1.

Record Nr.

UNINA9910983043103321

Autore

Lin Zhouchen

Titolo

Pattern Recognition and Computer Vision : 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part V / / edited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu

Pubbl/distr/stampa

Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025

ISBN

981-9786-20-7

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (641 pages)

Collana

Lecture Notes in Computer Science, , 1611-3349 ; ; 15035

Altri autori (Persone)

ChengMing-Ming

HeRan

UbulKurban

SilamuWushouer

ZhaHongbin

ZhouJie

LiuCheng-Lin

Disciplina

006

Soggetti

Image processing - Digital techniques

Computer vision

Artificial intelligence

Application software

Computer networks

Computer systems

Machine learning

Computer Imaging, Vision, Pattern Recognition and Graphics

Artificial Intelligence

Computer and Information Systems Applications

Computer Communication Networks

Computer System Implementation

Machine Learning

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Sommario/riassunto

This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18–20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

2.

Record Nr.

UNINA9910878059903321

Autore

Dehuri Satchidananda

Titolo

Machine Intelligence, Tools, and Applications : Proceedings of the International Conference on Machine Intelligence, Tools, and Applications—ICMITA 2024 / / edited by Satchidananda Dehuri, Sung-Bae Cho, Venkat Prasad Padhy, Poonkuntrun Shanmugam, Ashish Ghosh

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024

ISBN

3-031-65392-0

Edizione

[1st ed. 2024.]

Descrizione fisica

1 online resource (435 pages)

Collana

Learning and Analytics in Intelligent Systems, , 2662-3455 ; ; 40

Altri autori (Persone)

ChoSung-Bae

PadhyVenkat Prasad

ShanmugamPoonkuntrun

GhoshAshish

Disciplina

006.3

Soggetti

Computational intelligence

Artificial intelligence

Machine learning

Computational Intelligence

Artificial Intelligence

Machine Learning

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Nota di contenuto

Fuzzy Guided Genetic Algorithm Routing for Energy Conservation in Wireless Sensor Networks -- Comparative Analysis of Prediction Models for Software Bug Prediction -- Mandelbug Classification Engine Transfer Learning and NLP Approach -- Software Maintenance Prediction Using Regression Models -- Rough Set ELM Classifier and Deep Architecture for Remote Sensing Images -- Design of an Efficient Model for Satellite Image Classification Using Graph Neural Networks and Elephant Herding Optimization -- Multifaceted Analysis of Climate Trends and Air Quality in Indian Metropolises A Machine Learning and Time Series Forecasting Approach -- Machine Learning based Analysis and Forecasting of Electricity Demand in Misamis Occidental Philippines -- Implementation and Optimization of Swarm based System for Multi Agent Coordination and Task Execution in Marine Environment -- Implementing Deep NN for Plant Disease Detection and Diagnosis -- A PSO approach for two warehouse inventory problem with imperfect quality and variable discount -- Design of Intraday Stock Price Prediction Model using Machine Learning via Technical Indicators.

Sommario/riassunto

This book presents the recent advances including tools and techniques in the constantly changing landscape of machine learning (ML). This would enable the readers with a strong understanding of critical issues in ML by providing both broad and detailed perspectives on cutting-edge theories, algorithms, and tools. This will become a single source of reference on conceptual, methodological, technical, and managerial issues, as well as provide insight into emerging trends and future opportunities in the discipline of ML. This book contains altogether 36 chapters in the area of ML and its applications.