Atjaunināt sīkdatņu piekrišanu

E-grāmata: Deep Learning Concepts in Operations Research

Edited by (Institute of Engineering and Management, India), Edited by (Chief Scientific Advisor, Bio Tech Sphere Research, India), Edited by (Kalyani Mahavidyalaya, India), Edited by (Brainware University, India)
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 256,72 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Deep Learning Concepts in Operations ResearchPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:

  • An overview of applications and computing devices
  • Deep learning impacts in the field of AI
  • Deep learning as state-of-the-art approach to AI
  • Exploring deep learning architecture for cutting-edge AI solutions

Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.



The book provides mathematicians an overview of AI and machine learning relevant to operations research. It focuses decision modeling and optimization models as well as algorithms.

1. Deep Learning: Overview, Applications and Computing Devices
2. Deep Learning Impacts in the Field of Artificial Intelligence
3. Deep Learning is a State-of-the-Art Approach to Artificial Intelligence
4. Unleashing the Power: Exploring Deep Learning Architecture for Cutting-Edge AI Solutions
5. Deep Learning for ECG Classification: Techniques, Applications, and Challenges
6. Social Distancing Detection System Using Single Shot Detection (SSD) and Neural Networks
7. Recognition of Voice and Speech Using NLP Techniques
8. Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis
9. Machine Learning for Traffic Flow Prediction Addressing Congestion Challenges
10. Enhancing Autistic Spectrum Disorder Diagnosis Using ML Techniques: A Study on Deep Neural Network and Drop-out Deep Neural Network
11. Deep Learning: A State-of-the-Art Approach to Artificial Intelligence
12. An Approach through Different Mathematical Models to Enhance the Utility in Different Areas of Machine Learning
13. Study of Different Regression Methods, Models and Application in Deep Learning Paradigm
14. Deep Learning Impacts in the Field of Artificial Intelligence
15. Stock Prices Prediction of the FMCG Sector in NSE India: An Artificial Intelligence Approach
16. Multi-Attribute Decision Modelling
17. Regression Methods and Models
18. The Machine Learning Pipeline: Algorithms, Applications, and Managerial Implications
19. Role of Fertamean Neutrosophic Sets for Decision Making Modelling in Machine Learning
20. Performance Evaluation of Machine Learning Algorithms in the Field of Security-Malware Detection

Dr. Biswadip Basu Mallik is an associate professor of Mathematics in the Department of Basic Science & Humanities at Institute of Engineering & Management, University of Engineering & Management, Kolkata, India.

Dr. Gunjan Mukherjee is an associate professor in the Department of Computational Science, Brainware University, Barasat, India.

Rahul Kar holds a master's degree in mathematics from Burdwan University and is currently working as a SACT-II Mathematics faculty of Kalyani Mahavidyalaya, Kalyani, Nadia, West Bengal.

Aryan Chaudhary is the chief scientific advisor at BioTech Sphere Research, India, and a recognized researcher of healthcare and technology.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97810401023816e.html