Mohammad Subhi Al-Batah

Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

In this paper, a new learning algorithm, called the Modified Recursive Least Square (MRLS), is introduced for the Hybrid Multilayered Perceptron (HMLP) network. Adopting the Recursive Least Square (RLS) algorithm as its basis, the MRLS algorithm differs from RLS in the way that the weight of the linear connections for the HMLP network is estimated. The convergence rate of the MRLS algorithm is further improved by varying the forgetting factor, optimizing the way the momentum and learning rate are assigned. To investigate its applicability, the MRLS algorithm is demonstrated on the HMLP network using six benchmark data sets obtained from the UCI repository. The classification performance of the HMLP network trained with the MRLS algorithm is compared with those of the HMLP network trained with the Modified Recursive Prediction Error (MRPE) algorithm and the MLP trained with the standard RLS algorithm as well as with other commonly adopted machine learning classifiers. The comparison results indicated that the proposed MRLS trained HMLP network provides significant improvement over RLS trained MLP network, MRPE trained HMLP network, and other machine learning classifiers in terms of accuracy, convergence rate and mean square error (MSE).

Modeling and Testing Landslide Hazard Using Decision Tree

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

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 Zhous 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.

Feature based 3D Object Recognition using Artificial Neural Networks

recognition of objects is one of the main goals for computer vision research. This paper formulates and solves the problem of three-dimensional (3D) object recognition for Polyhedral objects. A multiple view of 2D intensity images are taken from multiple cameras and used to model the 3D objects. The proposed methodology is based on extracting set of features from the 2D images which include the Affine, Zernike and Hu moments invariants to be used as inputs to train artificial neural network (ANN). Various architectures of ANN were explored to recognize a shape of Polyhedral objects. The experiments results show that 3D objects can be sufficiently modeled and recognized by set of multiple 2D views. The best ANN architecture was twenty input and single output model.

Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

Testing Reliability Equivalence Factors of a Series-Parallel Systems in Burr Type X Distribution

In reliability analysis for improving the system performance, the scale parameter of the life time model has mainly considered to obtain equivalence factors for the system designs. In this paper, we propose a new approach through modifying the shape parameter of the Burr type X distribution. The proposed approach is applied to the general series parallel systems. Three different methods are used to improve the system reliability: (i) the reduction method, (ii) the hot duplication method and (iii) the cold duplication method. Numerical example is presented to compare performance of the applied methods, to find limitations for the equivalence factors and to illustrate the overall theoretical analysis.

Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

3D object recognition using fuzzy mathematical modeling of 2D images

Recent research in the area of 3D object recognition claim that it is possible to recognize the objects based on 2D images. The recognition process depends on using set of features extracted from the collected images. In this paper, we captured 792 images for eleven 3D Polyhedral objects. A complete image processing stages were implemented. They include image acquisition, pre-processing, feature extraction and classification using a fuzzy mathematical model of the extracted features. The recognition results show that the proposed fuzzy system is significant for classifying the 3D objects with excellent performance.

Bayesian Inference Based on the Interval Grouped Data from the Weibull Model with Application

In this paper, based on the interval grouped data, Bayesian approach is used to obtain estimator for the Weibull scale parameter and some lifetime such as the reliability and hazard functions. The estimation procedures have been developed and modified under squared error loss function (SELF) and general entropy loss function (GELF). The estimators are derived using the inverted gamma conjugate prior. Credible intervals and high posterior density (HPD) credible intervals are also obtained. Prediction for the future number of failures in the corresponding intervals is presented. Finally, real life example is applied to illustrate the performance of the estimation procedures.

A survey of energy conservation and management options for Malaysia

ABSTRACT The electrical energy consumption in Malaysia has increased sharply in the past few years, and modern energy-efficient technologies are desperately needed for the national energy policy. This article presents a comprehensive picture of the current status of energy consumption and various energy conservation options viable for the Malaysian environment. A detailed survey is made to assess the consumption pattern and the existing techniques for energy efficiency. Based on the survey, the feasibility of improving the available systems and adopting new programs in different sectors is investigated. The proposed options are then categorized into four main sectors such as building, industrial, electrical and transportation. The study reveals the fact that the energy conservation policy of the country has been fairly improved in the last ten years. However the country has to pay more attention to this area and make urgent measures to adopt more energy-efficient technologies in various sectors.