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The editors are immensely gratified to introduce this Handbook of Intelligent Computing and Optimization for Sustainable Development, with the intent of compiling information that advances sustainability as a fundamental factor for intelligent computing and optimization.

Intelligent and smart technologies are at the forefront of technological advances that represent potential transformations in smart living. These technologies and their applications are already rapidly impacting many industries and occupations. With applications in a number of fields such as the Internet of Things (IoT), optimization, renewable energy, manufacturing, agriculture, healthcare and smart cities, research on the developments of artificial intelligence is essential for sustainable development. The recent rise of emerging networking technologies such as 5G networks, social networks, IoT networks, etc., have attracted significant attention from academia as well as industry professionals looking to utilize these technologies for efficient communication purposes. Future citizens of the world will face increasing sustainability issues and need to be better prepared for energy transformation and sustainable future economic development in a smart world. Advances in artificial intelligence (AI) paradigms and smart informatics systems domains highlight the need for systems that aim to improve the quality and security of human life; therefore, this book discusses applications of artificial intelligence in sustainable development.

Recently, optimization has also received enormous attention along with the rapidly increasing use of communication technology and development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local optima.

This book provides the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including the IoT, manufacturing, optimization and healthcare. In general, the book is intended as a presentation of a wide range of publicly advanced achievements in the field of intelligent computing and optimization, which will be useful for a wide range of readers and will doubtless have a positive impact on a solution to the problem of sustainable development.

This book is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on emerging perspectives in the field of artificial intelligence to improve their products or research. Therefore, it will be a great resource for both Master’s and research students of computing sciences, engineering, environmental sciences, medical sciences, mathematics, statistics, economics and agricultural sciences. Moreover, industry experts from telecommunications, the energy sector, manufacturing industry, health sector, and finance and planning sectors can use this book as a reference resource.

Based on double-blind review processes, the 41 chapters were accepted for publication according to their suitability for the five parts of the book. A brief description of each chapter is given below.

Part I: Intelligent Computing and Applications

Chapter 1 proposes a mental workload prediction model using a neural network and the Bernoulli Boltzmann machine. For measuring mental workload, eye movement metrics were considered. The eye metrics were computed from raw eye movement data, which were recorded using an eye tracking device while solving coding problems. The authors found that the Bernoulli Boltzmann machine provides better accuracy in predicting mental workload from eye metrics.

Chapter 2 discusses the development of an artificial neural network (ANN) using short DNA strands, i.e., oligonucleotides. The short sequences of DNA molecules can be used to code input and output signals and to build the basic architecture of the neuron. The authors also illustrate the design methodology of DNA logic gates and DNA logic circuits. Because of a few drawbacks, viz. immense energy consumption, vast memory requirement and heat dissipation, the traditional computation approaches the limitations of its processing power and design strategy. The aim of the authors is a paradigm shift in the computational world; from silicon to carbon. The design strategies discussed in this chapter are essential for effective development of a practical DNA computer.

Chapter 3 focuses on a novel framework for detection of garments of interest from the footage of a surveillance camera. The video frames are processed using the GMG background subtraction model to obtain relevant foreground information along with foreground masks. The Mask R-CNN object detection model is used to identify customers and multiple image processing techniques are used to obtain the active garments in these frames. The detected customers are tracked and the OpenPose human pose estimation framework is utilized on them to obtain useful landmarks. The garments of interest are then determined based on the filtration of confidence scores calculated for each active garment. The framework was tested on a CCTV video dataset and was found to be effective despite facing arduous obstacles such as background noise and occlusions.

Chapter 4 studies matrix algebra and elliptic curve arithmetic computing based on the integration of modular arithmetic and complex number arithmetic. It describes the intelligent computing of nonlinear transformations based on the residue matrices and the elliptic curve arithmetic over complex plane that can be applied in the computer science fields, which deal with cryptographic applications for more security. Their mathematical properties over complex plane are applied to create the cryptographic nonlinear transformation techniques in traditional ciphers, elliptic curve cryptography and quantum cryptography.

