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001484143 001__ 1484143
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001484143 019__ $$a1409546394
001484143 020__ $$a9789819959747$$qelectronic book
001484143 020__ $$a9819959748$$qelectronic book
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001484143 0247_ $$a10.1007/978-981-99-5974-7$$2doi
001484143 035__ $$aSP(OCoLC)1409679015
001484143 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dYDX
001484143 049__ $$aISEA
001484143 050_4 $$aQ334$$b.I58 2023
001484143 08204 $$a006.3$$223/eng/20231122
001484143 1112_ $$aInternational Conference on Advances and Applications of Artificial Intelligence and Machine Learning$$d(2022).
001484143 24510 $$aAdvances and applications of artificial intelligence and machine learning :$$bProceedings of ICAAAIML 2022 /$$cBhuvan Unhelkar, Hari Mohan Pandey, Arun Prakash Agrawal, Ankur Choudhary, editors.
001484143 264_1 $$aSingapore :$$bSpringer,$$c2023.
001484143 300__ $$a1 online resource (xii, 802 pages) :$$billustrations (chiefly color).
001484143 336__ $$atext$$btxt$$2rdacontent
001484143 337__ $$acomputer$$bc$$2rdamedia
001484143 338__ $$aonline resource$$bcr$$2rdacarrier
001484143 4901_ $$aLecture Notes in Electrical Engineering ;$$vv.1078
001484143 5058_ $$aIntro -- Contents -- About the Editors -- Development of Big Data Dimensionality Reduction Methods for Effective Data Transmission and Feature Enhancement Algorithms -- 1 Introduction -- 2 Works -- 3 Objectives -- 4 Proposed Dimensionality Reduction Method -- 5 Analysis of the Obtained Results -- 6 Conclusion -- References -- IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based Computer Vision -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Preparation -- 3.2 Our Experimentation on Object Detection Models -- 4 Results -- 5 Conclusion -- References
001484143 5058_ $$aPrediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids -- 1 Introduction -- 2 Experimental Setup -- 3 Methodology -- 3.1 Data Set -- 3.2 Feature Representation -- 3.3 Support Vector Machines (SVM) -- 4 Results and Discussion -- 4.1 Evaluation Metrics -- 4.2 Performance of Proposed Model -- 4.3 Proposed Model Comparison Against Various Predictors -- 5 Conclusion -- References -- A Computational Comparison of VGG16 and XceptionNet for Mango Plant Disease Recognition -- 1 Introduction -- 2 Methodology and Dataset
001484143 5058_ $$a2.1 Architecture of the Proposed System -- 2.2 Dataset Description -- 2.3 Data Pre-processing -- 2.4 Models Used -- 2.5 Training and Compiling the Model -- 3 Result and Analysis -- 4 Conclusion -- References -- Generate Artificial Human Faces with Deep Convolutional Generative Adversarial Network (DCGAN) Machine Learning Model -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Setup -- 3.2 Dataset Description -- 3.3 Model Description -- 4 Results -- 5 Future Scope and Conclusion -- References -- Robust Approach for Person Identification Using Three-Triangle Concept
001484143 5058_ $$a1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Block Diagram of Recommended System -- 3.2 Algorithm Used -- 4 Circuit Layout -- 5 Interfacing of Components -- 6 Experimental Results -- 7 Conclusions -- 8 Future Scope -- References -- COVID-19 Disease Detection Using Explainable AI -- 1 Introduction -- 2 Explainable Artificial Intelligence -- 3 Dataset Description -- 4 Approach to the Proposed System -- 4.1 Support Vector Machine -- 4.2 Convolutional Neural Networks -- 4.3 ResNet50 -- 4.4 Implementation of Explainable AI -- 5 Proposed Methodology -- 6 Results
001484143 5058_ $$a7 Conclusion and Future Scope -- References -- Towards Helping Visually Impaired People to Navigate Outdoor -- 1 Introduction -- 1.1 Convolutional Neural Network -- 1.2 Visual Geometry Group -- 2 Literature -- 3 Methodology -- 3.1 Create the Dataset -- 3.2 Applying Existing Approach -- 3.3 Analyzing the Existing Approach -- 3.4 Detect Objects in Image -- 3.5 Train and Test the Model -- 3.6 Analyzing the Results -- 4 Experimentation -- 5 Conclusion and Future Work -- References -- An Analysis of Deployment Challenges for Kubernetes: A NextGen Virtualization -- 1 Introduction -- 2 Origin, History of Kubernetes, and the Community Behind
001484143 506__ $$aAccess limited to authorized users.
001484143 520__ $$aThis volume comprises the select peer-reviewed proceedings of the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning 2022 (ICAAAIML 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in the areas of artificial intelligence, machine learning, deep learning, and their advanced applications in computer vision and blockchain. It also covers research in core concepts of computers, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, software engineering, image processing, and cloud computing. This volume will provide a valuable resource for those in academia and industry.
001484143 650_6 $$aIntelligence artificielle$$vCongrès.
001484143 650_0 $$aArtificial intelligence$$vCongresses.$$xMedical applications$$0(DLC)sh 88003000
001484143 655_7 $$aproceedings (reports)$$2aat
001484143 655_7 $$aConference papers and proceedings.$$2lcgft
001484143 655_7 $$aActes de congrès.$$2rvmgf
001484143 655_0 $$aElectronic books.
001484143 7001_ $$aUnhelkar, Bhuvan.
001484143 7001_ $$aPandey, Hari Mohan.
001484143 7001_ $$aAgrawal, Arun Prakash.
001484143 7001_ $$aChoudhary, Ankur.
001484143 77608 $$iPrint version:$$aUnhelkar, Bhuvan$$tAdvances and Applications of Artificial Intelligence and Machine Learning$$dSingapore : Springer,c2023$$z9789819959730
001484143 830_0 $$aLecture notes in electrical engineering ;$$vv. 1078.
001484143 852__ $$bebk
001484143 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-5974-7$$zOnline Access$$91397441.1
001484143 909CO $$ooai:library.usi.edu:1484143$$pGLOBAL_SET
001484143 980__ $$aBIB
001484143 980__ $$aEBOOK
001484143 982__ $$aEbook
001484143 983__ $$aOnline
001484143 994__ $$a92$$bISE