Shruti Mishra – författare

Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.

Provides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets Presents and analyzes three different types of gene selection algorithms: Support Vector Machine-Bayesian T-Test-Recursive Feature Elimination (SVM-BT-RFE), Canonical Correlation Analysis-Trace Ratio (CCA-TR), and Signal-To-Noise Ratio-Trace Ratio (SNRTR) Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource

Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.- Provides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets- Presents and analyzes three different types of gene selection algorithms: Support Vector Machine-Bayesian T-Test-Recursive Feature Elimination (SVM-BT-RFE), Canonical Correlation Analysis-Trace Ratio (CCA-TR), and Signal-To-Noise Ratio-Trace Ratio (SNRTR)- Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field.

This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification.

Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field.This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification.- Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures- Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers- Provides a number of experimental analyses, with their results discussed and appropriately validated

"It is time for us to start conversations around these misfit stories and talk about things that are often ignored."

Have you ever felt out of place in society, your culture, or even your own family? Do you feel like you''re fighting against what is expected of you based on norms, traditions and values? Would you consider yourself a misfit? You''re not alone. 

Shruti Mishra''s MISFIT is a book filled with personal stories and researched data that explores the intersection of social stigmas and stereotypes, controversial and outdated traditions, social values, and unpopular opinions and choices. It touches on self-exploration, difference of perspective based on gender, and aspects of human life. You''ll explore issues within major topics such as: 

Love, marriage, and familyEducation and communityWealth and social standing

If you love to connect with others on a deeper level, feel passionate about social issues, or have ever felt like an outcast, MISFIT belongs on your bookshelf. It''s time to live life on your terms; no one should feel unwanted, unheard, or like a misfit. Remember, you are the main character in your own story and no one can play your role better than you.