Applications of Mathematical Modeling, Machine Learning, and Intelligent Computing for Industrial Development

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors’ biographies
List of contributors
Section 1: Mathematical modeling
Chapter 1: An interactive weight-based portfolio system using goal programming
1.1 Introduction
1.2 Interactive weight-based portfolio system
1.3 Stock data
1.4 Experimental work
1.5 Result analysis
1.6 Comparative analysis
1.7 Prototype of interactive weight-based portfolio system
1.8 Conclusion
References
Chapter 2: Generalized knowledge measure based on hesitant fuzzy sets with application to MCDM
2.1 Introduction
2.2 Fuzzy sets and hesitant fuzzy sets
2.3 Projection measure of HFEs
2.4 Entropy and knowledge measure
2.4.1 Applications of entropy and knowledge measures
2.5 Generalization of information measures
2.6 Comparative study
2.7 MCDM
2.8 Problem Statement
2.9 Conclusion
References
Chapter 3: Queuing system with customers’ impatience, retention, and feedback
3.1 Introduction
3.2 Assumptions of the model
3.3 Formulation of mathematical model
3.4 Solution of the model in steady-state
3.5 Measures of performance
3.5.1 Expected system size (Ls)
3.5.2 Expected waiting time of the customer in the system
3.5.3 Expected waiting time of a customer in the queue
3.5.4 Expected queue length Lq = λWq
3.6 Particular cases
3.7 Numerical illustration and sensitivity analysis
3.8 Conclusion and future work
References
Chapter 4: Controllable multiprocessor queueing system
4.1 Introduction
4.2 Formulation
4.3 Queue size distribution and execution indices
4.4 Estimated waiting time
4.5 Numerical results
4.6 Discussion
References
Chapter 5: Multi-server queuing system with feedback and retention
5.1 Introduction
5.2 Queuing model
5.3 Mathematical formulation
5.4 Time dependent solution of the model
5.5 Particular cases
5.6 Conclusion
References
Chapter 6: Retrial queueing system subject to differentiated vacations with impatient customers
6.1 Introduction
6.2 Model description
6.3 Steady-state solutions
6.4 Performance measures
6.5 Numerical results
6.6 Conclusions and future works
References
Chapter 7: A Preorder discounts and online payment facility on a sustainable inventory model with controllable carbon emission
7.1 Introduction
7.2 Review of literature
7.2.1 Literature review on inventory model with controllable carbon emission
7.2.2 Literature review on inventory model with non-instantaneous deteriorating items
7.2.3 Literature review of inventory model with the preorder program and multiple discount facility
7.2.4 Literature review of inventory model with inflation
7.2.5 Research gap
7.2.6 Problem definition
7.3 Assumptions and notations
7.3.1 Assumptions
7.4 Mathematical treatment
7.5 Algorithm
7.6 Numerical illustration
7.6.1 Example 1 when E ≥ a
7.6.2 Example 2 when E ≥ a
7.6.3 Example 3 when E < a
7.6.4 Example 4 when E < a
7.7 Result summary
7.8 Concavity
7.9 Sensitivity analysis
7.10 Observations
7.10.1 For case 1
7.10.2 For case 2
7.11 Managerial insights
7.12 Conclusion
References
Chapter 8: Green inventory systems with two-warehouses for rubber waste items
8.1 Introduction
8.1.1 Concept of two-warehouse in rubber waste items green inventory systems
8.1.2 Genetic algorithm
8.2 Related work
8.3 Notations and assumptions
8.4 Formulation and solution of the rubber waste items green inventory
8.5 Numerical simulations
8.5.1 Implementation of GA
8.6 Conclusions
References
Section 2: Machine learning
Chapter 9: Cyber-attack detection applying machine learning approach
9.1 Introduction
9.2 Known attack detection methods
9.3 Unknown attack
9.3.1 Anomaly-based approaches to defending zero-day cyber-attacks
9.3.2 Graph-based approaches to defending zero-day cyber-attacks
9.3.3 ML and deep learning-based approaches to defend against unknown (zero-day) cyber-attacks
9.4 Feature reduction and data generation for intrusion detection
9.4.1 Feature reduction techniques for attack data
9.4.2 Data generation methods to handle class imbalance problem
9.5 Conclusion
References
Chapter 10: Feature extraction methods for intelligent audio signal classification
10.1 Introduction
10.2 Theoretical background
10.3 Deployment of the three methods
10.3.1 Sound event detection
10.4 Performance Measures Metrics (PMMs)
10.4.1 Quantification of evaluation criteria
10.4.2 PMMs criteria for evaluation and selection
10.4.3 Manual microphones calibration process
10.5 Conclusions
References
Chapter 11: Feature detection and extraction techniques with different similarity measures using bag of features scheme
11.1 Introduction
11.2 Theoretical background
11.3 Deployment of the method
11.3.1 Data set
11.3.2 Proposed methodology
11.3.3 Proposed algorithm
11.3.4 MATLAB implementation
11.4 Results
11.5 Performance measures
11.5.1 Comparison of different methods descriptors
11.5.2 Evaluation
11.5.3 Performance compared with different algorithms
11.6 Conclusions
References
Chapter 12: Stratification based initialization model on partitional clustering for big data mining
12.1 Introduction
12.2 Partitional based clustering approach for big data
12.3 Clustering techniques for big data
12.3.1 Incremental method
12.3.2 Divide and conquer method
12.3.3 Data summarization
12.3.4 Sampling-based methods