Chapter 5 highlights the trends in fifth generation (5G) wireless communication and beyond. Further discussed is the use of machine learning algorithms in future wireless networks (5G and B5G). ML-based signal processing algorithms have the potential to solve network issues with 5G networks, thereby paving the way to the development of future intelligent wireless networks (B5G). This chapter presents a unified application of data science for wireless communication and analyzes the physical layer challenges for automatic modulation classification (AMC), resource allocation, channel estimation and millimeter wave communication. Further case studies have been carried out on AMC and CSI feedback in massive multi-input and multi-output (MIMO) systems. The performance of the proposed deep learning methods has been presented through extensive simulation results.

Chapter 6 envisions how all present networks can be compiled into a single crowd associated network called a CrAN. This chapter contains a routing protocol for the proposed network. All applications for the proposed network are also discussed. The authors highlight the importance of creating this network and how it tackles transmission problems better than other networks. Some limitations of the proposed network are also mentioned.

Chapter 7 demonstrates the application of a neural network (NN)-based group method of data handling (GMDH) for prediction of permeate flux (%) in disc-shaped membrane. The permeate flux is predicted using three parameters for this study such as pore size, operating pressure, and feed velocity. Different statistical techniques, such as mean absolute error, root mean square error (RMSE), RMSE-observation standard deviation error, and Pearson’s correlation coefficient, are analyzed in order to show the precision of the GMDH-NN models. To demonstrate the performance of the GMDH-NN model, the total error values are compared with the developed artificial neural network model. The study illustrates that the GMDH model predicts permeate flux of disc membrane with high accuracy.

Chapter 8 introduces a new approach to identify nonfunctional needs by using nonfunctional requirements (NFR) catalogs via machine-learning methods, and proposes a process to acquire these catalogs by using a systemic mapping study of “lightweight.” The authors analysis provides a way of generating data sets used to classify non-functional specifications by NFR catalogs extracted from the mapping research in order to address the circumstance. They focus on the definition of the various NFR forms, with an emphasis on usability, security, performance and adaptability.

Chapter 9 proposes an efficient and simple image recognition classification system, which consists of components from both reinforcement learning and deep learning. More specifically, Q-Learning is used with an agent having 2 states, and 2 to 3 actions. This classifier is different from others, because the latter use features of convolutional neural networks and also uses past histories in addition to Q states. Since the novel technique proposed has only 2 Q states, it has the advantage of being simple and also having significantly less parameters to optimize. The classifier given in this work performs better than other classifiers on the various datasets used experimentally.

Chapter 10 zeroes in on minimizing human involvement in changing control program code whenever some nonlinear disturbance affects the industrial control process, viz. measurement and control of temperature and flow, etc., as used in real-time process control applications. The PID technique has certain control-related limitations, especially in the control of vital process parameters like temperature acting as nonlinear disturbances. The fuzzy control simulation results worldwide have proved their superiority over conventional controllers and this technique has emerged as a harbinger in the implementation of artificial techniques. In this work, the vast processing power inherited in the biological neural structure has motivated the use of neural networks along with fuzzy logic in solving the control problem in the area of process control.

Chapter 11 discusses applications of artificial neural networks in the manufacturing sector in detail. The Industry 4.0 approach, which calls for exhaustive useof computers in different sectors of industry, is discussed along with suitable diagrams. Seven different types of ANN architecture are explained, along with different types of learning techniques exhibited by neural networks. A case study involving real-time hard machining experiments is explained with the help of MATLAB software NNTool module. Finally, an optimization model is derived and explained with ANN. The chapter also describes the advantages and applications of ANN in mechanical and manufacturing technology.

Chapter 12 proposes a system for the multilingual translation of speech to text. The conversion is based on speech signal knowledge. The speech-to-text (STT) process takes as input the utterances of human speech and includes as output a string of words. The purpose of this system is to extract, classify and acknowledge speech information. The project aims to automate the application to overcome the language barrier between countries and even states throughout the world; the above program will perform the different features in the application. The application recognizes speech (human matter) in one language to communicate expressively to another language specified by the user.

