Applied Intelligence for Industry 4.0

1. Auflage 2023
ISBN: 978-1-032-16415-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 278 Seiten, Format (B × H): 261 mm x 184 mm, Gewicht: 654 g

We are all aware that artificial intelligence (AI) has brought a change in our lives, driven by a new form of interaction between man and machine. We are in the era of the fourth Industrial Revolution (IR) where AI plays vital roles in human development by enabling extraordinary technological advances making fundamental changes to the way we live, work and relate to one another. It is an opportunity to help everyone, including leaders, policymakers and people from all income groups and nations, to harness converging technologies in order to create an inclusive, human-centered future. We need to prepare our graduates as well as researchers to conduct their research with 4.0 IR-related technologies. We need to develop policies and implement those policies to focus on the components of 4.0 IR for sustainable developments. Applied Intelligence for Industry 4.0 will cover cutting edge topics in the fields of AI and industry 4.0. The text will appeal to beginners and advanced researchers in computer science, information sciences, engineering and robotics.

Features

- Discusses advance data mining, feature extraction and classification algorithms for disease detection, cyber security detection and prevention, soil quality assessment and other industrial applications

- Includes the parameter optimization and explanation of intelligent approaches for business applications

- Presents context-aware smart insights and energy efficient and smart computing for the next-generation of smart industry

Zielgruppe

Academic

Autoren/Hrsg.

Kayes, Asm

Siddique, Nazmul

Kaiser, M Shamim

Arefin, Mohammad Shamsul

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

- Multi-labelled Bengali Public Comments Sentiment Analysis with Bidirectional Recurrent Neural Networks (Bi-RNN). 2. Machine Learning and Blockchain based Privacy-aware: Cognitive Radio Internet of Things. 3. Machine Learning Based Models for Predicting Autism Spec-trum Disorders. 4. Implementing Machine Learning Through the Neural Network for the Time Delay SIR Epidemic Model for the Future Forecast. 5. Prediction of PCOS Using Machine Learning and Deep Learning Algorithms. 6. Malware Detection: Performance Evaluation of ML Algo-rithms based on Feature Selection and ANOVA. 7. An Efficient Approach to Assess the Soil Quality of Sundar-bans Utilizing Hierarchical Clustering. 8. A Machine Learning Approach to Clinically Diagnose Human Pyrexia Cases. 9. Prediction of the Dengue Incidence in Bangladesh using Ma-chine Learning. 10. Detecting DNS over HTTPS Traffic Using Ensemble Feature Based Machine Learning. 11. Development of Risk-Free COVID-19 Screening Algorithm from Routine Blood Test using Ensemble Machine Learning. 12. A Transfer Learning Approach to Recognize Pedestrian At-tributes. 13.TF-IDF Feature-based Spam Filtering of Mobile SMS using Machine Learning Approach. 14. Content-based Spam Email Detection Using N-gram Machine Learning Approach. 15. AI Poet: A Deep Learning Based Approach to Generate Arti-ficial Poetry in Bangla. 16. Document Level Comparative Sentiment Analysis on Bangla News using Long-Short Term Memory and Machine Learning Approaches. 17. Employee Turnover Prediction Using Machine Learning Ap-proach. 18. A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets. 19. Analyzing IT Job Market and Classifying IT Jobs Using Ma-chine Learning Algorithms

Über Autor(innen)

Nazmul Siddique received the Dipl.-Ing. degree in Cybernetics and Automation from Dresden University of Technology, Dresden, Germany, MSc in Computer Science from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh and the Ph.D in Intelligent Control from the University of Sheffield, England, U.K. He has been a Lecturer with the School of Computing, Engineering and Intelligent Systems, University of Ulster Magee Campus, Londonderry, U.K since 2001. He was previously with the Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh. He has been a guest editor of seven special issues of several reputed journals. He has served as committee member and chair of a number of national and international conferences. He is a senior member of IEEE. He is on the Editorial Board of a number of International Journals. Dr. Siddique has published over 170 journal, refereed conference papers, book chapters, and five books (John Wiley, Springer, Taylor & Francis). His research interests are in the fields of intelligent systems, computational intelligence, stochastic systems, and Markov modeling.