After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.
Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.
The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in process location and dispersion. For efficient monitoring of process variability, we present several CUSUM control charts for monitoring changes in standard deviation of a normal process. The newly developed control charts based on well-structured sampling techniques - extreme ranked set sampling, extreme double ranked set sampling and double extreme ranked set sampling, have significantly enhanced CUSUM chart ability to detect a wide range of shifts in process variability. The relative performances of the proposed CUSUM scale charts are evaluated in terms of the average run length (ARL) and standard deviation of run length, for point shift in variability. Moreover, for overall performance, we implore the use of the average ratio ARL and average extra quadratic loss. A comparison of the proposed CUSUM control charts with the classical CUSUM R chart, the classical CUSUM S chart, the fast initial response (FIR) CUSUM R chart, the FIR CUSUM S chart, the ranked set sampling (RSS) based CUSUM R chart and the RSS based CUSUM S chart, among others, are presented. An illustrative example using real dataset is given to demonstrate the practicability of the application of the proposed schemes.
Measurements of environmental terrestrial gamma radiation dose-rate (TGRD) have been made in Johore, Malaysia. The focus is on determining a relationship between geological type and TGRD levels. Data were compared using the one way analysis of variance (ANOVA), in some instances revealing significant differences between TGRD measurements and the underlying geological structure.
A statistical prediction of terrestrial gamma radiation dose rate has been performed, covering the Kota Tinggi district of Peninsular Malaysia. The prediction has been based on geological features and soil types. The purpose of this study is to provide a methodology to statistically predict the gamma radiation dose rate with minimum surveying in an area. Results of statistical predictions using the hypothesis test were compared with the actual dose rate obtained by measurements.
Multivariate control charts have been applied in many sectors. One of the sectors that employ this method is network intrusion detection. However, the issue arises when the conventional control chart faces difficulty monitoring the network-traffic data that do not follow a normal distribution as required. Consequently, more false alarms will be found when inspecting network traffic data. To settle this problem, support vector data description (SVDD) is suggested. The control chart based on the SVDD distance can be applied for the non-normal distribution, even the unknown distributions. Kernel density estimation (KDE) is the nonparametric approach that can be applied in estimating the control limit of the non-parametric control charts. Based on these facts, a multivariate chart based on the integrated SVDD and KDE (SVDD-KDE) is proposed to monitor the network's anomaly. Simulation using the synthetic dataset is performed to examine the performance of the SVDD-KDE chart in detecting multivariate data shifts and outliers. Based on the simulation results, the proposed method produces better performance in detecting shifts and higher accuracy in detecting outliers. Further, the proposed method is applied in the intrusion detection system (IDS) to monitor network attacks. The NSL-KDD data is analyzed as the benchmark dataset. A comparison between the SVDD-KDE chart with the other IDS-based-control chart and the machine learning algorithms is executed. Although the it has high computational cost, the results show that the IDS based on the SVDD-KDE chart produces a high accuracy at 0.917 and AUC at 0.915 with a low false positive rate compared to several algorithms.
There are few reports of congenital disorders of glycosylation (CDGs) in the Asian population, although they have been reported worldwide. We identified a Malaysian infant female at 2 days of life with CDG type Ia. The diagnosis was suspected on the basis of inverted nipples and abnormal fat distribution. She had cerebellar hypoplasia and developed coagulopathy, hypothyroidism and severe pericardial effusion and died at 7 months of life. The diagnosis was supported by abnormal serum transferrin isoform pattern that showed elevated levels of the disialotransferrin isoform and trace levels of the asialotransferrin isoform. Enzyme testing of peripheral leukocytes showed decreased level of phosphomannomutase (PMM) activity (0.6 nmol/min per mg protein, normal range 1.6-6.2) and a normal level of phosphomannose isomerase activity (19 nmol/min per mg protein, normal range 12-25), indicating a diagnosis of CDG type Ia. Mutation study of the PMM2 gene showed the patient was heterozygous for both the common p.R141H (c.422T>A) mutation and a novel sequence change in exon 7, c.618C>A. The latter change is predicted to result in the replacement of the highly conserved phenylalanine residue at position 206 with a leucine residue (p.F206L) and occurs in the same codon as the previously reported p.F206S mutation. Analysis of 100 control chromosomes has shown that the p.F206L sequence change is not present, making it highly likely that this change is functionally important. To the best of our knowledge, this is the first report of CDG in the Malay population. Prenatal diagnosis was successfully performed in a subsequent pregnancy for this family.
The usual practice of using a control chart to monitor a process is to take samples from the process with fixed sampling interval (FSI). In this paper, a synthetic X control chart with the variable sampling interval (VSI) feature is proposed for monitoring changes in the process mean. The VSI synthetic X chart integrates the VSI X chart and the VSI conforming run length (CRL) chart. The proposed VSI synthetic X chart is evaluated using the average time to signal (ATS) criterion. The optimal charting parameters of the proposed chart are obtained by minimizing the out-of-control ATS for a desired shift. Comparisons between the VSI synthetic X chart and the existing X, synthetic X, VSI X and EWMA X charts, in terms of ATS, are made. The ATS results show that the VSI synthetic X chart outperforms the other X type charts for detecting moderate and large shifts. An illustrative example is also presented to explain the application of the VSI synthetic X chart.
