Interactive and Dynamic Dashboard: Design Principles

Cover Half Title Series Page Title Page Copyright Page Table of Contents About the Editors List of Contributors Chapter 1: Bibliometric analysis on visual data analysis and dynamic dashboard tools: A literature review 1.1 Introduction: data analysis 1.1.1 Dynamic dashboard tools 1.2 Methodology 1.3 Results and discussion 1.3.1 Production cycle of authors 1.3.2 Properties of query string and keywords 1.3.3 Relationship among terms in keywords 1.3.4 Citation analysis based on country of author 1.4 Conclusion References Chapter 2: Visual data analysis and inference through dimensionality reduction techniques 2.1 Introduction 2.1.1 Interactive dashboard 2.1.2 Dynamic interactive dashboard 2.2 Classification of dynamic interactive dashboard tools 2.2.1 Tableau: powerful data visualization tool 2.2.2 Microsoft power BI: unleashing data insights 2.2.3 QlikView and Qlik Sense: powerful tools for associative exploration 2.2.4 Domo: a single platform for analytics and data management 2.2.5 Looker: powerful data visualization and business intelligence 2.2.6 Sisense: unlocking data insights with ease and flexibility 2.2.7 Plotly dash: interactive data visualization for python developers 2.2.8 Dundas BI: feature-rich platform for data visualization and analytics 2.2.9 Zoho analytics: feature-rich and user-friendly data analytics platform 2.3 Dimensionality reduction 2.3.1 Principal component analysis 2.3.2 t-Distributed stochastic neighbour embedding 2.3.3 Uniform manifold approximation and projection 2.4 Conclusion References Chapter 3: Visual data analysis of temperature, ground water level, precipitation for climate-driven socio-economic prediction 3.1 Introduction 3.2 Artificial intelligence models 3.2.1 Machine learning models 3.2.1.1 Vector regression model 3.2.1.2 Random forest (rf) model creation 3.2.1.3 K-nearest-neighbor (KNN) method 3.2.1.4 ML model significance 3.2.2 Deep learning models 3.2.2.1 Adaptive neuro fuzzy inference system (ANFIS) 3.2.2.2 Feedforward neural network (FFNN) 3.2.2.3 Gaussian model (global climate modeling) 3.2.2.4 Long short-term memory (LSTM) (ground water modeling) 3.2.3 Hybrid models 3.2.3.1 ConvLSTM 3.2.4 Challenges in AI models 3.2.4.1 Statistical reasoning 3.2.4.2 Computational reasoning 3.2.4.3 Representational reasoning 3.3 Dataset 3.3.1 Dataset collection 3.3.2 Data mining 3.4 Results and analysis 3.4.1 Ways of weather prediction 3.4.1.1 Synoptic weather prediction 3.4.1.2 Statistical weather prediction 3.4.1.3 Numerical weather prediction 3.4.2 Visual representation 3.4.2.1 Line graph 3.4.2.2 Scatter plot 3.4.2.3 Bar charts 3.4.2.4 Residual scatter plots 3.5 Conclusion References Chapter 4: AI-based online interview bot with an interactive dashboard 4.1 Introduction 4.2 Related works 4.3 Proposed work 4.3.1 Proposed algorithms 4.4 Results & analysis 4.5 Conclusions Acknowledgements References Chapter 5: Visualizing food quality and safety: A dynamic dashboard approach with near-infrared imaging and machine learning 5.1 Introduction 5.2 Related works 5.3 NIR imaging: a non-invasive and insightful technique 5.3.1 Wavelengths of NIR imaging 5.3.2 Techniques in NIR imaging Reflective NIR imaging Hyperspectral NIR imaging NIR spectroscopy 5.3.3 NIR imaging system NIR light source Camera Spectrometer (Hyperspectral systems only) Computer and software 5.4 A symbiotic relationship: NIR imaging and machine learning 5.5 Interactive dashboards: transforming data into actionable insights 5.5.1 Tools for creating a dashboard 5.5.1.1 Creating dashboards with python 5.5.1.2 Creating dashboards with MATLAB 5.5.2 Revolutionizing the food industry: The road ahead 5.6 Conclusion References Chapter 6: Interactive dashboard and 3D visualization using t-SNE dimensionality reduction technique Introduction 3D visualization Literature review Methodology Results and discussion Difference between PCA and t-SNE Challenges of t-SNE Conclusion References Chapter 7: Dynamic dashboard creation for sales trends and optimize pricing strategies 7.1 Introduction 7.2 Literature review 7.2.1 Introduction to dynamic dashboards 7.2.2 Power BI as a dynamic dashboard tool 7.2.3 Design principles for dynamic dashboards 7.2.4 Evaluation metrics for dynamic dashboards 7.2.5 Challenges and future directions 7.3 Methodology 7.3.1 Data collection 7.3.2 Algorithm for filling NaN and outlier values 7.3.3 Dashboard design 7.4 Results 7.4.1 Dashboard design and features 7.4.2 User feedback and adoption 7.4.3 Business impact 7.5 Conclusion 7.5.1 Recapitulation of findings 7.5.2 Key insights 7.5.3 Contributions to practice and research 7.5.4 Implications for business 7.5.5 Future directions References Chapter 8: Scaling up the business with the aid of power query tool 8.1 Introduction 8.2 Literature study 8.3 Research approach 8.4 Results and discussions 8.5 Conclusion References Chapter 9: Interactive visualization techniques for thermal imaging analysis in ophthalmology: Comparative insights and future directions 9.1 Introduction 9.1.1 Evolution of ophthalmological imaging techniques 9.1.2 Rationale for exploring thermal imaging 9.1.3 Objectives of the comparative analysis 9.2 Principles of thermal imaging in ophthalmology 9.2.1 Mechanisms of heat generation and transfer in the eye 9.2.2 Thermal imaging modalities and technologies 9.3 Comparative analysis of imaging techniques 9.4 Applications of thermal imaging in ophthalmology for eye anomaly detection 9.4.1 Diabetic retinopathy 9.4.2 Glaucoma 9.4.3 Dry eye 9.4.4 Scleritis 9.5 Advancements in thermal imaging technology 9.6 Thermal imaging and camera 9.7 Interactive visualization techniques for thermal imaging 9.7.1 FLIR thermal studio software 9.7.2 Data exploration tools: flyr tools 9.7.3 Bokeh 9.7.4 Matplotlib and ipywidgets 9.8 Future directions and emerging applications 9.9 Conclusion References Chapter 10: Mind scan: Dynamic brain cancer detection dashboard with MRI imaging 10.1 Introduction 10.2 Methodology 10.2.1 Morphological process 10.3 Expected results 10.4 Conclusion References Chapter 11: Interactive and dynamic stock market dashboard 11.1 Introduction 11.2 Literature review 11.2.1 Power BI 11.2.2 Benefits of business intelligence (BI) 11.2.3 The importance of dashboards 11.2.4 Previous research 11.3 Methodology 11.3.1 Development steps 11.3.1.1 Data collection and preparation 11.3.1.2 Dashboard development 11.3.2 Data analysis method 11.4 Results and discussions 11.4.1 Business requirement analysis 11.4.2 Planning 11.4.3 Design of the stock market dashboard 11.4.4 Implementation and control 11.5 Conclusion References Chapter 12: Performance analysis of hierarchical clustering and high-dimensional clustering algorithms on network IDS benchmark datasets using interactive dynamic dashboard 12.1 Introduction 12.2 Dataset selection 12.3 Hierarchical clustering 12.4 High-dimensional clustering 12.5 Analysis of implementation results 12.6 Conclusion References Chapter 13: Breaking boundaries: The next frontier in skin cancer diagnosis combining transfer learning and multi-scale deep learning I Introduction Background Rationale and knowledge gap II Literature review III Methodology Preprocessing Transfer learning IV Calculations used V Algorithm VI Results VII Fuzzy logic Prior to processing Segmentation Phase-1 Phase Two Performance evaluation metrics VIII Dash board results IX Discussion X Conclusion References Chapter 14: Nourish net: Machine learning innovations in food recognition and calorie monitoring 14.1 Introduction 14.2 Existing work 14.3 Research problem 14.4 Methodology 14.4.1 System development methodology 14.4.2 Calorie measuring algorithm 14.4.3 Plan of implementation 14.5 Working of the proposed system 14.6 Algorithm 14.6.1 System architecture 14.7 Convention used 14.8 Conclusion References Chapter 15: Comprehensive study of coral reef assessment and colour correction using deep learning 15.1 Introduction 15.2 Dataset and materials 15.3 Mechanism 15.4 Image processing techniques 15.4.1 Study and comparison of the different color correction algorithms 15.4.2 Deep learning for image classification 15.4.3 Interactive visualization technique 15.4.4 Tool analysis 15.4.5 Colab library 15.5 Evaluation 15.6 Analysis table 15.7 Conclusion References Index