Jonathan Lethem

Elizabeth Gilbert

Richard Blanco

Ali Mohamed

Telling Room

The Story I Want to Tell is a compilation of forty stories, essays, and poems, half of them written by young writers who work with The Telling Room of Portland, Maine, and the other half by experienced writers who were inspired by the work of their younger counterparts. This one-of-a-kind collection includes work by Richard Blanco, Richard Russo, Elizabeth Gilbert, George Saunders, Dave Eggers, Ann Beattie, Campbell McGrath, Jonathan Lethem, Bill Roorbach, Betsy Sholl, Ari Meil, Lily King, Melissa Coleman, Jaed Coffin, Gibson Fay-LeBlanc, Lewis Robinson, Monica Wood, Susan Conley, Sara Corbett, and Mike Paterniti. By breaking down the barriers of age, experience, education, and knowledge, these authors celebrate the shared act of writing. Through the pairs of writing, a Q&A with the authors, and writing prompts, The Story I Want to Tell encourages writers—young and old—to explore their own writing and make their voices heard. For high school writing teachers, this book is an invaluable tool.

Explorations of the Art of Writing

The Story I Want to Tell

The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems

Handbook of Nature-Inspired Optimization Algorithms

Diego Oliva

Said Ali Hassan

This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.

Artificial Intelligence for COVID-19

Ali Mohamed

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