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Öğe Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models(Elsevier, 2015) Ünal, Yavuz; Polat, Kemal; Koçer, Hasan Erdinç; Hariharan, MuthusamyDisc abnormalities cause a great number of complaints including lower back pain. Lower back pain is one of the most common types of pain in the world. The computer-assisted detection of this ailment will be of great use to physicians and specialists. With this study, hybrid models have been developed which include feature extraction, selection and classification characteristics for the purpose of determining the disc abnormalities in the lumbar region. In determining the abnormalities, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people. In the feature extraction stage, 27 appearance characteristics and form characteristics were acquired from both sagittal and transverse images. In the feature selection stage, the F-Score-Based Feature Selection (FSFS) and the Correlation-Based Feature Selection (CBFS) methods were used to select the best discriminative features. The number of features was reduced to 5 from 27 by using the FSFS, and to 22 from 27 by using the CBFS. In the last stage, five different classification algorithms, i.e. the Multi-Layer Perceptron, the Support Vector Machine, the Decision Tree, the Naive Bayes, and the k Nearest Neighbor algorithms were applied. In addition, the combination of the classifier model (the combination of the bagging and the random forests) has been used to improve the classification performance in the detection of lumbar disc datasets. The results which were obtained suggest that the proposed hybrid models can be used safely in detecting the disc abnormalities. (C) 2015 Elsevier B.V. All rights reserved.Öğe Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech(Elsevier, 2017) Yogesh, C. K.; Hariharan, Muthusamy; Ngadiran, Ruzelita; Adom, A. H.; Yaacob, Sazali; Polat, KemalThe aim of the present study is to select a set of higher order spectral features for emotion/stress recognition system. 50 Bispectral (28 features) and Bicoherence (22 features) based higher order spectral features were extracted from speech signal and its glottal waveform. These features were combined with Inter-Speech 2010 features to further improve the recognition rates. Feature subset selection (FSS) was carried out in this proposed work with the objective of maximizing emotion recognition rate for subject independent with minimum features. The FSS contains two stages: Multi-cluster feature selection was adopted in Stage 1 to reduce feature space and identify relevant feature subset from Interspeech 2010 features. In Stage 2, Biogeography based optimization (BBO), Particle swarm optimization (PSO) and proposed BBO_PSO Hybrid optimization were performed to further reduce the dimension of feature space and identify the most relevant feature subset, which has higher discrimination ability to distinguish different emotional states. The proposed method was tested in three different databases: Berlin emotional speech database (BES), Surrey audio-visual expressed emotion database (SAVEE) and Speech under simulated and actual stress (SUSAS) simulated domain. The proposed feature set was evaluated with subject independent (SI), subject dependent (SD), gender dependent male (GD-male), gender dependent female (GD-female), text independent pairwise speech (TIDPS), and text independent multi-style speech (TIDMSS) experiments by using SVM and ELM classifiers. From the results obtained, it is evident that the proposed method attained accuracies of 93.25% (SI), 100% (SD), 93.75% (GD-male), and 97.58% (GD-female) for BES; 62.38% (SI) and 76.19% (SD) for SAVEE; and 90.09% (TIDMSS), 97.04% (TIDPS - Angryvs. Neutral), 98.89% (TIDPS - Lombard vs. Neutral), 99.07% (TIDPS - Loud vs. Neutral) for SUSAS. (c) 2017 Elsevier B.V. All rights reserved.Öğe A hybrid SCA inspired BBO for feature selection problems(Hindawi Ltd, 2019) Sindhu, Ru; Ngadiran, Ruzelita; Yacob, Yasmin Mohd; Zahri, Nik Adilah Hanin; Hariharan, Muthusamy; Polat, KemalRecent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.Öğe Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification(Elsevier Ireland Ltd, 2018) Hariharan, Muthusamy; Sindhu, Ru; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, KemalBackground and objective: Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods: Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results: Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. (c) 2017 Elsevier B.V. All rights reserved.