This book discusses practical applications of reliability and statistical methods and techniques in various disciplines, using Machine Learning, Artificial Intelligence, optimization, and other computation methods. Bringing together research from international experts, each chapter aims to cover both methods and practical aspects on reliability or statistical computations with emphasis on applications.
5G and IoT are set to generate an estimated 1 billion terabytes of data by 2025 and companies continue to search for new techniques and tools that can help them practice data collection effectively in promoting their business. This book explores the era of Big Data through reliability and statistical computing, showcasing how almost all applications in our daily life have experienced a dramatic shift in the past two decades to a truly global industry.
The requirement for Software Reliability Growth Models (SRGMs) has increased exponentially in response to the growing demand for strong and reliable software systems. During the testing phase of the Software Development Life Cycle (SDLC), SRGMs are particularly effective for estimating fault content, minimizing testing expenses, and maximizing software reliability. There has been a lot of research into selecting the best SRGMs for a certain failure dataset and then ranking all the SRGMs against the dataset. In this chapter, we have studied the mentioned problem and the solution to automate it with the developed compact Decision Support System (DSS), which includes all the functionalities and computational analysis of error logs and ensure error-free software to achieve the desired objective. The DSS is developed in Python utilizing several well-known packages such as Numpy, Scipy, Tkinter, and Pandas. To rank SRGMs employed in the DSS, we used Entropy & Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranking methodology. The implemented schema provides highly accurate performance indexes for the SRGMs required for efficient ranking, emphasizing the significance of the proposed prototype of DSS in the open literature, being a novel and ingenious development in the domain of software reliability.
Software reliability is defined as a program's ability to perform its functions under specific conditions for a specific time. Software reliability, according to ANSI, is defined as the likelihood of fault-free software functioning in a predefined environment for a specified time. Software quality, usability, functionality, serviceability, performance, maintainability, capability, and documentation all contribute to software reliability. Due to the high convolution of software, obtaining software reliability is difficult. Due to the rapid growth of system space and the feasibility of upgrading the software, system developers tend to increase the level of complexity within layers of software. It is difficult to achieve a satisfactory level of reliability for a highly complex system containing software. The number of users is directly influenced by software reliability. In practice, a reliable piece of software should have backup (redundant) code that runs the exception handling checks. As a result, the space complexity increases, necessitating more storage, which has an impact on the execution speed. However, for the following reasons, reliability still takes precedence over efficiency since compared to reliability, efficiency can easily be solved by utilizing better hardware resources. A user (or organization) would never use a less reliable or unreliable software that would hinder its growth and productivity. Furthermore, for deadline-based applications, an unreliable software might lead to the loss of invaluable information due to system crash. Origin of unreliability is unclear in a software system with distributed faults through its structure.
Including numerous illustrations and worked examples, the book is of interest to researchers, practicing engineers, and postgraduate students in the fields of reliability engineering, statistical computing, and Machine Learning.
Author(s): Hoang Pham
Publisher: Springer
Year: 2023
Language: English
Pages: 310
Preface
Contents
Editor and Contributors
Forecasting The Long-Term Growth of S&P 500 Index
1 Introduction
1.1 Objectives
1.2 Data Sources, Tools, and Abbreviations
2 Total Return Index and Equity Risk Premium
2.1 Equity Risk Premium
2.2 Discussion—A Naive 10-Year Forecast
3 Wavelet Analysis on the 36-Year Long Term Cycle
3.1 Introduction to the Wavelet Transform
3.2 Wavelet Regression of the 10-Year Returns
3.3 Wavelet Regression of the 20-Year Returns
4 Channel Deviation Framework
4.1 Mean-Reversion Decomposition
4.2 Closed Form Solution
4.3 Optimal Choice of Look-back Channel at 45 Years
4.4 Discussion on the Outputs
5 Relation Between Channel Deviation and CAPE
5.1 Regression of Log-CAPE
5.2 Discussion
5.3 Tectonic CAPE
6 The Tectonic Forecast Model
6.1 The 5-Factor Tectonic Forecast Model for Equity Returns
6.2 20-Year Nominal Return Forecast
References
Smart Maintenance and Human Factor Modeling for Aircraft Safety
1 Introduction
2 Impact of COVID-19 Pandemic on Commercial Aviation
3 Smart Technology in Aviation
4 COVID-19-Induced Problem
5 Safe Aircraft System (SAS) Model
6 SAS Modeling Procedure
7 SAS Modeling Case Study
7.1 Transport of Cargo in Passenger Aircraft
7.2 System Description and Analysis
7.3 Potential Hazard/Consequences Identification
7.4 System Safety Risk Analysis
7.5 System Safety Risk Assessment
7.6 System Risk Controls
8 Conclusion
References
Feedback-Based Algorithm for Negotiating Human Preferences and Making Risk Assessment Decisions
