In the current age of manufacturing revival in the U.S. and other developed countries, companies are looking for ways to maintain or gain advantage over competitors from low-cost countries. This book presents a firsthand account of actual projects in three different industries to help leaders and middle managers structure their own turnaround or improvement projects. The book uses a case-based approach for Lean implementation and presents a summary of lessons learned and insights on change management. It includes examples of Robotic Process Automation concepts used in manufacturing and offers several examples of analytical tools used to solve operations management problems. The first-person, story-based approach makes this book an engaging read for operations leaders and middle management in the areas of quality management, engineering management, business management, and manufacturing, as well as postgraduate students.

Resolving and offering solutions to your machine learning problems with R About This Book * Implement a wide range of algorithms and techniques for tackling complex data * Improve predictions and recommendations to have better levels of accuracy * Optimize performance of your machine-learning systems Who This Book Is For This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected. What You Will Learn * Get equipped with a deeper understanding of how to apply machine-learning techniques * Implement each of the advanced machine-learning techniques * Solve real-life problems that are encountered in order to make your applications produce improved results * Gain hands-on experience in problem solving for your machine-learning systems * Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance In Detail Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. Style and approach Following a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.

Alopecia was induced in mice by administration of Paclitaxel. As we know that chemotherapy signatured for miniaturization of hair follicle and changes the proliferation rate of hair growth by inhibiting growth cycle, which further leads to hair falling and defined as alopecia. To recover and compensate alopetic condition the drug of choice available i.e. Minoxidil, a vasodilator medication which slow and steady stop the hair falling or also have potential to proliferate hair follicles by widening blood vessels and opening potassium channels, it allows more oxygen, blood and nutrients to the follicle. This can also cause follicles in the telogen phase to shed, usually soon to be replaced by new, thicker hairs (in a new anagen phase). The alopecia, induced in the mice by Paclitaxel, was counteracted when solvent Ethanol: PEG: Water was administered simultaneously to the mice. The alopecia was not observed in groups that were treated with crude extract or Minoxidil along with Paclitaxel. Besides visual observation, quantitative data (e.g., A/T ratio and follicular density) also suggest inhibition of alopetic activity of the drug.