Hybrid Computational Intelligence Models With Symbolic Rule Extraction For Pattern Classification

Tesis ini adalah berkenaan dengan pembangunan model kecerdikan berkomputer hibrid bagi menangani masalah pengelasan corak. This thesis is concerned with the development of hybrid Computational Intelligence (CI) models for tackling pattern classification problems

Application Of The Fuzzy Min-Max Neural Networks To Medical Diagnosis.

Abstract. In this paper, the Fuzzy Min-Max (FMM) neural network along with two modified FMM models are used for tackling medical diagnostic problems. The original FMM network establishes hyperboxes with fuzzy sets in its structure for classifying input patterns into different output categories

Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%

A novel population-based local search for nurse rostering problem

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments

Rule pruning in a fuzzy rule-based classification system

In this paper, we purpose a rule pruning strategy to reduce the number of rules in a fuzzy rule-based classification system.A confidence factor, which is formulated based on the compatibility of the rules with the input patterns is under deployed for rule pruning.The pruning strategy aims at reducing the complexity of the fuzzy classification system and, at the same time, maintaining the accuracy rate at a good level.To evaluate the effectiveness of the pruning strategy, two benchmark data sets are first tested. Then, a fault classification problem with real senor measurements collected from a power generation plant is evaluated.The results obtained are analyzed and explained, and implications of the proposed rule pruning strategy to the fuzzy classification system are discussed. <br /

Techno-economical study of solar water pumping system: optimum design, evaluation, and comparison

Solar water pumping systems are fundamental entities for water transmission and storage purposes whether it is has been used in irrigation or residential applications. The use of photovoltaic (PV) panels to support the electrical requirements of these pumping systems has been executed globally for a long time. However, introducing optimization sizing techniques to such systems can benefit the end-user by saving money, energy, and time. This paper proposed solar water pumping systems optimum design for Oman. The design, and evaluation have been carried out through intuitive, and numerical methods. Based on hourly meteorological data, the simulation used both HOMER software and numerical method using MATLAB code to find the optimum design. The selected location ambient temperature variance from 12.8 °C to 44.5 °C over the year and maximum insolation is 7.45 kWh/m2/day, respectively. The simulation results found the average energy generated, annual yield factor, and a capacity factor of the proposed system is 2.9 kWh, 2016.66 kWh/kWp, and 22.97%, respectively, for a 0.81 kW water pump, which is encouraging compared with similar studied systems. The capital cost of the system is worth it, and the cost of energy has compared with other systems in the literature. The comparison shows the cost of energy to be in favor of the MATLAB simulation results with around 0.24 USD/kWh. The results show successful operation and performance parameters, along with cost evaluation, which proves that PV water pumping systems are promising in Oman

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don\u27t care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.<br /