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Full Description
This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today's software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.
Contents
Genetic Algorithm based Two Tiered Load Balancing Scheme for Cloud Data Centers.- KNN-DK: A Modified k-nn Classifier With Dynamic k-Nearest Neighbors.- Identification of Emotions from Sentences using Natural Language Processing For Small Dataset.- Comparison and Analysis of RNN-LSTMs and CNNs for Social Reviews Classification.- Blockchain Based Model for Expanding IoT Device Data Security.- Linear Dynamical Model as Market Indicator of the National Stock Exchange of India.- E- Focused Crawler and Hierarchical Agglomerative Clustering approach for Automated Categorization of Feature Level Health Care sentiments on Social Media.- Error Detection Algorithm for Cloud Outsourced Big Data.- Framing Fire Detection System of higher efficacy Using Supervised Machine Learning Techniques.- Twitter Data Sentiment Analysis using Naive Bayes Classifier and Generation of Heat Map for Analyzing Intensity Geographically.- Computing Mortality for ICU Patients using Cloud based Data.- Early Detection ofPoisonous Gas Leakage in Pipe-lines in An Industrial Environment UsingGas Sensor, Automated with IoT(Internet of Things).
