Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures.

Image segmentation is a fundamental task in computer vision in which an image is divided into many regions or segments, each of which corresponds to a separate object or part of an item within the image. Image segmentation’s major purpose is to simplify an image’s representation for analysis and interpretation, making it easier for a computer to comprehend and extract meaningful information from visual data. Adaptive K-means clustering is a variant of the classic K-means clustering algorithm in which the number of clusters (K) is continuously adjusted during the clustering process. Unlike classic K-means, which requires you to choose the number of clusters before executing the algorithm, adaptive K-means identifies the best number of clusters based on the features of the data. The proposed model works as follows. Firstly, pre-processing is performed by acquiring all the input images. Secondly, adaptive k-means clustering is employed for segmentation. Thirdly, important features are automatically extracted from X-ray images by making use of a feature-based image registration technique. Then, the detection of bone fractures is automatically carried out. The results are compared with those of existing studies, and it is observed that this model provides better results.