CNN-Based Image Segmentation Enhanced with K-Means Clustering: Insights from CIFAR-10 and YOLOv3

The contributions of Optuna in optimizing CNNs on CIFAR-10 increase model performance by a remarkable extent. It runs gradient boosting machines against linear models of regression to check whether they improve the accuracy of residential property price prediction by handling complex relationships. GBM proves very good at handling nonlinearities and predictor interactions, hence improving the real estate value prediction. It investigates advanced methodologies for improving neural network architectures, particularly on the problems of image segmentation and real-time clustering. This includes a description of CNNs with K-means and DBSCAN clustering, online K-means algorithms, semi-supervised learning, and spatial analysis. This work demonstrates improved accuracy across a range of datasets, including MNIST and real estate data. Performance metrics have been given for deep learning in image classification and object detection using CNNs on CIFAR-10. Object Detection with YOLOv3 in OpenCV and some notions of hyperparameter tuning using Optuna to increase the quality of a CNN.