Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions

A boosted SVM classifier trained by incremental learning and decremental unlearning approach

The present work gives a comparative analysis of two different classifiers, namely, Support Vector Machine (SVM) and Artificial Neural Network (ANN) to classify defective and non-defective fabric images. The image dataset is prepared by considering all the varieties of fabric materials. The morphological operations, namely, erosion and dilation are used. A total of twelve morphological features are considered for fabric image analysis. Significant morphological features are selected by adopting Feed Backward Selection Technique (FBST) that is applied in the feature reduction process. The overall classification accuracies of 94% and 86.5% are obtained using SVM and ANN classifiers respectively. The SVM classifier is found to give better classification rate than ANN classifier. The work finds applications in apparel industry, quality analysis, cost estimation, online purchase of fabric etc.

In this present work, a technique for differentiation of normal and cirrhotic liver segmented regions of interest (SROIs) based on Laws' masks analysis is reported. Thirty four B-mode ultrasound images taken from 22 normal volunteers and 12 patients suffering from liver cirrhosis were collected from Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India. The filtered texture images are obtained by convolving the SROIs with twenty five, 2D (5×5) special filters based on laws' masks. Metrics that can quantify the texture can be obtained by computing the statistics from these filtered texture images. Similar features are combined to remove the directional information as texture directionality is not important here. This results into 15 rotational invariant filtered texture images for each SROI. For each of the filtered images, five statistics namely, mean, standard deviation, skewness, kurtosis and energy are computed. Thus, a total of 75 Laws' texture features (15 filtered texture images × 5 statistical features) are computed for 82 normal SROIs and 39 cirrhotic SROIs taken from 34 B-Mode ultrasound liver images. Correlation based feature selection (CFS) method is used to find the optimal subset of Laws' texture features which can provide best discrimination between normal and cirrhotic SROIs. It has been observed that CFS method results in an optimal subset of 8 Laws' texture features {LLmean, LLsd, LEsd, SSskewness, RRenergy, LEenergy, LSenergy and LWenegy}. The classification performance of neural network (NN) classifier is compared with support vector machine (SVM) classifier. By using all 75 Laws' texture features the classification accuracy of 90.08% and 90.90% is obtained with NN and SVM classifier respectively. By using 8 Laws' features selected by CFS method the classification accuracy of 91.73% and 92.56% is obtained with NN and SVM classifier respectively. From the comparison it is can be concluded that only 8 Laws' texture features namely {LLmean, LLsd, LEsd, SSskewness, RRenergy, LEenergy, LSenergy and LWenegy} can be used to build an efficient computer aided diagnostic (CAD) system for predicting of liver cirrhosis.

The problem of detecting depression is multi-faceted because of variability in depressive symptoms caused by individual differences. The variations can be seen in historical information (like decreased physical activity etc.) and also in verbal/non-verbal behaviors (like lower pitch, downward eye gaze etc.). The primary goal of this research is to develop a novel classification system for diagnosing depression that considers both historical information and also verbal/non-verbal behaviors. For this purpose, we created a realworld multimodal dataset of depressed and non-depressed subjects with fourteen-day real-time smartphone usage records and audio-visual recordings. We extracted numerous features related to physiological/physical activity from smartphone usage records to capture historical information and features like pitch and eye gaze (verbal and non-verbal manifestations) from audio-visual clues. We experimented with early fusion using Decision trees classifier (along with several feature selection strategies) and Support Vector Machine (SVM) classifier with several late fusion methods. Then, we conducted a comparative study among both fusion strategies. Our findings showed that SVM classifier using late fusion strategy achieves best accuracy of 89%. In addition, a popular benchmarking multimodal dataset (DAIC-WOZ database) is used to further validate the effectiveness of our approach by fusing multi-faceted feature vectors for depression detection.

