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To be in the competitive industrial world, industries required high quality, speed in completing the required work, and commitment to the delivery dates. One of the most important issues in the field of production management is the job shop scheduling problem (JSSP). In this paper, the researchers tried to solve JSSP of factory by presenting a method to improve the factory's production. Job shop scheduling (JSS) is a suitable method for solving these types of problems, which aims to improve the production flow through minimizing the whole operation time of the products. Moreover, considering the factory that depends on workers as same as machine, human factor should be considered while scheduling by using the workers' weightage, in order to improve the workers' working time flexibility in terms of their waiting time among their tasks by proposed model of JSS. In addition, the researchers proposed a new combination of weightage values by using Taguchi method, regarding to improve the workers' working time and using genetic algorithm (GA) to solve the proposed model of JSS. One of the factories which is located in Jordan, and it is considered as one of the important factories; nevertheless, it can cover the local demands hardly, and hence, it deserves to be as a study case for this research. The findings of the studies decreased the whole operation time of the products by saving 75 min for each production line and 90 min by using GA, and the proposed model improved the flexibility of the workers' working time in terms of their waiting times among their tasks.

Prediction analysis has drawn signi?cant interest in numerous ?elds Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as helping to eliminate variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multivariate data restrains the optimization accuracy. The binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process for better prediction accuracy. A comparison between the T-Method+OA and T- Method+BPSO in four di?erent case studies shows that the T-Method+BPSO performs better with a higher coe?cient of determination (R2) value and means relative error (MRE) value compared to the T-Method+OA. The T-Method with the BPSO element as variables screening optimization is able to increase or even maintain the prediction accuracy for cases that are normally distributed, have a high R2value, and with low sample data.

Missing value or sometimes synonym as missing data, is an unavoidable issue when collecting data. It is uncontrollable and happen in almost any research fields. Hence, this study focused on identifying the current publications trend on missing data imputation techniques (1991- 2021) specifically in classification problems using bibliometric analysis. Most importantly, this research aims to uncover the potential missing data imputation methods. Two software were used; VOSViewer and Harzing Publish or Perish. Based on the Scopus database extracted in June 2021, the findings indicate an emerging trend in missing data imputation research to date, while there are two imputation methods that get the most attention; the random forest and the nearest neighbor methods.

The Mahalanobis–Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. The MD metric provides a measurement scale to classify classes of samples (Abnormal vs. Normal) and gives an approach to measuring the level of severity between classes. An accurate classification result depends on a threshold value or a cut-off MD value that can effectively separate the two classes. Obtaining a reliable threshold value is very crucial. An inaccurate threshold value could lead to misclassification and eventually resulting in a misjudgment decision which in some cases caused fatal consequences. Thus, this paper compares the performance of the four most common thresholding methods reported in the literature in minimizing the misclassification problem of the MTS namely the Type I–Type II error method, the Probabilistic thresholding method, Receiver Operating Characteristics (ROC) curve method and the Box–Cox transformation method. The motivation of this work is to find the most appropriate thresholding method to be utilized in MTS methodology among the four common methods. The traditional way to obtain a threshold value in MTS is using Taguchi’s Quadratic Loss Function in which the threshold is obtained by minimizing the costs associated with misclassification decision. However, obtaining cost-related data is not easy since monetary related information is considered confidential in many cases. In this study, a total of 20 different datasets were used to evaluate the classification performances of the four different thresholding methods based on classification accuracy. The result indicates that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Nevertheless, the study recommends the use of the Type I–Type II error method due to its less computational complexity as compared to the other three thresholding methods.

Taguchi’s T-Method is one of the Mahalanobis Taguchi System- (MTS-) ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. The prediction model’s complexity aspect can be further enhanced by removing features that do not provide valuable information on the overall prediction. In order to accomplish this, a matrix called orthogonal array (OA) is used within the existing Taguchi’s T-Method. However, OA’s fixed-scheme matrix and its drawback in coping with the high-dimensionality factor led to a suboptimal solution. On the contrary, the usage of SNR (dB) as its objective function was a reliable measure. The application of Binary Bitwise Artificial Bee Colony (BitABC) has been adopted as the novel search engine that helps cater to OA’s limitation within Taguchi’s T-Method. The generalization aspect using bootstrap was a fundamental addition incorporated in this research to control the effect of overfitting in the analysis. The adoption of BitABC has been tested on eight (8) case studies, including large and small sample datasets. The result shows improved predictive accuracy ranging between 13.99% and 32.86% depending on cases. This study proved that incorporating BitABC techniques into Taguchi’s T-Method methodology effectively improved its prediction accuracy.

