Applied Machine Learning for Smart Data Analysis
1st Edition
Published on 5. June 2019
Book
Hardback
244 pages
978-1-138-33979-8 (ISBN)
Description
The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.
Key Features
Follows an algorithmic approach for data analysis in machine learning
Introduces machine learning methods in applications
Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics
Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets
Case studies are covered relating to human health, transportation and Internet applications
Key Features
Follows an algorithmic approach for data analysis in machine learning
Introduces machine learning methods in applications
Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics
Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets
Case studies are covered relating to human health, transportation and Internet applications
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic
Illustrations
105 s/w Abbildungen, 16 s/w Tabellen
16 Tables, black and white; 105 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
520 gr
ISBN-13
978-1-138-33979-8 (9781138339798)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nilanjan Dey | Sanjeev Wagh | Parikshit N. Mahalle
Applied Machine Learning for Smart Data Analysis
E-Book
05/2019
1st Edition
CRC Press
€73.49
Available for download
Nilanjan Dey | Sanjeev Wagh | Parikshit N. Mahalle
Applied Machine Learning for Smart Data Analysis
E-Book
05/2019
1st Edition
CRC Press
€73.49
Available for download
Persons
Nilanjan Dey, Sanjeev Wagh, Parikshit N. Mahalle, Mohd. Shafi Pathan
Content
1. Hindi and Urdu To English Named Entity Statistical Machine Transliteration Using Source Language Word Origin Context. 2. Anti-Depression Psychotherapist Chat-Bot for Exam And Study Depression. 3. Deep Learning for HealthCare Information's. 4. Priority based Message Forwarding Scheme in VANET with Intelligent Navigation. 5. Plagiasil: "A Plagiarism Detector"(MAS Scalable Framework for Research Effort Evaluation by Unsupervised Machine Learning - Hybrid Plagiarism Model). 6. Digital image processing using Wavelets Basic principles and application. 7. Placements Probability Predictor Using Data Mining Techniques. 8. Big Data Summarization using Modified Fuzzy Clustering Algorithm, Semantic Feature and Data Compression Approach. 9. Topic specific Natural Language Chatbot as General Advisor for College. 10. Implementing Ubiquitous Environment In Museum. 11. Implementation of Machine Learning in Education Sector: Analyzing causes behind average student grades. 12. Traffic Zone Warning and Violation Detection using Mobile Computing. 13. A Comparative Analysis and Discussion of Email Spam Classification Methods using Machine Learning Techniques. 14. Malware Prevention and Detection System for SmartPhone: A Machine Learning Approach. 15. Spam Review Detection and Recommendation of Correct Outcomes Based on Appropriate Reviews.