An improved genetic algorithm procedure is introduced in this work based on the theory of the most highly fit parents (both male and female) are most likely to produce healthiest offspring. It avoids the destruction of near optimal information and promotes further search around the potential region by encouraging the exchange of highly important information among the fittest solution. A novel crossover technique called Segmented Multi-chromosome Crossover is also introduced. It maintains the information contained in gene segments and allows offspring to inherit information from multiple parent chromosomes. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of multi-layer perceptron network in medical disease diagnosis. Compared to the previous works, the average accuracy of the proposed algorithm is the best among all algorithms for diabetes and heart dataset, and the second best for cancer dataset.
This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
In this paper, the method of differentiating asthmatic and non-asthmatic patients using the frequency analysis of capnogram signals is presented. Previously, manual study on capnogram signal has been conducted by several researchers. All past researches showed significant correlation between capnogram signals and asthmatic patients. However all of them are just manual study conducted through the conventional time domain method. In this study, the power spectral density (PSD) of capnogram signals is estimated by using Fast Fourier Transform (FFT) and Autoregressive (AR) modelling. The results show the non-asthmatic capnograms have one component in their PSD estimation, in contrast to asthmatic capnograms that have two components. Furthermore, there is a significant difference between the magnitude of the first component for both asthmatic and non-asthmatic capnograms. The effectiveness and performance of manipulating the characteristics of the first frequency component, mainly its magnitude and bandwidth, to differentiate between asthmatic and non-asthmatic conditions by means of receiver operating characteristic (ROC) curve analysis and radial basis function (RBF) neural network were shown. The output of this network is an integer prognostic index from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 95.65% and an error rate of 4.34%. This developed algorithm is aspired to provide a fast and low-cost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.
The ability of speech synthesis system to synthesize emotional speech enhances the user's experience when using this kind of system and its related applications. However, the development of an emotional speech synthesis system is a daunting task in view of the complexity of human emotional speech. The more recent state-of-the-art speech synthesis systems, such as the one based on hidden Markov models, can synthesize emotional speech with acceptable naturalness with the use of a good emotional speech acoustic model. However, building an emotional speech acoustic model requires adequate resources including segment-phonetic labels of emotional speech, which is a problem for many under-resourced languages, including Malay. This research shows how it is possible to build an emotional speech acoustic model for Malay with minimal resources. To achieve this objective, two forms of initialization methods were considered: iterative training using the deterministic annealing expectation maximization algorithm and the isolated unit training. The seed model for the automatic segmentation is a neutral speech acoustic model, which was transformed to target emotion using two transformation techniques: model adaptation and context-dependent boundary refinement. Two forms of evaluation have been performed: an objective evaluation measuring the prosody error and a listening evaluation to measure the naturalness of the synthesized emotional speech.
As Malaysia is one of the world's largest producer of palm oil, large amounts of palm oil mill effluent (POME) is generated. It was found that negatively charged components are accountable for POME color. An attempt was made to remove residual contaminants after conventional treatment using anion base resin. Adsorption experiments were carried out in fixed bed column. Various models such as the Thomas, the Yoon-Nelson, the Wolborska and BDST model were used to fit the experimental data. It was found that only the BDST model was fitted well at the initial breakthrough time. A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system. The results showed that the WNN model described breakthrough curves better than the commonly used models. The effects of pH, flow rate and bed depth on column performance were investigated. It was found that the highest uptake capacity was obtained at pH 3. The exhaustion time appeared to increase with increase in bed length and decrease in flow rate.
Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.
Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.
Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.
This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical Capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.
Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, X(max), and lipid yield, P(max)/X(max). RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Pr(lipid). A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of X(max) to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation.
Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.
Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.
We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.
Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.
The most important targets for vaccine development are the proteins that are highly expressed by the microorganisms during infection in-vivo. A number of Mycobacterium tuberculosis (Mtb) proteins are also reported to be expressed in-vivo at different phases of infection. In the present study, we analyzed multiple published databases of gene expression profiles of Mtb in-vivo at different phases of infection in animals and humans and selected 38 proteins that are highly expressed in the active, latent and reactivation phases. We predicted T- and B-cell epitopes from the selected proteins using HLAPred for T-cell epitope prediction and BCEPred combined with ABCPred for B-cell epitope prediction. For each selected proteins, regions containing both T- and B-cell epitopes were identified which might be considered as important candidates for vaccine design against tuberculosis.