This paper analysed the effects of bending on the performance of a textile antenna wherein the antenna under test was made of felt substrate for both industrial, scientific, and medical (ISM) band and WBAN applications at 2.45 GHz. Moreover, the conductive material was used for the patch, and the ground plane used a 0.17 mm Shieldit textile. Meanwhile, the antenna structure was in the form of rectangular, with a line patch in between elements to abate the mutual coupling effect. The measured operating frequency range of the antenna spanned from 2.33 GHz to 2.5 GHz with a gain of 4.7 dBi at 2.45 GHz. In this paper, the antenna robustness was examined by bending the structure on different radii and degrees along both X- and Y-axis. Next, the effects on return loss, bandwidth, isolation, and radiation characteristics were analysed. This paper also discovered that the antenna’s performance remained acceptable as it was deformed, and the measured results agreed well with the simulation.

Objective. The aim of this study was to investigate the effects of surface stiffness on multisegment foot kinematics and temporal parameters during running. Methods. Eighteen male subjects ran on three different surfaces (i.e., concrete, artificial grass, and rubber) in both heeled running shoes (HS) and minimal running shoes (MS). Both these shoes had dissimilar sole profiles. The heeled shoes had a higher sole at the heel, a thick base, and arch support, whereas the minimal shoes had a flat base sole. Indeed, the studied biomechanical parameters responded differently in the different footwear during running. Subjects ran in recreational mode speed while 3D foot kinematics (i.e., joint rotation and peak medial longitudinal arch (MLA) angle) were determined using a motion capture system (Qualysis, Gothenburg, Sweden). Information on stance time and plantar fascia strain (PFS) was also collected. Results. Running on different surface stiffness was found to significantly affect the peak MLA angles and stance times for both HS and MS conditions. However, the results showed that the joint rotation angles were not sensitive to surface stiffness. Also, PFS showed no relationship with surface stiffness, as the results were varied as the surface stiffness was changed. Conclusion. The surface stiffness significantly contributed towards the effects of peak MLA angle and stance time. These findings may enhance the understanding of biomechanical responses on various running surfaces stiffness in different shoe conditions.

To date, in modern industrial competitions, there are several industries demanded a high quality in terms of efficiency, adherence of delivery dates, and speed to achieve a demand work. One company in Jordan, specialized to produce a glass utilizing advanced machines and high-quality raw materials, whereas, there is a difficulty to fulfill the coverage of local demands. This paper, the researchers aims for an improvement of glass factory to minimize the unnecessary issues. To do so, it requires an effective method in order to enhance the production process in factory. As stated earlier, the effective method is required in this study namely Job Shop Scheduling (JSS), it is appropriate method to be used to deal with unnecessary problems that usually occur and present a suitable schedule which can guarantee an optimal solution in terms of minimizing the completion time that required to complete the whole production flow. This study aims to enhance the flow of the production line by diminishing products operation time. Thus, recent studies highlighted the outcomes declination in the product operation time by saving 85 minutes using random search (RS) algorithm, and each production line by 75 minutes via classical computation method. Thereupon, solving classical job shop scheduling (CJSS) model, which related to the study case of this paper, via RS algorithm is more significant than classical computation methods.

Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.

The use of Differential Evolution (DE) to simultaneously optimize parameter \( t \) and middle control points \( \left( {P_{1} } \right. \) and \( \left. {P_{2} } \right) \) of cubic Bezier curve is presented in this paper. The main objective of this research is to reduce the error of the numerical result. In addition, the numerical result from this research also being compared with the previous article [4] and [5]. The main steps involved are boundary extraction and corner point detection of the images. Then, followed by the process of DE in optimizing the parameter \( t \) and middle control points. Finally, by using the optimized value, the piecewise cubic Bezier curve is fitted to each segment. In addition, the Sum Square Error (SSE) has been used as an objective function to calculate the distance of the fitted Cubic Bezier curve with the boundary of the original images. The numerical result for this research produced more errors because of two parameter value that were optimized has been run simulataneously. The result that has been obtained does not guarantee that this algorithm is not good because the use of DE is subjective. DE is an interesting algorithm that can be explored in more detail and can be applied in various problems. The new research of DE can be extended more to its various mutation strategies.

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far.

