Applied Machine Learning and Data Analytics
portes grátis
Applied Machine Learning and Data Analytics
5th International Conference, AMLDA 2022, Reynosa, Tamaulipas, Mexico, December 22-23, 2022, Revised Selected Papers
Jabbar, M. A.; Siarry, Patrick; Tiwari, Sanju; Ortiz-Rodriguez, Fernando
Springer International Publishing AG
05/2023
242
Mole
Inglês
9783031342219
15 a 20 dias
Descrição não disponível.
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.
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artificial intelligence;software design;software engineering;neural networks;computer vision;data mining;information retrieval;image processing;3d modelling;augmented reality;digital libraries;digital heritage;digital humanities
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