Fault diagnosis, centered on pattern recognition techniques employing online measurements of process data, has been studied during the past decades. Amongst those techniques, artificial neural networks classifiers received an enormous attention due to some of their remarkable features. Recently, a new machine learning method based on statistical learning theory known as the Support Vector Machine (SVM) classifier is offered in the pattern recognition field. Support vector machine classifiers were originally used to solve binary classification problems. Subsequently, methods were proposed to apply support vector machine classifier to multiclass problems. Two of these mostly used methods are known as one versus one and one versus all. This paper deals with the application of the above mentioned classifiers for fault diagnosis of a chemical process containing a continuous stirred tank reactor and a heat exchanger. The results show a superior classification performance of the support vector machine versus the selected artificial neural network. In addition, the support vector machine classifier is very sensitive to the proper selection of the training parameters. It is shown that the utilization of genetic algorithm for optimal selection of these parameters is feasible and can help to improve the support vector machine classifier performance.

In this paper, we deal with the problem of authorship attribution (AA) on short Arabic texts. So, we make a survey on a set of several features and classifiers that are employed for the task of AA. This investigation uses characters, character bigrams, character trigrams, character tetragrams, words, word bigrams and rare words. The AA is ensured by 4 different measures, 3 classifiers (Multi-Layer Perceptron (MLP), Support Vector Machines (SVM) and Linear Regression (LR)) and a new proposed fusion called VBF (i.e. Vote Based Fusion). The evaluation is done on short Arabic texts extracted from the AAAT dataset (AA of Ancient Arabic Texts). Although the task of AA is known to be difficult on short texts, the different results have revealed interesting information on the performances of the features and classification techniques on Arabic text data. For instance, character-based features appear to be better than word-based features for short texts. Furthermore, the proposed VBF fusion provided high performances with an accuracy of 90% of good AA, which is higher than the score of the original classifier using only one feature. Globally, the results of this investigation shed light on the efficiency and pertinency of several features and classifiers in AA of short Arabic texts.

Deep learning is far and wide considered to be the most powerful method in computer vision fields, which has a lot of applications such as image recognition, robot navigation systems, and self-driving cars. Recent developments in neural networks have led to an efficient end-to-end architecture to human activity representation and classification. In light of these recent events in deep learning, there is now much considerable concern about developing less expensive computation and memory-wise methods. This paper presents an optimized end-to-end approach named stochastic deep conviction network (SDCN) formulated using the deep learning method. It comprises of deep learning method namely deep belief network (DBN), two supervised machine learning algorithm support vector machine (SVM) and decision tree (DT) with optimization capability for speech emotion identification. In the beginning, pre-processing is performed and the features are automatically extracted from the input speech signal by the DBN. Since speech signal features loses most of the information and the performance cannot be guaranteed because dynamic interactions can generate uncountable emotion-specific experiences that have the same core feeling state but different perceptual inclinations so DBN provides more robust features. The next step is to classify the emotions in the training phase; here the SVM classifier is chosen which performs dual classification. In order to enhance this classification process, defects must be reduced and the best discrimination of the extracted features should be obtained hence particle swarm optimization (PSO) technique is being added along with SVM classifier in the training phase. To reduce the over fitting problem and risks of a single classifier a DT is being used in the testing phase for the exact identification of emotions (anger, disgust, fear, happiness, neutral and sadness) and therefore it obtains better performance than a single classifier. The complication of the decision tool is that it can increase the computation time. Thus to eliminate this defect whale optimization (WO) technique is being added to the decision tree to reduce the complexity of the system, which in turn lessens the time taken for recognizing the emotion of the speech signal. This formulated proposed SDCN system improves the recognition rate accurately. In this work, theMATLAB environment is being preferred to perform speech emotion recognition. Using the proposed technique the achieved accuracy of emotion detection is above 95% and the identification of various emotions exceeds 98% recognition rate with a computation time of 23 seconds, which has not been achieved so far by any other existing techniques.

In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to put into a classifier-ensemble is a crucial issue of multiple classifier-ensemble design. With this observation in mind, we proposed a generic genetic classifier-ensemble method for the classifier selection in biomedical NER. Various diversity measures and majority voting are considered, and disjoint feature subsets are selected to construct individual classifiers. A basic type of individual classifier – Support Vector Machine (SVM) classifier is adopted as SVM-classifier committee. A multi-objective Genetic algorithm (GA) is employed as the classifier selector to facilitate the ensemble classifier to improve the overall sample classification accuracy. The proposed approach is tested on the benchmark dataset – GENIA version 3.02 corpus, and compared with both individual best SVM classifier and SVM-classifier ensemble algorithm as well as other machine learning methods such as CRF, HMM and MEMM. The results show that the proposed approach outperforms other classification algorithms and can be a useful method for the biomedical NER problem.KeywordsSupport Vector MachineHide Markov ModelNatural Language ProcessingEnsemble MethodConditional Random FieldThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.

