Human gait recognition using orthogonal least square as feature selection

<span>This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel.</span>

Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once the optimal feature set is obtained, PCA and OLS selected the most significant features and the optimized SVM then classified the selected cry patterns. In PCA-SVM, eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE) were used to select the most significant features. SVM with radial basis function (RBF) kernel was chosen in the classification stage. The classification accuracy and computation time were computed to evaluate the performance of each method. The best method for classifying asphyxiated infant cry is PCA-SVM with EOC since it produces the highest classification accuracy which is 94.84%. Using PCA-SVM, the classification process was performed in 1.98s only. The results also show that employing feature selection techniques could enhance the classifier performance.</p>

Jaringan Saraf Radial Basis Probabilistic untuk Identifikasi Morfologi Benih Padi Rawa Kalimantan Selatan

Machine vision berbasis jaringan saraf tiruan dan pemrosesan gambar digital merupakan metode alternatif yang dapat dilakukan untuk mengidentifikasi dan mengevaluasi keragaman varietas padi. Berbeda dengan metode pengamatan langsung yang memiliki tingkat subjektivitas tinggi dan metode kimiawi (PCR) yang bersifat destruktif dan mahal, machine vision berbasis jaringan saraf tiruan menawarkan sistem identifikasi dan evaluasi secara cepat, praktis, murah, akurat, serta bersifat non-destruktif. Paper ini membahas machine vision berbasis jaringan saraf tiruan sebagai teknologi alternatif untuk identifikasi varietas padi rawa Kalimantan Selatan berdasarkan ciri morfologinya, yaitu area, perimeter, major axis, minor axis, circularity, aspect ratio, roundness, dan feret untuk setiap sampel benih padi. Dalam paper ini, sistem identifikasi varietas benih padi menggunakan jaringan saraf radial basis probabilistic dengan optimalisasi bobot hidden center menggunakan algoritme orthogonal least square. Dari proses learning dihasilkan performa pelatihan sebesar 88.32% dan performa pengujian sebesar 88.21% dengan tingkat keberhasilan pada proses pelatihan dari masing-masing varietas bayar papuyu, bayar putih, benih kuning, benih putih, ketan, siam gadis, siam unus, dan karan dukuh masing-masing sebesar 100.00%, 92.59%, 88.89%, 92.59%, 92.59%, 81.48%, 100.00%, dan 100.00%. Untuk proses pengujian, tingkat keberhasilan masing-masing varietas ialah 100.00%, 87.50%, 88.89%, 100.00%, 88.89%, 88.89%, 100.00%, dan 100.00%.<br /><br />Kata Kunci: benih padi, machine vision, morfologi, RBP-OLS