Background: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. Objective: The present study was conducted to predict cervical cancer and identify its important predictors using Machine Learning classification algorithms. Material and Methods: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017– 2018, were analyzed by Machine Learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). Results: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95. 55, 90. 48, 100, and 95. 20, 95. 55, 90. 48, 100, and 95. 20, those of RBF 95. 45, 90. 00, 100 and 91. 50, those of SVM 93. 33, 90. 48, 95. 83 and 95. 80 and those of MLP 90. 90, 90. 00, 91. 67 and 91. 50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. Conclusion: This investigation confirmed that ML can enhance the prediction of cervical cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing cervical cancer prevention.

Road construction and maintenance organizations usually use certain criteria to qualify asphalt mixtures before use in construction. One of the most important features that is measured in the asphalt mixing and quality control plan is the Marshall asphalt stability. This study examines the use of Machine Learning techniques to predict Marshall asphalt stability. Due to the time-consuming and costly process of asphalt production and quality control, it is necessary to use new methods in this process. In this research, two Supervised support vector Machine and random forest algorithms, which are Machine Learning algorithms, were used to predict the marshal asphalt stability. For this purpose, the test results of 2000 asphalt samples including Granulation of aggregate, Fracture percentage, bitumen adsorption, bitumen specific gravity, actual specific gravity of materials, bitumen consumption percentage, dust to effective binder ratio and Marshall asphalt stability for training and evaluation Models were used. After modeling and evaluation, the value of R2 is 87.5 for the support vector Machine method and 82.69 for the random forest. Also, MAPE, RMES and SDE values for SVM were 3.1073, 40.042 and 0.0208, respectively, and for RF were 3.1641, 41.870 and 0.0211, respectively. The results show the efficiency of the models used against laboratory methods for predicting marshal asphalt stability, which SVM method has a better performance than RF. Machine Learning methods can be used to predict other parameters of the asphalt mixing plan and reduce the time, cost and human error of the tests.

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs Machine Learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a Machine Learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naï ve Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘ #lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.