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Rajamanickam D: Causal Inference for
Machine Learning Engineers

Causal Inference for Machine Learning Engineers

A Practical Guide

Deep Learning in Computer Vision

Medical Robots and Devices: New Developments and Advances

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing. Furthermore, it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.

Machine Learning For Malware Detection

Harnessing the Power of AI for Your Organization—Profitably and Safely

The AI Conundrum

Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

Resistance in the Age of Artificial Intelligence

Algorithmic Capital

Key Technologies on New Energy Vehicles

This book focuses on the current advanced technology field of battery operation big data and comprehensively describes the frontier technology of big data analysis and intelligent control of battery safe operation. The content systematically introduces the coupling mechanism of battery system safety and consistency, battery SOH estimation and prediction, battery safety state prediction, battery fault diagnosis, battery system risk pre-warning, and safety control strategy research.This book is suitable for teachers and students of universities and colleges in the automotive and related industries, engineers and technicians in the R&amp;D departments of enterprises or automotive enthusiasts to read and refer.The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.

Big Data Analysis and Application of Power Battery Operation Safety

A Comprehensive Examination of Industry 5.0 and Society 5.0

Transforming Industries, Empowering Societies

Quantum Computing, Machine Learning, and Bio-inspired Optimization

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

Green Federated Learning for Sustainable Smart Computing

AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies. The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

AI and Data Science in Medical Research

Das Handbuch leistet eine erste Bestandsaufnahme der im Fach Medienwissenschaft genutzten Methoden zur Untersuchung digitaler Kulturen. Es vermittelt einen Überblick über medienkulturwissenschaftliche Forschungsdesigns und den Umgang mit Forschungsdaten. Dabei stellt das Handbuch sowohl methodische Ansätze zur Erhebung und Analyse digitaler Artefakte, Praktiken und Phänomene vor, wie auch spezifisch digitale Methoden der Datenanalyse und -visualisierung. Die Beiträge stellen konkrete Anwendungen vor und reflektieren den jeweiligen methodischen Ansatz in der Fachkultur und den Arbeitsgebieten der Medienwissenschaft.

Handbuch Digitale Medien und Methoden

Causal Inference for Machine Learning Engineers

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