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EN
Purpose: Investigate the potential of vacuum dewatering process of on three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. Design/methodology/approach: For this study a data set of 90 experimental observations obtained from laboratory testing with and without application of vacuum dewatering after designing and casting the concrete of said three grades. The standard cubes of size 150 mm × 150 mm × 150 mm were obtained by core cutting and tested for compression after 3, 7, 14, 21 and 28 days of proper curing. Accuracy of prediction of compressive strength of concrete by application of M5P, ANN and SVM as artificial intelligence techniques and their feasibility are assessed to estimate the compressive strength of the concrete enacted with vacuum dewatering technique. A total data set was segregated in two groups. A group of 63 observations was used for model development and smaller group of 27 observations was used for testing the models. Findings: Overall performance of ANN based developed model is better than M5P and SVM based models for predicting the compressive strength of concrete for this data set. Research limitations/implications: Investigated three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. The experimental research involved only testing of cubes only. Practical implications: Using ANN based developed model makes it possible to quickly and accurately predict the compressive strength of concrete. Originality/value: The results of comparing three models for predicting the compressive strength of concrete and the optimal values of ANN based developed models are presented. Earlier no one has applied M5P, ANN and SVM modelling to predict the compressive strength of vacuum dewatered concrete.
EN
Glaucoma is the prime cause of blindness and early detection of it may prevent patients from vision loss. An expert system plays a vital role in glaucoma screening, which assist the ophthalmologists to make accurate decision. This paper proposes a novel technique for glaucoma detection using optic disk localization and non-parametric GIST descriptor. The method proposes a novel area based optic disk segmentation followed by the Radon transformation (RT). The change in the illumination levels of Radon transformed image are compensated using modified census transformation (MCT). The MCT images are then subjected to GIST descriptor to extract the spatial envelope energy spectrum. The obtained dimension of the GIST descriptor is reduced using locality sensitive discriminant analysis (LSDA) followed by various feature selection and ranking schemes. The ranked features are used to build an efficient classifier to detect glaucoma. Our system yielded a maximum accuracy (97.00%), sensitivity (97.80%) and specificity (95.80%) using support vector machine (SVM) classifier with nineteen features. Developed expert system also achieved maximum accuracy (93.62%), sensitivity (87.50%) and specificity (98.43%) for public dataset using twenty six features. The proposed method is efficient and computationally less expensive as it require only nineteen features to model a classifier for the huge dataset. Therefore the proposed method can be effectively utilized in hospitals for glaucoma screening.
EN
P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliabil-ity in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.
EN
Issues surrounding the misuse of illegal drugs in animals destined for food production have be an enormous challenge to regulatory authorities charged with enforcing their control. A method has been proposed recently which compared the bovine blood biochemistry profiles between control and treated animals, using the support vector machine (SVM) as the classification tool. Whether an animal has been treated is determined by the classification outcome of the SVM on an individual serum sample taken off the animal. However, the acquisition time of the serum sample is essential in the classification performance of the SVM. Thus, the paper proposed to collect and analyze a pair of samples, in order to obtain at least one sample whose acquisition time resulted in an SVM with the highest sensitivity. The power of the strategy in improving sensitivity was theoretically proven to be up to 0.25 and empirically confirmed on a bovine blood biochemistry data. Furthermore, classification rules of the SVM were proposed to be adapted to meet higher levels of demands on sensitivity. Schemes were described which optimized the time apart between the collection of the two samples and the impact of the proposed strategy on specificity was also investigated.
EN
Hematological malignancies i.e. acute lymphoid leukemia and acute myeloid leukemia are the types of blood cancer that can affect blood, bone marrow, lymphatic system and are the major contributors to cancer deaths. In present work, an attempt has been made to design a CAC (computer aided classification system) for diagnosis of myeloid and lymphoid cells and their FAB (French, American, and British) characterization. The proposed technique improves the AML and ALL diagnostic accuracy by analyzing color, morphological and textural features from the blood image using image processing and to assist in the development of a computer-aided screening of AML and ALL. This paper endeavors at proposing a quantitative microscopic approach toward the discrimination of malignant from normal in stained blood smear. The proposed technique firstly segments the nucleus from the leukocyte cell background and then computes features for each segmented nucleus. A total of 331 geometrical, chromatic and texture features are computed. A genetic algorithm using support vector machine (SVM) classifier is used to optimize the feature space. Based on optimized feature space, an SVM classifier with various kernel functions is used to eradicate noisy objects like overlapped cells, stain fragments, and other kinds of background noises. The significance of the proposed method is tested using 331 features on 420 microscopic blood images acquired from the online repository provided by the American society of hematology. The results confirmed the viability or potential of using a computer aided classification method to reinstate the monotonous and the reader-dependent diagnostic methods.
EN
The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes. A closer look at the pairwise strategy shows the problem which impacts the final result. Each binary classifier votes for each object even if it does not belong to one of the two classes which it is trained on. This problem is addressed in our strategy. We propose to use additional classifiers to select the objects which will be considered by the pairwise classifiers. A similar solution was proposed by Moreira and Mayoraz (1998), but they use classifiers which are biased according to imbalance in the number of samples representing classes.
EN
The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.
