東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

World of business with data and anal...

Sharma, Neha.

FindBook

Google Book

Amazon

博客來

World of business with data and analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	World of business with data and analytics/ edited by Neha Sharma, Mandar Bhatavdekar.
其他作者:	Sharma, Neha.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xiv, 201 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1. Dynamic Demand Planning for Distorted Historical Data Due to Pandemic -- Chapter 2. Cognitive Models to Predict Pipeline Leaks and Ruptures -- Chapter 3. Network Optimization of the Electricity Grid to manage Distributed Energy Resources using Data & Analytics -- Chapter 4. Enhancing Market Agility Through Accurate Price Indicators using Contextualized Data Analytics -- Chapter 5. Infrastructure for Automated Surface Damage Classification and Detection in Production industries using ResUNet based Deep Learning Architecture -- Chapter 6. Cardiac Arrhythmias Classification & Detection for Medical Industry Using Wavelet Transformation & Probabilistic Neural Network Architecture -- Chapter 7. Investor Behavior towards Mutual Fund -- Chapter 8. iMask - An Artificial Intelligence Based Redaction Engine -- Chapter 9. Artificial Intelligence for Proactive Vulnerability Prediction and interpretability using Occlusion -- Chapter 10. Intrusion Detection System using Signature based Detection and Data Mining Technique. Chapter 11. Cloud Cost Intelligence using Machine Learning -- Chapter 12. Mining deeper Insights using Unsupervised NLP -- Chapter 13. Explainable AI for ML OPS.
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-981-19-5689-8
ISBN:	9789811956898

World of business with data and analytics
World of business with data and analytics[electronic resource] /edited by Neha Sharma, Mandar Bhatavdekar. - Singapore :Springer Nature Singapore :2022. - xiv, 201 p. :ill. (some col.), digital ;24 cm. - Studies in autonomic, data-driven and industrial computing,2730-6445. - Studies in autonomic, data-driven and industrial computing..

Chapter 1. Dynamic Demand Planning for Distorted Historical Data Due to Pandemic -- Chapter 2. Cognitive Models to Predict Pipeline Leaks and Ruptures -- Chapter 3. Network Optimization of the Electricity Grid to manage Distributed Energy Resources using Data & Analytics -- Chapter 4. Enhancing Market Agility Through Accurate Price Indicators using Contextualized Data Analytics -- Chapter 5. Infrastructure for Automated Surface Damage Classification and Detection in Production industries using ResUNet based Deep Learning Architecture -- Chapter 6. Cardiac Arrhythmias Classification & Detection for Medical Industry Using Wavelet Transformation & Probabilistic Neural Network Architecture -- Chapter 7. Investor Behavior towards Mutual Fund -- Chapter 8. iMask - An Artificial Intelligence Based Redaction Engine -- Chapter 9. Artificial Intelligence for Proactive Vulnerability Prediction and interpretability using Occlusion -- Chapter 10. Intrusion Detection System using Signature based Detection and Data Mining Technique. Chapter 11. Cloud Cost Intelligence using Machine Learning -- Chapter 12. Mining deeper Insights using Unsupervised NLP -- Chapter 13. Explainable AI for ML OPS.

This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.

ISBN: 9789811956898

Standard No.: 10.1007/978-981-19-5689-8doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / W67 2022

Dewey Class. No.: 006.312

World of business with data and analytics
LDR:03213nmm a2200337 a 4500 001 2303898
003 DE-He213
005 20220928105107.0
006 m d
007 cr nn 008maaau
008 230409s2022 si s 0 eng d
020 $a 9789811956898 $q (electronic bk.)
020 $a 9789811956881 $q (paper)
024 7 $a 10.1007/978-981-19-5689-8 $2 doi
035 $a 978-981-19-5689-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b W67 2022
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b W927 2022
245 0 0 $a World of business with data and analytics $h [electronic resource] / $c edited by Neha Sharma, Mandar Bhatavdekar.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xiv, 201 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in autonomic, data-driven and industrial computing, $x 2730-6445
505 0 $a Chapter 1. Dynamic Demand Planning for Distorted Historical Data Due to Pandemic -- Chapter 2. Cognitive Models to Predict Pipeline Leaks and Ruptures -- Chapter 3. Network Optimization of the Electricity Grid to manage Distributed Energy Resources using Data & Analytics -- Chapter 4. Enhancing Market Agility Through Accurate Price Indicators using Contextualized Data Analytics -- Chapter 5. Infrastructure for Automated Surface Damage Classification and Detection in Production industries using ResUNet based Deep Learning Architecture -- Chapter 6. Cardiac Arrhythmias Classification & Detection for Medical Industry Using Wavelet Transformation & Probabilistic Neural Network Architecture -- Chapter 7. Investor Behavior towards Mutual Fund -- Chapter 8. iMask - An Artificial Intelligence Based Redaction Engine -- Chapter 9. Artificial Intelligence for Proactive Vulnerability Prediction and interpretability using Occlusion -- Chapter 10. Intrusion Detection System using Signature based Detection and Data Mining Technique. Chapter 11. Cloud Cost Intelligence using Machine Learning -- Chapter 12. Mining deeper Insights using Unsupervised NLP -- Chapter 13. Explainable AI for ML OPS.
520 $a This book covers research work spanning the breadth of ventures, a variety of challenges and the finest of techniques used to address data and analytics, by subject matter experts from the business world. The content of this book highlights the real-life business problems that are relevant to any industry and technology environment. This book helps us become a contributor to and accelerator of artificial intelligence, data science and analytics, deploy a structured life-cycle approach to data related issues, apply appropriate analytical tools & techniques to analyze data and deliver solutions with a difference. It also brings out the story-telling element in a compelling fashion using data and analytics. This prepares the readers to drive quantitative and qualitative outcomes and apply this mindset to various business actions in different domains such as energy, manufacturing, health care, BFSI, security, etc.
650 0 $a Data mining. $3 562972
650 0 $a Business $x Data processing. $3 527441
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Analysis and Big Data. $3 3538537
700 1 $a Sharma, Neha. $3 3209829
700 1 $a Bhatavdekar, Mandar. $3 3605596
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in autonomic, data-driven and industrial computing. $3 3494523
856 4 0 $u https://doi.org/10.1007/978-981-19-5689-8
950 $a Computer Science (SpringerNature-11645)