12.3.5 Efficient Nearest Neighbor (NN) Search
12.3.6 Dimension reduction-based techniques
12.3.7 Parallel computing methods
12.3.8 Condensation-based methods
12.3.9 Granular computing
12.4 Sampling methods for big data mining
12.4.1 Uniform random sampling
12.4.2 Systematic sampling
12.4.3 Progressive sampling
12.4.4 Reservoir sampling
12.4.5 Stratified sampling
12.5 Proposed model for partitional clustering algorithms using stratified sampling
12.5.1 Stratified sampling
12.5.2 Stratification
12.5.3 Proposed initialization model for partitional based clustering
12.5.4 Experimental environment and selected algorithm
12.5.5 Validation criteria
12.5.6 Results and discussion
12.6 Conclusion
References
Chapter 13: Regression ensemble techniques with technical indicators for prediction of financial time series data
13.1 Introduction
13.2 Literature review
13.3 Dataset and technical indicators
13.4 Framework of proposed work
13.5 Methodology
13.5.1 Regression
13.5.2 Ensemble model
13.5.3 K-Fold cross validation
13.6 Result and analysis
13.7 Conclusion
References
Chapter 14: Intelligent system for integrating customer’s voice with CAD for seat comfort
14.1 Introduction
14.2 Quality Function Deployment (QFD)
14.2.1 QFD design parameters (HOWs)
14.2.2 Comfort evaluation using QFD
14.3 CAD for seat comfort assessment
14.3.1 The seat models
14.3.2 Seat features
14.3.3 Cushions materials
14.3.4 The human model
14.3.5 Human’s material properties
14.3.6 Occupant anthropometric analysis
14.3.7 The simulation technique
14.3.8 Finite Element Analysis (FEA)
14.4 The prediction model
14.4.1 Machine learning predictive model
14.5 System validation and calibration
14.6 Results and discussions
14.7 Conclusions
References
Chapter 15: An implementation of machine learning to detect real time web application vulnerabilities
15.1 Introduction
15.2 Architecture of vulnerability scanners
15.2.1 Objective
15.2.2 Techniques for vulnerability scanning
15.3 Recognition of vulnerabilities automatically
15.3.1 Crawling component
15.3.2 Component of attack
15.3.3 Analysis modules
15.4 Principles of attack and analyzation
15.4.1 SQL injection attack
15.4.2 Simple reflected XSS attack
15.4.3 Encoded reflected XSS
15.4.4 Form-redirecting XSS
15.5 Outcome
15.6 Estimated consequences
References
Section 3: Intelligent computing
Chapter 16: Intelligent decision support system for air traffic management
16.1 Introduction
16.2 Theory and technical approach
16.3 Experimental work
16.4 Discussion
16.5 Conclusion and future work
References
Chapter 17: Bio-mechanical hand with wide degree of freedom
17.1 Introduction
17.2 Theoretical background
17.2.1 Steps to extract EMG signals
17.2.2 Types of designs
17.2.3 Problem formulation
17.2.4 Proper fitting
17.2.5 Delayed impulse response
17.2.6 Recent discovered materials
17.3 Electronics circuits
17.4 Hardware designing
17.4.1 Hardware used
17.4.2 Software used
17.4.3 Designing steps
17.4.4 Image capturing by fyuse
17.4.5 File format conversion in blender
17.4.6 Flattening design in solid works
17.4.7 Conversion to mesh file
17.4.8 Conversion to g-code (Geometric codes)
17.4.9 3D printing
17.4.10 Printed hand
17.4.11 Circuit outline
17.4.12 Pseudo code/Algorithm for Arduino
17.5 Result and discussion
17.5.1 Appearance
17.6 Conclusion and future scope
17.6.1 Conclusion
17.6.2 Future scope
References
Chapter 18: Analyses of repetitive motions in goods to person order picking strategies using intelligent human factors system
18.1 Introduction
18.1.1 Background
18.1.2 Goods to person versus person to goods
18.1.3 Goods to persons repetitive motion’s effects on human pickers
18.2 Methodology
18.2.1 The digital pickers
18.2.2 Warehouse layout components
18.2.3 Repetitive task analyses
18.3 Data collection and results
18.3.1 Comfort assessment
18.3.2 Fatigue and recovery analysis
18.3.3 Lower Back Analysis (LBA)
18.3.4 Metabolic Energy Expenditure (MEE)
18.3.5 Static Strength Prediction (SSP)
18.3.6 Rapid Upper Limb Assessment (RULA) and Ovako working posture analysis
18.3.7 Ergonomic results for Eva (50th percentile)
18.3.8 Ergonomic results for Bill (50th percentile)
18.3.9 Ergonomic results for Jill (5th percentile)
18.4 Discussion
18.5 Conclusion
References
Chapter 19: Hybridization of IoT networks with intelligent gateways
19.1 Introduction
19.2 Basic IoT architecture
19.3 Proposed intelligent gateway-based hybridized IoT network
19.4 Machine learning algorithms for proposed model
19.4.1 k-Nearest Neighbor (kNN) algorithm
19.4.2 SVM (Support Vector Machine)
19.5 Comparative analysis
19.5.1 Accuracy
19.5.2 Receiver Operating Characteristic (ROC)
19.6 Transmission reliability of proposed model
19.7 Conclusion
References
Chapter 20: M/M1 + M2/1/N/K-policy queues to monitor waiting times via intelligent computing
20.1 Introduction
20.1.1 False alarm rate for control chart and average run length (ARL)
20.1.2 Applications of queueing models
20.1.3 Examples of queueing systems
20.1.4 Statistical Process Control (SPC)
20.2 Steady state distributions of M/M1 + M2 /1/N/K-policy queues
20.2.1 Moments of waiting time process and control limits
20.3 Sampling plan and simulation
20.3.1 Embedded Markov chains
20.3.2 Simulation study
20.4 Application: find an optimum K-policy
20.5 Discussion, conclusion, and scope
References
Index