Sri Lankan journal of applied statistics最新文献

{"title":"Identification of Reasons for Culpable Homicides and Attempted Murders: A Case Study for the Kelaniya Police Division","authors":"H. L. A. Weerakoon, N. Chandrasekara","doi":"10.4038/sljastats.v24i1.8092","DOIUrl":"https://doi.org/10.4038/sljastats.v24i1.8092","url":null,"abstract":"Culpable Homicides and attempted murders are ultimate crimes that could create ripple effects on a society which could go far beyond the original loss of human life. Owing to the unpredictable nature of such crimes that require complex investigations the objective of this study was to come up with an appropriate model to identify the reason for a culpable homicide or an attempted murder using a statistical approach. This study use data collected from 12 Police stations in Kelaniya Police Division relating to the incidents happened between 2010 and 2020. The Pearson Chi-square test was used in identifying the influential explanatory variables. Out of the 18 variables, 8 predictors including Weapon used, Relationship, Location, Civil Status of the perpetrator were statistically associated with the identified reasons at 5 % level of significance. Multinomial logistic regression followed by four data mining models including classification tree, support vector machine (SVM), k-nearest neighbour (KNN), and probabilistic neural network (PNN) were employed initially with a training and testing set which was randomly selected in the ratio 90:10. The 4 data mining models were then fitted separately by using the bagging technique. The accuracies were compared using the confusion matrixes and rates of misclassifications of the critical classes. Out of the fitted models, the highest accuracy of 93.75 % was shown by the PNN model with a spread of 0.6. The identified model can be used as a decision support tool by crime investigators and relevant authorities for wise decision making.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43165764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study on Factors Associated with Child Sexual Abuse and Recognizing the Severity: Special Reference to Galle District","authors":"L. Dilshan, N. Withanage, N. Chandrasekara","doi":"10.4038/sljastats.v24i1.8091","DOIUrl":"https://doi.org/10.4038/sljastats.v24i1.8091","url":null,"abstract":"Child Sexual Abuse has been a global epidemic with devastating consequences. One in four girls and one in six boys have been experienced some form of sexual abuse in their tender age in the world. According to Police statistics, Child Sexual Abuse (CSA) cases is growing in recent years in Sri Lanka too. Galle is among the four districts where the reported child abuse cases high and the reported CSA complaints are increasing extraordinarily. Also, there is no previous research have been done in the Southern part of the country regarding the crisis of CSA. So, main objective of this study is to determine the key risk factors that affected to a CSA in Galle Police Division, and to develop suitable regression and machine learning models to predict the severity of CSA. 225 CSA cases reported to Police Child and Women Bureau of Galle Police Division during the period 2017 – 2020 were treated for this study. Out of twenty-one risk which were found from literature and knowledge of domain experts, sixteen variables showed a significant relationship with response variable severity of CSA according to chi-square test of association. Traditional OLR model was performed to predict severity of CSA and to detect key risk factors to a CSA with two different data selection methods. Next, machine learning techniques: Decision Tree, SVM, and PNN were trained to classify severity of CSA. Random over-sampling technique was used to overcome the class imbalanced problem persists in the dataset. Finally, bagging technique was executed to conserve robustness of models and to improve performance. The OLR model classified the severity of CSA with 68.85% accuracy. Machine learning techniques, Decision Tree, SVM and PNN model classified the severity of CSA with an accuracy of 82.15%, 77.68% and 85.25% respectively. PNN model performed with higher accuracy better than other fitted models. The results obtained from this study can be used to take precautions and to arrange awareness sessions for adults to reduce CSA in Galle Police Division. Also, the study can be extended to the whole island to reduce CSA and to make it a better place for children.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49668892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Social Media Advertisement Features on the Online Purchase Intention: A Case Study in Sri Lanka","authors":"S. S. K. T. Seelanatha, N. Abeynayake","doi":"10.4038/sljastats.v23i3.8075","DOIUrl":"https://doi.org/10.4038/sljastats.v23i3.8075","url":null,"abstract":"With the development of technology, most people got the chance to engage in digital marketing activities. Online shopping is a trending facility that improves day by day and social media advertisements play a major role in customers’ online purchase intention. The purpose of this study was to observe how the features of social media advertisements affect the online purchase intention of customers in Sri Lanka when purchasing products including agricultural products. The social media advertisement features that affect online purchase intention (creativity, customer feedback, entertainment and information in advertisements) were considered in this study. A google form questionnaire was used to gather data and 312 responses were collected. Confirmatory Factor Analysis and Structural Equation Modeling were used for the data analysis. After analyzing the gathered data, it was found that informative advertisements and creative advertisements on social media platforms have a direct impact on online purchasing intention. Also, the results indicated that customer feedback affects purchase intention through information. Entertaining online advertisements have an impact on purchase intention through their creativity. This study focused on only four features in social media advertisements. Therefore, future researchers should address the other advertisement features as well. The findings of this research can be used to make advertisements more useful and profitable for advertisers as well as sellers.