About the Book
The new book presents a valuable selection of state-of-the-art technological advancements using the concepts of AI and machine learning, highlighting the use of predictive analytics of data to find timely solutions to real-time problems. It helps to identify applicable approaches in order to enhance, automate, and develop effective solutions to challenges in data science and artificial intelligence. The various novel approaches include applications in healthcare, natural language processing, and smart cities. As such, the book is divided into sections that address:
Computational Intelligence in Image Processing
Computational Intelligence in Healthcare
Techniques for Natural Language Processing
Computational Intelligence in Smart Cities
The very diverse range of topics include AI and machine learning applications for
In security: For using digital image processing for image fusion (face recognition, feature extraction, object detection as well tracking, moving object identification), for person re-identification for security purposes.
In healthcare and medicine: For diagnosis and prediction of breast cancer, other cancers, diabetes, heart disease; for predicting susceptibility to COVID-19; for prediction of mood and anxiety disorders.
In agriculture: For prediction of crop profit; for prediction of cropping patterns and recommendation for crop cultivation.
In traffic science/smart cities: For understanding road scene images, for detection of traffic signs, for devising a fog-based intelligent traffic phase timing regulation system
In language/speech/text: For automatic text summarization, for document indexing for unstructured data, for speech/accent recognition, for sound separation, for American Sign Language interpretation for nonsigners, for emotional recognition and analysis through speech, body postures with facial expressions, and other body movements (to improve the performance of virtual personal assistants / emotion recognition using speech, body postures with facial expressions and other body movements.
This volume offers valuable information for researchers working in interdisciplinary or multidisciplinary areas of healthcare, image analysis, natural language processing, and smart cities. This includes academicians, people in industry, and students with engineering background with research interest in these areas.
These peer-review chapters were selected from the International Conference on Computational Intelligence in Analytics and Information Systems (CIAIS- 2021), held in April 2021 at Manav Rachna University, India. Together with Volume 2: Advances in Digital Transformation, this 2-volume set offers an abundacne of valuable information on emerging technologies in computational intelligence in information systems focusing on data science and artificial intelliegence.
Table of Contents:
PART I: COMPUTATIONAL INTELLIGENCE IN IMAGE PROCESSING 1. A Study of Issues and Challenges with Digital Image Processing 2. A Methodical View on Prerequisites of Picture Combination, Strategies, Key Indicators with Usage in Real Life and Scientific Domains Facilitating Smart Ubiquitous Environment 3. A Study of Emerging Issues and Possibilities for Breast Cancer Diagnosis Using Image Modalities 4. Pap Smear Image Segmentation Using Chan-Vese-Based Adaptive Primal Dual Splitting Algorithm 5. Satellite Image Compression by Random Forest Optimization Techniques and Performance Comparison Using a Multispectral Image Compression Method 6. Learning Spatio-Temporal Features for Movie Scene Retrieval Using a 3D Convolutional Autoencoder 7. Person Re-Identification Using Deep Learning and Neural Networks PART II: COMPUTATIONAL INTELLIGENCE IN HEALTHCARE 8. A Systematic Literature Review in Health Informatics Using Data Mining Techniques 9. Utilization of Artificial Intelligence Based Methods for Preoperative Prediction in Shoulder Arthroplasty: Survey 10. Role of Computer-Based Intelligence for Prognostication a Social Well-Being and Identifying Frailty and Drawbacks 11. Health Informatics Support for Occurrence Administration Using Artificial Intelligence and Deep Learning: COVID-19 Pandemic Response 12. Machine Learning Approach for Prediction Analysis of COVID-19 13. Assessment of Generalized Anxiety Disorder and Mood Disorder in Undergraduate Students during the Coronavirus Disease (COVID-19) Pandemic 14. Evaluation of Deep Learning Models for Medical Tools Classification 15. Cervical Cancer Diagnosis and Prediction: An Application of Machine Learning Techniques 16. The Working Analysis on Machine Learning Algorithms to Predict Diabetes and Breast Cancer 17. An Ensemble of AdaBoost with Multilayer Perceptron for Heart Disease Prediction PART III: TECHNIQUES FOR NATURAL LANGUAGE PROCESSING 18. An Empirical Study of Text Summarization Techniques Using Extractive Approaches 19. Design and Comparative Analysis of Inverted Indexing of Text Documents 20. Acoustic Musical Instrument Recognition 21. Classification of Accented Voice Using RNN and GAN 22. Speech Emotion Recognition Using LSTM 23. Interpretation of American Sign Language Using Convolutional Neural Networks 24. Emotional Intelligence: An Approach to Analyze Stress Using Speech and Face Recognition 25. Proposed Integrated Framework for Emotion Recognition: A Futuristic Approach PART IV: COMPUTATIONAL INTELLIGENCE IN SMART CITIES 26. A Review on Machine Learning Techniques for Human Actions Recognition 27. Fog-Based Intelligent Traffic Phase Timing Regulation System 28. Deep Learning Classification Model for Detection of Traffic Signs 29. Understanding Road Scene Images Using CNN Features 30. Profitable Crop Prediction for the State of Odisha Using Machine Learning Algorithms 31. CapGAN: IoT-Based Cropping Patterns Prediction and Recommendation for Crop Cultivation
About the Author :
Hardeo Kumar Thakur, PhD, is working as an Associate Professor in the Department of Computer Science and Technology of Manav Rachna University, Faridabad, India. With over 10 years of teaching and research experience, he focuses on data mining, dynamic graph mining, machine learning and big data analytics.
Manpreet Kaur, PhD, is an Associate Professor in the Department of Computer Science and Technology, Manav Rachna University, India. She has more than 14 years of teaching and research experience. Her current research is on machine learning, deep learning, and natural language processing.
Parneeta Dhaliwal, PhD, has over 16 years of experience in teaching and research. Presently, she is an Associate Professor in the Department of Computer Science and Technology, Manav Rachna University, India. She is also Head of the Research Cluster of Computing (RCC) to facilitate students in their research projects.
Rajeev Kumar Arya, PhD, is an Assistant Professor with the Department of Electronics and Communication Engineering at National Institute of Technology, Patna, India. His current research interests are in wireless communication, soft computing techniques, cognitive radio, signal processing, communication systems, and circuit design.
Joan Lu, PhD, is a Professor in the Department of Computer Science and the Research Group Leader of Information and System Engineering in the Centre of High Intelligent Computing at the University of Huddersfield, UK, having previously been team leader in the IT Department of the Charlesworth Group.