A comparison between Chi-squared Automatic Interaction Detector (CHAID) and logistic regression analysis was performed for classification problems on bank direct marketing data. CHAID Performance Comparison and comparison with Logistic Regression (LR) performance were also conducted. Priority performance with two statistical measures was evaluated: classification accuracy and sensitivity in the presence of data containing categorical imbalances. Random over sampling (ROS) was then applied to deal with class balance problems to get better performance of CHAID analysis. Segmentation analysis was also performed using the CHAID approach to improve the performance of the analysis results. CHAID outperforms LR because of its advantages that it can be used to perform segmentation modeling. Direct marketers should pay attention to traits are Duration, Month, Contact, and Housing. To get a higher subscription, the bank must extend the call duration. Based on these results, the banking industry needs to prepare regulations related to human resources, infrastructure, costs, and government support to achieve higher subscriptions.JEL Classification A10; C10; G21

The goal of this study is to assess the efficiency of Artificial Bee Colony (ABC) algorithm in finding the optimal solution of curve fitting problem specifically for medical images. Data of Computed Tomography (CT) images from two different patients were collected. The procedure of curve fitting for medical images include conversion of Digital and Communications in Medicine (DICOM) images to binary images, boundary and corner point detection, parameterization, and curve reconstruction by using ABC algorithm. Then, Sum Square Error (SSE) was used to calculate the distance of the fitted Cubic Bezier curve with the boundary of the original images. Based on the calculation and parameter tuning that had been done, the smallest error of both skulls is57.5754and28.8628, respectively. The finding of this study illustrated that the proposed method had efficiently produced fitted Bezier curve that resemble the original medical images. In addition, the used of Douglas Peucker algorithm helps to improve the performance of the proposed method since computational time can be minimized. This study had shown that the proposed method can be used as an alternative method in order to reconstruct or redesigned the medical images since it produces a small error. For future work, we are planning to explore and applied the ABC algorithm to reconstruct the missing part of the skull since it can reduce the time taken to produce the skull implant as well as reducing the cost of producing it.

Mahalanobis-Taguchi system (MTS) is a data mining method that employs Mahalanobis distance (MD) and Taguchi robust engineering philosophy, in order to explore and exploit data in a multidimensional system. Furthermore, MD calculation provides a measurement scale that is used to discriminate and interpret the relationships among data samples (abnormal vs normal). Another function of MD calculation is that it is an approach of measuring the level of dissimilarity between them (severity). One unique feature of MTS lies on its precision in assessing the variability among all levels of samples (noise). Besides, MTS possesses the ability to evaluate the significant and insignificant factors that contribute to the performance of system (optimize). This evaluation is performed through a simplistic yet effective technique (orthogonal array and signal to noise ratio). Moreover, MTS receives a tremendous amount of appreciations by various industries due to its ability to perform various functions in diverse industrial applications (among others) of optimization, inspection, diagnostics, monitoring, estimation, prediction, classification and discrimination. Despite the appreciations received by this method, some criticize the reliability of MTS on its effectiveness, while some contribute ingenious ideas (hybrid systems, intelligent search algorithms, novel mathematical computation methods, etc.) for a further improvement of this method’s implementation. Therefore, this paper provides a general review on MTS research trends and studies conducted on this method outside Japan, in order to identify future research prospects.

Previous studies have examined the role of arm swing for various types of jumping technique, but none have been found to study about the gender differences in term of the role of arm swing on forward and backward jump. This study aimed to compare the jumping performance between male and female for forward and backward jump. Seven male and seven female subjects performed four trials of forward and backward jump with (FJA, BJA) and without arm swing (FJ, BJ) respectively. Qualisys Track Manager System, EEGO Sports, Visual3D and MATLAB software was used to record and analyze the performance. According to the result, the triceps brachii muscle is the most active muscle compared to other muscles during jumping. The normalized vGRF showed significant correlation with jump height when jumping forward and backward (p

A concrete justification unavailability of the roughly estimated warranty claim quantity is leading to also inability to understand defect trend behavior and its effect upcoming warranty claim quantity. This is where an equation model is derived by considering the previous actual warranty data, to represent warranty claim defect quantity impact. Taking into consideration, identified parameters which link with pricing and cost, it also includes the observation and monitoring of warranty trend from the existing actual warranty data, by plotting cumulative defect quantity over Vehicle Line-off Date, as well as plotting cumulative defect quantity over Vehicle Submission Date. Multiple Linear Regression is deployed to define the best Multiple Regression Equation. Predictors, response and predictors’ validations defined by using normality and probability test. The successfully developed equation model, takes into account the existing warranty data and trend, As a result, the equation model managed to provide forecasted warranty claim quantity, based on a complete 36-month warranty period cycle, which has a significant impact on a reliable and convincing figure – a key factor in warranty budgeting and accrual task.

