00000nam a22000004a 4500 UP-1685675941131487477 Buklod 20231019150244.0 m |o d | ta 231019s2022 flua rb |001 0|eng 9780367628826 (hbk) BC-74020 New Century Books & General Merchandise Php 5,845.50 Department of Mathematics and Computer Science WSU Libraries BAG rda eng QA 76.9 B45 D38 2022 Data science and data analytics opportunities and challenges edited by Amit Kumar Tyagi. Boca Raton, FL CRC Press, an imprtint of the Taylor & Francis Group 2022. xvii, 464 pages illustrations 26 cm text rdacontent unmediated rdamedia volume rdacarrier "A Chapman & Hall book" Includes bibliographical references and index. Section I: Introduction about Data Science and Data Analytics -- 1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach -- 2. IoT Analytics/Data Science for IoTT. -- 3. A Model to Identify Agriculture Production Using Data Science Techniques -- 4. Identification and Classification of Paddy Crop Diseases Using Big Data Machine Learning Techniques -- Section II Algorithms, Methods, and Tools for Data Science and Data Analytics -- 5. Crop Models and Decision Support Systems Using Machine Learning -- 6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random Forest -- 7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning -- 8. Hybrid Cellular Automata Models for Discrete Dynamical Systems -- 9. An Efficient Imputation Strategy Based on Adaptive Filter for Large Missing Value Datasets -- 10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models -- Section III: Applications of Data Science and Data Analytics -- 11. Wheat Rust Disease Detection Using Deep Learning -- 12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary Disease -- 13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical LSTM Methods -- 14. A Tier-based Educational Analytics Framework -- 15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images -- 16. Prediction of Acoustic Performance Using Machine Learning Techniques -- Section IV: Issue and Challenges in Data Science and Data Analytics -- 17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony -- 18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian Information Technology Stocks -- 19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments -- 20. Churn Prediction in Banking the Sector -- 21. Machine and Deep Learning Techniques for Internet of Things Based Cloud Systems -- Section V: Future Research Opportunities towards Data Science and Data Analytics -- 22. Dialect Identification of the Bengali Language -- 23. Real-Time Security Using Computer Vision -- 24. Data Analytics for Detecting DDoS Attacks in Network Traffic -- 25. Detection of Patterns in Attributed Graph Using Graph Mining -- 26. Analysis and Prediction of the Update of Mobile Android Version. "Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. It is the future of Artificial Intelligence (AI) and the necessity of future to make things easier and more productive. In simple terms, Data science is the discovery of data or uncovering hidden patterns (like complex behaviors, trends, and inferences) from data. Moreover, Big Data Analytics/Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, R, etc., are being used to analyse this large amount of data that can be used in predicting the valuable information/ making decisions. Note that structured data can be easily analysed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in unstructured form that requires advanced analytics tools. But while analysing, we face several concerns like complexity, scalability, privacy leaking and trust issues. Data science helps us in extracting meaningful information (or insights) from the unstructured or complex or large amount of data (available or stored around us virtually at cloud). In summary, this book will cover all the possible areas, applications with arising serious concerns, and challenges towards this emerging area/ field in detail (with a comparative analysis/ taxonomy). This books provides information to its readers This book gives concept of Data Science, Tools and Algorithms existed for many useful applications This book provides many challenges and Opportunities in Data Science and Data Analytics, which help researchers in identifying research gaps or problems to continue their research work All possible areas and uses of data science in this smart era This book is in written many areas like agriculture, healthcare, Graph mining, Education, Security, etc., for providing a clear understanding to readers. Academician, Data Scientist, and Stockbrokers from Industry/Business will find this book useful in knowing optimal strategies for enhancing their firm's productivity"--Provided by publisher. Big data. Tyagi, Amit Kumar 1988- editor. FO UPBAG UPBAG-MAIN QA 76.9 B45 D38 2022 Book