One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
Continuous depletion in tin productions has led to a newly emerging industry that is a tin by-product (amang) processing industry to harness mega tons of tin by-products produced in the past. Amang composed of profitable multi-heavy minerals and rare-earth elements. With poorly established safety and health practices in operating plant, amang poses extremely high radioactivity problem associated with high occupational ionizing radiation exposures to workers and continuously impacting the local environment with radioactive contamination from industrial effluent and solid waste into lithosphere and water bodies. The radioactivity level of 238U and 232Th series in the mineral varies from few hundreds up to ~200,000 and ~400,000 Bq kg-1 respectively and are potential to yield more than ~ 30,000 nGy h-1 of gamma (γ) radiation exposure to plant workers. The study found out that for 8 h of work time, a worker is estimated to receive an average effective dose of 0.1 mSv per day from external γ radiation source with a maximum up to 2 mSv per day for extreme exposure situation. Interferences of different exposure routes for examples inhalation of equivalent equilibrium concentration (ECC) of 222Rn and 220Rn progenies and airborne long-lived α particles from the dusty working environment could pose a higher total effective dose as much as 5 mSv per day and 115 mSv per year. The value is 5 times higher than the annual dose limit for designated radiation worker (20 mSv) in Peninsular Malaysia. The study found that 41% of the total received an effective dose received by a worker is contributed by 222Rn, 32% of airborne particulates and dust, 23% from external γ exposure and 4% from 220Rn. Based on radioecological risk assessment, the study found out that the aquatic environment is the highly exposed group to ionizing radiation from industrial effluent discharge and sand residues. With the impotent establishment of radiation protection in the industry, plus the country newly introduced long-term plan to revive tin mining as well as its accessory amang mineral, it is necessary for the government to harmonize current regulation to improve the worker safety and health as well as sustaining local environment.
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
The economic-statistical design of the synthetic np chart with estimated process parameter is presented in this study. The effect of process parameter estimation on the expected cost of the synthetic np chart is investigated with the imposed statistical constraints. The minimum number of preliminary subgroups is determined where an almost similar expected cost to the known process parameter case is desired for the given cost model parameters. However, the available number of preliminary subgroups in practice is usually limited, especially when the number of preliminary subgroups is large. Consequently, the optimal chart parameters of the synthetic np chart are computed by considering the practical number of preliminary subgroups in which the cost function is minimized. This leads to a lower expected cost compared to that of adopting the optimal chart parameter corresponding to the known process parameter case.
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.
A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
We report the case of a 23-year-old woman who presented with prolonged menstruation and multiple bruises on the limbs and trunk. Investigations revealed severe thrombocytopenia and deranged coagulation profile with markedly prolonged activated partial thromboplastin time (aPTT). Lupus anticoagulant, anti-cardiolipin antibody and anti-beta-2-glycoprotein 1 antibody were positive. She was diagnosed with Immune Thrombocytopenic Purpura (ITP) with positive antiphospholipid antibody serology and given a course of intravenous methylprednisolone and tapering doses of oral prednisolone. She was steroid free and had no bleeding or thrombotic event over two years follow up.
In this work, the mixed multivariate T2 control chart's detailed performance evaluation based on PCA mix is explored. The control limit of the proposed control chart is calculated using the kernel density approach. Through simulation studies, the proposed chart's performance is assessed in terms of its capacity to identify outliers and process shifts. When 30% more outliers are included in the data, the proposed chart provides a consistent accuracy rate for identifying mixed outliers. For the balanced percentage of attribute qualities, misdetection happens because of the high false alarm rate. For unbalanced attribute qualities and excessive proportions, the masking effect is the key issue. The proposed chart shows the improved performance for the shift in identifying the shift in the process.
Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper presents the methodology for two hybrid methods that can be used for time series forecasting. The first combines singular spectrum analysis with linear recurrent formula (SSA-LRF) and neural networks (NN), while the second combines the SSA-LRF and weighted fuzzy time series (WFTS). Some of the highlights of these proposed methodologies are:•The two hybrid methods proposed here are applicable to load data series and other time series data.•The two hybrid methods handle the deterministic and the nonlinear stochastic pattern in the data.•The two hybrid methods show a significant improvement to the single methods used separately and to other hybrid methods.
A morphology study was essential to the development of the cementless femoral stem because accurate dimensions for both the periosteal and endosteal canal ensure primary fixation stability for the stem, bone interface, and prevent stress shielding at the calcar region. This paper focused on a three-dimensional femoral model for Asian patients that applied preoperative planning and femoral stem design. We measured various femoral parameters such as the femoral head offset, collodiaphyseal angle, bowing angle, anteversion, and medullary canal diameters from the osteotomy level to 150 mm below the osteotomy level to determine the position of the isthmus. Other indices and ratios for the endosteal canal, metaphyseal, and flares were computed and examined. The results showed that Asian femurs are smaller than Western femurs, except in the metaphyseal region. The canal flare index (CFI) was poorly correlated (r < 0.50) to the metaphyseal canal flare index (MCFI), but correlated well (r = 0.66) with the corticomedullary index (CMI). The diversity of the femoral size, particularly in the metaphyseal region, allows for proper femoral stem design for Asian patients, improves osseointegration, and prolongs the life of the implant.