time, cricket is growing rapidly in popularity worldwide. Given this context, it's a great moment to explore how well LLMs can keep up with the latest cricket knowledge. This study evaluates the performance of three LLMs Co-Pilot, ChatGPT, and Liner in generating accurate summaries of bilateral Test and One Day Internationals (ODI) cricket series played in 2024. The evaluation focused on three main tasks: reporting series results, identifying the top three batsmen with their scores, and listing the top three bowlers with their wickets. Among the models, Co-Pilot stood out, consistently delivering the highest accuracy across all tasks and formats, especially for matches involving Australia, India, and South Africa. ChatGPT showed mixed results, excelling in some areas but struggling with task-specific accuracy. Liner, on the other hand, had the lowest accuracy and faced significant challenges in providing relevant detailed cricket-related information. The study also noted instances where the models generated unrelated or incorrect outputs, highlighting the need to validate LLM-generated cricket data to ensure it is reliable and correct.

The game of cricket is one of the most popular team games. Its influence continues globally, with efforts by the International Cricket Council (ICC) to expand the game to other regions and make it more inclusive beyond the commonwealth countries. Weather has become an integral part of cricket because the game is played outdoors, and the weather can impact the game in many ways. Though there are numerous cricket-related studies in the literature, there is a shortage of research work focusing on the impact of the weather on the game. This study considers one-day international cricket games played during the last decade and conducts an in-depth analysis to quantify the impact of weather-related factors on batting and bowling. Furthermore, we extend the analysis to explore weather-prone cricket venues, and at the end, we accurately predict the result of a weather-affected game. According to the experimental outcomes, the nature of the weather (clear, overcast, rainy, sunny) and some of the weather–related parameters such as temperature, heat index, humidity, dewpoint, precipitation, win-speed, pressure, and the amount of light significantly impact both batting and bowling at various stages of the game. When predicting the winner of the weather-affected game, the Bagging model can predict the winner with an accuracy of 79%, recall value of 84%, and precision of 76%.

Elementary education is critical as it lays the foundation for learning, critical thinking, social and emotional development, individual growth, and self-confidence. Hence, studying the elementary students’ educational progress is immensely important. This manuscript aims to investigate the factors that impact elementary students’ academic performance, predict their academic performance, and use the above factors to identify the student’s appropriate skilled level group based on their academic performance. In this study, the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) score, a measurement to assess students’ early literacy skills for K-6 graders, is used to quantify students' academic performance. A sample of 185 first and second graders and their features, including student’s BMI (body mass index), amount of time the student spends watching television, student’s gender, number of hours that a student sleeps each night, amount of time that the student spends reading books, student’s race, and amount of time that the student spends on physical activity are used. Based on the regression analysis, second graders’ academic performances are significantly impacted by their BMI values (β = − 4.002, p < 0.05) and the amount of time students spend reading books (β = 29.14, p < 0.05). The first graders’ academic performances are significantly impacted by the amount of sleeping time (β = 41.89, p < 0.01) and their gender (β = − 37.129, p < 0.05). Furthermore, the experimental findings indicate that machine-learning techniques accurately predict the students' appropriate academic group. In the naive Byers classifier, students in the lowest academic performance group can be identified successfully with a sensitivity of 92%, and the students in the highest academic group can be identified with a specificity value of 100%.

Featuring recent advances in the field, this new textbook presents probability and statistics, and their applications in stochastic processes. This book presents key information for understanding the essential aspects of basic probability theory and concepts of reliability as an application. The purpose of this book is to provide an option in this field that combines these areas in one book, balances both theory and practical applications, and also keeps the practitioners in mind. Features Includes numerous examples using current technologies with applications in various fields of study Offers many practical applications of probability in queueing models, all of which are related to the appropriate stochastic processes (continuous time such as waiting time, and fuzzy and discrete time like the classic Gambler’s Ruin Problem) Presents different current topics like probability distributions used in real-world applications of statistics such as climate control and pollution Different types of computer software such as MATLAB^®, Minitab, MS Excel, and R as options for illustration, programing and calculation purposes and data analysis Covers reliability and its application in network queues

This paper presents findings of a study to predict the winners of an One Day International (ODI) cricket game, after the completion of the first inning of the game. We use Naive Bayes (NB) approach to make this prediction using the data collected with 15 features, comprised of variables related to batting, bowling, team composition, and other. Upon the construction of an initial model, our objective is to improve the accuracy of predicting the winner using some feature selection algorithms, namely univariate, recursive elimination, and principle component analysis (PCA). Furthermore, we examine the contribution of the appropriate ratios of training sample size to testing sample size on the accuracy of prediction. According to the experimental findings, the accuracy of winner-prediction can be improved with the use of feature selection algorithm. Moreover, the accuracy of winner prediction becomes the highest (85.71%) with the univariate feature selection method, compared to its counterparts. By selecting the appropriate ratio of the sample sizes of training sample to testing sample, the prediction accuracy can be further increased.