Off-line handwritten Thai name recognition for student identification in an automated assessment system

<abstract> <bold><sc>Abstract.</sc></bold> This study evaluated four optical sensor systems in an effort to characterize a robust nondestructive technique to estimate tree-specific oil palm leaflet nutrient deficiency and aid in determining the required fertilizer dosage rates. The four sensor systems used were a handheld reflectance sensor (GreenSeeker 505), a chlorophyll meter (SPAD 502 Plus), a fluorescence sensor (Multiplex 3), and a spectroradiometer (FieldSpec 3). Spectral responses were collected using the above sensors for leaf samples from 164 randomly selected trees from three commercial farms in Malaysia. The spectral data were processed using either stepwise multiple linear regression (SMLR) or rank features techniques for feature extraction. The feature datasets were analyzed using support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and artificial neural network (ANN) classifiers. The SVM and ANN classifiers performed better compared to SIMCA for most nutrient prediction cases. Overall, with SMLR as a feature extraction technique and SVM as a classifier, a spectroradiometer can be used to effectively predict N, P, K, Mg, Ca, and B nutrient levels in oil palm leaflets. The average overall prediction accuracies for the above conditions were in the range of 59% to 85%. The spectroradiometer dataset was further analyzed using SVM regression models for estimating nutrient levels. Either SMLR-selected features or partial least square (PLS) latent variables were used for establishing the above regression models. The models fitted on the SMLR features performed better for N, Mg, Ca, and B, with correlation coefficients (r) for test datasets in the range of 0.45 to 0.78. The best fitted SVM regression models were for boron estimation, with a correlation coefficient of 0.78 and root mean squared error of prediction (RMSEP) of 2.82.

This paper describes the study of techniques used in hand gesture recognition in sign language interpretation. The study is discussed from two aspects: the process of hand gesture recognition and the techniques of feature extraction in hand gesture recognition. The techniques of feature extraction in hand gesture recognition are grouped into five categories: Hidden Markov Model (HMM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Dynamic Bayesian Network (DBN), and Dynamic Time Warping (DTW). The main ideas and the application of each technique are described in detail.

Communication is the ultimate of man’s search for conveying his ideas, emotions, and concepts. Dance is one of the media of communication through which dancers share notion of feelings, with the spectators through gestures, i.e., mudra. Gesture recognition propagates a concept without verbal speech or listening, and in dance recognition, the notion is transferred through various dance poses and actions. This activity in a way really paves way to enhance Indian Sign Language. This study focuses to solve the mudra resemblance in Bharatanatyam through a new system developed with image processing and classification technique using histogram of oriented gradient (HOG) feature extraction techniques and support vector machine (SVM) classifier. SVM classifies the features of HOG into mudras as text labels. Popular feature vectors such as scale-invariant feature transform (SIFT), speed up robust feature (SURF), and local binary pattern (LBP) are hardened against HOG for accuracy and speediness, and this innovative proposed concept is useful for online dance learners.

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553-1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

In this article, we proposed a novel teleconferencing system that combines a facial muscle model and the techniques of face detection and facial feature extraction to synthesize a sequence of life‐like face animation. The proposed system can animate realistic 3D face images in a low‐bandwidth environment to support virtual videoconferencing. Based on the technique of feature extraction, a face detection algorithm for the virtual conferencing system is proposed in this article. In the proposed face detection algorithm, the YCbCr skin color model is used to detect the possible face area of the image; the feature points of the face is determined by using the symmetry property of the face and the gray level characteristics of the eyes and the mouth. According to the positions of the feature points on a facial image, we can compute the transformation values of the feature points. These values will then be sent via a network from the sender's side to the receiver's side frame by frame. We can synthesize the realistic facial animations on the receiver's side based on these. Experimental results show that the proposed system can achieve a practical animated face‐to‐face virtual conference with good facial expressions and a low‐bandwidth requirement. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 323–332, 2010

