000 04175nam a22005535i 4500
001 978-981-19-5689-8
003 DE-He213
005 20240423130142.0
007 cr nn 008mamaa
008 220928s2022 si | s |||| 0|eng d
020 _a9789811956898
_9978-981-19-5689-8
024 7 _a10.1007/978-981-19-5689-8
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aWorld of Business with Data and Analytics
_h[electronic resource] /
_cedited by Neha Sharma, Mandar Bhatavdekar.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXVI, 201 p. 141 illus., 114 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Autonomic, Data-driven and Industrial Computing,
_x2730-6445
505 0 _aChapter 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 _aThis 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 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 0 _aQuantitative research.
650 1 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
650 2 4 _aData Analysis and Big Data.
700 1 _aSharma, Neha.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBhatavdekar, Mandar.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811956881
776 0 8 _iPrinted edition:
_z9789811956904
776 0 8 _iPrinted edition:
_z9789811956911
830 0 _aStudies in Autonomic, Data-driven and Industrial Computing,
_x2730-6445
856 4 0 _uhttps://doi.org/10.1007/978-981-19-5689-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185609
_d185609