Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications (Studies in Big Data, 132)

✍ Scribed by Gilberto Rivera (editor), Laura Cruz-Reyes (editor), Bernabé Dorronsoro (editor), Alejandro Rosete (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 597
• Category: Library

✦ Synopsis

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics.

Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling.

With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields.

Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.

✦ Table of Contents

Preface
Contents
Descriptive and Diagnostic Analytics
Cluster Analysis Using k-Means in School Dropout
1 Introduction
2 Related Works
3 k-Means Clustering Processing
4 Dataset
5 Experimentation and Results
5.1 Kappa Statistic
5.2 Mean Absolute Error
5.3 Root Mean Square Error
6 Discussion
7 Conclusions
References
Topic Modeling Based on OWA Aggregation to Improve the Semantic Focusing on Relevant Information Extraction Problems
1 Introduction
2 Background
2.1 Topic Modeling in Keyphrases Extraction
2.2 Topic Modeling in Extractive Summarization
3 Topic Modeling Aimed at Extracting Relevant Information
3.1 Fuzzy-Based Topic Modelling
3.2 Candidate Topics Extraction
3.3 Topics Identification
3.4 Topics Ranking Construction
3.5 Fuzzy-Based Topic Modelling Applied to Keyphrases Extraction
3.6 Fuzzy-Based Topic Modelling Applied to Text Summarization
4 Evaluation and Discussion
4.1 Experimental Results in Keyphrases Extraction Problem
4.2 Experimental Results in a Multi-document Summarization Problem
5 Conclusions and Future Works
References
An Affiliated Approach to Data Validation: US 2020 Governor’s County Election
1 Introduction
2 Literature Review
3 Benford’s Law
4 Zipf’s Law
5 Application
5.1 US 2020 Election
6 Comparative Study
7 Conclusion
References
Acquisition, Processing and Visualization of Meteorological Data in Real-Time Using Apache Flink
1 Introduction
2 Theoretical Fundamentals
2.1 Big Data
2.2 Data Streaming
2.3 NoSQL Databases
2.4 Apache Kafka
2.5 Apache Flink
2.6 Elasticsearch
2.7 Kibana
3 Proposed Architecture
3.1 Software and Computer Equipment Used
3.2 Construction of the Weather Station
3.3 Streaming of Data from the Weather Station to the Tools
3.4 Data Storage in Elasticsearch
3.5 Visualization of Data with Kibana
4 Verification
5 Results
6 Discussions
7 Conclusions
References
Topological Data Analysis for the Evolution of Student Grades Before, During and After the COVID-19 Pandemic
1 Introduction
2 Preliminaries
2.1 Topological Data Analysis
2.2 Topological Spaces
2.3 Simplicial Complexes
2.4 Homology Groups
3 Simplicial Complexes from Data
3.1 Vietoris-Rips Complex
3.2 Čech Complex
3.3 Nerves
3.4 Filtrations
4 Persistent Homology
4.1 Persistence Diagrams
4.2 Bottleneck Distance
5 Mapper Algorithm
6 Application
6.1 Datasets
6.2 Some Results
7 Conclusion and Future Work
References
Redescending M-Estimators Analysis on the Intuitionistic Fuzzy Clustering Algorithm for Skin Lesion Delimitation
1 Introduction
2 Redescending M-Estimation
2.1 Enhanced Intuitionstic Fuzzy Clustering Algorithm Trough Redes-Cending M-Estimators
3 Experimental Results
3.1 Metrics
3.2 Simulation Results
4 Conclusion and Future Scope
References
Big Data Platform as a Service for Anomaly Detection
1 Introduction
2 Problem Description and Motivation
3 Background
3.1 Modern Distributed Computing and Frameworks
3.2 Platforms as a Service with Container Orchestators
4 Big Data
4.1 Big Data Reference Architecture
5 Big Data Architecture Proposal Built-In PaaSCO DC/OS
5.1 Assembly Frameworks in PaasCO DC/OS for Big Data Anomaly Detection
5.2 Test Case for Prediction of Severe Diabetic Retinopathy
5.3 Experimental Analysis
5.4 General Conclusions in Test Case
6 General Discussion and Conclusions
References
Predictive Analytics
An Overview of Model-Driven and Data-Driven Forecasting Methods for Smart Transportation
1 Introduction
2 Model-Driven Versus Data-Driven Approaches
3 Model-Driven for Traffic State Estimation
3.1 Macroscopic Models
3.2 Microscopic Modeling
3.3 Mesoscopic Modeling
