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Engineering (R0)

Manoj Diwakar

Vinayakumar Ravi

Prabhishek Singh

Hoang Pham

Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care

&lt;p&gt;Enhancing dysarthric speech for improved clinical communication: A deep learning approach.- Speech-based real-world scene understanding for assistive care of the visually impaired.- Medical image segmentation with deep learning: &amp;nbsp;An overview.- Lightweight generative model for synthetic biomedical images with enhanced quality.- Pediatric dental disease detection using X-ray image enhancements and deep learning algorithms.- Evaluation of Parkinson disease from MRI images using deep learning techniques.- Analyzing the effect of eyes open and eyes closed states on EEG in Parkinson&amp;rsquo;s disease with ON and OFF medication.- Automated detection of diabetic retinopathy using ResNet-50 deep learning model.- Deep learning model for decoding subcortical brain activity from simultaneous EEG-FMRI multi-model data.- Secure transmission of medical images in IoMT for smart cities using data hiding scheme.- Deep learning approaches to heart stroke prediction: Model evaluation and insights.- Harnessing predictive modeling techniques for early detection and management of diseases: Challenges, innovations, and future directions.- Fundamentals of machine learning and deep learning for healthcare applications.- Automatic detection of Parkinson disease through various machine learning models.- Transforming healthcare: The role of AI and ML in disease prediction, treatment, and patient satisfaction.- Multi-modality medical (CT, MRI, ultrasound etc.) Image fusion using machine learning/deep learning.- Leveraging digital devices for objective behavioral health assessment: Computational machine learning methods for sleep and mental health evaluation.- Optimizing medical image quality through hybrid machine learning techniques and convolutional denoising autoencoders.- Image segmentation in multimodal medical imaging using deep learning models.- Brain MRI analysis for multiple sclerosis detection using deep learning techniques.&lt;/p&gt;

HBR's 10 Must Reads

How to stay ahead in a world transformed by AI.If you read nothing else on artificial intelligence, read this book. We've chosen a new selection of current and classic Harvard Business Review articles that will help you make sense of the evolving generative AI landscape, manage regulation and risk, and develop a strategy that keeps pace with technological change.This book will inspire you to: <ul><li>Help your people and technology work better together</li><li>Redesign roles, workflows, and capabilities</li><li>Keep your AI projects on track</li><li>Address gen AI's quality control and waste problems</li><li>Harness the potential of agentic AI</li><li>Embed AI in the core of your business</li></ul>This collection of articles includes "For Success with AI, Bring Everyone on Board," by David De Cremer; "Bring Human Values to AI," by Jacob Abernethy, Francois Candelon, Theodoros Evgeniou, Abhishek Gupta, and Yves Lostanlen; "From Prediction to Transformation," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "How AI Can Help Managers Think Through Problems," by Elisa Farri and Gabriele Rosani; "The Gen AI Playbook for Organizations," by Bharat N. Anand and Andy Wu; "Agentic AI Is Already Changing the Workforce," by Jen Stave, Ryan Kurt, and John Winsor; "Keep Your AI Projects on Track," by Iavor Bojinov; "How to Marry Process Management and AI," by Thomas H. Davenport and Thomas C. Redman; "The Hidden Penalty of Using AI at Work," by Oguz A. Acar, Phyliss Jia Gai, Yanping Tu, and Jiayi Hou; "Heavy Machinery Meets AI," by Vijay Govindarajan and Venkat Venkatraman; "AI Is Changing the Structure of Consulting Firms," by David S. Duncan, Tyler Anderson, and Jeffrey Saviano; "Stop Running So Many AI Pilots," by Goutam Challagalla, Mahwesh Khan, and Fabrice Beaulieu; "Addressing Gen AI's Quality-Control Problem," by Stefan Thomke, Philipp Eisenhauer, and Puneet Sahni; "AI's Growing Waste Problem--and How to Solve It," by Samsurin Welch, Khaled Soufani, and Eva Morales; and "Don't Let an AI Failure Harm Your Brand," by Julian De Freitas.HBR's 10 Must Reads are definitive collections of classic ideas, practical advice, and essential thinking from the pages of Harvard Business Review. Exploring topics like disruptive innovation, emotional intelligence, and new technology in our ever-evolving world, these books empower any leader to make bold decisions and inspire others.This Updated and Expanded edition features new, breakthrough articles, additional short-form pieces, and a detailed discussion guide to give you and your team the tools you need for sustained success.

