On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.

The classification and prediction of medical diseases is a cutting edge research problem in the medical field. The experts of machine learning are continuously proposing new classification methods for the prediction of diseases. The discovery of classification rules from medical databases for classification and prediction of diseases is a challenging and nontrivial task. It is very significant to investigate the more promising and efficient classification approaches for the discovery of classification rules from the medical databases. This paper focuses on the problem of selection of more efficient, promising and suitable classifier for the prediction of specific diseases by performing empirical studies on bunch mark medical databases. The research work under the focus concentrates on the benchmark medical data sets i.e. arrhythmia, breast-cancer, diabetes, hepatitis, mammography, lymph, liver-disorders, sick, cardiotocography, heart-statlog, breast-w, and lung-cancer. The medical data sets are obtained from the open-source UCI machine learning repository. The research work will be investigating the performance of Decision Tree (i.e. AdaBoost.NC, C45-C, CART, and ID3-C) and Support Vector Machines. For experimentation, Knowledge Extraction based on Evolutionary Learning (KEEL), a data mining tool will be used. This research work provides the empirical performance analysis of decision tree-based classifiers and SVM on a specific dataset. Moreover, this article provides a comparative performance analysis of classification approaches in terms of statistics.

Suicide is an important issue to address, especially in rural areas. Rural areas are facing unique challenges such as poor health care facilities, lack of awareness, financial constraints and many more for such matters. Aims: To find the social, educational and medical attributes which may lead a person to deliberate self harm. Study Design: Retrospective study. Methodology: Total 100 cases of suicidal attempts taken from DHQ teaching hospital Sargodha from (June to December) 2019. We considered all the suicidal and self harm cases admitted through emergency and medicolegal clinic. Moreover cases less than 9 years of age and autopsy cases were excluded. All the cases were analysed with reference to 10 features (age, gender, locality, education, marital status, duration of stay in hospital, treatment given, prevalence of psychiatric disorder, suicidal attempts, the method used for suicidal attempt). Statistical analysis: ML models work on numeric data. However the dataset we collected have categorical features except age. The most used method for such purpose is python get dummies function. The get dummies() function is used to convert categorical variable into dummy/indicator variables. Results: In this study, more preponderance of suicidal attempts at age less than 40 in males which shows the development of more mature attitude with increasing age. Conclusion: It was concluded that suicide is influenced by many personal factors that cannot be shared publicly on social platforms. However, such information can be used to lower the risk of suicide attempts in rural areas. Keywords: Machine Learning, Suicide Prevention, Suicide Detection and Ideation.