3.4 Critiques and Limitations of Model-Driven Approaches
4 Traffic Flow Prediction Based on Data-Driven
4.1 The Challenges of Data-Driven Traffic Prediction
4.2 Review of Data-Driven Approaches
5 Naïve Methods
6 Parametric Methods
6.1 Historical Average Algorithm (HA)
6.2 Smoothing Techniques
6.3 Kalman Filtering Technique (KFT)
6.4 Auto-Regressive Linear Processes
7 Non-parametric Methods or Machine Learning Approach
7.1 Support Vector Regression (SVR)
7.2 Artificial Neural Networks (ANN)
7.3 Hybrid Prediction Methods
8 Conclusions
References
Data Augmentation Techniques for Facial Image Generation: A Brief Literature Review
1 Introduction
2 Generation of Artificial Facial Images
2.1 Generic Transformations
2.2 Component Transformation
2.3 Attribute Transformation
2.4 Age Progression and Regression
3 Generative Adversarial Networks (GANs)
3.1 Definition
3.2 Architecture
3.3 Training Process
3.4 Challenges
3.5 Face Image Generation Evolution with GANs
4 Related Work
5 Methodology
6 Face Image Generation with GANs
7 Conclusion and Future Work
8 Code Repository
References
A Review on Machine Learning Aided Multi-omics Data Integration Techniques for Healthcare
1 Introduction
2 Multi-omics
2.1 Genomics
2.2 Epigenomics
2.3 Transcriptomics
2.4 Proteomics
2.5 Metabolomics
3 Machine Learning
4 Multi-omics Data Integration Strategies
4.1 Early Integration
4.2 Mixed Integration
4.3 Intermediate Strategies
4.4 Late Integration
4.5 Hierarchical Integration
5 Machine Learning-Based Data Integration Methods
5.1 Concatenation-Based Integration Methods
5.2 Model-Based Integration Methods
5.3 Transformation-Based Integration Methods
6 Application
6.1 IMPaLA (Integrated Molecular Pathway-Level Analysis)
6.2 MixOmics
6.3 MOFA (Multi-omics Factor Analysis)
6.4 BioMiner
6.5 TCGA (The Cancer Genome Atlas)
6.6 ICGC
6.7 CPTAC (Clinical Proteomic Tumor Analysis Consortium)
6.8 DepMap
6.9 PaintOmics
7 Multi-omics Research Contributions
7.1 PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models)
7.2 iCluster
7.3 Patient-Specific Data Fusion (PSDF)
7.4 Bayesian Consensus Clustering (BCC)
8 Challenges
9 Future Prospects
10 Conclusion
References
Learning of Conversational Systems Based on Linguistic Data Summarization Applications in BIM Environments
1 Introduction
2 Model of the Conversational System with Learning Based on Linguistic Summaries of Data
2.1 BRasa Assistant Subsystem Architecture
2.2 Architecture and Algorithms of BRasa_LDS Learning Subsystem
2.3 Example of Application of BRasa on BIM Project Management Environment
2.4 Indicators Used to Evaluate the Conversational System Knowledge Databased on Linguistic Summaries
3 Results Analysis
3.1 Validation of BRasa Performance in BIM Project Management Environment (BusinessRedmine Ecosystem)
4 Conclusions
References
Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0
1 Introduction
1.1 Data Analytics, Computational Intelligence, and Predictive Maintenance
1.2 Emerging Technologies in Predictive Maintenance
1.3 Basic Concepts
2 Related Work
2.1 Models, Algorithms, and Applications for Solving Production Loss in Industry 4.0
2.2 Application of Cased-Based Reasoning in Industry 4.0
3 Proposed FCBRB Framework
3.1 Overview of the Framework
3.2 Methodology
3.3 Sim (FnSa, FnSb)
4 Implementation and Discussion
4.1 Experimentation and System Implementation
4.2 Discussion
5 Conclusions
References
Machine Learning for Identifying Atomic Species from Optical Emission Spectra Generated by an Atmospheric Pressure Non-thermal Plasma
1 Introduction
1.1 Motivation
2 Automatic Recognition with Machine Learning
2.1 Optical Emission Spectroscopy
2.2 Characterization Techniques Based on Machine Learning
3 Method
3.1 Ensemble-Classifier Based on Decision Trees Algorithms
4 Results
5 Discussion
6 Conclusion
References
Agent-Based Simulation: Several Scenarios
1 Introduction
2 Signal Configuration
2.1 Case 1: Signal Configuration with Agent-Based Simulation
2.2 Traffic Simulation Tool
2.3 Discussion: Case 1
3 Simulation of Drifting Objects
3.1 Case 2: Simulation of Drifting Objects in Cuban Territorial Waters
3.2 GAMA Platform
3.3 Modeling Proposal
