In the media and entertainment industry, the infusion of Artificial Intelligence has set the stage for a remarkable change. AI has emerged as a formidable force in the realms of game development, movie production, and advertising, innovating creative processes across industries. AI has become a catalyst in the media and entertainment sector, sparking strategic investments and anchoring a determined pursuit to satisfy evergrowing viewer demands. In this era of innovation, robots and augmented intelligence have become the architects of unforgettable, next-generation consumer experiences. Giants in both the media and entertainment and technology spheres, including Blizzard Entertainment, Walt Disney World, Google, Microsoft, and Intel, have converged their expertise to craft, launch, and refine a plethora of AI-driven innovations, propelling the industry into uncharted territories of imagination and spectacle. Artificial Intelligence plays a pivotal role in transitioning from generic, uniform content to personalised experiences tailored for individualistic approach. Utilising sophisticated algorithms, AI platforms analyse user sentiments, behaviours and engagement patterns to curate tailored content, news feed, videos, articles and advertisements. This book aims at understanding different viewpoints from authors on Artificial Intelligence, technology and the contemporary media scenario. Looking at it from a modern outlook, it won't be justified on our part to define new media through some hardcore definition as such. With the onset of the 'latest' and 'what was new yesterday not being new today', the lines have become blurred and the system itself is expanding its wings at a rate comparable to universal expansion (just kidding). The more we think about this concept of New Media, the more we feel as to how tremendous it is! Two words 'New' and 'Media' both with well defined meanings merge and together we get a combination which is for the entire world to explore. It is more than just a media, it's an extension of ourselves. New Media encompasses all sorts of interaction between new technology and established media form to start with and goes way beyond that to bringing and visualising what could be the next new cool, thereby encompassing everything that we have the capacity to think of, if we look at it from a neutral perspective. The very definition of new media is less settled upon, known and identified. It spans a complex path with computer sitting at it's locus of convergence. This book delves into the profound upheaval taking place at the junction of artificial intelligence and the media industry. As AI technologies advance, they are changing the way content is created, disseminated, consumed, and even sold. From newsrooms to social media platforms, AI is creating new opportunities while also posing distinct obstacles.
Dr. Richard Shepherd is an Associate Professor in the Department of Computer Science at Louisiana State University. He earned his Ph.D. and M.S. in Computer Science at the University of Delaware, and his B.S. in Computer Science at Virginia Commonwealth University. David has since worked as a postdoctoral fellow in the Department of Computer Science at the University of British Columbia, built sweat equity as employee #9 at Tasktop Technologies, and risen to Senior Principal Scientist at ABB Corporate Research. His research has produced tools that have been used by thousands, innovations that have been featured in the popular press, and practical ideas that have won business plan competitions. Dr. Shepherd currently serves as the Co-Editor-in-Chief of the Journal of Systems & Software. His current work focuses on enabling end-user programming for industrial machines and increasing diversity in computer science. His research interests include data mining, deep learning, bioinformatics, medical image analysis, and graph learning. He is dedicated to the interdisciplinary study of artificial intelligence (AI) in healthcare and medicine. He focuses on utilizing advanced computational techniques including data mining, machine learning, and deep learning to address critical biomedical challenges such as AI fairness and multimodal learning for robust diseases screening. The objective is to enhance the understanding, diagnosis and clinical management of human diseases through cutting-edge AI-driven tools and methodologies.
Preface 1. INTRODUCTION TO MEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE .....1 1.1 Introduction 1.2 A Bit of History 2. OVERVIEW OF ARTIFICIAL INTELLIGENCE..................................................................5 2.1 Introduction to Artificial Intelligence 2.2 Machine Learning 2.3 Support Vector Machines 2.4 Neural Networks 2.5 Naïve Bayesian Classifier 2.6 Hidden Markov Models 2.7 k-Means Clustering 2.8 Principal Component Analysis 3. DATA MINING METHODS WITH EXAMPLE APPLICATIONS TO THE MEDICAL DOMAIN .............................................................24 3.1 Introduction 3.2 Overview of Machine Learning and Data Mining 3.3 Machine Learning and Data Mining Resources 3.4 Example/Illustrative Medical Applications 3.5 Conclusions 4. COMPUTATIONAL INTELLIGENCE TECHNIQUES AND AREAS OF THEIR APPLICATIONS IN MEDICINE ..........................................................................43 4.1 Introduction 4.2 Fuzzy Logic 4.3 Genetic Algorithm 4.4 ANNs 4.5 Conclusion 5. SATISFICING OR THE RIGHT INFORMATION AT THE RIGHT TIME........................57 5.1 Introduction 6. SOFT TISSUE CHARACTERIZATION USING GENETIC ALGORITHM .......................64 6.1 Introduction 6.2 Biomechanical Models Contents 6.3 GA 6.4 Performance Analysis 6.5 Conclusions 7. MACHINES AND WAVELET TRANSFORM IN ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION.............................................................................................78 7.1 Introduction 7.2 Data Analysis 7.3 Support Vector Machines 7.4 Description of Multiclass Methods 7.5 Experimental Results 7.6 Conclusion 8. BUILDING NAÏVE BAYES CLASSIFIERS WITH HIGH DIMENSIONAL AND SMALL-SIZED DATA SETS ............................................................................................95 8.1 Introduction 8.2 Naïve Bayes Classifier 8.3 Experiments 8.4 Discussions and Problems 8.5 Conclusion and Future Work 9. PREDICTING TOXICITY OF CHEMICALS COMPUTATIONALLY..............................114 9.1 Introduction 9.2 Data Set 9.3 Preprocessing and Computation 9.4 RF with Boosting Algorithm 9.5 Toxicity Prediction Using Bayesian Classifiers 9.6 Concluding Remarks 10. CANCER PREDICTION METHODOLOGY USING AN ENHANCED ARTIFICIAL NEURAL NETWORK...........................................................127 10.1 Introduction 10.2 Review of Related Research 10.3 Dominant Gene Prediction Using ANN 10.4 Results and Discussion 10.5 Discussion 10.6 Conclusion 11. A SYSTEM FOR MELANOMA DIAGNOSIS BASED ON DATA MINING............................................................................................139 11.1 Introduction 11.2 Data Set 11.3 Rule Induction and Validation 11.4 Optimization of the ABCD Formula 11.5 Internet Melanoma Diagnosing and Learning System 11.6 Synthetic Skin Lesions 11.7 Conclusions 12. IMPLEMENTATION AND OPTIMIZATION................................................................146 12.1 Introduction 12.2 Retinal Layer Detection in OCT Images 12.3 Experiments and Results 12.4 Comp
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