Applied Machine Learning and Data Analytics 5th International Conference, AMLDA 2022, Reynosa, Tamaulipas, Mexico, December 22–23, 2022, Revised Selected Papers

This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. 

Deep Learning Methods to Automate Embryo Classification and Evaluation.- Univariate feature fitness measures for classification problems: An empirical assessment.- Securing Advanced Metering Infrastructure using Blockchain for Effective Energy Trading.- Keratoconus Classification using Feature Selection and Machine Learning Approach.- Semantic Segmentation of The Lung to Examine the Effect of COVID-19 using UNET Model.- SMDKGG: A Socially Aware Meta Data Driven Knowledge Graph Generation for Disaster Tweets.- Brain MRI Image Classification using Deep Learning.- A Real Time Face Recognition Attendance using Machine Learning.- Towards abalone differentiation through machine learning.- Better Qualitative Searching for Effecting the Performance of Machine Translation.- RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation using Convolutional Neural Network.- A credit card fraud detection model using machine learning methods with a hybrid of undersampling and oversampling for handling imbalanced datasets for high scores.-  Implementation of YOLOv7 Model for Pest Detection.- Online Grocery Shopping: - Key Factors to Understand Shopping Behavior from Data Analytics Perspective.- A novel approach: Semantic web enabled subject searching for Union Catalogue.- How People in South America is Facing Monkeypox Outbreak?.- Multi-level Classification of Satellite Images using Pre-trained AlexNet Architecture.- Handwriting Recognition for Predicting Gender and Handedness using Deep Learning.- Handwriting Recognition for Predicting Gender and Handedness using Deep Learning.- Performance evaluation of Smart Flower Optimization Algorithm over Industrial Non-Convex Constrained Optimization Problems.