3.4 Results of the Experiments
3.5 Discussion: Case 2
4 Simulation for Training
4.1 Case 3: Simulator for the Training of Boiler Operators
4.2 Discussion: Case 3
5 Conclusion
References
Prescriptive Analytics
Multihop Ridesharing in NPC
1 Introduction
2 Related Work
3 Multi-hop Ride Sharing Problem Definition
4 MHRS in NP
5 Vertex Covering and VC leqp MHRS
5.1 If i is Into the Yes Instances of VC Then The Transformation Function of i Will Produce a YES Instance of MHRS
5.2 If i is Not Into the YES Instances of VC Then The Transformation Function of i Will Produce an Instance that is Not Into the YES Instances of MHRS
6 Conclusions
References
A Content-Based Group Recommender System Using Feature Weighting and Virtual Users Aggregation
1 Introduction
2 Preliminaries
2.1 Content-Based Recommendation
2.2 Group Recommendation
2.3 Previous Works in Content-Based Group Recommender Systems
3 A New Hybrid Method for Content-Based Group Recommender Systems
3.1 Modeling Users and Items
3.2 Aggregation of User Profiles
3.3 Addition of Group Profile
3.4 Calculation of Weighted User-Item Similarity
3.5 Aggregation of Values and Final Recommendation
4 Experimentation
4.1 Datasets
4.2 Experimental protocol
4.3 Results and Discussion
5 Conclusions
References
Performance Evaluation of AquaFeL-PSO Informative Path Planner Under Different Contamination Profiles
1 Introduction
2 Related Work
3 Statement of the Problem
3.1 Monitoring Problem
3.2 Assumptions
4 PSO-Based Path Planning Algorithms
4.1 Classic Particle Swarm Optimization (PSO)
4.2 Enhanced GP-Based PSO
4.3 AquaFeL-PSO
5 Results and Discussion
5.1 Ground Truth
5.2 Performance Metric
5.3 Setting Simulation Parameters
5.4 Performance Comparison
6 Summary of the Results
7 Conclusions
References
Adapting Swarm Intelligence to a Fixed Wing Unmanned Combat Aerial Vehicle Platform
1 Introduction
2 Proposed Work
3 Swarm Formation
3.1 Arrowhead Formation
3.2 Rectangular Prism Formation
3.3 Simulation Analysis
4 Mission Execution
4.1 Mission Initiation Module
4.2 Route Planning
5 Autonomous Lock-On Target Tracking
5.1 Determining the Target and the Route
5.2 Target Image Processing
5.3 Target Tracking via LVFG
6 Communication
6.1 Communication with Ground Station
6.2 Intercommunication of UCAVs
6.3 Collision Avoidance
6.4 Flight Control Module
7 Conclusion
References
Cellular Processing Algorithm for Time-Dependent Traveling Salesman Problem
1 Introduction
2 State of the Art
2.1 Time-Dependent Traveling Salesman Problem Contributions
2.2 Cellular Processing Algorithms Contributions
3 Time Dependent-Traveling Salesman Problem (TD-TSP)
3.1 Instance Structure
3.2 Calculation Process Example
4 Greedy Randomized Adaptive Search Procedure Algorithm for Time Dependent-Traveling Salesman Problem
4.1 Greedy Randomized Adaptive Search Procedure Construction
4.2 Roulette Procedure
4.3 Influence on the Candidate List
4.4 Shared Memory and Normalization
4.5 Reactive Greedy Randomized Adaptive Search Procedure
5 Experimental Results
5.1 Configuration and Instances
5.2 Parameter Comparison
5.3 Comparison Between GRASP Methods
5.4 Comparison Between CPA Methods
5.5 Comparison Between CPA and GRASP Methods
6 Conclusions
References
Portfolio Optimization Using Reinforcement Learning and Hierarchical Risk Parity Approach
1 Introduction
2 Related Work
3 Data and Methodology
3.1 Choosing the Sectors
3.2 Data Acquisition
3.3 Hierarchical Risk Parity Portfolio Design
3.4 Portfolio Design Using Reinforcement Learning
3.5 Backtesting the Portfolios on the Training and Test Data
4 Results
5 Conclusion
References
Reducing Recursion Costs in Last-Mile Delivery Routes with Failed Deliveries
1 Introduction
2 Formal Description of the Proposed Model
3 Solution Method
4 Case Study
4.1 Solution of the Instance A
4.2 Solution of the Instance B
5 Conclusions
References
Intelligent Decision-Making Dashboard for CNC Milling Machines in Industrial Equipment: A Comparative Analysis of MOORA and TOPSIS Methods
1 Introduction
2 Developing
3 Obtaining the Data
4 Analysis of Data Capture
5 Results
6 Conclusions
7 Future Research
References