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001450313 001__ 1450313
001450313 003__ OCoLC
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001450313 019__ $$a1346533891$$a1347029157
001450313 020__ $$a9783031120978$$q(electronic bk.)
001450313 020__ $$a3031120973$$q(electronic bk.)
001450313 020__ $$z9783031120961
001450313 020__ $$z3031120965
001450313 0247_ $$a10.1007/978-3-031-12097-8$$2doi
001450313 035__ $$aSP(OCoLC)1347384750
001450313 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCQ
001450313 049__ $$aISEA
001450313 050_4 $$aQA75.5
001450313 08204 $$a004$$223/eng/20221013
001450313 1112_ $$aConference on Computer Systems and Applications$$n(5th :$$d2022 :$$cAlgiers, Algeria)
001450313 24510 $$aAdvances in computing systems and applications :$$bproceedings of the 5th Conference on Computing Systems and Applications /$$cMustapha Reda Senouci, Said Yacine Boulahia, Mohamed Akrem Benatia, editors.
001450313 2463_ $$aCSA 2022
001450313 264_1 $$aCham :$$bSpringer,$$c2022.
001450313 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and color).
001450313 336__ $$atext$$btxt$$2rdacontent
001450313 337__ $$acomputer$$bc$$2rdamedia
001450313 338__ $$aonline resource$$bcr$$2rdacarrier
001450313 4901_ $$aLecture notes in networks and systems ;$$vvolume 513
001450313 500__ $$aIncludes author index.
001450313 5050_ $$aIntro -- Preface -- Organization -- Contents -- Artificial Intelligence and Data Science -- AttR2U-Net: Deep Attention Based Approach for Melanoma Skin Cancer Image Segmentation -- 1 Introduction -- 2 Background and Related Work -- 2.1 R2U-Net Architecture -- 2.2 Attention Mechanism -- 3 AttR2U-Net Configurations -- 3.1 AttR2U-Net-V1 -- 3.2 AttR2U-Net-V2 -- 3.3 AttR2U-Net-V3 -- 4 Experiments and Results -- 4.1 ISIC Dataset -- 4.2 Experimental Results -- 5 Conclusion -- References -- Causality Analysis Method and Model Related to Why-Question Answering in Business Intelligence Context
001450313 5058_ $$a1 Introduction -- 2 Causality Analysis Approaches -- 3 Proposed Causality Perception Model in BI Context -- 4 Proposed Causality Analysis Method -- 5 Experimental Study -- 5.1 Granger Causality Tests -- 5.2 Association Rules Algorithms Results -- 6 Conclusion and Future Works -- References -- Markovian Segmentation of Non-stationary Data Corrupted by Non-stationary Noise -- 1 Introduction -- 2 Two-jumping Conditional Triplet Markov Models -- 2.1 Two-jumping Conditional Triplet Markov Chain -- 2.2 Two-jumping Conditional Triplet Markov Field -- 3 Performance Evaluation
001450313 5058_ $$a3.1 Segmentation of Simulated Images -- 3.2 Segmentation of Synthetic Images -- 3.3 Results and Discussion -- 4 Conclusion -- References -- Aster: A DSL for Engineering Self-Adaptive Systems -- 1 Introduction -- 2 Illustrative Example -- 3 Modeling the Aircraft Arrival Planning System -- 3.1 Architecture of the Aircraft Arrival Planning System -- 3.2 Aster Syntax -- 4 The Aircraft Arrival Planning System Formal Semantics -- 4.1 A Petri Net-Based Semantics for Aster -- 4.2 Generating Formal Specifications -- 4.3 The Aircraft Arrival System Semantics -- 5 Conclusion -- References
001450313 5058_ $$aCo-rating Aware Evidential User-Based Collaborative Filtering Recommender System -- 1 Introduction -- 2 Dempster-Shafer Theory Basic Concepts -- 3 Evidential Collaborative Filtering -- 3.1 A Brief Overview of ECF Research -- 3.2 Evidential K-Nearest Neighbors -- 4 Problem Formulation -- 5 Experimental Evaluation -- 5.1 Dataset -- 5.2 Metrics -- 5.3 Results -- 6 Conclusion and Perspectives -- References -- Graph Representation Learning for Covid-19 Drug Repurposing -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Computational Workflow -- 3.2 Data Collection -- 3.3 Method
001450313 5058_ $$a4 Results -- 4.1 Model Training -- 4.2 Model Evaluation -- 4.3 Drugs Ranking and Validation -- 5 Conclusion -- References -- A Scalable Adaptive Sampling Based Approach for Big Data Classification -- 1 Introduction -- 2 Prior Works in Big Data Sampling -- 3 Scalable Adaptive Sampling Based on ScaSRS, BLB and Chebyshev Inequality -- 3.1 Selecting Data with ScaSRS Algorithm -- 3.2 Learning and Creating Model -- 3.3 Calculating the Variance -- 3.4 Improved Sample Accuracy Using Active Learning -- 3.5 SGDAS Algorithm -- 4 Results and Discussion -- 4.1 Test Dataset -- 4.2 Empirical Results
001450313 506__ $$aAccess limited to authorized users.
001450313 520__ $$aThe book is a valuable reference work for students, researchers, academics, and industry practitioners interested in the latest scientific and technological advances across the conference topics. The CSA 2022 proceedings provide a collection of new ideas, original research findings, and experimental results in the field of computer science covering: artificial intelligence, data science, computer networks and security, information systems, software engineering, and computer graphics.
001450313 588__ $$aDescription based on print version record.
001450313 650_0 $$aComputer systems$$vCongresses.
001450313 655_0 $$aElectronic books.
001450313 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001450313 7001_ $$aReda Senouci, Mustapha,$$eeditor.
001450313 7001_ $$aBoulahia, Said Yacine,$$eeditor.
001450313 7001_ $$aBenatia, Mohamed Akrem,$$eeditor.
001450313 77608 $$iPrint version:$$aConference on Computer Systems and Applications (5th : 2022), creator.$$tAdvances in computing systems and applications.$$dCham : Springer, 2022$$z9783031120961$$w(OCoLC)1346321789
001450313 830_0 $$aLecture notes in networks and systems ;$$vv. 513.
001450313 852__ $$bebk
001450313 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12097-8$$zOnline Access$$91397441.1
001450313 909CO $$ooai:library.usi.edu:1450313$$pGLOBAL_SET
001450313 980__ $$aBIB
001450313 980__ $$aEBOOK
001450313 982__ $$aEbook
001450313 983__ $$aOnline
001450313 994__ $$a92$$bISE