Electronic and ultrasonic welding of plastics

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals

The current semantic recognition methods for long and short texts have low recognition accuracy and slow recognition speed for texts with obvious length differences, which can not accurately identify the deep semantics contained in the texts and restrict the development of text data. To solve this problem, based on the traditional text semantic recognition method, the natural language processing (NLP) artificial intelligence principle is introduced, and a new design of long and short text semantic recognition method is proposed. Extract the semantic features of the original text, and use the frequency method to process the lexical features in the text by weighting. Then, obtain the feature weights of the long and short texts and use the NLP artificial intelligence method to distinguish the unknown texts and identify the semantics of the long and short texts online. The experimental analysis shows that the accuracy of the semantic recognition methods proposed in this paper is more than 96. 87%. The accuracy is higher. The recall is less than 35. 27%. The recall is lower and the recognition advantage is significant compared with the traditional methods.

As in many fields, the use of artificial intelligence methods in the classification of weather images will be very useful. In this study, a data set consisting of five classes such as cloudy, foggy, rainy, shine, and sunrise was used. A hybrid model has been developed to classify the images in the dataset. First of all, the features of the images in the dataset are obtained by using MobilenetV2, Densenet201, and Efficientnetb0 architectures, which are the most popular Convolutional Neural Network (CNN) architectures. These features are combined and optimized so that these optimized features are classified in the Support Vector Machine (SVM) classifier, one of the most popular classifier methods in machine learning. As a result, the developed hybrid model has outperformed the existing pre-trained architectures in the study. In addition, it has been proven that classification by concatenating the features obtained with CNN architectures is a successful method.

Biomedical data are widely accepted in developing prediction models for identifying a specific tumor, drug discovery and classification of human cancers. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. There are a lack of studies on evaluation of data pre-processing techniques, such as resampling and feature selection, on imbalanced biomedical data learning. The relationship between data pre-processing techniques and the data distributions has never been analysed in previous studies. This article mainly focuses on reviewing and evaluating some popular and recently developed resampling and feature selection methods for class imbalance learning. We analyse the effectiveness of each technique from data distribution perspective. Extensive experiments have been done based on five classifiers, four performance measures, eight learning techniques across twenty real-world datasets. Experimental results show that: (1) resampling and feature selection techniques exhibit better performance using support vector machine (SVM) classifier. However, resampling and Feature Selection techniques perform poorly when using C4.5 decision tree and Linear discriminant analysis classifiers; (2) for datasets with different distributions, techniques such as Random undersampling and Feature Selection perform better than other data pre-processing methods with T Location-Scale distribution when using SVM and KNN (K-nearest neighbours) classifiers. Random oversampling outperforms other methods on Negative Binomial distribution using Random Forest classifier with lower level of imbalance ratio; (3) Feature Selection outperforms other data pre-processing methods in most cases, thus, Feature Selection with SVM classifier is the best choice for imbalanced biomedical data learning.

Biomedical data are widely accepted in developing prediction models for identifying a specific tumour, drug discovery and human cancers detection. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. This paper mainly focuses on reviewing and evaluating some popular and recently developed resampling and feature selection (FS) methods for class imbalance learning with data distribution being considered. Experimental results show that: 1) resampling and FS techniques exhibit better performance using support vector machine (SVM) classifier; 2) techniques such as random undersampling and FS perform better than other data pre-processing methods with T location-scale distribution when using SVM and K-nearest neighbours (KNN) classifiers. Random oversampling outperforms other methods on negative binomial distribution using Random Forest with lower level of imbalance ratio; 3) FS outperforms other data pre-processing methods in most cases, thus, FS with SVM classifier is the best choice for imbalanced biomedical data learning.