Z. Saad

Weather Forecasting Using Photovoltaic System and Neural Network

This paper presents the applicability of Artificial Neural Network (ANN) for weather forecasting using a Photovoltaic system. The main objective is to predict daily weather conditions based on various measured parameters gained from the PV system. In this work, Multiple Multilayer Perceptron (MMLP) network with majority voting technique was used and trained using Levenberg Marquardt (LM) algorithm. Voting technique is widely used in many applications to solve real world problem. Different techniques of voting are used such as majority rules, decision making, consensus democracy, consensus government and supermajority. The way of the voting technique is different depending on the problem involved. Majority voting technique was applied in the study so that the performance of MMLP can be approved as compared to single MLP network. The proposed work has been used to classify four weather conditions; rain, cloudy, dry day and storm. The system can be used to represent a warning system for likely adverse conditions. Experimental results demonstrate that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.

A genetic algorithm-neural network approach for Mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue slide images

This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image segmentation, feature extraction and identification. It uses Ziehl-Neelsen stained tissue slides images which are acquired using a digital camera attached to a light microscope for diagnosis. To separate the tubercle bacilli from its background, moving k-mean clustering that uses C-Y colour information is applied. Then, seven Hus moment invariants are extracted as features to represent the bacilli. Finally, based on the input features, a GA-NN approach is used to classify into two classes: ‘true TB’ and ‘possible TB’. In this study, genetic algorithm (GA) is applied to select significant input features for neural network (NN). Experimental results demonstrated that the GA-NN approach able to produce better performance with fewer input features compared to the standard NN approach.

Colour Image Segmentation of Tuberculosis Bacilli in Ziehl-Neelsen-Stained Tissue Images Using Moving K-Mean Clustering Procedure

Segmentation of tuberculosis bacilli in Zeihl-Neelsen tissue slide images is a crucial step in computer-assisted tuberculosis bacilli detection. In this paper, an automatic colour image segmentation using moving k-mean clustering was proposed. First, initial filter is used to remove the tissues images which remain blue after counterstaining process. After that, moving k-mean clustering using green component of RGB colour model and R_y component of C-Y colour model are used to segment the TB bacilli from its undesirable background which also remains red even after decolourization process. Then a 5×5 median filter and region growing was used to eliminate small regions and noises. The proposed methods have been analysed for several TB slide images under various conditions. Experimental results indicate that the proposed techniques were successfully segment TB bacilli from its background.

Improved Classification Performance for Multiple Multilayer Perceptron (MMLP) Network Using Voting Technique

This project investigates the capability of multiple multilayer perceptron (MMLP) system with majority voting technique. It is a system which consists of all the best-performed MLPs and a single final output from these MLPs is selected by the voting system. The MLP networks are trained using two types of learning algorithm, which are the Levenberg Marquardt and the Resilient Back Propagation algorithms. The performance of the MMLP networks are calculated based on the percentage of correct classificition. Data from three case studies, triangular waveform classification, breast cancer detection and transport classification, have been used to test the performance of the developed system. The results show that the MMLP system with voting technique has the capability of improving the classification correctness. The most well-known artificial neural network (ANN) architecture is a Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. By approaching these innovated MMLP system with automatic voting, the better classification result will be produced.

Comparative study of PID controlled modes on automatic water level measurement system

This paper presents the performances of Proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) modes controller to control an automatic water level control system. This project is developed to verify the performance of water level control system using PID control modes. The measurements of water level control system were collected from process plant which is located at Process Laboratory, UiTM Pulau Pinang. The measurements that have been carried out were analyzed based on three modes controlled that are Proportional, Proportional-Integral and Proportional-Integral-Derivative. The response of each control mode has been determined to identify the time constant, rise time, peak time and percentage overshoot. The evaluation of the output has been carried out and compared by software simulation using MATLAB toolbox. The results indicate that measured data and simulated data were showing a similarity in the responses and time constant.

