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001484389 019__ $$a1412620991
001484389 020__ $$a9789819965502$$q(electronic bk.)
001484389 020__ $$a9819965500$$q(electronic bk.)
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001484389 0247_ $$a10.1007/978-981-99-6550-2$$2doi
001484389 035__ $$aSP(OCoLC)1410862084
001484389 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dEBLCP$$dOCLCO$$dGW5XE$$dOCLCO
001484389 049__ $$aISEA
001484389 050_4 $$aQA76.9.D3
001484389 08204 $$a005.75/65$$223/eng/20231213
001484389 1112_ $$aInternational Conference on Data Analytics and Management.$$n(4th :$$d2023 :$$cLondon, England).
001484389 24510 $$aProceedings of data analytics and management :$$bICDAM 2023.$$nVolume 3 /$$cAbhishek Swaroop, Zdzislaw Polkowski, Sérgio Duarte Correia, Bal Virdee, editors.
001484389 24630 $$aICDAM 2023
001484389 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001484389 264_4 $$c©2023
001484389 300__ $$a1 online resource (xxvi, 687 pages) :$$billustrations (chiefly color).
001484389 336__ $$atext$$btxt$$2rdacontent
001484389 337__ $$acomputer$$bc$$2rdamedia
001484389 338__ $$aonline resource$$bcr$$2rdacarrier
001484389 4901_ $$aLecture notes in networks and systems ;$$vvolume 787
001484389 500__ $$aInternational conference proceedings.
001484389 500__ $$aIncludes author index.
001484389 5058_ $$aIntro -- ICDAM-2023 Steering Committee Members -- Preface -- Contents -- Editors and Contributors -- Enhancing Computational Thinking Based on Virtual Robot of Artificial Intelligence Modeling in the English Language Classroom -- 1 Introduction -- 2 Method -- 2.1 Participants -- 2.2 Questionnaire -- 2.3 The Procedure of Robotics Instructional Design (RID) in English Class -- 3 Results and Discussions -- 3.1 Questionnaire and Observation Results of CT Through Virtual Robotics of Artificial Intelligence -- 3.2 Discussion -- 4 Conclusions -- References
001484389 5058_ $$aSoftware Change Prediction Model Using Ensemble Learning -- 1 Introduction -- 2 Data Collection -- 2.1 Data Acquisition -- 2.2 Extraction of Objective Oriented Metrics -- 2.3 Assessment of Changes Made Between Two Versions -- 2.4 Dealing with Data Imbalance -- 3 Training of Base Classifiers -- 3.1 Selection of Classifiers -- 3.2 Training Base Classifiers -- 4 Implementation of Prediction Network -- 5 Aggregation of the Outcome -- 6 Results and Analysis -- 6.1 AUC Score -- 6.2 Precision Result -- 7 Conclusion -- References
001484389 5058_ $$aDiscerning Monkeypox from Other Viruses of the Poxviridae Family in a Deep Learning Paradigm -- 1 Introduction -- 1.1 History of Monkeypox -- 1.2 Need for Monkeypox Detection -- 1.3 Machine Learning and AI for Monkeypox Detection and a Brief History for the Researches Using ML -- 1.4 Proposed Method -- 2 Recent Works in the Field -- 3 Proposed Methodology -- 3.1 Models Used -- 3.2 Image Preprocessing, Augmentation and Data Separation -- 3.3 Basic Flow and Classification -- 3.4 Ensemble -- 4 Dataset Used -- 5 Metrics Used for Evaluation of the Research -- 6 Results -- 7 Conclusion -- References
001484389 5058_ $$aAn Exploratory Study to Classify Brain Tumor Using Convolutional Neural Networks -- 1 Introduction -- 1.1 Overview -- 1.2 Motivation -- 1.3 Organization of the Paper -- 1.4 Main Contributions -- 2 Background Study -- 2.1 Convolutional Neural Network -- 2.2 Brain Tumor -- 2.3 Magnetic Resonance Imaging -- 3 Literature Survey -- 4 Proposed Methodology -- 4.1 Dataset -- 4.2 Image Preprocessing -- 4.3 Model Training -- 4.4 Model Deployment -- 5 Results -- 6 Comparative Study -- 7 Conclusion -- References -- Skin Cancer Detection with Edge Devices Using YOLOv7 Deep CNN -- 1 Introduction
001484389 5058_ $$a2 Related Work -- 3 Methodology -- 3.1 Data Description -- 3.2 Transfer Learning Approach -- 3.3 Experimental Setup -- 4 Result and Discussion -- 5 Conclusion -- References -- Effective Image Captioning Using Multi-layer LSTM with Attention Mechanism -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 Encoder-Decoder Architecture -- 3.2 Feature Extraction -- 3.3 Text Preprocessing -- 3.4 Attention Mechanism -- 3.5 Language Modeling -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Evaluation Metric -- 4.3 Hyperparameters Used -- 5 Results and Discussion -- 6 Conclusion -- References
001484389 506__ $$aAccess limited to authorized users.
001484389 520__ $$aThis book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.
001484389 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 13, 2023).
001484389 650_6 $$aBases de données$$xGestion$$vCongrès.
001484389 650_0 $$aDatabase management$$vCongresses.$$vCongresses$$0(DLC)sh2008102037
001484389 655_0 $$aElectronic books.
001484389 655_7 $$aproceedings (reports)$$2aat
001484389 655_7 $$aConference papers and proceedings$$2fast
001484389 655_7 $$aConference papers and proceedings.$$2lcgft
001484389 655_7 $$aActes de congrès.$$2rvmgf
001484389 7001_ $$aSwaroop, Abhishek,$$eeditor.
001484389 7001_ $$aPolkowski, Zdzislaw,$$eeditor.
001484389 7001_ $$aCorreia, Sérgio Duarte,$$eeditor.
001484389 7001_ $$aVirdee, Bal,$$eeditor.
001484389 77608 $$iPrint version: $$z9819965497$$z9789819965496$$w(OCoLC)1394115006
001484389 830_0 $$aLecture notes in networks and systems ;$$vv. 787.
001484389 852__ $$bebk
001484389 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-6550-2$$zOnline Access$$91397441.1
001484389 909CO $$ooai:library.usi.edu:1484389$$pGLOBAL_SET
001484389 980__ $$aBIB
001484389 980__ $$aEBOOK
001484389 982__ $$aEbook
001484389 983__ $$aOnline
001484389 994__ $$a92$$bISE