http://repository.wyb.ac.lk/handle/1/3059 Fri, 08 May 2026 14:09:21 GMT 2026-05-08T14:09:21Z http://repository.wyb.ac.lk/handle/1/3589 A Method to Minimize Rework Amount Using Mean Particle Diameter: A Case Study of Activated Carbon Manufacturing Preena, TN; Dilanthi, MGS Rework is a major determinant of activated carbon manufacturing industry. This study investigated rework as a case study by determining its severity by identifying its causes. The considered work categories included only the rework of particle size preparation process. The objective of this research was to suggest a method to minimize the rework amount of size category and analyze the root causes for it. The analysis was done mainly over two objects, the crusher machine and particle size distribution process. Feed materials were categorized into two size combinations in the crushing process. The mean particle diameter analysis was done for both. The study found that activated carbon crushing process needed separate gap settings for each size combination. Further, feeding those two size combinations separately, would give more benefits. Further, it would maintain the specified product range at a higher level. Improving the quality of raw materials, checking the size distribution of the materials before feed, adjusting the new gap settings, replacing the rollers after checking attrition, conducting employee awareness programs and systemizing the procedure of issuing bin for feed were suggested as possible solutions along with the feasibility analysis. Thus, the technical team staff, engineers or mechanics can implement suitable programs or methods of gap setting procedures using their knowledge based on these findings. Tue, 01 Jan 2019 00:00:00 GMT http://repository.wyb.ac.lk/handle/1/3589 2019-01-01T00:00:00Z http://repository.wyb.ac.lk/handle/1/3588 SVM Based Classification Approach to Identify Suitable Beginners for Tennis Based on Anthropometric Measurements Amarasena, PT; Kumara, BTGS; Jointion, S Anthropometric measurements are generally used to determine and predict achievement in different sports. An athlete’s anthropometric and physical characteristics may be an important precondition for successful participation in any given sport. Further, anthropometric profiles indicate whether the player would be suitable for the competition at the highest level in a specific sport. The main aim of this study is to measure accuracy of the given classification algorithms. Each and every data mining algorithm provides separate prediction accuracy details. This study investigates an integration of four data mining algorithms (Naïve Bayes, Decision Trees, Random Forest, and Support Vector Machine) and an Ensemble approach (bagging, boosting, and stacking). In this research, Push up, Sit-up, Cardiac respect endurance, flexibility, agility, Height, Weight, Upper Arm Relaxed Girth, Fore Arm Girth, Chest Girth, Wrist Girth, Waist Girth, Thigh Girth, Calf Girth, Angle Girth, Acromial Radial Length, Radials lion Dactyl ion, Foot Length, body composition, fore arm length, hand length, Explosive power, Breath hold in time, resting heart rate, volume of oxygen, Force vital capacity, force explotaty volume in 1 second, Upper arm radius, and Leg Length are used. In this paper, classification performance of these models has been evaluated using Accuracy, Precision, Recall, F-Measure, MCC, ROC Area, PRC Area, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) etc. The results of this study indicated that SVM was the most suitable algorithm to classify Anthropometric measurements of tennis players. Tue, 01 Jan 2019 00:00:00 GMT http://repository.wyb.ac.lk/handle/1/3588 2019-01-01T00:00:00Z http://repository.wyb.ac.lk/handle/1/3587 Movie Success Prediction and Movie Rating using Data Mining Kudagamage, UP; Kumara, BTGS; Baduraliya, CH This study aims to analyze different data mining approaches to explore the attributes affecting the success or failure of a movie and develop a rating for movies. Four data mining algorithms and an Ensemble approach are considered in movie success prediction and also this study demonstrates the correlation between successes or failure of a movie and the different attributes of movies. Various prediction criteria are used to evaluate the prediction performance of these models. Further, a spatial clustering technique called the Associated Keyword Space (ASKS) was applied for this study. Similarities between movies were calculated using the Cosine Similarity and these affinity values were used for this clustering model. Movies were categorized under the success or failure of movies by clustering them into four clusters as Most Successful Movies, Successful Movies, Unsuccessful Movies and Least Successful Movies. The most effective attributes towards the success or failure of a movie were identified. Movie makers can use these results to identify which movie attributes are the most effective and can consider them for the success of their future movie productions. Also, using the Correlation Coefficient, a mathematical model that can be used to predict the movie’s success or failure is proposed and a movie rating (from 1-10) is developed. Tue, 01 Jan 2019 00:00:00 GMT http://repository.wyb.ac.lk/handle/1/3587 2019-01-01T00:00:00Z http://repository.wyb.ac.lk/handle/1/3586 IoT and Fuzzy Logic Based Smart Mirror for the Hospitality Industry Suraweera, SAPD; Nagahamulla, HRK; Vidanagama, VGTN Automation has been a best topic in the hospitality industry over the past few months. From AI concierge’s electronic butlers to fully intelligent hotels, hotels have been embracing this technology to improve their operations and revolutionize the guest experience. To monitor guests at hotels constantly, there should be a system that can be easily handled, user friendly and smart in accordance with the rapid advancements in technology. Though the applications of Internet of Things (IoT) are diverse, this system is based on IoT which can be implemented by using Raspberry pi technology. Tue, 01 Jan 2019 00:00:00 GMT http://repository.wyb.ac.lk/handle/1/3586 2019-01-01T00:00:00Z