HBR's 10 Must Reads on Artificial Intelligence

A timely, practical guide to AI—its strengths, weaknesses, and real-world applications—for business professionals and policymakers. Artificial intelligence, or AI, can recognize a pattern from any set of data it is given, which is what makes it such an extraordinarily powerful tool. But because not all patterns are authentic or reliable, AI's pattern-finding superpower can lead to spurious patterns—and to disastrous results for business and government entities that rely on them. Hence the conundrum at the heart of AI: its greatest strength can also be its greatest weakness. Targeting the businessperson who needs to know how to use AI profitably and responsibly, Caleb Briggs and Rex Briggs offer in this book a foundational understanding of AI that is easy to grasp yet thorough enough to be used effectively.

A timely, practical guide to AI its strengths, weaknesses, and real-world applications for business professionals and policymakers.

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

The AI Conundrum

Discover how to adapt, adopt, and thrive in an AI-anxious era, written by the man with the compass for our maze Expecting the apocalypse around the corner? This book is your playbook to surviving the rise of machine learning. 71% of workers are anxious about AI. The good news: the machines still need you, and you can make them work for you. Don't Panic cuts through the noise, showing you exactly how this technology works and where it fails, and why your human judgment is more valuable than ever. Instead of focusing on technical skills or abstract predictions, author Nate B. Jones ("AI Nate") begins with the emotional reality behind AI anxiety, drawing on his 20-years experience in the tech industry. He's garnered trust from more than 1 million people to help them stay relevant in a rapidly changing technological landscape. AI might have a planet-sized brain, but just like Marvin the Android from The Hitchhiker’s Guide to the Planet, it’s permanently stuck at factory settings. It can’t learn, it can’t adapt, and it can’t gain wisdom. This book reveals why AI's permanent blind spots make human judgment more valuable today than ever before.&nbsp; Worry less, and start owning your future.

AI Optimism for the Mildly Alarmed

Don't Panic

Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!Highlights include:1)Neural networks2)Gradient descent3)Classification4)Regularization5)Convolutional neural networks (CNNs)6)Functional API7)Transformer architecture8)Reinforcement learning9)Autoencoders10)Stable Diffusion

Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!Highlights include:1)Neural networks2)Gradient descent3)Classification4)Regularization5)Convolutional neural networks (CNNs)6)Functional API7)Transformer architecture8)Reinforcement learning9)Autoencoders10)Stable Diffusion

The Comprehensive Guide to Deep Learning with the Keras API and Python

Keras 3

The Omicron Forex Trading Manual is a literate and focused explanation for non-specialists of Forex strategies for automated (algorithmic) trading.

The manual is also a guide to the essential general requirements of Forex trading which all traders must master. These include the ability to think in probabilities, to maintain discipline (which the automated strategies are designed to assist with) and master the psychology of Forex Trading. It improves the Forex trading learning process. It shows how discipline can be maintained. It also explains how the software does back-testing and it demonstrates why algorithms are important.

You need automation in your Forex trading to:Significantly improve the Forex trading learning process.
Carry out research and do back-testing.
Help you impose discipline and appreciate the need for it.
Allow for risk reduction through diversification in currency pairs, which would be onerous and error prone if attempted manually.
Take over certain repetitive functions that are more appropriate for computers to handle.