Öğe A new feature constituting approach to detection of vocal fold pathology(Taylor & Francis Ltd, 2014) Hariharan, Muthusamy; Polat, Kemal; Yaacob, SazaliIn the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.Öğe A new hybrid intelligent system for accurate detection of Parkinson's disease(Elsevier Ireland Ltd, 2014) Hariharan, Muthusamy; Polat, Kemal; Sindhu, RavindranElderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset. (C) 2014 Elsevier Ireland Ltd. All rights reserved.Öğe A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal(Pergamon-Elsevier Science Ltd, 2017) Yogesh, Chinnakalai K.; Hariharan, Muthusamy; Ngadiran, Ruzelita; Adom, Abdul Hamid; Yaacob, Sazali; Polat, KemalSpeech signals and glottal signals convey speakers' emotional state along with linguistic information. To recognize speakers' emotions and respond to it expressively is very much important for human-machine interaction. To develop a subject independent speech emotion/stress recognition system, by identifying speaker's emotion from their voices, features from OpenSmile toolbox, higher order spectral features and feature selection algorithm, is proposed in this work. Feature selection plays an important role in overcoming the challenge of dimensionality in several applications. This paper proposes a new particle swarm optimization assisted Biogeography-based algorithm for feature selection. The simulations were conducted using Berlin Emotional Speech Database (BES), Surrey Audio-Visual Expressed Emotion Database (SAVEE), Speech under Simulated and Actual Stress (SUSAS) and also validated using eight benchmark datasets. These datasets are of different dimensions and classes. Totally eight different experiments were conducted and obtained the recognition rates in range of 90.31%-99.47% (BES database), 62.50%-78.44% (SAVEE database) and 85.83%-98.70% (SUSAS database). The obtained results convincingly prove the effectiveness of the proposed feature selection algorithm when compared to the previous works and other metaheuristic algorithms (BBO and PSO). (C) 2016 Elsevier Ltd. All rights reserved.Öğe Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal's characteristics features(Elsevier, 2015) Kavsaoğlu, Ahmet Reşit; Polat, Kemal; Hariharan, MuthusamyHemoglobin can be measured normally after the analysis of the blood sample taken from the body and this measurement is named as invasive. Hemoglobin must continuously be measured to control the disease and its progression in people who go through hemodialysis and have diseases such as oligocythemia and anemia. This gives a perpetual feeling of pain to the people. This paper proposes a non-invasive method for the prediction of the hemoglobin using the characteristic features of the PPG signals and different machine learning algorithms. In this work, PPG signals from 33 people were included in 10 periods and 40 characteristic features were extracted from them. In addition to these features, gender information (male or female), height (as cm), weight (as kg) and age of each subjects were also considered as the features. Blood count and hemoglobin level were measured simultaneously by using the "Hemocue Hb-201TM" device. Using the different machine learning regression techniques (classification and regression trees - CART, least squares regression - LSR, generalized linear regression - GLR, multivariate linear regression - MVLR, partial least squares regression - PLSR, generalized regression neural network GRNN, MLP - multilayer perceptron, and support vector regression - SVR). RELIEFF feature selection (RFS) and correlation-based feature selection (CFS) were used to select the best features. Original features and selected features using RFS (10 features) and CFS (11 features) were used to predict the hemoglobin level using the different machine learning techniques. To evaluate the performance of the machine learning techniques, different performance measures such as mean absolute error - MAE, mean square error - MSE, R-2 (coefficient of determination), root mean square error - RMSE, Mean Absolute Percentage Error (MAPE) and Index of Agreement - IA were used. The promising results were obtained (MSE-0.0027) using the selected features by RFS and SVR. Hence, the proposed method may clinically be used to predict the hemoglobin level of human being clinically without taking and analyzing blood samples. (C) 2015 Elsevier B.V. All rights reserved.