The Mahalanobis-Taguchi system (MTS) is a relatively new statistical methodology combining various mathematical concepts and is used in the field of diagnosis and classification in multidimensional systems. MTS is a very efficient method and has already been applied to a wide range of disciplines. However, its feature selection phase (optimization stage), which uses experimental designs (orthogonal array, OA), is susceptible to improvement. In MTS, selection of important features or variables to improve classification accuracy is done using signal-to- noise (S/N) ratios and OA. OA has been noted for limitations in handling a large number of variables. Therefore, in this research, we propose the use of a random binary search (RBS) algorithm incorporated within MTS for optimizing the procedure for selecting the most useful variables. Ensemble is a powerful technique to achieve improvement in the accuracy of predictive models, whereby individual methods, which are not consistently the best performers in different problems and datasets, are brought together to provide predictions which are more accurate than those made by individual methods.

Gait analysis is recognized as a method used in quantifying gait disorders and in clinical evaluations of patients. However, the current guidelines for the evaluation of post anterior cruciate ligament reconstruction (ACLR) patient outcomes are primarily based on qualitative assessments. This study aims to apply gait analyses and mathematical, index-based health management, using the Mahalanobis Taguchi System (MTS) and the Kanri Distance Calculator (KDC) to diagnose the level of the gait abnormality and to identify its contributing factors following ACLR. It is hypothesized that (1) the method is able to discriminate the gait patterns between a healthy group (HG) and patients with ACLR (PG), and (2) several contributing factors may affect ACLR patients&rsquo, rehabilitation performance. This study compared the gait of 10 subjects in the PG group with 15 subjects in the HG. The analysis was based on 11 spatiotemporal parameters. Gait data of all subjects were collected in a motion analysis laboratory. The data were then analyzed using MTS and KDC. In this study, two significant groups were recognized: the HG, who achieved results which were within the Mahalanobis space (MS), and (ii) the PG who achieved results above the MS. The results may be seen as being on-target and off-target, respectively. Based on the analysis, three variables (i.e., step width, single support time, and double support time) affected patient performance and resulted in an average mark of above 1.5 Mahalanobis distance (MD). The results indicated that by focusing on the contributing factors that affect the rehabilitation performance of the patients, it is possible to provide individualized and need-based treatment.

Methadone Flexi Dispensing Service (MFlex) has been officially re-branded from Methadone 1Malaysia Service (M1M) since 2nd January 2019. Patients under MFlex are frequently taking their methadone according to a plan provided by pharmacist at public clinic. From the dose monitoring taken annually, pharmacists can predict critical patients based on high monthly dose increases. However, the current monitoring system is written documentation with total doses that cannot accurately measure addiction levels and slow down the distribution process to appropriate incentives as provided by the government. The main objective of this work is to develop a new data monitoring system by evaluating all factors contributed to the addiction level. Mahalanobis-Taguchi System (MTS) is a method of predicting and diagnosing system performance using multivariate data in order to make quantitative decisions with the construction of a multivariate measurement scale using an analytical method. The results show that the minimum Mahalanobis Distance (MD) for healthy data is 0.2245 while the maximum is 2.3380. The minimum and maximum MD of unhealthy data is 0.6077 and 24.5719 respectively. Thus, parameters of blood, bilirubin, nitrite, specific gravity, leukocytes are considered as significant parameters by considering positive value signal-to-noise ratio (SNR) gain. Graphical user interface (GUI) has been developed for analyzing the normal and abnormal patients in detail. Meanwhile, mobile application has been developed as a decision-making tool to classify that the patients is either normal or abnormal.

Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.

Digital image is considered as a powerful tool to carry and transmit information between people. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge for the researchers in this field. A lot of image denoising techniques have been introduced in order to remove the noise by taking care of the image features; in other words, getting the best similarity to the original image from the noisy one. However, the findings are still inconclusive. Beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), there is also the scarcity of review papers which carry an important role in the development and progress of research. Thus, this review paper intorduce an overview of the different fundamental approaches that yield the several image-denoising techniques, presented with a new classification. Furthermore, the paper presents the different evaluation tools needed on the comparison between these techniques in order to facilitate the processing of this noise problem, among a great diversity of techniques and concepts.

Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. Therefore, this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. This technique permits the narrowing of the gap between the original and the restored images, visually and quantitatively by adopting the mathematical concept ''arithmetic progression''. Through this paper, this concept is integrated into the image denoising, due to its ability in modelling the variation of pixels’ intensity in the image. The principle of the proposed denoising technique relies on the precision, where it keeps the uncorrupted pixels by using effective noise detection and converts the corrupted pixels by replacing them with other closest pixels from the original image at lower cost and with more simplicity.

Factors affecting students’ performance determinants of students’ performance have been the subject of ongoing debate among educators, academics, and policy makers. Several factors may affect students’ academic performance at universities. This study aims to measure and study these factors using theories on academic performance. Kanri calculator and Mahalanobis Taguchi System are used to determine which factor affects the academic performance. There are two programs with different school involve in this research; Program A and Program B. The results show that the variable 4 which contain course outcome 2 result are meaningfully related to the academic performance.

In recent years, university course timetabling at University of Malaysia Perlis (UniMAP) given by Bahagian Pengurusan Akademik (BPA) applied based on their experience with a little guidance from Microsoft Excel to generate the timetable. The most challenges is not about the generating a timetable that works, but generating one that is fairly, practical and good as possible. In general, the university course timetabling problem is an optimization problem in which a set of events has to be scheduled in timeslots, located in suitable rooms under specific constraints. Free Educational Timetabling (FET) is FET is open source free software for automatically scheduling the timetable of a school, high-school or university. It uses a fast and efficient timetabling algorithm, developed with C language in 2002. In this paper, the process of adaptation in presented to produce university course timetable, which has been practically used starting from Semester II, 2012/2013. Due to the complexity and constraints of generating the course timetable, an experimental methodology for choosing proper parameter setting. In particularly, the procedures allow an automatic and semi-automatic configuration of the software with a good save in time. The successful example shows that the implementation of FET software can provide a new alternative on solving the university course timetable problems through an experimental methodology leads to high performing algorithms and viable solutions for hard combinatorial optimization problems in comparison to previous practices.

This paper presents an issue related to examination timetabling raised in the University of Malaysia Perlis (UniMAP). UniMAP is a distributed campus university in Perlis, North of Malaysia, which is comprised of four branch campuses that offers a total of 31 programmes at 12 schools or faculties. This study looked into an issue related to examination timetabling based on intricacy, time, and several challenges. As such, real world data were analysed by employing the Free Educational Timetable (FET) software program in the attempt to seek a solution for the issue at hand. The FET refers to an open-source and free software program can schedule timetable automatically for any learning institutes by applying an efficient and rapid algorithm meant for timetabling. Nevertheless, due to absence of solution in the literature and the employment of new dataset, the outcomes from the analyses were compared with a manual solution made available. It was discovered that the solution generated from this study appeared better in terms of its capacity of offering an examination timetable that is high in quality within a shorter period of time, as well as improved proximity cost. In conclusion, the results generated from this study have the ability to lessen the burden of the Examination Unit related to examination timetabling.

Solving a real-world university examination timetabling is an intractable problem. Examination timetabling problems (ETP) can be defined as a problem of assigning a set of final examinations schedules to a given number of timeslots and rooms subject to a set of constraints. These constraints are usually divided into hard and soft constraints. Hard constraints must be satisfied under any circumstances. Whilst, soft constraints are not only essential but also should be satisfied as much as possible. Due to the complexity of the problem, it is usually impossible to have solutions that satisfy all soft constraints. The quality of the timetable is measured based on soft constraints violation. A weighted penalty/value is attached to each violation of the soft constraint and the objective is to minimize the total penalty value of these violations. Although there have been a numerous approaches in solving the ETP, but this paper only focus on Genetic Algorithm (GA) method based on the fact that it is robust and can fits into complex problem space. This paper gives a summary of the GA method on ETP and it aims in helping other researcher to shorten their time in understanding the application of GA in the selected problem.

