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on1391983845 |
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OCoLC |
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20240909213021.0 |
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m o d |
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cr un|---aucuu |
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230802s2023 sz a o 101 0 eng d |
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|a 9783031373039
|q electronic book
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|a 3031373030
|q electronic book
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|a 10.1007/978-3-031-37303-9
|2 doi
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|a (OCoLC)1391983845
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|a Q334
|b .I58 2022
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|a UYQ
|2 bicssc
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|a COM004000
|2 bisacsh
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|a UYQ
|2 thema
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|a HCDD
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2 |
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|a International Conference on Artificial Intelligence and Internet of Things
|n (1st :
|d 2022 :
|c Jamshedpur, India)
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| 245 |
1 |
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|a Recent trends in artificial intelligence and IoT :
|b first International Conference, ICAII 2022, Jamshedpur, India, April 4-5, 2023, Revised selected papers /
|c Rajesh Kumar Tiwari, G. Sahoo, editors.
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3 |
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|a ICAII 2022
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| 264 |
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1 |
|a Cham :
|b Springer,
|c 2023.
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| 300 |
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|a 1 online resource (xiii, 330 pages) :
|b illustrations (some color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Communications in computer and information science,
|x 1865-0937 ;
|v 1822
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| 520 |
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|a This book constitutes selected papers presented at the First International Conference on Artificial Intelligence and Internet of Things, ICAII 2022, held in Jamshedpur, India. ICAII 2022 has been postponed to April 2023. The 23 papers were thoroughly reviewed and selected from the 86 submissions. They are arranged in topical sections on artificial Intelligence, and Internet of Things.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed August 1, 2023).
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| 505 |
0 |
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|a Intro -- Preface -- Organization -- Contents -- Artificial Intelligence -- Student Performance Monitoring System Using Artificial Intelligence Models -- 1 Introduction -- 2 Collaboration Through Decision-Making -- 3 Artificial Intelligence -- 3.1 Impact of AI on Education -- 3.2 Machine Learning -- 3.3 Decision-Making -- 4 Proposed Model of 4 -Tier DSS Using AI -- 4.1 Decision-Making Systems -- 5 Methodology -- 5.1 Decision-Making with Data Mining -- 5.2 Typical Attributes -- 6 Result and Discussion -- 6.1 Accuracy of Prediction -- 6.2 DSS in E-Learning -- 6.3 Decision-Making in Education
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| 505 |
8 |
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|a 7 Conclusion -- References -- On Assaying the T-score Value for the Detection and Classification of Osteoporosis Using AI Learning Techniques -- 1 Introduction -- 2 Background -- 3 Analysis -- 4 Discussion -- 5 Conclusion -- References -- Evaluation of Healthcare Data in Machine Learning Model Used in Fraud Detection -- 1 Introduction -- 2 Literature Review -- 3 Evaluation Method -- 3.1 Accuracy -- 3.2 Precision or Positive Predicted Value -- 3.3 Negative Predicted Value -- 3.4 Sensitivity or Recall or TPR -- 3.5 Specificity or True Negative Rate -- 3.6 False Positive Rate
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| 505 |
8 |
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|a 3.7 False Negative Rate -- 3.8 FDR -- 3.9 F 1 Score or Beta -- 3.10 GLC -- 3.11 F2 Score or Beta 2 -- 3.12 ROC (Receiver Operating Characteristic) Curve -- 3.13 AUC -- 3.14 Average Precision or PR AUC Score -- 3.15 Brier Score -- 4 Evaluation Measure -- 5 Conclusion -- References -- A New Approach to Heart Disease Prediction Using Clustering and Classification Algorithms -- 1 Introduction -- 2 Literature Review -- 3 Clustering Algorithms -- 4 Classification Algorithms -- 5 Problem Statement -- 6 Methodology -- 6.1 K-Nearest Neighbors (KNN) and K-Means Algorithm -- 6.2 Logistic Regression (LR)
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| 505 |
8 |
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|a 7 Results and Discussions -- 8 Conclusion -- References -- Skin Lesion Classification: Scrutiny of Learning-Based Methods -- 1 Introduction -- 2 Skin Lesion Imaging Techniques -- 2.1 Optical Coherence Tomography (OCT) -- 2.2 Reflectance Confocal Microscopy (RCM) -- 2.3 Dermoscopy -- 2.4 Ultrasound -- 3 Benchmark Dataset -- 3.1 Hospital Pedro Hispano (PH2) -- 3.2 International Skin Imaging Collaboration (ISIC) Archive -- 4 Deep Learning-Based Skin Lesion Detection Systems -- 5 Challenges and Future Recommendations -- 6 Conclusion -- References
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| 505 |
8 |
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|a Lung Disease Classification Using CNN-Based Trained Models from CXR Image -- 1 Introduction -- 2 Related work -- 3 Methodology -- 3.1 System Architecture -- 3.2 AlexNet -- 3.3 SqueezeNet -- 3.4 Feature Selection -- 4 Dataset -- 5 Preprocessing -- 6 Proposed Work -- 7 Result and Discussion -- 8 Evaluation -- 9 Conclusion -- References -- A Novel Framework for Satellite Image Denoising and Super Resolution Using CNN-GAN -- 1 Introduction -- 2 Related Work -- 2.1 SAR Image Multiplicative Noise Degradation Model -- 2.2 CNNs for SAR Image Despeckling -- 2.3 Residual Learning -- 3 Proposed Model
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| 650 |
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0 |
|a Artificial intelligence
|v Congresses.
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| 650 |
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0 |
|a Internet of things
|v Congresses.
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| 650 |
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7 |
|a Artificial intelligence
|2 fast
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| 650 |
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7 |
|a Internet of things
|2 fast
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Tiwari, Rajesh Kumar,
|e editor.
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| 700 |
1 |
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|a Sahoo, G.
|e editor.
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| 830 |
|
0 |
|a Communications in computer and information science ;
|v 1822.
|x 1865-0937
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-37303-9
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
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