Physical disabilities affect many people worldwide, but therapies and rehabilitation can help one's recovery. Rehabilitation can be done manually. However, modern developments in technology enabled the use of robotics in rehabilitation, and has proved to be with comparable effectivity with manual rehabilitation. In line with this, a locally developed rehabilitation for upper limb extremities was developed. Regarding features, it can be at par with commercially available rehabilitation devices. To further improve the developed device, an algorithm to assist the user while rehabilitation is studied. Electromyography (EMG) based signals were used for input. Multiple models were compared, namely artificial neural networks (ANN), support vector machines (SVM), and recurrent neural networks (RNN). Six feature extraction techniques were used for training. These are integrated EMG (IEMG), root-mean-square (RMS), discrete wavelet transform (DWT), fast Fourier transform (FFT), waveform length (WL), and zero crossing (ZC). The best performing algorithm based on processing speed and training time was with IEMG as feature extraction technique, and ANN as training algorithm. Recorded training time was at 21 seconds, with the model's accuracy averaging at 96.3% and its processing time logged at 203 milliseconds. It was also interfaced with the existing rehabilitation device via simulation. Future directives include baseline compensation, correction techniques for muscle fatigue, and gathering data from healthy subjects and actual stroke patients.

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.

The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system.

Surface electromyogram (sEMG) signals are widely used to control the myoelectric prosthetic arm for amputees. In this study, the authors investigated the usefulness of discrete wavelet transform (DWT) features from multiple levels of approximation and detail coefficients obtained from sEMG signals. DWT is used for de-noising as well as feature extraction in this study and further tested using Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) Classifier. The performance of SVM classifier is compared with ANN classifier. The classification accuracy of SVM classifiers was found better as compared to ANN in term of speed and robustness. In the first section of paper the authors presented the introduction related to the study. The experimental set-up for recording surface EMG signal is presented in section 2. In section 3 the experimental results are depicted and in section 4 the results are concluded.

International audience

This paper presents the development of novel and efficient algorithms for early detection of cardiac ischemia from ECG signal using different feature extraction techniques. The proposed work mainly involves three stages namely denoising, feature extraction and classification. The removal of noise from ECG signal is achieved by applying wavelet threshold technique. The extraction of clinically useful features is carried out through morphological technique, statistical analysis, principal component analysis (PCA)-based technique and independent component analysis-wavelet packet decomposition (ICA-WPD) technique. The extracted features are used as inputs for artificial neural network (ANN), support vector machines (SVM) and K-nearest neighbour (KNN) classifier models for detecting ischemic beats. The performance of all models are compared and validated with ECG signal acquired from physiobank database in terms of performance indices such as classification accuracy, sensitivity and positive prediction accuracy. The results have confirmed that the ANN model trained and tested with features extracted by ICA-WPD provides highest classification accuracy of 96.85%, PPA of 99.59% and sensitivity of 97.22%. Results clearly demonstrated that the ANN classifier model combined with ICA-WPD-based feature is more effective in diagnosing myocardial ischemia at early stages.

In recent scenario speaker recognition is used in many places for security purpose. Speaker recognition is technique which can verify or identify the person who is speaking. It is different than speech recognition system. In this paper we have discussed the feature extraction techniques like Mel Frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) etc. and classification techniques like Gaussian Mixture Model (GMM), Artificial Neural Networks (ANN) etc. that is available for speaker recognition. Also the survey is about how to get better efficiency in terms of speaker recognition rate by simply modifying the existing feature extraction and classification techniques.

Lung cancer is one of the most prevalent causes of mortality worldwide, making early detection essential for improving patient survival rates. Computed tomography (CT) imaging serves as a crucial diagnostic tool; however, the large volume of generated images poses challenges in precise interpretation by radiologists. This study evaluates the effectiveness of lung cancer classification by utilizing various feature extraction techniques in combination with support vector machine (SVM) and k-nearest neighbours (KNN) classifiers. By analysing different feature sets, the research aims to identify the most effective combination for enhanced classification accuracy. The findings indicate notable improvements in classification performance, facilitating more reliable lung cancer detection.

PurposeCurrent practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.Design/methodology/approachThree novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.FindingsThe proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.Originality/valueTo the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

We report about some experiments on the fingerprint database NIST-4 using different combinations of Support Vector Machine (SVM) classifiers. Images have been preprocessed using the feature extraction technique as in [10]. Our best classification accuracy is 89.3 percent (with 1.8 percent rejection due to the feature extraction process) and is obtained by an error-correction scheme of SVM classifiers. Our current system does not outperform previously proposed classification methods, but the focus here is on the development of novel algorithmic ideas. In particular, as far as we know, SVM have not been applied before in this area and our preliminary findings clearly suggest that they are an effective and promising approach for fingerprint classification.