Iranian Journal of Electrical and Electronic Engineering

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The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experimentally that the proposed IRGA-classifier has removed two important weak points of the conventional GA-classifiers. These problems are the large number of training points and the large number of iterations to achieve a comparable performance with the Bayes classifier, which is an optimal conventional classifier. Three examples have been chosen to compare the performance of designed IRGA-classifier to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The results show clearly a considerable improvement for the performance of IRGA-classifier compared with a conventional GA-classifier.

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Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in applications dealing with images, it is still in its infancy in speech processing. Age classification, on the other hand, is also concerned as a useful tool in different applications, like issuing different permission levels for different aging groups. This paper concentrates on a comparative study of gender and age classification algorithms applied to speech signal. Experimental results are reported for the Danish Emotional Speech database (DES) and English Language Speech Database for Speaker Recognition (ELSDSR). The Bayes classifier using sequential floating forward selection (SFFS) for feature selection, probabilistic Neural Networks (PNNs), support vector machines (SVMs), the K nearest neighbor (K-NN) and Gaussian mixture model (GMM), as different classifiers, are empirically compared in order to determine the best classifier for gender and age classification when speech signal is processed. It is proven that gender classification can be performed with an accuracy of 95% approximately using speech signal either from both genders or male and female separately. The accuracy for age classification is about 88%.

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Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT), probabilistic neural network (PNN) and support vector machine (SVM) are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

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Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets.

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Circuit Breakers (CBs) are critical components in power system for reliability and protection. To assure their accurate performance, a comprehensive condition assessment is of an imminent importance. Based on dynamic resistance measurement (DRM), this paper discusses a simple yet effective fuzzy approach for evaluating CB’s electrical contacts condition. According to 300 test results obtained from healthy and three defected electrical contacts, the authors describe the special effect of common failures on DRM characteristics and propose seven deterioration indicators. Using these parameters, a fuzzy classifier is suggested to accurately determine contact sets condition. The salient advantage of the proposed model is its capability to recognize the type of contact failure. The feasibility and effectiveness of the proposed scheme has been validated through 40 real life recorded data of some electrical contacts. 

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With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over Sampling (ROS), Synthetic Minority Over Sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively.

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This study proposes a descriptor-based approach combined with deep learning, which recognizes facial emotions for safe driving.  Paying attention to the driver's facial expressions is crucial to address the increasing road accidents. This project aims to develop a Facial Emotion Recognition (FER) system that monitors the driver's facial expressions to identify emotions and provide instant assistance for safety control. In the initial stage, Viola-Jones face detection was employed to detect the facial region, followed by Butterworth high-pass filtering to enhance the identified region for locating the eye, nose, and mouth regions, using Viola-Jones face detection. Secondly, the Local Binary Patterns (LBP) feature descriptor is utilized to extract features from the identified eye, nose, and mouth regions. Using 3 RGB channels, the extracted features from these three regions are fed into RessNet-50 and EfficientNet deep networks. The outputs of the two deep learning models' classifiers are combined and integrated using two ensemble methods: ensemble maximum voting and ensemble mean. Based on these combining classifier rules, the performance was evaluated on the JAFFE and KMU-FED databases. The experimental results demonstrate that the proposed method can effectively and with higher accuracy than other competitors recognize emotions in the JAFFE and KMU-FED datasets. The novelty and originality of this paper lie in its significant application in the automotive industry. Implementing our proposed method in a system capable of high accuracy and precision can help mitigate numerous driving hazards. Our approach has achieved 99% and 98% accuracy on the JAFFE and KMU-FED databases, respectively. This high level of accuracy, coupled with its practical relevance, underscores the innovative nature of our work.