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001442223 0247_ $$a10.1007/978-981-16-2422-3$$2doi
001442223 035__ $$aSP(OCoLC)1266904753
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001442223 049__ $$aISEA
001442223 050_4 $$aQA76.76.E95
001442223 08204 $$a006.3/3$$223
001442223 1112_ $$aInternational Conference on Intelligent Sustainable Systems$$n(4th :$$d2021 :$$cTirunelveli, India)
001442223 24510 $$aIntelligent Sustainable Systems :$$bProceedings of ICISS 2021 /$$cJennifer S. Raj, Ram Palanisamy, Isidoros Perikos, Yong Shi, editors.
001442223 2463_ $$aICISS 2021
001442223 260__ $$aSingapore :$$bSpringer,$$c2022.
001442223 300__ $$a1 online resource (844 pages)
001442223 336__ $$atext$$btxt$$2rdacontent
001442223 337__ $$acomputer$$bc$$2rdamedia
001442223 338__ $$aonline resource$$bcr$$2rdacarrier
001442223 347__ $$atext file
001442223 347__ $$bPDF
001442223 4901_ $$aLecture Notes in Networks and Systems ;$$vv. 213
001442223 500__ $$a5 Simulation Results.
001442223 500__ $$aIncludes author index.
001442223 5050_ $$aIntro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- Deep Learning-Based Approach for Parkinson's Disease Detection Using Region of Interest -- 1 Introduction -- 1.1 Subject-Level Classification -- 1.2 Region of Interest -- 1.3 Model Generalizability -- 1.4 Data Leakage -- 2 Literature Survey -- 3 Dataset Description -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Architecture -- 4.3 Evaluation Metrics -- 5 Experiments and Results -- 5.1 Subject-Level Classification -- 5.2 Region of Interest -- 5.3 Model Generalizability -- 5.4 Effect of Data Leakage
001442223 5058_ $$a6 Conclusion -- References -- Classification of Class-Imbalanced Diabetic Retinopathy Images Using the Synthetic Data Creation by Generative Models -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Description -- 2.2 Retinal Synthetic Image Generation -- 2.3 CNN Classifier -- 3 Experimental Results and Discussions -- 4 Conclusion -- References -- A Novel Leaf Fragment Dataset and ResNet for Small-Scale Image Analysis -- 1 Introduction -- 2 Dataset Preparation -- 2.1 Data Collection -- 2.2 Image Pre-processing -- 2.3 Post-processing -- 2.4 Filename-Format
001442223 5058_ $$a3 Residual Neural Network for Cotyledon-Type Identification and Plant Species Classification -- 3.1 Dataset Formulation and Feature Description -- 3.2 Residual Block -- 3.3 Methodology -- 4 Results -- 4.1 Cotyledon-Type Identification -- 4.2 Plant Species Classification -- 5 Discussion -- 6 Comparison Between Applied ResNet and ResNet-152 V2 -- 7 Conclusion and Possible Future Contributions -- References -- Prediction of Covid 19 Cases Based on Weather Parameters -- 1 Introduction -- 2 Review of Related Literature Paper -- 3 Methodology Used -- 3.1 Dataset Used
001442223 5058_ $$a3.2 Correlation Study of the Factors -- 3.3 Mean Squared Error -- 3.4 Models Used for Study -- 3.5 Analysis Performed -- 4 Experiment Results -- 4.1 Linear Regression -- 4.2 Decision Tree Regression -- 4.3 Random Forest Regression -- 5 Conclusion -- References -- CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Approaches -- 3.1 Unsupervised K-Means Clustering Algorithm -- 3.2 Secured Multi-party Addition Algorithm -- 3.3 Pailier Homomorphic Encryption -- 4 Work Done -- 5 Experimental Results and Discussion
001442223 5058_ $$a5.1 Communication Overhead -- 5.2 Storage Overhead -- 5.3 Scalability -- 5.4 Encryption Cost -- 5.5 Runtime Analysis on Data Points -- 5.6 Runtime Analysis on Data Dimensionality -- 6 Limitations and Future Work -- 7 Concluding Remarks -- References -- Performance Evaluation of Hierarchical Clustering Protocols in WSN Using MATLAB -- 1 Introduction -- 2 Radio Model -- 3 Overview of Clustering Protocols -- 3.1 LEACH (Low-Energy Adaptive Clustering Hierarchy) -- 3.2 LEACH in Heterogeneous Environment -- 3.3 LEACH-C -- 3.4 SEP -- 3.5 DEEC -- 3.6 DDEEC -- 4 Simulation Scenario and Performance Metrics
001442223 506__ $$aAccess limited to authorized users.
001442223 520__ $$aThis book features research papers presented at the 4th International Conference on Intelligent Sustainable Systems (ICISS 2021), held at SCAD College of Engineering and Technology, Tirunelveli, Tamil Nadu, India, during February 26-27, 2021. The book discusses the latest research works that discuss the tools, methodologies, practices, and applications of sustainable systems and computational intelligence methodologies. The book is beneficial for readers from both academia and industry.
001442223 588__ $$aDescription based on print version record.
001442223 650_0 $$aExpert systems (Computer science)$$vCongresses.
001442223 650_0 $$aElectronic systems$$vCongresses.
001442223 650_0 $$aInformation technology$$vCongresses.
001442223 650_0 $$aSustainable engineering$$vCongresses.
001442223 650_6 $$aSystèmes experts (Informatique)$$vCongrès.
001442223 650_6 $$aSystèmes électroniques$$vCongrès.
001442223 650_6 $$aTechnologie de l'information$$vCongrès.
001442223 650_6 $$aIngénierie durable$$vCongrès.
001442223 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001442223 655_7 $$aConference papers and proceedings.$$2lcgft
001442223 655_7 $$aActes de congrès.$$2rvmgf
001442223 655_0 $$aElectronic books.
001442223 7001_ $$aRaj, Jennifer S.
001442223 7001_ $$aPalanisamy, Ram.
001442223 7001_ $$aPerikos, Isidoros.
001442223 7001_ $$aShi, Yong,$$d1956-
001442223 77608 $$iPrint version:$$aRaj, Jennifer S.$$tIntelligent Sustainable Systems.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811624216
001442223 830_0 $$aLecture notes in networks and systems ;$$vv. 213.
001442223 852__ $$bebk
001442223 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2422-3$$zOnline Access$$91397441.1
001442223 909CO $$ooai:library.usi.edu:1442223$$pGLOBAL_SET
001442223 980__ $$aBIB
001442223 980__ $$aEBOOK
001442223 982__ $$aEbook
001442223 983__ $$aOnline
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