1 Introduction
2 Literature Review
2.1 Consistency and Feedback Exchange in Decision-Making
2.2 MCDM Methods to Support the Problem of Research
3 New Developments Within the AHP Framework
3.1 Problem Setting
3.2 Negotiation Algorithm for Feedback Exchange with Decision Makers
4 Hybrid MCDM Perspective
4.1 The ELECTRE I Method for Work Equipment Selection
5 Industrial Case Study
5.1 Calculating Risk Factors Weights Via the Negotiation Process
5.2 Selecting the Priority Work Equipment Via the ELECTRE I
5.3 Discussion of Results and Practical Insights for Management
6 Conclusions
References
Joining Aspect Detection and Opinion Target Expression Based on Multi-Deep Learning Models
1 Introduction
2 Related Works
3 The Proposed Method
3.1 Multi Deep Learning Models
3.2 Joining Aspect Detection and Opinion Target Expression
4 Experiments
4.1 Dataset
5 Conclusion
References
Voting Systems with Supervising Mechanisms
1 Introduction
2 Modeling of Voting Systems
2.1 General Voting Systems
2.2 Weighted Voting Systems
3 Inspection Models
4 Recent Studies
4.1 Motivation
4.2 Intervened Decision-Making Systems
4.3 Numerical Example
5 Conclusion and Future Research
References
Assessing the Severity of COVID-19 in the United States
1 Introduction
2 Related Work
3 Methodology
3.1 Basic Metrics
3.2 Derived Metrics
3.3 Four New Proposed Metrics
4 Results
5 Discussion
6 Conclusion
References
Promoting Expert Knowledge for Comprehensive Human Risk Management in Industrial Environments
1 Introduction
2 Literature Review
2.1 FCMs Easing Decision-Making
2.2 Formal Human Risks Identification
2.3 FMECA Supporting Risk Management
2.4 MCDM Approaches in the Analysed Field
3 Integrated Methodological Approach
3.1 FCMs Modelling Human Risks
3.2 Nonlinear Hebbian Algorithms on FCMs
3.3 Modified FMECA Integrating the Total Effect
3.4 The Z-TODIM Method for Risk Prioritisation
4 Case Study
4.1 FCM Based on NHL Algorithm to Derive Total Effects
4.2 Z-TODIM Prioritising Risks and Discussion of Results
5 Conclusions
References
Data Quality Assessment for ML Decision-Making
1 Introduction
2 Data Quality Assessment: Recent Metrics
3 Quality Metrics in Text Recognition
4 Quality Metrics in ECG Based Biometric Identification
5 ML Based Timeline Data Analysis
6 Conclusion
References
From Holistic Health to Holistic Reliability—Toward an Integration of Classical Reliability with Modern Big-Data Based Health Monitoring
1 Introduction
2 Manifestation and Underlying Mechanisms of Physical and Virtual Failures
3 An HDD Example of Virtual Failure Determination
3.1 Critical Parameters Identification, Their Degradation Pattern and Quantification
3.2 Parametric Trigger Limits Determination
4 Health Philosophy and the Need for Holistic Reliability Assessment: Human-Machine Analogy
5 Holistic Integration of Physical Failure with Virtual Failure
5.1 Complex Number (or Vector) Analogy
5.2 Ying-Yang Analogy
6 Holistic Failure Quantification and Applications
7 Holistic Reliability Analysis Considering Parametric-Degradation Based Virtual Failures—An Example
8 Concluding Remarks
References
On the Aspects of Vitamin D and COVID-19 Infections and Modeling Time-Delay Body's Immune System with Time-Dependent Effects of Vitamin D and Probiotic
1 Introduction
2 Prevalence Rates Analysis of Vitamin D Deficiency in European and Asian Countries
3 Mathematical Model Development
3.1 Numerical Examples
4 Conclusion
References
A Staff Scheduling Problem of Customers with Reservations in Consideration with Expected Wait Time of a Customer without Reservation
1 Introduction
2 Reservation and Assignment
3 Assignment Evaluation
4 Tabu Search
5 Numerical Example
6 Conclusion
References
Decision Support System for Ranking of Software Reliability Growth Models
1 Introduction
2 Literature Review
3 Performance Indexes
4 Ranking Methodology
5 Environment of the DSS
6 Result and Analysis
7 Conclusion
References
Human Pose Estimation Using Artificial Intelligence
1 Introduction
1.1 Human Body Models
1.2 Risks and Errors in Fitness Apps Based on Human Pose Estimation
1.3 Human Pose Estimation Metrics
1.4 2D Versus 3D Pose Estimation
1.5 Applications of Pose Estimation
1.6 Working of Pose Estimation
1.7 Computer Vision
1.8 Artificial Intelligence
1.9 Project Description
1.10 Benefits of Our Gym Tracker
2 Literature Review
2.1 Deep Neural Based Approach (Toshev et al.)
2.2 Estimation Using Multi-scale Template Model
2.3 Generative Approaches
2.4 Human Pose Estimation Features with Convolutional Networks
2.5 Deep Learning Framework Using Motion Features for Human Pose Estimation
2.6 Movenet
2.7 Human Pose Estimation Via DNN
2.8 TFPOSE
2.9 POSEAUG
2.10 EMPOSE
2.11 DSPNET
2.12 ADAFUSE
2.13 PIFPAF
2.14 HigherHRNet
2.15 Sim2Real
2.16 ContextPose
2.17 CVFusion
3 Methodology
3.1 Mediapipe Based Approach
3.2 Calculating Angles
3.3 Calculating Repetitions for Curls
3.4 Calculating Repetitions for Pushups
3.5 Calculating Repetitions for Pullups
3.6 Calculating Repetitions for Squats
4 Result
5 Conclusion
References
Neural Network Modeling and What-If Scenarios: Applications for Market Development Forecasting
1 Introduction
2 Neural Networks
3 Data
4 Modeling
5 Interpretation
5.1 Political and Electoral Cycles: Relationships with Economic Cycles
5.2 Cause-Effects: Factor Impact Analysis
5.3 Automotive Sales Forecast: Long-Term Accuracy
6 Conclusion
References
Mental Health Studies: A Review
1 Introduction
2 Current Research
2.1 Data Collection, Self-Reporting Surveys, and Reviews
2.2 Data Collection with Machine Learning Applications
2.3 Modeling of Mental Health Using Survey Data and Machine Learning
3 Concluding Remarks
References