Chapter 13 presents a survey on the classification of automatic summarization techniques. Searching for relevant information in summaries typically consumes less time as opposed to searching the entire collection of web pages or documents. Summary generation is helpful in many natural language processing tasks such asretrieving the relevant documents, indexing the text documents, generating personalized summaries, document classification, question and answering system. Extractive summarization techniques are easy to develop as opposed to abstractive summarization, but abstractive summarization models are capable of producing more coherent summaries than extractive methods. This chapter also includes a discussion of different types of datasets used. Intrinsic and extrinsic methods for evaluating summaries are also discussed by the authors. From this survey it is observed that different summarization techniques are found suitable for different datasets. The chapter concludes with a discussion about open research problems to be solved in automatic text summarization (ATS).

Chapter 14 proposes a framework for sentiment analysis of twitter data. The authors measured tweets posted by users in the format of hashtags (#) to state their belief about existing trends. Basically, the sentiment of tweets was investigated using Google Cloud Platform, BigQuery, and Google App Engine. Word intellect recapitulation and WordNet sign inputs were used to amplify the precision. Later, with the help of a classification method, information or data was segregated in the form of positive, negative and neutral. Significant insights are acquired by data visualization. The sentiment analysis was executed based on the ranking produced.

Chapter 15 explores the applications of topic modeling in research. The authors found that applying topic models on various applications is helpful in scientific research. With the help of topic modeling, we can generate topics that are really useful and interesting in an automated way. The literature survey shows that latent dirichlet allocation (LDA) is widely used in text analysis. Social networks are also mainly involvedin this field of text analysis. The use and applications of topic modeling research discussed in this chapter can be a significant source for text mining research, with topic modeling based on popular techniques for researchers in future works.

Part II: Optimization

Chapter 16 provides a comparison of the performance of four machine learning algorithms—Naïve Bayes, Neural Network, Support Vector Machine, and K-nearest Neighbors—in spam classification. The implementation of the algorithms is carried out in R and performance is evaluated by using AUC of the ROC curve, Accuracy, Kappa, and F-Measure. The results revealed that the SVM algorithm performed better than the other algorithms. This work showed that the receiver operating curve–area under the curve (ROC-AUC) is better suited for use in the machine learning world when compared to the accuracy metrics which are generally used in assessing the performance measurement of a classification algorithm.

Chapter 17 deals with an inventory system where urea bags of varying bulk sizes arrive at the warehouse, in which the arrivals follow the Poisson process and the inter arrival times follow exponential distribution. Probability distribution of inventory levels and total expected cost per unit time are obtained, supported with numerical calculations and graphical representations.

Chapter 18 represents a single-objective prototype for supply chain optimization considering disruption scenarios. The goal is to lessen the amount of the setup cost, shipping cost, production price, inventory expense, purchasing cost and scenario cost. A mixed integer linear programming model is developed which as a result of multiple entities is complex. The intention is to find a solution to such a model by developing a solver with the intent of providing a comparative study with different evolutionary approaches and numerical methods like branch and bound.

Chapter 19 studies the tax risk profile of South African construction companies, which is characterized by the book value, cash flow position, headcount, firm earnings, debt size and type of firm. The model that defines this tax risk is a neural network (NN) boosted generalized linear model (GLM). The main aim of this study was to develop an artificially intelligent pricing model. The study, which was conducted using an examination of financial statements of construction companies, highlights the key determinants of the price of tax risk for construction firms. Modeling techniques used to build the pricing model are discussed in detail along with their challenges.

Chapter 20 proposes a design of a simplified Type-A Schiffman phase shifter (SPS) based on microstrip transmission line (TL) technology. This phase shifter (PS) is designed to obtain a phase shift of 90 degrees at the resonance frequency. In this design, the stub matching technique is employed to match the impedance. This design is tunable, thereby obtaining a phase shift from 45 to 90 degrees with a phase deviation of 5 degrees in the resonance frequency of 2.4GHz. A varactordiode is introduced to make the design tunable. The design is carried out using the FR4 substrate, with an operating frequency of 2.4GHz. An IE3D full-wave simulation platform is used for simulation purposes.

Chapter 21 explores manufacturing competencies and sustainability issues for automobile manufacturing companies. The work is based on organizations in north Indian automobile and auto parts manufacturing companies, with various manufacturers being treated alike irrespective of the manufacturing sector. A qualitative model was developed for depicting competency and strategy relation. The research provided an insight into manufacturing competencies and their relation tostrategic success; it also discovered areas that could be improved with further research.