You do not want to use automation in your Forex trading to "Set and forget" and leave to run unsupervised on a live account. This way lies potential disaster.

The Omicron Forex Trading Manual contains an introduction to the automated strategies and does not include the software routines themselves.

The Omicron Forex Trading Manual

This book explores one of the most pressing questions of our time: What happens to law when technological systems-algorithms, smart contracts, digital platforms-begin to regulate our lives as effectively as, or even more effectively than, traditional legal rules?While many scholars have framed this debate in terms of efficiency, compliance, and enforcement, this book takes a step back to ask a deeper question: What is law, and what distinguishes it from mere technological control? Through three interconnected chapters, the book moves across theory, contemporary issues, and history. It critiques the popular thesis of the "death of law" under conditions of algorithmic regulation, arguing that law itself can be seen as a technology of a special kind. The book's central argument is that technological regulation does not "perfect" the law by guaranteeing compliance. Instead, it abolishes the normative dimension that defines law: the distinction between what is and what ought to be. When algorithms collapse this gap-by making non-compliance impossible-the unique reality that law creates disappears. Accessible, provocative, and interdisciplinary, this is not simply a book about legal theory. It is a book about the future of human freedom, responsibility, and political life in a world increasingly governed by technology.This book will appeal primarily to scholars and high-level students working in the areas of legal theory, the philosophy of law, and law and technology.

Technology and the Future of Normativity

Law Beyond Code

AN INSTANT NATIONAL BESTSELLER “Evenhanded and insightful.”&nbsp; —Publisher’s Weekly (starred review) “An accessible, often fascinating primer.... Essential, illuminating reading.” —Kirkus From the author of the New York Times bestseller The Digital Doctor comes an engaging, clear-eyed, and ultimately hopeful examination of healthcare’s efforts to embrace generative artificial intelligence. In A Giant Leap, physician and thought leader Robert Wachter navigates between hype and skepticism to make a compelling case for AI’s power to transform healthcare. He argues that, in a system buckling under the weight of bureaucratic pressures, soaring costs, and clinician burnout, AI doesn’t need to be perfect—it only needs to be better. Drawing on extensive research and more than 100 interviews with pioneers across medicine, technology, policy, and business, Wachter shows how AI is already entering hospitals and clinics to draft notes, field patient questions, recommend treatments, interpret images, and guide surgeries. He unflinchingly confronts risks like hallucinations, biases, and misinformation, while revealing how AI can now match, and sometimes surpass, physicians in areas ranging from diagnosis to empathy. But this isn’t simply a technology story. It’s about the human choices that will determine whether AI becomes healthcare’s salvation or another source of harm and frustration.&nbsp; Blending clinical insight, vivid storytelling, and journalistic precision, A Giant Leap offers an indispensable roadmap for healthcare leaders, clinicians, and patients. It&nbsp;is a vibrant and timely account of how AI is changing what it means to care—and be cared for—in this age of astonishing technology.

AN INSTANT NATIONAL BESTSELLER “Evenhanded and insightful.”&nbsp; —Publisher’s Weekly (starred review) “An accessible, often fascinating primer.... Essential, illuminating reading.” —Kirkus From the author of the New York Times bestseller The Digital Doctor comes an engaging, clear-eyed, and ultimately hopeful examination of healthcare’s efforts to embrace generative artificial intelligence. In A Giant Leap, physician and thought leader Robert Wachter navigates between hype and skepticism to make a compelling case for AI’s power to transform healthcare. He argues that, in a system buckling under the weight of bureaucratic pressures, soaring costs, and clinician burnout, AI doesn’t need to be perfect—it only needs to be better. Drawing on extensive research and more than 100 interviews with pioneers across medicine, technology, policy, and business, Wachter shows how AI is already entering hospitals and clinics to draft notes, field patient questions, recommend treatments, interpret images, and guide surgeries. He unflinchingly confronts risks like hallucinations, biases, and misinformation, while revealing how AI can now match, and sometimes surpass, physicians in areas ranging from diagnosis to empathy. But this isn’t simply a technology story. It’s about the human choices that will determine whether AI becomes healthcare’s salvation or another source of harm and frustration.&nbsp; Blending clinical insight, vivid storytelling, and journalistic precision, A Giant Leap offers an indispensable roadmap for healthcare leaders, clinicians, and patients. It&nbsp;is a vibrant and timely account of how AI is changing what it means to care—and be cared for—in this age of astonishing technology.

