No / Several bridges failed because of scouring and erosion around the bridge elements. Hence, precise prediction of abutment scour is necessary for the safe design of bridges. In this research, experimental and computational investigations have been devoted based on 45 flume experiments carried out at the NIT Warangal, India. Three innovative ensemblebased data intelligence paradigms, namely categorical boosting (CatBoost) in conjunction with extra tree regression (ETR) and K-nearest neighbor (KNN), are used to accurately predict the scour depth around the bridge abutment. A total of 308 series of laboratory data (a wide range of existing abutment scour depth datasets (263 datasets) and 45 flume data) in various sediment and hydraulic conditions were used to develop the models. Four dimensionless variables were used to calculate scour depth: approach densimetric Froude number (Fd50), the upstream depth (y) to abutment transverse length ratio (y/L), the abutment transverse length to the sediment mean diameter (L/d50), and the mean velocity to the critical velocity ratio (V/Vcr). The Gradient boosting decision tree (GBDT) method selected features with higher importance. Based on the feature selection results, two combinations of input variables (comb1 (all variables as model input) and comb2 (all variables except Fd50)) were used. The CatBoost model with Comb1 data input (RMSE = 0.1784, R = 0.9685, MAPE = 10.4724) provided better accuracy when compared to other machine learning models.

Diabetic Retinopathy (DR) is one of the principal sources of blindness due to diabetes mellitus. It can be identified by lesions of the retina, namely microaneurysms, hemorrhages, and exudates. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior studies on diabetic retinopathy typically extract features manually but are time-consuming and not accurate. In this research, we propose a research framework using advanced retina image processing, deep learning, and a boosting algorithm for high-performance DR grading. First, we preprocess the retina image datasets to highlight signs of DR, then follow by a convolutional neural network to extract features of retina images, and finally apply a boosting tree algorithm to make a prediction based on extracted features. Experimental results show that our pipeline has excellent performance when grading diabetic retinopathy images, as evidenced by scores for both the Kaggle dataset and the IDRiD dataset. / Master of Science / Diabetes is a disease in which insulin can not work very well, that leads to long-term high blood sugar level. Diabetic Retinopathy (DR), a result of diabetes mellitus, is one of the leading causes of blindness. It can be identified by lesions on the surface of the retina. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior image processing studies of diabetic retinopathy typically detect features manually, like retinal lesions, but are time-consuming and not accurate. In this research, we propose a framework using advanced retina image processing, deep learning, and a boosting decision tree algorithm for high-performance DR grading. Deep learning is a method that can be used to extract features of the image. A boosting decision tree is a method widely used in classification tasks. We preprocess the retina image datasets to highlight signs of DR, followed by deep learning to extract features of retina images. Then, we apply a boosting decision tree algorithm to make a prediction based on extracted features. The results of experiments show that our pipeline has excellent performance when grading the diabetic retinopathy score for both Kaggle and IDRiD datasets.