Breast cancer is becoming the major factor of death amongst women in the world. However, it is found that longer lifespan of this disease’s patients can be guaranteed through early detection and accurate diagnosis of this disease. When it comes to the treatment given to patients, a doctor needs to put his/her knowledge and experience in practice by specifying the source of the suspected disease (out of a list of the possible causes with similar symptoms). This is followed by confirming the diagnosis through a number of tests. Therefore, identifying the disease without receiving assistance from intelligence systems is time consuming. The objective of this study is to introduce the intelligence system which develops Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS). It is also with the purpose to validate the techniques of feature selection problems which are computationally efficient, and to apply Random Binary Search algorithm in solving medical classification problems. In this study, in order to improve the step of choosing the most useful variables, Random Binary Search (RBS) algorithm is proposed, which is incorporated between MTS. Besides being a relatively new statistical methodology where various mathematical concepts are combined, MTS is used in the field of diagnosis and classification in multidimensional systems. It is also a highly efficient method, and it has been utilized in a wide range of disciplines such as engineering, medical, financial, and more. Datasets of medical fields, which were concerning cancer, diabetes and hepatitis, were used in this study. Besides, binary class classification problems were also represented by these data sets.Breast cancer is becoming the major factor of death amongst women in the world. However, it is found that longer lifespan of this disease’s patients can be guaranteed through early detection and accurate diagnosis of this disease. When it comes to the treatment given to patients, a doctor needs to put his/her knowledge and experience in practice by specifying the source of the suspected disease (out of a list of the possible causes with similar symptoms). This is followed by confirming the diagnosis through a number of tests. Therefore, identifying the disease without receiving assistance from intelligence systems is time consuming. The objective of this study is to introduce the intelligence system which develops Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS). It is also with the purpose to validate the techniques of feature selection problems which are computationally efficient, and to apply Random Binary Search algorithm in solving medical classification prob...

The wide usage of credit scoring is to determine credit, credit analysis cost reduction, and faster decision making. However, traditional credit scoring models are not the factor for the influence of noises. There are several objectives of this study: 1) to develop Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS), 2) to validate the techniques of feature selection problems which are computationally efficient, and 3) to apply Random Binary Search algorithm in solving credit problems. In this study, Random Binary Search (RBS) algorithm was proposed, which was incorporated between MTS. The purpose of this was to optimize the procedures for determining the most useful variables. Besides being a relatively new statistical methodology where various mathematical concepts are integrated, there is an involvement of MTS in the field of diagnosis and classification in multidimensional systems. Besides, it is a highly effective method and has been implemented to a range of disciplines such as engineering, medical, financial, and others. As for the main problem of this study, data sets of credit scoring were used.

MBA is a popular choice post-graduate degree program choices. It is regularly observed that the characteristic and achievement of an MBA program be contingent on the criteria required of received students. The purpose of this report is to evaluate the contribution of individual entry criteria for each candidate and to identify the optimal impact variable. In this effort, a multi-criteria assessment basis using the MTS methodology and the Kanri Distance Calculator. Criteria measured in this study include the scores achieved by a candidate before joining the degree program. MD used in MTS to measure the abnormality of a candidate's score from apparently ultimate scores. The important criteria for evaluation are identified using an OA and SNR. This work could support institutions to exclusive good-quality candidates.

The theory of linear elastic fracture mechanic (LEFM) has proven that we can evaluate the amount of stress located at the crack tip by determining the stress intensity factor (). The stress at the tip of a sharp crack has the highest stress which can lead to failure on the material. Thus, the cracks within human bones are quite complicated because of the bone microstructure. There are a few factors that can minimize the effect of the cracks so that patients can heal much faster. Hence, this paper focuses on how several crack distances, between two parallel edge cracks can affect the value of stress intensity factor (). Using the LEFM theory, the interaction between two neighboring crack tips was investigated.

This paper describes particular pain events to be located as in infant face images with feature extraction algorithm. Nonlinear Fuzzy Robust PCA (NFRPCA) feature extraction is implemented to test its effectiveness in recognizing pain in images. In this work, two classifiers, Fuzzy k-nearest neighbors (Fuzzy k-NN) and k-nearest neighbors (k-NN) are employed. Result shows that the NFRPCA and classifier (Fuzzy k-NN and k-NN) can be used for the recognition of infant pain images with the best accuracy of 89.77%.

Fibonacci retracement implicates a forecast of future movements in foreign exchange rates (forex) of the previous movement inductive analysis. Fibonacci ratios are used to forecast the retracements level of 0.382, 0.500 and 0.618 and to determine the current trend which provide the mathematical foundation for the Elliott wave theory. K-nearest neighbour (KNN) and linear discriminant analysis (LDA) algorithm are the pattern recognition method for nonlinear feature mining of Elliott wave patterns. Results show that LDA is better than KNN in terms of classification accuracy data which are 99.43%. Among of three levels of Fibonacci retracement results, the 38.2% shows the best forecasting for Great Britain Pound pair to US Dollar currency as major pair by using mean absolute error (MAE), root mean square error (RMSE) and pearson correlation coefficient (r) as the statistical measurements which are 0.001884, 0.000019 and 0.992253 for uptrend and 0.001685, 0.000019 and 0.998806 for downtrend.