How AI Is Transforming Healthcare and What That Means for Our Future

A Giant Leap

International Series in Operations Research & Management Science

This book focuses on the innovative use of multi-source heterogeneous data to address key issues in public transit systems. Readers will explore advanced methodologies for data mining, problem diagnosis, mechanism analysis, and stochastic optimization tailored to the public transit context.

This book stands out by integrating the latest in data processing techniques, pattern recognition, and mechanism analysis, providing a comprehensive framework for systematic diagnosis and optimization of transit systems. By examining both microscopic and macroscopic perspectives, it offers insights into passenger behavior, system reliability, and personalized transit solutions.

The unique approach of this book lies in its holistic perspective, emphasizing the study of patterns, problem diagnosis, mechanism analysis, and model construction through the use of multi-source heterogeneous public transit data. It bridges the gap between multi-source data analysis, Bayesian network technology, and synergistic optimization models, serving as a crucial resource for researchers, practitioners, and policymakers who aim to enhance the efficiency, reliability, and passenger satisfaction of public transit systems.

This book focuses on the innovative use of multi-source heterogeneous data to address key issues in public transit systems. Readers will explore advanced methodologies for data mining, problem diagnosis, mechanism analysis, and stochastic optimization tailored to the public transit context.This book stands out by integrating the latest in data processing techniques, pattern recognition, and mechanism analysis, providing a comprehensive framework for systematic diagnosis and optimization of transit systems. By examining both microscopic and macroscopic perspectives, it offers insights into passenger behavior, system reliability, and personalized transit solutions.The unique approach of this book lies in its holistic perspective, emphasizing the study of patterns, problem diagnosis, mechanism analysis, and model construction through the use of multi-source heterogeneous public transit data. It bridges the gap between multi-source data analysis, Bayesian network technology, and synergistic optimization models, serving as a crucial resource for researchers, practitioners, and policymakers who aim to enhance the efficiency, reliability, and passenger satisfaction of public transit systems.

Problem Diagnosis and Stochastic Optimization

Harnessing Multi-Source Heterogeneous Data for Public Transit

The dogma that brain function relied on the cortex has dominated clinical neurology, neurosurgery and psychiatry for the last 100 years. Since the start of the 2000s, it has become evident that brain function is orchestrated as a network through white matter connections. This framework provides an understanding of brain function and dysfunction, and has radically changed how neurosurgical resections are performed. There is currently no manual for clinicians to visualize this functional anatomy in a fast, easy and user-friendly way. This is particularly important for senior clinicians who may have an understanding of cortical anatomy but may struggle with newly described white matter connections as they may now be visualized with MRI, and also for trainees who are learning the subject of applied neuro-anatomy. With this book, we aim to bridge this gap.


 


In 1995 Jackson and Duncan produced "MRI Neuroanatomy: a new angle on the brain" using the best clinical MRI available at the time and did not demonstrate white matter tracts. This was very well received, and it is time now to produce an atlas that shows the 3D anatomy of gray and white matter to contemporary standards.


 


This atlas is based on a high-quality MRI of a healthy subject which resembles the type of imaging is regularly available to clinicians. It is structured in sections (cortical anatomy, subcortical anatomy, and network anatomy) that are intended to guide clinicians from the classical cortical paradigms into a network neuroscience perspective. The 2D orthogonal slices are organized in two orientations:


 


(A) following the plane of the anterior and posterior commissure, as has been traditionally used in stereotactic atlases


(B) following the plane of the hippocampus, as is commonly used in clinical epilepsy practice.


