Prediction and Analysis for Knowledge Representation and Machine Learning
Herausgeber: Kumar, Avadhesh; Kumar, T Ganesh; Sagar, Shrddha
Prediction and Analysis for Knowledge Representation and Machine Learning
Herausgeber: Kumar, Avadhesh; Kumar, T Ganesh; Sagar, Shrddha
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This book illustrates different techniques and structures that are used in knowledge representation and machine learning. The aim of this book is to draw the attention of graduates, researchers and practitioners working in field of information technology and computer science (in knowledge representation in machine learning).
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This book illustrates different techniques and structures that are used in knowledge representation and machine learning. The aim of this book is to draw the attention of graduates, researchers and practitioners working in field of information technology and computer science (in knowledge representation in machine learning).
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 220
- Erscheinungstermin: 7. Oktober 2024
- Englisch
- Abmessung: 234mm x 156mm x 12mm
- Gewicht: 331g
- ISBN-13: 9780367649111
- ISBN-10: 036764911X
- Artikelnr.: 71560858
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
- Verlag: Taylor & Francis Ltd (Sales)
- Seitenzahl: 220
- Erscheinungstermin: 7. Oktober 2024
- Englisch
- Abmessung: 234mm x 156mm x 12mm
- Gewicht: 331g
- ISBN-13: 9780367649111
- ISBN-10: 036764911X
- Artikelnr.: 71560858
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- 06621 890
Avadhesh Kumar is PVC at Galgotias University, Greater Noida, Uttar Pradesh, India. He has more than 21 years of Academic and Research Experience. He was awarded Ph.D. in Computer Science & Engineering in 2010 from Thapar University, Patiala, Punjab, India. He did his M.Tech. in Information Technology and B.Tech. in Computer Science & Engineering from Harcourt Butler Technological Institute (HBTI), Kanpur, UP, India. His Research Area includes Software Engineering, Aspect-Oriented Software Systems, Component-Based Software Development, Soft Computing, and Artificial Intelligence. He has published more than 30 Research papers in reputed Journals and conferences. He has authored 3 books. He is Reviewer of many International Journals and Conferences. He has been Keynote Speaker in many International Conferences. Shrddha Sagar is working as Associate Professor in School of Computing Science and Engineering, Galgotias University, NCR Delhi, India. She has completed Ph. D in Computer Science from Banasthali University, Jaipur, India. Her main thrust research areas are Artificial Intelligence, Internet of Things, Machine learning and Big Data. She is a pioneer researcher in the areas of Artificial Intelligence, Internet of Things, Machine learning and has published more than 25 papers in various national / international journals. She has presented paper in National/International Conferences, published book chapters in Taylor & Francis Group (CRC Press), IGI global. Dr. T. Ganesh Kumar works as an Associate Professor at the School of Computing Science and Engineering in Galgotias University, NCR, Delhi. He received ME degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tamilnadu, India. He completed his full time PhD degree in Computer Science and Engineering at Manonmaniam Sundaranar University. He was Co-Investigator for two government of India sponsored funded projects He has published many reputed international SCI and Scopus indexed journals and conferences. He is a reviewer of many reputed journals. He has published five patents in India. Dr. K Sampath Kumar Professor & Research Coordinator in the School of Computing Science and Engineering, Galgotias University, Greater Noida, UP, NCR- Delhi, India. He has complete his Ph.D in Data Mining from Anna University-Chennai, Tamil Nadu, India and obtained his ME from Sathyabama University-Chennai,Tamil Nadu, India. He has over 20 years of teaching and industry experience. His expertise in Big Data, Cloud Computing, IOT, Artificial Intelligence and Real Time Systems. He published more than 50 research articles in the international journals and Conferences and also published 5 patents (IPR).
1. Machine Learning. 2. Design of a knowledge representation and Indexing:
Background and Future. 3. Prediction Analysis of Noise Component using
Median Based Filters Cascaded With Evolutionary Algorithms. 4. Construction
of Deep Representations. 5. Knowledge Representation using Probabilistic
model and Reconstruction based algorithms. 6. Multi-Ontology Mapping for
Internet of Things (MOMI). 7. Higher Level Abstraction of Deep
Architecture. 8. Knowledge Representation and Learning Mechanism Based on
Networks of Spiking Neurons. 9. Multiview Representation learning. 10.
Covid-19 Applications
Background and Future. 3. Prediction Analysis of Noise Component using
Median Based Filters Cascaded With Evolutionary Algorithms. 4. Construction
of Deep Representations. 5. Knowledge Representation using Probabilistic
model and Reconstruction based algorithms. 6. Multi-Ontology Mapping for
Internet of Things (MOMI). 7. Higher Level Abstraction of Deep
Architecture. 8. Knowledge Representation and Learning Mechanism Based on
Networks of Spiking Neurons. 9. Multiview Representation learning. 10.
Covid-19 Applications
1. Machine Learning. 2. Design of a knowledge representation and Indexing:
Background and Future. 3. Prediction Analysis of Noise Component using
Median Based Filters Cascaded With Evolutionary Algorithms. 4. Construction
of Deep Representations. 5. Knowledge Representation using Probabilistic
model and Reconstruction based algorithms. 6. Multi-Ontology Mapping for
Internet of Things (MOMI). 7. Higher Level Abstraction of Deep
Architecture. 8. Knowledge Representation and Learning Mechanism Based on
Networks of Spiking Neurons. 9. Multiview Representation learning. 10.
Covid-19 Applications
Background and Future. 3. Prediction Analysis of Noise Component using
Median Based Filters Cascaded With Evolutionary Algorithms. 4. Construction
of Deep Representations. 5. Knowledge Representation using Probabilistic
model and Reconstruction based algorithms. 6. Multi-Ontology Mapping for
Internet of Things (MOMI). 7. Higher Level Abstraction of Deep
Architecture. 8. Knowledge Representation and Learning Mechanism Based on
Networks of Spiking Neurons. 9. Multiview Representation learning. 10.
Covid-19 Applications