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001453734 001__ 1453734
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001453734 019__ $$a1357016305
001453734 020__ $$a9783031214387$$q(electronic bk.)
001453734 020__ $$a3031214382$$q(electronic bk.)
001453734 020__ $$z9783031214370
001453734 020__ $$z3031214374
001453734 0247_ $$a10.1007/978-3-031-21438-7$$2doi
001453734 035__ $$aSP(OCoLC)1356891271
001453734 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCQ$$dUKAHL
001453734 049__ $$aISEA
001453734 050_4 $$aQA76.758
001453734 08204 $$a005.1$$223/eng/20230113
001453734 1112_ $$aComputational Methods in Systems and Software$$n(6th :$$d2022 :$$cOnline)
001453734 24510 $$aData science and algorithms in systems :$$bproceedings of 6th Computational Methods in Systems and Software 2022.$$nVol. 2 /$$cRadek Silhavy, Petr Silhavy, Zdenka Prokopova, editors.
001453734 264_1 $$aCham :$$bSpringer,$$c[2023]
001453734 264_4 $$c©2023
001453734 300__ $$a1 online resource (xviii, 1022 pages) :$$billustrations (some color).
001453734 336__ $$atext$$btxt$$2rdacontent
001453734 337__ $$acomputer$$bc$$2rdamedia
001453734 338__ $$aonline resource$$bcr$$2rdacarrier
001453734 4901_ $$aLecture notes in networks and systems ;$$vvolume 597
001453734 500__ $$aConference proceedings.
001453734 500__ $$aIncludes author index.
001453734 5050_ $$aIntro -- Preface -- Organization -- Contents -- Understanding the General Framework for Teaching Semantics and Syntaxes of Visual Languages to Computer Education Students Based on Notion of Abstract Visual Syntax Graphs -- 1 Introduction -- 2 Related Work -- 2.1 Syntax of Visual Languages -- 2.2 Semantics of Visual Languages -- 2.3 Graph Representation -- 2.4 Abstract Visual Syntax Graph and Graph Grammar -- 2.5 Logical Semantics -- 3 The three Notable Visual Languages -- 3.1 Euler Diagrams (Circle) -- 3.2 VEX -- 3.3 Show and Tell -- 4 Conclusions and Future Work -- References
001453734 5058_ $$aA Prediction System Using AI Techniques to Predict Students' Learning Difficulties Using LMS for Sustainable Development at KFU -- 1 Introduction -- 1.1 Practitioner Notes -- 2 Related Work -- 3 Machine Learning -- 3.1 Logistics Regression (LR) -- 3.2 K-Nearest Neighbor (KNN) -- 3.3 Decision Tree (DT) -- 3.4 Naive Bayes Algorithm (NB) -- 3.5 Random Forest (RF) -- 3.6 Stochastic Gradient Descent (SGD) -- 3.7 Ridge Classifier -- 3.8 Nearest Centroid -- 4 Dataset Description -- 5 Methodology -- 6 Data Transformation -- 7 Data Partitioning -- 8 Performance Evaluation -- 9 Results -- 10 Conclusion
001453734 5058_ $$a11 Discussion -- References -- COVID-19 Detection from Chest X-Ray Images Using Detectron2 and Faster R-CNN -- 1 Introduction -- 2 Deep Learning Based Object Detection -- 2.1 R-CNN -- 2.2 Fast R-CNN -- 2.3 Faster R-CNN -- 2.4 YOLO -- 3 Methodology -- 3.1 Dataset -- 3.2 Baseline Models -- 3.3 Evaluating Object Detection Models -- 3.4 Training Process for Different Models -- 4 Results and Discussion -- 5 Conclusion -- References -- Effective SNOMED-CT Concept Classification from Natural Language using Knowledge Distillation -- 1 Introduction -- 2 Related work
001453734 5058_ $$a2.1 SNOMED-CT (Systemized Nomenclature of Medicine Clinical Term) -- 2.2 Medical Natural Language Document -- 2.3 Methods for Inferring Terms for Binding SNOMED-CT -- 2.4 Knowledge Distillation -- 2.5 BioBert ch4ref11 -- 3 Methodology -- 3.1 Problem statement -- 3.2 Proposed Model -- 3.3 Data Preprocessing -- 3.4 Learning Method and Architecture -- 4 Results and Discussions -- 5 Conclusion -- References -- Analyze Mental Health Disorders from Social Media: A Review -- 1 Introduction -- 2 Methodology -- 3 Result
001453734 5058_ $$a3.1 RQ1: What Technique Is Most Commonly Used in the Mental Health Analysis in the Last Five Years? -- 3.2 RQ2: What Data Sources or Applications Are Widely Used to Retrieve Test Data? -- 3.3 Synthetic Result -- 4 Conclusion -- References -- Methods of Solution to the Task on Early Detection of Fire Outbreaks Based on Images and Video Streams from Controlled Territories -- 1 Introduction -- 2 Review of Existing Methods -- 3 Set up of the Task -- 4 Realization of Experiments -- 4.1 Task of Binary Classification -- 4.2 Extraction of Places with Fire Based on YOLO -- 5 Conclusion -- References
001453734 506__ $$aAccess limited to authorized users.
001453734 520__ $$aThis book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.
001453734 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 13, 2023).
001453734 650_0 $$aSoftware engineering$$vCongresses.
001453734 650_0 $$aSystem design$$vCongresses.
001453734 655_0 $$aElectronic books.
001453734 655_7 $$aConference papers and proceedings.$$2lcgft
001453734 7001_ $$aSilhavy, Radek,$$eeditor.
001453734 7001_ $$aSilhavy, Petr,$$eeditor.
001453734 7001_ $$aProkopova, Zdenka,$$eeditor.
001453734 77608 $$iPrint version: $$z3031214374$$z9783031214370$$w(OCoLC)1347695597
001453734 830_0 $$aLecture notes in networks and systems ;$$vv. 597.
001453734 852__ $$bebk
001453734 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-21438-7$$zOnline Access$$91397441.1
001453734 909CO $$ooai:library.usi.edu:1453734$$pGLOBAL_SET
001453734 980__ $$aBIB
001453734 980__ $$aEBOOK
001453734 982__ $$aEbook
001453734 983__ $$aOnline
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