 


The first part shows the 3D anatomy of the cerebral hemisphere, deep nuclei, brainstem and cerebellum.


 


The second part shows the same brain cut in 2D orthogonal slices (axial, coronal, sagittal) with a raw T1-weighted image, accompanied by a labelled image showing the gross anatomy of the brain and grey matter structures and, also, the labelled white matter tracts in that slice. This is particularly relevant for neurosurgeons, who will be able to appreciate before planning a resection the relationship between each tract's trajectory and the gray matter. This will also benefit neurologists, enabling clarity as to how single lesions can cause multiple disconnection and impact on different functions and behaviours.


 


The third part demonstrates the 3D anatomy of the major white matter tracts in the brain, to indicate how distant lesions can impact the same function.

Cortex, Nuclei and Connections

MRI Neuroanatomy

A dramatic new future of work in which managers assemble exactly the expertise they need—within minutes. A USA Today Bestseller Thinkers50 Talent Award Shortlist The Next Big Idea Club: “October 2025 Must-Read Books” 2026 PROSE Award Winner: Business, Finance, and Management 2026 Axiom Business Book Awards: Gold Medal, Business Disruption/Reinvention Shortlisted for the 2026 getAbstract International Book Award, Business Impact category "If you’ve ever wished you could assemble your version of The Avengers at work, this book will help you make it happen.” —Adam Grant, #1 New York Times-bestselling author of Think Again and host of the podcast Re:Thinking Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement, too; some of those experts give a second opinion or a moment of brainstorming, whereas others join as full-fledged team members for a sustained collaborative effort. This is the future promised by flash teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations. Drawing on original research and industry examples, this book will help readers to: <ul><li>Recognize and source needed expertise within minutes</li><li>Anticipate and plan team designs with AI</li><li>Redesign team structures on the fly as projects evolve</li></ul> Industries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

Leading the Future of AI-Enhanced, On-Demand Work

Flash Teams

Wie lassen sich Entscheidungen in komplexen Situationen treffen? Dieses Lehrbuch bietet eine fundierte Einführung in die Entscheidungstheorie – ein Fachgebiet, das auf logischen und modelltheoretischen Grundlagen beruht und dabei ein praktisches Ziel verfolgt: Entscheidungsregeln und -verfahren zu entwickeln, die rationalen Akteuren in vielen realen Problemsituationen nützen.Das Lehrbuch liefert Grundlagenwissen für alle Studierenden der Wirtschaftswissenschaften und eine sorgfältige didaktische Aufbereitung auf hohem wissenschaftlichen Niveau. Es bietet nicht nur einen systematischen Überblick über zentrale Konzepte und Methoden, sondern auch eine erkenntnistheoretische und historische Einordnung. Verweise auf Originalquellen laden dazu ein, die Entwicklung entscheidungstheoretischer Ideen nachzuvollziehen – und so ein lebendiges Verständnis dieses faszinierenden Forschungsfeldes zu gewinnen.Der Inhalt<ul><li>Grundmodell der Entscheidungstheorie</li><li>Gewinn als Zielgröße</li><li>Gewinnmaximierungsmodelle</li><li>Nutzen als Zielgröße</li><li>Entscheidung unter Unsicherheit</li><li>Entscheidungsverfahren in Gremien</li><li>Wissenschaftliche Methodik</li></ul>Der Autor Prof. Dr. Thomas Bonart 
 verfügt über eine umfangreiche Lehr- und Forschungserfahrung im Bereich Entscheidungs- und Planungsrechnung.

Gewinn - Nutzen - Gemeinwohl

Entscheidungstheorie

The Society of Fire Protection Engineers Series

SFPE Handbook of Fire Protection Engineering

Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care

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