Machine learning

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.

This book gathers selected research papers presented at the AICTE-sponsored International Conference on IoT Inclusive Life (ICIIL 2019), which was organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India, on December 19-20, 2019.

The book discusses various aspects of biophysics.

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain.

This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019).

This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience.

This book highlights reliable, valid and practical testing and assessment of interpreting, presenting important developments in China, where testing and assessment have long been a major concern for interpreting educators and researchers, but have remained largely under-reported.

This book covers applications of machine learning in artificial intelligence.

This book features best selected research papers presented at the International Conference on Machine Learning, Internet of Things, and Big Data (ICMIB 2021) held at Indira Gandhi Institute of Technology, Sarang, India, during December 2021.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience.

This book is a collection of outstanding papers presented at the 1st International Conference on Advances in Computational Intelligence and Informatics (ICACII 2019), organized by the Department of Computer Science & Engineering, Anurag Group of Institutions (AGI), Hyderabad, on 20-21 December 2019.

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data.

Deep reinforcement learning has attracted considerable attention recently.

This book highlights contemporary state of research in multidisciplinary areas in computer science, computer engineering, chemical engineering, mechanical engineering, physics, biomedical sciences, life sciences, medicine, and health care.

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques.

The book presents recent advances in the generation and detection of fake multimedia.

This book focuses on the impact of artificial intelligence (AI) and machine learning (ML) models on supply chain operations in industry 4.

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods.

This book gathers selected high-quality research papers presented at Arab Conference for Emerging Technologies 2020 organized virtually in Cairo during 21-23 June 2020.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience.

This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining.

This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios.

This book gathers selected high-quality research papers presented at International Conference on Renewable Technologies in Engineering (ICRTE 2021) organized by Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India, during 15-16 April 2021.

This book features selected research papers presented at the First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), organized by Northwest Group of Institutions, Punjab, India, Southern Federal University, Russia, and IAC Educational Trust, India along with KEC, Ghaziabad and ITS, College Ghaziabad as an academic partner and held on 12-13 October 2019.

This book focuses on privacy and security concerns in big data and differentiates between privacy and security and privacy requirements in big data.

This book includes high-quality research papers presented at the Fifth International Conference on Innovative Computing and Communication (ICICC 2022), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 19-20, 2022.

This book includes a collection of peer-reviewed best selected research papers presented at the Third International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2022), organized by Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana, India, during 15-16 January 2022.

This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design.

This book constitutes the Third Diabetic Foot Ulcers Grand Challenge, DFUC 2022, which was held on September 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 in Singapore.

This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.

Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning.

This Element provides theoretical and practical introductions to machine learning for social scientists in applying it to experimental data.

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges.

This book illustrates how data can be useful in solving business problems.

This book presents recent developments in multivariate and robust statistical methods.

This book presents recent developments in multivariate and robust statistical methods.

Traditional DevOps is struggling with new challenges in today's fast-changing software world.

Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.

A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.

Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

A user-friendly, introductory, learning-by-doing bridge between classical and machine learning time series analysis with R.

A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.

A comprehensive introduction to computational analysis of sentiments, opinions, emotions, and moods.

Machine learning algorithms allow computers to learn without being explicitly programmed.

Machine learning algorithms allow computers to learn without being explicitly programmed.

Chapters "e;On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based SentimentAnalysis"e; and "e;Contextualized Graph Embeddings for Adverse Drug Event Detection"e; are licensed under theterms of the Creative Commons Attribution 4.

This book prepares students to execute the quantitative and computational needs of the finance industry.

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems.

This book prepares students to execute the quantitative and computational needs of the finance industry.