Contrast enhancement for Ziehl-Neelsen tissue slide images using linear stretching and histogram equalization technique

Ziehl-Neelsen staining method is a common technique used to diagnose tuberculosis infection. Clinical samples such as sputum or tissue are stained using Ziehl- Neelsen procedure and analysed manually by microbiologist for detecting TB bacilli. However, the screening process is labour intensive and time consuming. Image enhancement tools can be used to improve the manual screening process. This paper analyses the performance of linear stretching and histogram equalization techniques for Ziehl-Neelsen tissue slide images. Both enhancement techniques which are originally implemented for gray-scale image have been adapted for colour images. Although the adapted image processing technique is simple, the results indicate that these methods may have some potential to be used for improving the quality of Ziehl-Neelsen slide images. Overall analyses show that the linear stretching using local minimum and maximum value has successfully improved the image contrast and enhance the image quality.

Performance Comparison of Different Multilayer Perceptron Network Activation Functions in Automated Weather Classification

Multilayer perceptron network (MLP) has been recognized as a powerful tool for many applications including classification. Selection of the activation functions in the multilayer perceptron (MLP) network plays an essential role on the network performance. This paper presents a comparison study of two commonly used MLP activation function, sigmoid and hyperbolic tangent for weather classification. Meteorological data such as solar radiation, ambient temperature, current, surface temperature, voltage, wind direction and wind speed data are acquired from a photovoltaic (PV) system. Then, the meteorological data are input to the MLP network to classify the weather condition. In this study, weather conditions are classified into four types, rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is used to train the MLP network since it is the fastest training and ensure the best converges towards a minimum error. Experimental results show that hyperbolic tangent activation function is more efficient compared to sigmoid activation function. The MLP network using hyperbolic tangent function has achieved higher classification accuracy with less number of hidden nodes compared to sigmoid activation function.

Performance of neural network feature subset selection in injected fuel flow forecasting

This paper compares the performance of some feature subset selection in injected fuel flow forecasting. Injected fuel flow leads to an accurate measurement of cars fuel consumption. The fuel consumption of a car depends on many factors like road, weather, and driver behaviour that are rigid for a car manufacturer to influence. Speed, stepper speed_step, stepper speed_angle, revolution, stepper rev_step, stepper rev_angle, fuel volume, stepper fuel_step, stepper fuel_angle, fuel transducer input, current fuel consumption, gear, distance to empty in volume, distance to empty in kilometre, current distance, and battery voltage are measured from the experimented car. The multilayered perceptron network trained by Levenberg-Marquardt algorithm was selected as a black box model for forecasting purposes. The input variables were taped from car sensors. The criterions for comparison are based on the coefficient of determination (R2). Three difference feature subset selection (A, B and C) consists of 1267 data samples have been collected. The first 700 data were used for training and the rest were used in validation and forecasting process. The three subset of the feature (A, B and C) are selected based on trial and error. The results show that feature subset selection B outperformed feature subset selection A and C significantly.

3D object recognition using MANFIS network with orthogonal and non-orthogonal moments

This paper addresses a performance analysis of two well known moments, namely Hus moments and Zernikes moments for 3D object recognition. Hus moments and Zernikes moments are the non-orthogonal and orthogonal moments respectively, which are commonly used as shape feature for 2D object or pattern recognition. The current study proved that with some adaptation to multiple views technique, Hu and Zernike moments are sufficient to model 3D objects. In addition, the simplicity of moments calculation reduces the processing time for feature extraction, hence increases the system efficiency. In the recognition stage, we proposed to use a neuro-fuzzy classifier called Multiple Adaptive Network based Fuzzy Inference System (MANFIS) for matching and classification. The proposed method has been tested using two groups of object, polyhedral and free-form objects. The experimental results show that Zernike moments combined with MANFIS network attain the best performance in both recognitions, polyhedral and free-form objects.

Vehicle Recognition System Using Singular Value Decomposition and Extreme Learning Machine

The purpose of this research is to develop a system that is able to recognize and classify a variety of vehicles using image processing and artificial neural network. In order to perform the recognition, first, all the images containing the vehicles are required to go through several images processing technique such as thresholding, histogram equalization and edge detection before obtaining the desired dataset for classification process. Then, the vehicle images are converted into data using singular value decomposition (SVD) extraction method and the data are used as an input for training process in the classification phase. A Single Layer Feedforward (SLFN) network trained by Extreme Learning Machine (ELM) algorithm is chosen to perform the recognition and classification. The network is evaluated in terms of classification accuracy, training time and optimum structure of the network. Then, the recognition performance using the ELM training algorithm is compared with the standard Levenberg Marquardt (LM) algorithm.