Chapter 22 creates a nonlinear continuous review inventory model for multiple products considering the quantity of the products received as uncertain with controllable lead time. The solution of the model was discussed with and without considering service level constraint. The Lagrangian method is applied for the model with service level constraints and an optimal solution is arrived at.

Part III: Metaheuristics – Applications and Innovations

Chapter 23 proposes a completely innovative metaheuristic optimization algorithm. This new method was inspired by the circular structures on the seafloor which are created by one of the pufferfish species (Torquigeneralbomaculosus). The basic parameters of the inventive method were determined by the parameters observed during the process of the circular structure of the pufferfish. The process of performing this natural phenomenon is detailed in this work. In addition, detailed information about the concept of optimization, development process and activity areas are given in the study. The performance of this proposed method, which is inspired by nature, has been compared with other methods used extensively in the literature.

Chapter 24 proposes a hybrid optimization approach called HGWOSSO based on the integration of two swarm-based approaches, namely grey wolf optimizer (GWO) and sperm swarm optimization (SSO). The aim ofthis hybridization is to merge and enhance the capabilities of exploitation and exploration in both SSO and GWO to generate both in varied strengths. The functions of fixed-dimension multimodal, multimodal, and unimodal benchmarks gleaned from the literature are utilized to check the solution quality and performance of the HGWOSSO variant. The results revealed that the local search in SSO increases the ability of the hybrid variant in solving the benchmark functions, which significantly outperforms the GWO variant in terms of quality of solutions and capability of reaching the global optimum.

Chapter 25 envisions accessible lines of research associated with metaheuristics and highlights less explored areas of considerable concern. The authors concentrate on other metaheuristic approaches, hybrid processes, parallel metaheuristics, metaheuristics under uncertainty and multi-objective optimization. A review of these methods shows that while they are linked to several works, they have not been thoroughly investigated, and there are several open lines of study. The work considered in this chapter is especially beneficial for those researchers looking for novel fields in metaheuristics for multi-objective research and multi-objective optimization.

Chapter 26 deals with the issue of order reduction and controller synthesis in a unified domain for the PMSM drive. Two basic algorithms, viz. the firefly technique and the bacterial foraging optimization technique, are integrated to constitute a new topology known as the hybrid firefly algorithm (HFA). Originally, a PMSM drive consisting of both speed and current controllers created a higher-order system that has been reduced to a lower-order model via an identification method used in signal processing technology. In cascade control with a PI controller, the reduced-order model is there upon compared with that of the reference plant to roughly assess the unknown three-term controller parameters. The control parameters in the unified domain resemble almost accurately the continuous-time parameters at a low sampling limit. A unified controller design framework is thus developed for the drive. The smart algorithm is therefore successfully used both for the order reduction and for the estimation of the controller parameter of PMSM drives.

Chapter 27 presents the three-diode model-based PV module. The Harris hawks’ optimization (HHO) algorithm is used to estimate all the nine parameters of the system for three types of commercially available PV modules; namely, KC200GT multi-crystal, CS6K-280M mono-crystalline, STM6 40-36 mono-crystalline, Pro. SW255 poly-crystalline. The competitive and statistical experimental results show that HHO is advantageous in the sense that the sum of square error is lower as compared to that with other wellknown algorithms. The suggested technique also exhibits better convergence than the salp swarm algorithm (SSA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), and dragonfly algorithm (DA).

Part IV: Sustainable Computing

Chapter 28 proposes a probability density function (PDF) optimized quantization (OQ) scheme for decision statistics at secondary user (SU) nodes to reduce communication overhead through the control channel in IoT cognitive radio network. A proposed approach was evaluated using software defined radio (SDR) setup consisting of RTL-SDR and Raspberry Pi (RPI). Using real-world signal, the proposed quantization strategy was tested with traditional soft-fusion, K-means clustering (KMC) and support vector machine (SVM)-based classifiers at the fusion center (FC). The results show a significant savings in bit requirement at FC to obtain an equivalent performance in comparison to similar schemes in the literature.

