001468044 000__ 06742cam\\22007577i\4500
001468044 001__ 1468044
001468044 003__ OCoLC
001468044 005__ 20230707003351.0
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001468044 008__ 230520s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001468044 020__ $$a9783031311536$$qelectronic book
001468044 020__ $$a3031311531$$qelectronic book
001468044 020__ $$z3031311523
001468044 020__ $$z9783031311529
001468044 0247_ $$a10.1007/978-3-031-31153-6$$2doi
001468044 035__ $$aSP(OCoLC)1379440314
001468044 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dEBLCP$$dYDX
001468044 049__ $$aISEA
001468044 050_4 $$aHD30.2$$b.I58 2022
001468044 08204 $$a658.4/038$$223/eng/20230524
001468044 1112_ $$aInternational Conference on Information Systems and Management Science$$n(5th :$$d2022)
001468044 24510 $$aKey digital trends shaping the future of information and management science :$$bproceedings of 5th International Conference on Information Systems and Management Science (ISMS) 2022 /$$cLalit Garg, Dilip Singh Sisodia, Nishtha Kesswani, Joseph G. Vella, Imene Brigui, Sanjay Misra, and Deepak Singh .
001468044 2463_ $$aISMS 2022
001468044 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001468044 300__ $$a1 online resource (xiii, 628 pages) :$$billustrations (chiefly color).
001468044 336__ $$atext$$btxt$$2rdacontent
001468044 337__ $$acomputer$$bc$$2rdamedia
001468044 338__ $$aonline resource$$bcr$$2rdacarrier
001468044 4901_ $$aLecture notes in networks and systems ;$$v671
001468044 500__ $$aIncludes author index.
001468044 5050_ $$aIntro -- Preface -- Organization -- Contents -- Improvisation of Predictive Modeling Using Different Classifiers for Predicting Thyroid Disease in Patients -- 1 Introduction -- 1.1 Data Analytics -- 1.2 Prediction and Predictive Analysis -- 2 Literature Review -- 3 Exploratory Data Analysis -- 4 Proposed Model -- 4.1 k-NN Classifier -- 4.2 Random Forest Classifier -- 4.3 XGBoost Classifier -- 4.4 CatBoost Classifier -- 5 Results -- 6 Conclusion -- References -- Application of IoT for Proximity Analysis and Alert Generation for Maintaining Social Distancing -- 1 Introduction -- 2 Related Work
001468044 5058_ $$a3 Materials and Methods -- 3.1 Methodology and Block Diagram of System -- 4 Experimental Setup -- 5 Mathematical Modeling -- 6 Theoretical Results -- 7 Device Simulation and Result -- 8 Conclusions and Future Scope -- References -- Analysis and Optimization of Fault -- Tolerant Behaviour of Motors in Electric Vehicular Systems -- 1 Introduction -- 2 Overview of Motors in Electric Vehicular Systems -- 3 EV Modelling -- 4 Exploratory Data Analysis of PMSM Characteristics Using Real - Time Data -- 4.1 About Dataset -- 4.2 Key Points About the Dataset -- 4.3 Determining Correlations
001468044 5058_ $$a4.4 Generating Heatmap -- 4.5 Observations -- 5 Modelling of the Dynamics of Electric Vehicle -- 6 Fault Analysis -- 6.1 Normal Operation -- 6.2 Abnormal Operation -- 6.3 Observation Table -- 7 Optimization Technique of PMSM for Electric Vehicular Application -- 7.1 Normal Operation -- 7.2 Abnormal Operation -- 7.3 Observation Table -- 8 Conclusion -- References -- Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter -- 1 Introduction -- 2 Contrast Enhancement -- 3 Histogram Equalization (HE)
001468044 5058_ $$a4 Contrast Limited Adaptive Histogram Equalization (CLAHE) -- 4.1 Histogram Modified - Local Contrast Enhancement (HM-LCE) -- 5 Related Works -- 6 Image Segmentation -- 6.1 Mathematical Morphological Operations -- 7 Proposed Contrast Enhancement Using Modified Bi-level Histogram with Homomorphic Filter (MBH-HF) -- 7.1 Modified Bi-level Histogram -- 8 Experimental Analysis -- 8.1 Dataset Used -- 8.2 Experimental Result Analysis -- 9 Conclusion -- References -- Deep Learning Based Bengali Image Caption Generation -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology
001468044 5058_ $$a3.1 Processing of Dataset -- 3.2 Extraction of Feature -- 3.3 Embedding -- 3.4 Encoder -- 3.5 Decoder -- 4 Experimental Setup -- 4.1 Output Generation -- 4.2 Result Analysis -- 5 Comparison -- 6 Future Scope and Conclusion -- References -- Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data -- 1 Introduction -- 2 Related Work -- 3 Research Methodology -- 3.1 Collection of Dataset -- 3.2 Data Pre-processing -- 3.3 Feature Extraction -- 3.4 Emotion Detection Using Deep Learning Models -- 4 Result and Analysis -- 5 Conclusion and Future Work -- References
001468044 506__ $$aAccess limited to authorized users.
001468044 520__ $$aThis book (proceedings of ISMS 2022) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of information systems and management science. This textbook shows how to exploit information systems in a technology-rich management field. The book introduces concepts, principles, methods, and procedures that will be valuable to students and scholars in thinking about existing organization systems, proposing new systems, and working with management professionals in implementing new information systems.
001468044 588__ $$aDescription based on online resource; title from digital title page (viewed on June 22, 2023).
001468044 650_0 $$aInformation resources management$$vCongresses.
001468044 650_0 $$aInformation technology$$vCongresses.
001468044 655_0 $$aElectronic books.
001468044 655_7 $$aConference papers and proceedings.$$2lcgft
001468044 7001_ $$aGarg, Lalit,$$d1977-$$eeditor.
001468044 7001_ $$aSisodia, Dilip Singh,$$d1977-$$eeditor.
001468044 7001_ $$aKesswani, Nishtha,$$eeditor.
001468044 7001_ $$aVella, Joseph G.,$$eeditor.
001468044 7001_ $$aBrigui, Imene$$eeditor.
001468044 7001_ $$aMisra, Sanjay$$eeditor.
001468044 7001_ $$aSingh, Deepak$$eeditor.
001468044 77608 $$iPrint version:$$aGarg, Lalit$$tKey Digital Trends Shaping the Future of Information and Management Science$$dCham : Springer International Publishing AG,c2023$$z9783031311529
001468044 830_0 $$aLecture notes in networks and systems ;$$vv. 671.
001468044 852__ $$bebk
001468044 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31153-6$$zOnline Access$$91397441.1
001468044 909CO $$ooai:library.usi.edu:1468044$$pGLOBAL_SET
001468044 980__ $$aBIB
001468044 980__ $$aEBOOK
001468044 982__ $$aEbook
001468044 983__ $$aOnline
001468044 994__ $$a92$$bISE