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|a 10.1007/978-981-99-5547-3
|2 doi
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|a (OCoLC)1410824708
|z (OCoLC)1410333378
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|a ASEAN-Australian Engineering Congress
|d (2022 :
|c Online)
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| 245 |
1 |
0 |
|a Proceedings of ASEAN-Australian Engineering Congress (AAEC2022) :
|b engineering solutions in the age of digital disruption /
|c Chung Siung Choo, Basil T. Wong, Khairul Hafiz Bin Sharkawi, Daniel Kong, editors.
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| 246 |
3 |
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|a AAEC 2022
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| 264 |
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1 |
|a Singapore :
|b Springer,
|c 2023.
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| 300 |
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|a 1 online resource (342 pages) :
|b illustrations (black and white, and color).
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| 336 |
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|a text
|b txt
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|a computer
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|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Lecture notes in electrical engineering ;
|v volume 1072
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| 520 |
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|a This book presents the proceedings of the ASEAN-Australian Engineering Congress (AAEC2022), held as a virtual event, 1315 July 2022 with the theme Engineering Solutions in the Age of Digital Disruption. The book presents selected papers covering scientific research in the field of Engineering Computing, Network, Communication and Cybersecurity, Artificial Intelligence & Machine Learning, Materials Science & Manufacturing, Automation and Sensors, Smart Energy & Cities, Simulation & Optimisation and other Industry 4.0 related Technologies. The book appeals to researchers, academics, scientists, students, engineers and practitioners who are interested in the latest developments and applications related to addressing the Fourth Industrial Revolution (IR4.0).
|
| 588 |
0 |
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|a Print version record.
|
| 504 |
|
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|a References -- Cough Sound Disease Detection with Artificial Intelligence -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Data Analysis of Fly Ash Geopolymer Compressive Strength Using Machine Learning Method -- 1 Introduction -- 2 Methodology -- 2.1 Sample Preparation -- 2.2 Compressive Strength Test -- 2.3 Neural Network -- 3 Result and Discussion -- 3.1 Compressive Strength -- 3.2 Neural Network -- 4 Conclusion -- 5 Recommendations -- References
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| 505 |
0 |
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|a Intro -- Foreword by Ir. Dennis Ong -- Foreword by Prof. Jugdutt Singh -- Preface -- Acknowledgements -- List of Reviewers -- Contents -- Artificial Intelligence and Machine Learning -- A Systematic Literature Review on Determining the Effectiveness of Short-Term COVID-19 Prediction Models -- 1 Introduction -- 2 Objectives -- 2.1 Inclusion and Exclusion Criteria -- 2.2 Quality Assessment and Coding -- 3 Methods -- 3.1 Machine Learning Models -- 3.2 Deep Learning Models -- 3.3 Predictive Analysis -- 3.4 GLEM and Hybrid EAMA Models -- 3.5 Other Methods -- 4 Discussion -- 5 Conclusion
|
| 505 |
8 |
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|a Feature Reduction of Relational Oil Drilling Data Before Propositionalization and Harmonization by Measuring Relational Data Missingness -- 1 Introduction -- 2 FSbP Via DAHFR -- 3 Data -- 4 Methodology -- 4.1 Determining a Baseline Feature -- 4.2 Relative Missing Rate Measurement -- 4.3 Feature Relevance Determination -- 4.4 Validation -- 5 Results and Discussion -- 6 Conclusion -- References -- Automation and Sensors -- Investigation of the Effect of Polyvinyl Acetate Coating on Multiple U-shaped Fibre Optic Sensors in Different Solution Conditions -- 1 Introduction -- 2 Background
|
| 505 |
8 |
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|a 2.1 U-shaped FOS -- 2.2 Polyvinyl Acetate (PVAc) -- 3 Methodology -- 3.1 Equipment and Material Used -- 3.2 Fabrication of Fibre Probe -- 3.3 Fibre Coating with Dripping Method Experiment -- 3.4 Glycerol Solution (RI) Experiment -- 3.5 Honey Solution (RI) Experiment -- 3.6 Data Analysis -- 4 Result and Discussion -- 4.1 Fibre Coating with Dripping Method Experiment -- 4.2 Glycerol Solution (RI) Experiment -- 4.3 Honey Solution (RI) Experiment -- 4.4 Sensor Prototype Testing Equipment -- 5 Conclusion -- References
|
| 505 |
8 |
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|a Temperature Modulation of Metal Oxide Semiconductor Gas Sensors for Black Pepper Geo-Tracing -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Education 4.0 -- Online Learning: Students' Barriers and Feedback on the Instructional Delivery Methods -- 1 Introduction -- 2 Materials and Methods -- 2.1 Target Respondent -- 2.2 Questionnaire Distribution -- 2.3 Questionnaire Design -- 2.4 Statistical Analysis -- 3 Results and Discussion -- 3.1 Students' Background -- 3.2 Students' Accessibility -- 3.3 Mode of Teaching -- 4 Conclusion
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| 650 |
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0 |
|a Engineering
|x Data processing
|v Congresses.
|
| 650 |
|
0 |
|a Industry 4.0
|v Congresses.
|
| 650 |
|
7 |
|a Engineering
|x Data processing.
|2 fast
|0 (OCoLC)fst00910334
|
| 650 |
|
7 |
|a Industry 4.0.
|2 fast
|0 (OCoLC)fst02024640
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 fast
|0 (OCoLC)fst01423772
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
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|a Choo, Chung Siung,
|e editor.
|
| 700 |
1 |
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|a Wong, B. T.
|q (Basil T.),
|e editor.
|1 https://isni.org/isni/0000000425428281
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| 700 |
1 |
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|a Sharkawi, Khairul Hafiz Bin,
|e editor.
|
| 700 |
1 |
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|a Kong, Daniel,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|a ASEAN-Australian Engineering Congress (2022 : Online), creator.
|t Proceedings of ASEAN-Australian Engineering Congress (AAEC2022).
|d Singapore : Springer, 2023
|z 9789819955466
|w (OCoLC)1402254232
|
| 830 |
|
0 |
|a Lecture notes in electrical engineering ;
|v v. 1072.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-99-5547-3
|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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