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001468306 001__ 1468306
001468306 003__ OCoLC
001468306 005__ 20230707003245.0
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001468306 019__ $$a1380464879
001468306 020__ $$a9789819903931$$q(electronic bk.)
001468306 020__ $$a9819903939$$q(electronic bk.)
001468306 020__ $$z9819903920
001468306 020__ $$z9789819903924
001468306 0247_ $$a10.1007/978-981-99-0393-1$$2doi
001468306 035__ $$aSP(OCoLC)1380686148
001468306 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP
001468306 049__ $$aISEA
001468306 050_4 $$aTA404.23
001468306 08204 $$a620.110285/631$$223/eng/20230606
001468306 24500 $$aMachine learning for advanced functional materials /$$cNirav Joshi, Vinod Kushvaha, Priyanka Madhushri, editors.
001468306 260__ $$aSingapore :$$bSpringer,$$c2023.
001468306 300__ $$a1 online resource
001468306 5050_ $$aIntro -- Preface -- Contents -- Solar Cells and Relevant Machine Learning -- 1 Introduction -- 1.1 Generations of Solar Cells -- 1.2 Machine Learning -- 2 Workflow of Machine Learning -- 2.1 Data Collection and Preparation -- 2.2 Model Building and Evaluation -- 3 Machine Learning for Solar Cells -- 3.1 Naïve Bayes (NB) -- 3.2 Artificial Neural Network (ANN) -- 3.3 Decision Trees (DT) -- 3.4 Other Machine Learning Techniques -- 4 Typical Applications of ML Tools for Solar Cells -- 4.1 Effect of Material Properties on PCE of Solar Cells -- 4.2 Prediction of Optimal Device Structure
001468306 5058_ $$a5 Conclusion and Future Recommendations -- References -- Machine Learning-Driven Gas Identification in Gas Sensors -- 1 Introduction -- 2 Gas Sensor and Electronic Nose -- 2.1 Gas Sensors Classification -- 2.2 Characteristics of Chemiresistive Type Gas Sensors -- 2.3 Gas Sensor with Identification Capability: Electronic Nose -- 3 Gas Sensing Response Features -- 3.1 Steady-State Features -- 3.2 Transient-State Features -- 4 Gas Sensing Signal Modulation Methods -- 5 Machine Learning-Enabled Smart Gas Sensor for Industrial Gas Identification -- 6 Summary and Outlook -- References
001468306 5058_ $$aA Machine Learning Approach in Wearable Technologies -- 1 Introduction -- 2 Machine Learning Algorithms Commonly Used in Wearable Technologies -- 2.1 Supervised Machine Learning -- 2.2 Non-supervised Machine Learning -- 2.3 Deep Learning -- 2.4 Evaluation Metrics -- 3 Application of Machine Learning in Wearable Technologies -- 3.1 Healthcare Applications -- 3.2 Sports Analytics -- 3.3 Smart Farming and Precision Agriculture -- 4 Conclusion and Outlooks -- References
001468306 5058_ $$aPotential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials -- 1 Introduction -- 2 Fundamentals of Machine Learning Algorithms: In Context of Material Science -- 3 Adoption of Machine Learning in Material Science -- 3.1 Principle -- 3.2 Automatic Information Acquisition -- 3.3 Physical Insights from Materials Learning -- 4 Model Generalizability and Performance in the Real World -- 4.1 Case Study: Prediction of TATB Peak Stress -- 4.2 Model Generalizability Takeaways -- 5 Conclusions -- References
001468306 5058_ $$aThe Application of Novel Functional Materials to Machine Learning -- 1 Introduction -- 2 Design of Experiments and Parameter Space Optimization -- 2.1 Device Fabrication -- 2.2 Synthesis of Materials -- 3 Identifying Next-Generation Materials -- 3.1 Plan for Achieving Carbon Neutrality -- 3.2 Technological Advancements -- 4 Algorithms for Machine Learning -- 5 Machine Learning Applications -- 5.1 Batteries -- 5.2 Photovoltaics and Light-Emitting Materials -- 6 Future Perspective -- 6.1 Materials for CO2 Capture -- 6.2 Materials for Catalysis
001468306 506__ $$aAccess limited to authorized users.
001468306 520__ $$aThis book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the materials electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
001468306 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 6, 2023).
001468306 650_0 $$aMaterials$$xData processing.
001468306 650_0 $$aMachine learning.
001468306 655_0 $$aElectronic books.
001468306 7001_ $$aJoshi, Nirav.
001468306 7001_ $$aKushvaha, Vinod.
001468306 7001_ $$aMadhushri, Priyanka.
001468306 77608 $$iPrint version: $$z9819903920$$z9789819903924$$w(OCoLC)1363100588
001468306 852__ $$bebk
001468306 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-0393-1$$zOnline Access$$91397441.1
001468306 909CO $$ooai:library.usi.edu:1468306$$pGLOBAL_SET
001468306 980__ $$aBIB
001468306 980__ $$aEBOOK
001468306 982__ $$aEbook
001468306 983__ $$aOnline
001468306 994__ $$a92$$bISE