Data science and algorithms in systems : proceedings of 6th Computational Methods in Systems and Software 2022. Vol. 2

Data science and algorithms in systems : Vol. 2 / proceedings of 6th Computational Methods in Systems and Software 2022. Radek Silhavy, Petr Silhavy, Zdenka Prokopova, editors.

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Bibliographic Details
Corporate Author: Computational Methods in Systems and Software Online
Other Authors: Silhavy, Radek (Editor), Silhavy, Petr (Editor), Prokopova, Zdenka (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2023]
Series:Lecture notes in networks and systems ; v. 597.
Subjects:
Software engineering > Congresses.
System design > Congresses.
Software engineering
System design
Electronic books.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • 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
  • A 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
  • 11 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
  • 2.1 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
  • 3.1 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

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