Pattern Recognition and Data Analysis with Applications [electronic resource] /

Chapter 1. Revolutions in Infant fingerprint Recognition-A Survey -- Chapter 2. A Review of High Utility Itemset Mining for Transactional Database -- Chapter 3. A Cross-sectional study on distributed mutual exclusion algorithms for ad hoc networks -- Chapter 4. Electromagnetic Pollution Index Estimation of Green Mobile Communication of Macrocell -- Chapter 5. Prediction of Train delay System in Indian Railways using Machine Learning Techniques: Survey -- Chapter 6. Valence of emotion recognition using EEG -- Chapter 7. A Deep Learning-based Approach for Automated Brain Tumor Segmentation in MR images -- Chapter 8. MZI based Electro-Optic Reversible XNOR/XOR Derived from Modified Fredkin Gate -- Chapter 9. Secured Remote Access of Cloud Based Learning Management System (LMS) Using VPN -- Chapter 10. Surface EMG signal classification for hand gesture recognition -- Chapter 11. Improved Energy Efficiency in Street Lighting: A Coverage based Approach -- Chapter 12. Security and Challenges for Cognitive IOT Based Future City Architecture -- Chapter 13. A Heuristic Model for Friend Selection in Social Internet of Things -- Chapter 14. A Fuzzy string matching based reduplication with morphological attributes -- Chapter 15. Accelerating LOF Outlier Detection Approach. etc.

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with theintegration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG). .