Textbook in PDF format

The primary objective of this book is to provide insight into the design and development of the intelligent system. The proposed book volume mainly focuses on a machine learning and deep learning-based intelligent system that would bring out the latest trends in the field of tourism, healthcare, agriculture, etc. This book provides security solutions for the intelligent system in different applications. The technological gaps between the traditional system and intelligent system are mentioned in the book, which will help in better understanding for the implementation of the intelligent system using Machine Learning (ML) and Deep Learning (DL) approaches. Although ML and DL have made great achievements in intelligent systems, there are still substantial open challenges that have not been fully studied. The main open challenges of using ML and DL in intelligent systems are: (i) Better performance of the system (ii) Time complexity of the jobs running inside an intelligent system (iii) Managing overload tasks (iv) Providing security towards the system. This book will definitely help academicians, researchers and industry people towards the security, design and development of the intelligent system.
Data Science is a growing field that involves various applications using statistical and Machine Learning techniques applied on large dataset. As technology continues to advance, data scientists are able to mine more information from ever-larger sets of data, making it easier to analyze patterns in the world around us. This book presents an overview of different primary models in intelligent systems using Machine Learning and Deep Learning. It begins by defining the concepts and principles of Machine Learning, including supervised, unsupervised, and reinforcement learning models. This also explains how Deep Learning extends these models through the interference of Artificial Neural Networks (ANN), and discusses popular Deep Learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The book indeed explores how the different models would be useful in various real-world applications, like, computer vision, natural language processing (NLP), and robotics. Finally, it concludes by discussing some of the challenges and future benefits of intelligent systems with reputed Machine Learning models and models in Deep Learning. In technical words, Intrusion detection system (IDS) refers to them as packets, and large packets are transferred across an internet network in a second's time.
Most of the tools used in the Data Science industry are Python, Machine Learning, NoSQL, MongoDB, Hadoop, Spark, etc. Fields directly or indirectly related to Data Science include statistical learning, Machine Learning, Deep Learning, image processing, signal processing, natural language processing, predictive modeling, etc.
Preface
Intelligent Systems for Future Applications Using Machine Learning
Fundamental Models in Intelligent Systems Using Machine Learning and Deep Learning
A Comparative Analysis of Machine Learning Algorithms on Intrusion Detection Systems
A Novel Approach for Requirement-Based Test Case Prioritization Using Machine Learning Techniques
The Detection and Prevention of Phishing Threats in OSN Using Machine Learning Techniques
A Novel Approach to Detecting Apple Disease Using CNN
A Novel Sigmoid Butterfly Optimization Deep Learning Model for Big Data Classification
An Analysis of Optical Character Recognition-Based Machine Translation for Low Resource Languages
Generative AI for Bio-Signal Analysis and Augmentation
Deep Learning for the Closed Loop Diabetes Management System
Digital Image Spatial Feature Learning and Mapping Using Geospatial Artificial Intelligence: A Case Study