Chapter 29 describes recent advancements in energy-saving practices and strategies for achieving a strong vision of green IoT-enabled smart farming coupled with machine learning provided with prediction intelligence. A G-IoT prototype is formulated using machine learning to determine the outline of irrigation conditional nonlinear weather changes. The core aspect of this review article consists of surveys and discussions of the vital topics in green IoT-based smart farming and their enabler technologies.

Chapter 30 proposes a disseminated equal preparation structure for online content examination to proficiently deal with vast amounts of information. This model speaks to semantic content rundown from electronic information with the assistance of a frequent pattern tree and semantic metaphysics by utilizing the space information semantic. Here, MapReduce structure is utilized as information and text information is spoken of as slant term grid with numerical qualities. Results are provided to establish the proposed efficient sentiment analysis method. This method is capable of handling large web-based data efficiently and also performs well for handling synonyms.

Chapter 31 devises a three-phase system using fuzzy logic for node deployment and inter-node data transmission using the A* algorithm for analyzing crop-related data in precision farming. The model shows faster data coverage with fewer iterations than existing models along with a cost-effective optimized deployment strategy, which will help users save money and have access to proper real-time data. The result analysis is shown in accordance with the real-time deployment scheme.

Chapter 32 focuses on smart and precise agriculture to achieve better crop yields. The authors include several essential categories of the agricultural domain and highlight the importance of every category. Many different technologies are implemented to enhance crop yield, food security and ease of work. Artificial intelligence, internet of things, and robotics are discussed. These technologies help farmers at every crop stage—from showing to harvesting the crop, from packing to transportation. Artificial intelligence helps farmers utilize assets more economically and get fair use of farmland.

Chapter 33 discusses the developing needs of both academicians and professionals for understanding the relationship of various sustainable green initiatives, advanced manufacturing techniques, maintenance techniques and performance attributes. In this admiration, notwithstanding exhibiting the most recent understanding of the present status of the impact of four capacities on execution of vehicles organizations, it has valuable ramifications for managers with respect to the technique development.

Part V: AI in Healthcare

Chapter 34 utilizes the Bayesian paradigm in understanding the association between the gender and lipid profile among coronary artery disease (CAD) patients and compares the results with classical approaches. The research is based on the secondary analysis of data (n=1045) from a National Health and Nutrition Examination Survey (NHANES) (2015–2016) of individuals older than 50 years in which measures of the lipid profile were available. The clinical diagnosis of CAD was positive in 91 individuals. The comparison of differences in the lipid profiles across gender was performed under the classical (independent sample t-test) and Bayesian paradigm. Males positive for CAD were younger (54-80) than females (57-80). The lipid parameters (total cholesterol, LDL, Direct HDL, non-HDL) differed significantly across gender under both paradigms, except for triglyceride and two ratios (TC:HDL, LDL:HDL). However, the Bayesian paradigm suggested differences even for triglycerides and TC:HDL ratio across gender. This clearly suggests that even when sample size is small, the Bayesian paradigm closely approximates our prior knowledge of lipid profile as the risk factor for CAD occurrence. The Bayesian paradigm unraveled the importance of clinical parameters (triglyceride and TC:HDL), which remained hidden under the classical t-test.

Chapter 35 proposes an architecture of cascaded convolutional long short-term memory (ConvLSTM) that uses the idea of combining the patch-based dictionary learning approach for reconstruction of dynamic MRI. K-space data of T2-weighted dynamic MRI sequences obtained from ADNI database is undersampled for accelerating the acquisition process using Cartesian masks of different undersampling rates. Proposed architecture is later used to reconstruct this undersampled sequence. Results are compared with state-of-the-art 2-dimensional cascaded convolutional neural network (CNN)-based reconstruction for all standardmetrics. The proposed methodology is capable of preserving anatomical structure modality even after manifold undersampling.

Chapter 36 presents a multispectral imaging-based gender classification with images collected in nine narrow spectrums across VIS and NIR spectrum. The authors also experimentally presented the comparative performance analysis study on gender classification using affine hull algorithm and wavelet averaging fusion. The experimental results are obtained on the multispectral facial database of 145 subjects corresponding to 78300 sample spectral band images. The extensive experimental results are carried out across six different illuminations using three different feature extraction methods for gender classification. The average classification obtained indicates the superiority of the wavelet fusion method over the affine hull subspace learning method in successfully extracting the unique characteristic information from spectral bands for improved performance.

