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Pattern Recognition: 15th Mexican Conference, MCPR 2023, Tepic, Mexico, June 21–24, 2023, Proceedings de Ansel Yoan Rodríguez-González

Pattern Recognition: 15th Mexican Conference, MCPR 2023, Tepic, Mexico, June 21–24, 2023, Proceedings: Lecture Notes in Computer Science, cartea 13902

Editat de Ansel Yoan Rodríguez-González, Humberto Pérez-Espinosa, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
en Limba Engleză Paperback – 20 mai 2023
This book constitutes the refereed proceedings of the 15th Mexican Conference on Pattern Recognition, MCPR 2023, held in Tepic, Mexico, during June 21–24, 2023.

The 30 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers are divided into the following topical sections: pattern recognition and machine learning techniques; deep learning and neural networks; medical applications of pattern recognition; language processing and recognition; and industrial applications of pattern recognition.
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Specificații

ISBN-13: 9783031337826
ISBN-10: 3031337824
Ilustrații: XIV, 328 p. 150 illus., 104 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.49 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Pattern Recognition and Machine Learning Techniques: Feature Analysis and Selection for Water Stream Modeling.- A Cloud-based (AWS) Machine Learning Solution to Predict Account Receivables in a Financial Institution.- A New Approach for Road Type Classification using Multi-Stage Graph Embedding Method.- Removing the Black-Box from Machine Learning.- Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment.- Fitness Function Comparison for Unsupervised Feature Selection with Permutational-Based Dierential Evolution.- A Method for Counting Models on Cubic Boolean Formulas.- Automatic Identication of Learning Styles through Behavioral Patterns.- Comparison of Classiers in Challenge Scheme.- Deep Learning and Neural Networks: Robust Zero-Watermarking for Medical Images based on Deep Learning Feature Extraction.- Plant Stress Recognition UsingDeep Learning and 3D Reconstruction.- Segmentation and Classification Networks for Corn/Weed Detection under Excessive Field Variabilities.- Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation.- Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks.- Estimation of Stokes Parameters using Deep Neural Networks.- Experimental Study of the Performance of Convolutional Neural Networks Applied in Art Media Classification.- Medical Applications of Pattern Recognition: Hadamard Layer to Improve Semantic Segmentation in Medical Images.- Patterns in Genesis of Breast Cancer Tumor.- Realistic Simulation of Event-Related Potentials and their usual Noise and Interferences for Pattern Recognition.- Chest X-ray Imaging Severity Score of COVID-19 Pneumonia.- Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples.- Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms.- Retinal Artery and Vein Segmentation using an Image-to-image Conditional Adversarial Network.- Evaluation of Heatmaps as an Explicative Method for Classifying Acute Lymphoblastic Leukemia Cells.- Language Processing and Recognition: Machine Learning Models Applied in Sign Language Recognition.- Urdu Semantic Parsing: An Improved SEMPRE Framework for Conversion of Urdu Language Web Queries to Logical forms.- Improving the Identification of Abusive Language through Careful Design of Pre-training Tasks.- Industrial Applications of Pattern Recognition: TOPSIS Method for Multiple-Criteria Decision-Making Applied to Trajectory Selection for Autonomous Driving.- Machine-learning based Estimation of the Bending Magnitude Sensed by a Fiber Optic Device.- Graph-based Semi-Supervised Learning using Riemannian Geometry Distance for Motor Imagery Classification.