Prediction of Malignant Breast Cancer Cases Using Ensemble Machine Learning: A Case Study of Pesticides Prone Area.

Cancer of the female breast is one of the leading types of cancers worldwide. This paper presents a case study of Malwa Belt in India that has witnessed the proliferation in the overall mortality rate due to breast cancer. The paper researches mortality aspect of the disease and its association with the various risk parameters including demographic characteristics, percentage of pesticides residue present in the water and soil, life style of the women in the affected area, water intake, and the amount of pesticide exposure to the patient. The levels of organochlorine pesticides like DDT and its metabolites and isomers of HCH in blood, tumor and surrounding adipose are estimated. Additionally, an extent of exposure of the subjects to environmental pollutants like heavy metals (Lead, Copper, Iron, Zinc, Calcium, Selenium, and Chromium etc.)are also examined. For the obtained experimental data, an efficient ensemble machine learning based framework called Bagoost is proposed to predict the risk of breast cancer in Malwa women. The performance of the proposed machine learning model results in an accuracy of 98.21 percent, when empirically tested using K-fold cross validation over the real time data of malignant and benign cases and is established to be efficacious than the existing approaches.

Deep learning-based bird eye view social distancing monitoring using surveillance video for curbing the COVID-19 spread.

The escalating transmission intensity of COVID-19 pandemic is straining the healthcare systems worldwide. Due to the unavailability of effective pharmaceutical treatment and vaccines, monitoring social distancing is the only viable tool to strive against asymptomatic transmission. Pertaining to the need of monitoring the social distancing at populated areas, a novel bird eye view computer vision-based framework implementing deep learning and utilizing surveillance video is proposed. This proposed method employs YOLO v3 object detection model and uses key point regressor to detect the key feature points. Additionally, as the massive crowd is detected, the bounding boxes on objects are received, and red boxes are also visible if social distancing is violated. When empirically tested over real-time data, proposed method is established to be efficacious than the existing approaches in terms of inference time and frame rate.

Deposition angle prediction of Fused Deposition Modeling process using ensemble machine learning.

In present study, artificial intelligence systems intertwine with mechanical systems for reducing the manufacturing time and cost of products. In Fused Deposition Modeling (FDM) optimum value of deposition angle significantly varies with product geometry; hence, prediction and validation is performed using ensemble based random forest machine learning model. The training data is generated using different shapes and geometries whereas correlation based feature selection technique is employed to explore the crucial features of products. To check the effectiveness of the random forest model K-fold cross validation method is used. The empirical evaluation shows a prediction accuracy of 94.57%, remarkably superior than other methods. The proposed robust model efficiently predicts the optimum deposition angle for any geometry which would further enhance the applicability of digitally manufactured products.