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_a10.1007/978-981-19-5689-8 _2doi |
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_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. |
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300 |
_aXVI, 201 p. 141 illus., 114 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
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700 | 1 |
_aBhatavdekar, Mandar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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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 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-19-5689-8 |
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912 | _aZDB-2-SXCS | ||
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