001471738 000__ 06255cam\\2200685\i\4500
001471738 001__ 1471738
001471738 003__ OCoLC
001471738 005__ 20230908003312.0
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001471738 008__ 230714s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001471738 020__ $$a9783031353147$$q(electronic bk.)
001471738 020__ $$a3031353145$$q(electronic bk.)
001471738 020__ $$z9783031353130
001471738 020__ $$z3031353137
001471738 0247_ $$a10.1007/978-3-031-35314-7$$2doi
001471738 035__ $$aSP(OCoLC)1390445335
001471738 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001471738 049__ $$aISEA
001471738 050_4 $$aQ334
001471738 08204 $$a006.3$$223/eng/20230714
001471738 1112_ $$aComputer Science On-line Conference$$n(12th :$$d2023 :$$cOnline)
001471738 24510 $$aArtificial intelligence application in networks and systems :$$bproceedings of 12th Computer Science On-line Conference 2023.$$nVolume 3 /$$cedited by Radek Silhavy, Petr Silhavy.
001471738 264_1 $$aCham :$$bSpringer,$$c2023.
001471738 300__ $$a1 online resource (xvii, 839 pages) :$$billustrations (chiefly color).
001471738 336__ $$atext$$btxt$$2rdacontent
001471738 337__ $$acomputer$$bc$$2rdamedia
001471738 338__ $$aonline resource$$bcr$$2rdacarrier
001471738 4901_ $$aLecture notes in networks and systems ;$$v724
001471738 500__ $$aInternational conference proceedings.
001471738 500__ $$aIncludes author index.
001471738 5050_ $$aIntro -- Preface -- Organization -- Contents -- Prediction Model for Tax Assessments Using Data Mining and Machine Learning -- 1 Introduction -- 2 Literature Review and Related works -- 3 Methodology -- 3.1 Model Design and Implementation -- 3.2 Web Application Design and Implementation -- 4 Results -- 4.1 Random Forest Score Classifier -- 4.2 Confusion Matrix -- 4.3 ROC Curve -- 5 Discussion and Conclusion -- References -- A Review of Evaluation Metrics in Machine Learning Algorithms -- 1 Introduction -- 2 Related Work -- 3 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion
001471738 5058_ $$a3 The Proposed Approach -- 3.1 The Dataset Description -- 3.2 The STS Model -- 3.3 The (SAF) Model -- 3.4 Factors Impacting Student Performance -- 3.5 Input Layer (SAF) Model -- 3.6 Hidden Layer (SAF) Model -- 3.7 Output Layer (SAF) Model -- 4 Final Decision -- 5 Optimization Procedure -- 6 Results -- 7 Conclusion -- References -- Data Mining, Natural Language Processing and Sentiment Analysis in Vietnamese Stock Market -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 General -- 3.2 Data Crawling -- 3.3 Labeling System -- 4 Experiments -- 4.1 Dataset Number One -- 4.2 Dataset Number Two
001471738 5058_ $$a4.3 Comments -- 5 Conclusions -- References -- Two Approaches to E-Book Content Classification -- 1 Introduction -- 2 An Initial Data and an "Image" Model -- 3 Classification Based on a Mixed Text-Formula Model -- 3.1 Data Preparation -- 3.2 Feature Extraction in a Text-Formula Model -- 4 Practical Results of Classification -- 5 Conclusions -- References -- On the Geometry of the Orbits of Killing Vector Fields -- 1 Introduction -- 2 The Geometry of Killing Vector Fields -- 3 The Classification of Geometry of Orbits -- 4 On the Compactness of the Orbits
001471738 5058_ $$a5 Applications in Partial Differential Equations -- 6 Conclusion -- References -- The Classification of Vegetations Based on Share Reflectance at Spectral Bands -- 1 Introduction -- 2 Materials, Data and Methods -- 3 Results -- 3.1 Preparation Data of Share Reflection for Vegetations -- 3.2 Classification of Vegetations Based on Share Reflectance at Bands -- 4 Discussion -- 5 Conclusions -- References -- The Problem of Information Singularity in the Storage of Digital Data -- 1 Introduction -- 2 Overview of Information Singularity Issues -- 2.1 Storing and Interpreting Streaming Videos
001471738 506__ $$aAccess limited to authorized users.
001471738 520__ $$aThe application of artificial intelligence in networks and systems is a rapidly evolving field that has the potential to transform a wide range of industries. The refereed proceedings in this book is from the Artificial Intelligence Application in Networks and Systems session of the Computer Science Online Conference 2023 (CSOC 2023), which was held online in April 2023. The section brings together experts from different fields to present their research and discuss the latest trends and challenges. One of the key themes in this section is the development of intelligent systems that can learn, adapt, and optimize their performance in real time. Researchers are exploring how AI algorithms can be used to create autonomous networks and systems that can make decisions without human intervention. Furthermore, this section highlights the use of AI in improving network performance and efficiency. Researchers are exploring how AI algorithms can be used to optimize network routing, reduce congestion, and improve the quality of service. These efforts can help organizations save costs and improve user experience.
001471738 588__ $$aDescription based on print version record.
001471738 650_0 $$aArtificial intelligence$$vCongresses.
001471738 650_0 $$aSoftware engineering$$vCongresses.
001471738 655_0 $$aElectronic books.
001471738 655_7 $$aConference papers and proceedings.$$2lcgft
001471738 7001_ $$aSilhavy, Radek,$$eeditor.
001471738 7001_ $$aSilhavy, Petr,$$eeditor.
001471738 77608 $$iPrint version:$$aComputer Science On-line Conference (12th : 2023 : Online), creator.$$tArtificial intelligence application in networks and systems : Volume 3.$$dCham : Springer, 2023$$z9783031353130$$w(OCoLC)1382791189
001471738 830_0 $$aLecture notes in networks and systems ;$$v724.
001471738 852__ $$bebk
001471738 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35314-7$$zOnline Access$$91397441.1
001471738 909CO $$ooai:library.usi.edu:1471738$$pGLOBAL_SET
001471738 980__ $$aBIB
001471738 980__ $$aEBOOK
001471738 982__ $$aEbook
001471738 983__ $$aOnline
001471738 994__ $$a92$$bISE