8
Content available remote DIFFRACT: DIaphyseal Femur FRActure Classifier SysTem
EN
Determining the types of fractured bones is the most important step of fracture treatment. Different fracture cases may be observed in daily life and each of them may require a specific treatment. It is not possible for a physician to know all fracture types and treatment methods by heart. Therefore, it is needed an effective solution to facilitate such a tedious process. Based on this need, we propose an auxiliary tool called a DIaphyseal Femur FRActure Classifier SysTem (DIFFRACT). The DIFFRACT can automatically classify diaphyseal femur fractures according to the Müller AO Classification system on X-ray images. In DIFFRACT, we have used the Niblack thresholding method to segment X-ray images. We have observed that Niblack is the most effective method for the segmentation of fractured bones since it does not lose information related to the fracture region. Moreover, we have developed a novel pre-processing method called a support vector machine (SVM) based sensitive noise remover to remove the noises occurring in the segmentation step. In addition, we have innovatively proposed two combined feature extraction methods, the bone completeness indicator (BCI) and fractured region mapping (FRM), to classify different types of fractures. We have used a multi-class SVM to determine the type of bone fractures. Based on the detailed experiments, 196 X-ray images were classified into nine classes according to AO-32 with 89.87% success rate. The DIFFRACT may be used as supplementary tool for the determination of fractured femur bones by physicians. It may facilitate decision making process of the physicians.
EN
Environmental microorganisms (EMs) are single-celled or multi-cellular microscopic organ-isms living in the environments. They are crucial to nutrient recycling in ecosystems as they act as decomposers. Occurrence of certain EMs and their species are very informative indicators to evaluate environmental quality. However, the manual recognition of EMs in microbiological laboratories is very time-consuming and expensive. Therefore, in this article an automatic EM classification system based on content-based image analysis (CBIA) techniques is proposed. Our approach starts with image segmentation that determines the region of interest (EM shape). Then, the EM is described by four different shape descriptors, whereas the Internal Structure Histogram (ISH), a new and original shape feature extraction technique introduced in this paper, has turned out to possess the most discriminative properties in this application domain. Afterwards, for each descriptor a support vector machine (SVM) is constructed to distinguish different classes of EMs. At last, results of SVMs trained for all four feature spaces are fused in order to obtain the final classification result. Experimental results certify the effectiveness and practicability of our automatic EM classification system.
EN
Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.
11
Content available remote Classification of speech intelligibility in Parkinson's disease
EN
A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD.
12
EN
In this paper, we present a classification of electronic components in the electronic factory. This classification provides relevant information for correcting the manufacturing process, thereby enhancing the production fields and the quality of product. Our classification system based on the support vector machine (SVM) classifies all the used electronic components into predefined categories that are learnt from the training samples. The system has been deployed in the manufacturing line and has met the design criteria of over 90% of the classification rate and 80% of the classification accuracy.
PL
W niniejszym artykule opisano automatyczną klasyfikację komponentów elektronicznych, która pozwala m.in. na ocenę jakości produktu. Klasyfikację tę przeprowadzono przy użyciu maszyny wektorów wspierających (ang. support vector machine, SVM). Dzięki zastosowanemu klasyfikatorowi uzyskano 80% dokładność klasyfikacji. Zbudowany system klasyfikacji został zainstalowany na linii produkcyjnej komponentów elektronicznych.
EN
This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.
14
Content available remote Human emotion recognition based on facial texture information
EN
The paper presents the system for automatic emotion recognition. Firstly, face detection algorithm [5] is performed on input image to create face representation. Then, face texture is encoded with Local Binary Patterns [11] and used as a feature set in emotion recognition. The Support Vector Machine [15] is used as a classifier. The proposed system was tested with spontaneous emotions.
PL
W niniejszym artykule opisany został system do automatycznego rozpoznawania emocji. Pierwszym etapem systemu są detekcja i lokalizacja twarzy [5] na obrazie wejściowym. Następnie tekstura twarzy kodowana jest przy użyciu Local Binary Patterns [11] i zastosowana jako zbiór cech opisujących emocję. Maszyna wektorów wpierających [15] pełni rolę klasyfikatora w rozpoznawaniu emocji. Skuteczność przedstawionego systemu została zbadana dla zadania rozpoznawania emocji spontanicznych.
PL
W artykule przedstawiono wykorzystanie maszyny wektorów wspierających (SVM) na użytek interfejsów mózg-komputer (BCI). W opracowanych algorytmach jako cechy sygnału EEG wykorzystano jego wariancję. Przedstawiono wyniki badań związanych z wykorzystaniem sieci SVM jako klasyfikatora. Eksperymenty przeprowadzono przy użyciu różnego rodzaju funkcji jądra.
EN
Implementing communication between man and machine by use of EEG signals is one of the biggest challenges in the signal theory. Such communication could improve the standard of living of people with severe motor disabilities. Some disable persons cannot move, however they can think about moving their arms, legs and this way produce stable motor-related EEG signals. These signals can be used to construct BCI systems. However, the proper interpretation of the EEG signals is a very difficult task. There are three main stages in EEG signal analysis: feature extraction, feature selection and classification. The main aim of the paper is to implement a support vector machine as a classifier for the brain-computer interface. The proposed algorithm uses the EEG signal variance in the frequency range 8-30Hz. Experiments were conducted with use of different kernel functions for the SVM classifier. The best results were achieved for the quadratic polynomial kernel function. The classification error for testing data was 0.13.
16
Content available remote Adaptive control scheme based on the least squares support vector machine network
EN
Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
PL
Motywacją do badań był pomysł wytworzenia robota-kosiarki wyposażonego w system komputerowego widzenia. Rozpoznawanie obrazu może zostać zrealizowane za pomocą klasyfikacji tekstur obiektów, które otaczają robota. Artykuł przedstawia przykład klasyfikacji tekstur za pomocą Maszyny wektorów wspierających SVM (ang. Support Vector Machine) Do badań wykorzystano oprogramowanie LIBSVM.
EN
Motivation for research was idea to create mower robot with computer vision system. Image recognition can be done by textures classification of objects that robot is surrounded. This article has reviewed example of texture classification by SVM Support vector machine. For research was used LIBSVM software.
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