Chapter 37 analyzes the performance of various deep learning models for polyp detection. Various image enhancement techniques, such as max, min, sobel and canny filters, are applied to improve the performance efficiency of the training networks, which further helps to increase the rate of polyp detection. The concept of transfer learning and fine tuning is implemented to improve the efficiency of VGG16 and VGG19 deep neural networks. The system model is tested to detect polyps and the results of the system are described using different performance metrics like accuracy, loss, precision and recall. This work concludes that VGG19 deep neural networks are more suitable for polyp detection than other methods.

Chapter 38 proposes a method based on a combined approach of extrinsic content sensors and ab-initio signal sensors to predict boundary exons in human sequences. Here, a homology-based exon prediction method is used which utilizes external information from sources like transcript and protein databases. The method is evaluated at a nucleotide as well as exon level. The experimental results indicate that the proposed method is appropriate for predicting boundary exons with a significant level of accuracy. It also demonstrated superior performance when compared with existing protein-coding gene prediction methods.

Chapter 39 discusses a blood glucose monitoring system with 5-fixed LED wavelengths in near-infrared (NIR) region at 2.12 μm, 2.24 μm, 2.27 μm, 2.31 μm and 2.33 μm as a source of excitation. The Jetson Nano board having ARM Cortex A57 is used to control these LED sources. The authors recorded 57 spectra on laboratory samples prepared to resemble blood, having proportions as per the major constituents (glucose, ascorbate, urea, lactate, and alanine) present in the blood. Out of 57spectra, 53 were used for calibration set and 4 were used for validating the model. Partial least square regression (PLSR) prediction algorithms are developed in Python and run on Jetson Nano board.With PLSR, the result of glucose prediction gave a root mean square error (RMSE) of 12.01mg/dL, determination coefficient R₂ = 0.97 and accuracy of 90.14%. A back propagation–artificial neural network (BP-ANN) model is developed on Jetson Nano board for accuracy. This BP-ANN model is used to train the same 53 sample data sets and 4 for validating the model. The system is validated with Clarke error grid analysis (CEGA) and Bland–Altman analysis.

Chapter 40 envisions the potential of geographic information systems (GIS) in combating COVID-19. These systems play an important role in many areas, including quick mapping of epidemic information, spatial monitoring of cases reported, forecasting of district transmission of epidemic hazard and mitigation, and social-emotional assistance for decision-making and control. The authors have collected the state-level variation of COVID-19 pandemic prevalence spreading across the Gujarat state of India. In the present study, they prepared four maps, namely, confirmed positive cases, cases tested for COVID-19, patients recovered, people under quarantine, and total deaths as per data collected from the government. Cluster zones can be easily identified by the GIS-based mapping approach. Outcomes of the study demonstrated that Ahmedabad city in Gujarat has suffered more as a result of this pandemic.

Chapter 41 proposes a mobile-based medical alert system for a COVID-19 detection system using ZigBee technology. The health report of the user will be sent to the caretaker or doctor via a cloud computing network for analysis. The real-time monitoring of body temperature and symptoms of COVID-19 and data transmission via remote sensing is also realized.

In conclusion, this book highlights the important role artificial intelligence playsin smart living and sustainable development along with the critical need for more research in the field. It provides a comprehensive overview of the latest breakthroughs and recent progress in intelligent technologies; and highlights relevant sustainable intelligent computing technologies, uses, and techniques across various industries. We hope that readers will significantly benefit from this book academically, scientifically and societally; and that it will expand opportunities and open new scientific paths to foster the discovery of knowledge and its applications.

Mukhdeep Singh Manshahia Punjabi University, Patiala, Punjab, India Valeriy Kharchenko Federal Scientific Agroengineering Center, VIM, Moscow, Russia Elias Munapo Department of Statistics & Operations Research, NWU, Mafikeng Campus, South Africa J. Joshua Thomas UOW Malaysia KDU Penang University College, Malaysia Pandian Vasant Universiti Teknologi PETRONAS, Malaysia