Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit

Image-based oil palm leaf disease detection using convolutional neural network

Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification. Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier

Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied to heterogeneous cancer classification using four different microarray benchmark datasets. The comparative experimental results showed that the proposed methodology achieved an almost 100% classification accuracy in multiclass cancer prediction, leading to superior performance with respect to other clustering algorithms. Subsequently, performance comparisons with other classifiers were made. An assessment analysis showed that this proposed approach outperformed most of the other classifiers

An effective and novel wavelet neural network approach in classifying type 2 diabetics

Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved

Optimization of cellulose phosphate synthesis from oil palmlignocellulosics using wavelet neural networks

Cellulose phosphate was synthesized from microcrystalline cellulose derived from oil palm lignocellu-losics via the H3PO4/P2O5/Et3PO4/hexanol method. The influence of process variables (viz. temperature,reaction time, and the H3PO4/Et3PO4ratio) on the properties of the resulting cellulose phosphate wasinvestigated using a wavelet neural network model with the goals of ascertaining which factors werecritical and of determining optimized reaction parameters for this synthesis. The experimental resultscorroborated the good fit of the wavelet neural network model. The prediction errors were quite small(less than 7%), and the regression values (R2greater than 0.99) were also satisfactory

Design and characterization of flat lens antenna using aperture-coupled microstrip patches

A planar discrete lens antenna is a low profile, light weight and cost effective solution to conventional and curved dielectric lenses. The basic theory of operation of flat lens antenna unit cell is to collimate the feed spherical electromagnetic incident wave into planar wavefront at the back of the aperture. Therefore, the array unit cell must be designed to establish the required phase adjustment. Flat lens antenna elements which are based on aperture-coupled microstrip patches are presented. The lens contains 7×7 elements with a diameter of 71 mm and operates in the X-band frequency range. The lens was experimentally validated and good agreement between simulation and measurement results were obtained. The achieved measured peak gain is 15.85 dB. This gives 6 dB gain enhancement for the system. The antenna 1-dB gain bandwidth and power efficiency are 7.8% and 58% respectively. A very good transmission phase shift of 340° is achieved with transmission coefficient of better than 2.25 dB. In addition, the measured radiation pattern results show that the antenna system has good symmetry between E and H plane with a half-power beamwidth of 16.2° and 16.6° in E-plane and H-plane respectively. Moreover, the proposed lens element employs a simple and less fabrication complexity mechanism for phase shift correction. Finally, the obtained results show that the proposed flat lens antenna is an attractive choice for the applications of wireless airborne systems such as VSAT (Very Small Aperture Terminal)

Modeling of acetosolv pulping of oil palm fronds using response surface methodology and wavelet neural networks

Mathematical models based on response surface methodology (RSM) and wavelet neural networks (WNNs) in conjunction with a central composite design were developed in order to study the influence of pulping variables viz. acetic acid, temperature, time, and hydrochloric acid (catalyst) on the resulting pulp and paper properties (screened yield, kappa number, tensile and tear indices) during the acetosolv pulping of oil palm fronds. The performance analysis demonstrated the superiority of WNNs over RSM, in that the former reproduced the experimental results with percentage errors and mean squared errors between 3 and 8% and 0.0054–0.4514 respectively, which were much lower than those obtained by the RSM models with corresponding values of 12–40% and 0.0809–9.3044, further corroborating the goodness of fit of the WNNs models for simulating the acetosolv pulping of oil palm fronds. Based on this assessment, it validates the exceptional predictive ability of the WNNs in comparison to the RSM polynomial model

Tracking of moving athlete from video sequences using fower pollination algorithm

Performance analysis, as related to sport, is a process underpinned by a systematic analysis of information, to accelerate the performance of athletes through crafted focused practice session based on the obtained analysis. Quantifcation of athlete performance profle using sports video has thus been put forward, where the athlete tracking in such video-based analysis is one of the critical elements for the success of an object tracking system. In this study, for the frst time the fower pollination algorithm (FPA) is utilised to track the motion of the moving athlete from the sports video. Initially, a search window with the attributes of centroid coordinates of the moving athlete, width and length of the search window is used to represent the current position of the athlete. Subsequently, the hue, saturation and value (HSV) histogram of the region within the search window is evaluated. In the consecutive frame, several potential positions of the athlete are identifed, and the Bhattacharyya distance between the HSV histogram of the athlete in the previous frame and the potential position in the current frame is calculated. Since the FPA attempts to maximise the similarity of both histograms, intuitively, the current position of the moving athlete should be only slightly diferent than his previous position. The comparative analysis shows that the FPA is comparable with other competing algorithms in terms of detection rate, tracking accuracy and processing time

Robotic arm system with computer vision for colour object Sorting

This study presents the development of robotic arm with computer vision functionalities to recognise the objects with different colours, pick up the nearest target object and place it into particular location. In this paper, the overview of the robotic arm system is first pre-sented. Then, the design of five-degrees of freedom (5-DOF) robotic arm is introduced, followed by the explanation of the image proc-essing technique used to recognize the objects with different colours and obstacle detection. Next, the forward kinematic modelling of the robotic arm using Denavit-Hartenberg algorithm and solving the inverse kinematic of the robotic arm using modified flower pollination algorithm (MFPA) are interpreted. The result shows that the robotic arm can pick the target object accurately and place it in its particular place successfully. The concern on user safety is also been taken into consideration where the robotic arm will stop working when the user hand (obstacle) is detected and resume its process when there is no obstacle

Reliable epileptic seizure detection using an improved wavelet neural network

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts