Avishek Nag – författare

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

Digital twin (DT) technology is a real-time evolving digital duplicate of a physical object or process that contains all its history. It is enabled by massive real-time multi-source data collection and analysis. While 6G is considered as an enabler of digital twins, DT can also be a facilitator for integrating AI and 6G towards reliable, pervasive and efficient intelligent technologies.While the DT concept is familiar among aerospace and industrial engineers, it is a relatively new topic among electronic, electrical, computer, communications and networking engineers. For future massive-scale industrial internet-of-things (IoT) applications facilitated by DTs, a 6G network will be much more advantageous than its 5G counterpart.Digital Twins for 6G: Fundamental theory, technology and applications aims to bring together knowledge from industrial practitioners and researchers, and to introduce novel concepts that can help address the challenges associated with this interdisciplinary topic. The authors will cover fundamentals, enabling technologies, standards and advanced topics of DT and 6G to demystify the DT concept and its networking requirements and benefits, support a broader understanding of DT and its relationship with 6G to a larger audience, support learning and understanding for researchers and professionals working on 5G and 6G, and create a foundation on DT and 6G for the international research community.This book is intended to be both a tutorial of the important topics around digital twin and advanced wireless communications technologies, including 6G, as well as an advanced overview for technical professionals in the communications industry, technical managers, and researchers in both academia and industry.