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44973380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Approach to Classify Breast Tissues: A Case Study Using Six-classed Breast Tissue Data","authors":"S. Santharooban, S. P. Abeysundara","doi":"10.4038/sljastats.v23i3.8081","DOIUrl":"https://doi.org/10.4038/sljastats.v23i3.8081","url":null,"abstract":"The present study investigates the effectiveness of six Machine Learning (ML) algorithms in classifying the breast tissue dataset generated using the electrical impedance spectroscopy method. This study used the breast tissue dataset available at the UCI machine learning repository, consisting of 106 spectral records with ten variables. The data were partitioned into train and test datasets. Sixty six percentage of data was allocated for the train dataset and balance for the test dataset. Six ML algorithms were tested for effectiveness using accuracy, Cohen’s Kappa, sensitivity and specificity. The results revealed that the backpropagation algorithm (BPN) produced the highest accuracy and Kappa compared to other machine learning algorithms in classifying the six-classed breast tissue dataset. Both Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) produced the second-highest accuracy and Kappa. The C5.0 decision tree algorithm takes the third level. The fourth and fifth levels of accuracy are Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ), respectively. The sensitivity of all classes by the classification of BPN was more than eighty percentage, which is higher than other machine learning algorithms. The specificity of all classes predicted by BPN was more than ninety six percentage and was comparatively at the highest level than other machine learning algorithms. Therefore, the study concludes that the backpropagation algorithm will effectively classify the six classed breast tissue data.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48760897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective Study; Comparison of the Stride Pattern of Elite 400 meters Hurdlers in Sri Lanka with Elite Athletes in Asia and the World","authors":"T. Bandara, D. Perera, H. Hapuarachchi","doi":"10.4038/sljastats.v23i3.8084","DOIUrl":"https://doi.org/10.4038/sljastats.v23i3.8084","url":null,"abstract":"This study aimed in identifying and comparing the stride pattern in the 400mH event of the top 10 Sri Lankan, Asian, and World levels athletes of 2019 top list. A retrospective research design was used and following a selective sampling method top 10 athletes were selected from each group as subjects (N 30). Each athlete’s 400 mH 2019 season best video was analysed. 400m event timings were recorded from World Athletics. Kinovea software version 0.8.26 and Minitab software version 19 were used for data analysis. One-way ANOVA, Tukey test and Pearson’s correlation coefficient tests were performed. It was significantly different from the 1st hurdle to the 6th hurdle in all three groups. Tukey test further revealed a significant difference in Sri Lankan athletes from the start to the 1st hurdle, and from the 6th hurdle to the 10th hurdle. Moreover, only the world-level athletes were significantly different from the 10th hurdle to the finish line. The 400m time was significantly different in all three levels. World-level athletes’ group have a moderate, and the Asian-level athlete group have a very weak correlation while the Sri Lankan athlete group have a strong correlation between 400m time and 400 mH time (r= 0.607, 0.135, 0.849) respectively. In conclusion, to improve the level of performance among Sri Lankan 400mH athletes compared with the other levels, the times taken from start to the first hurdle, between hurdles, and from last hurdle to the finish line needed to be improved while improving 400m performance.","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41439778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical Approach of Identifying Crime Hotspots for GIS Mapping in Sri Lanka","authors":"M. Munasingha, N. Napagoda","doi":"10.4038/sljastats.v23i2.8065","DOIUrl":"https://doi.org/10.4038/sljastats.v23i2.8065","url":null,"abstract":"","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44669382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Consequences of Population Aging in Sri Lanka","authors":"A. M. Shafna, L. Gunaratne","doi":"10.4038/sljastats.v23i2.8064","DOIUrl":"https://doi.org/10.4038/sljastats.v23i2.8064","url":null,"abstract":"","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48652493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared Frailty Model for Joint Survival Data - A Simulation Study","authors":"J. C. Liyanage, G. Karunarathna","doi":"10.4038/sljastats.v23i2.8071","DOIUrl":"https://doi.org/10.4038/sljastats.v23i2.8071","url":null,"abstract":"","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42916608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling Rare Events in an Adaptive Cluster Sampling Design with Heterogeneity among Networks and within the Network Units","authors":"O. Wale-Orojo, O. M. Olayiwola, F. S. Apantaku, I. T. Omoniyi, A. Ajayi","doi":"10.4038/sljastats.v23i1.8041","DOIUrl":"https://doi.org/10.4038/sljastats.v23i1.8041","url":null,"abstract":"","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45059839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unit Gamma/Gompertz Quantile Regression with Applications to Skewed Data","authors":"M. H. Mustapha, Suleman Nasiru","doi":"10.4038/sljastats.v23i1.8066","DOIUrl":"https://doi.org/10.4038/sljastats.v23i1.8066","url":null,"abstract":"","PeriodicalId":91408,"journal":{"name":"Sri Lankan journal of applied statistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43731192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}