Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network has become exceedingly serious. As urban water supply network is hidden underground asset, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network, decision tree algorithm (C4.5) has been carried out to classify the specific situation of water supply pipeline. Part of the historical data was used to establish a decision tree classification model, and the remaining historical data was used to validate this established model. Adopting statistical methods were used to access the decision tree model including basic statistical method, Receiver Operating Characteristic (ROC) and Recall-Precision Curves (RPC). These methods has been successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model was to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable (AU), near perfect condition (NPC) and serious deterioration (SD). Finally, this research focused on pipe classification which plays a significant role in maintaining water supply networks in the future.

Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Submucous fibroids classification - STEP-W classification

Objective: To develop a new submucous fibroids classification for evaluating the viability and the degree of difficulty and complexity of a hysteroscopic myomectomy.Methods: We have included more four parameters in addition to penetration the fibroid into the myometrium. The extra-parameters were: size of the fibroid, it topography, it extension of the base in relation the wall was set and the wall it was set. The fibroids were classified according to the Classification of the European Society for Gynaecological Endoscopy (ESGE) and to the new classification (STEP-W) in patients who were submitted to hysteroscopic resection of submucous fibroids. The possibility of total resection of the fibroid, the operating time, the fluid deficit and the frequency of any complications were considered. The Fisher test, the Student t test and the analysis of variance test were used in the statistical analyses. It was considered statistically significant when the p-value was less than 0.05 in the two-tailed test.Results: In group which the hysteroscopic surgery was considered complete there was no significant difference between the three ESGE levels (0, 1 and 2). Using the STEP-W, the difference between the numbers of complete surgeries was significant (p < 0.001) for the two levels (groups I and II). The difference between the operating times was significant for the two classifications. In relation to the fluid deficit, only the STEP-W showed significant differences between the levels (p=0.02).Conclusions: It seems to us that the new classification (STEP-W) gives more clues to the difficulties of a hysteroscopic myornectomy than the standard classification (ESGE).

Traumatic subdural hygromas: Proposed pathogenesis based classification

Background: Traumatic subdural hygroma (TSHy) is an accumulation of cerebrospinal fluid (CSF) in the subdural space after head injury. It appears to be relatively common, but its onset time and natural history are not well defined. Considered a benign epiphenomenon of trauma, the pathogenesis of TSHy is still unclear and many questions remain unanswered. This study adds to the information on TSHy, and proposes a classification based on pathogenesis.Methods: Thirty-four consecutive adult patients with TSHy were analyzed for clinical evolution and serial CT scan, during a period of several months. TSHy diagnosis was based on published CT scan criteria of hypodense subdural collection after trauma, without enhancement and neomembrane, with a minimum distance of 3 mm between the skull and brain. Ventricle size was analyzed by calculating the bicaudate index (BCI). For comparison, the BCI was measured from CT scan at three moments: admission, at time of TSHy diagnosis, and from last CT scan.Results: There were 34 patients, aged between 16 and 85 years (mean 40), half of them were below 40 years. Road traffic crashes were the main cause of head injury. The mean time for hygroma diagnosis was 9 days. Twenty-one patients (61.8%) underwent conservative treatment for TSHy and 13 (38.2%), surgical treatment. TSHy are early lesions and can be detected in the first 24 hours after trauma, usually as small subdural effusion (SSEff). Based on clinical and CT scan findings, we divided the 34 patients into 3 groups, (Ia and Ib) without evident mass effect and (II) with evident mass effect. Group Ia includes patients without ventricle dilation; Ib, patients with associated ventricle dilations.Conclusions: SSEff detected in the first 24 hours posttrauma in our series evolved into TSHy suggesting that this is an early lesion; all THSy were divided in three groups according to the pathophysiologic mechanism. These three groups probably represent a continuum of CSF absorption impairment. Group la represents what most authors consider a simple hygroma, with no impairment on CSF absorption. Group Ib represent the external hydrocephalus form with various degrees of CSF imbalance, and group II were the cases presenting marked mass effect.

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil.Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing.Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001).Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Evaluation of motivation methods used to obtain appropriate oral hygiene levels in schoolchildren.

The aim of the present study is to assess the behaviour of different motivation methods on levels of oral hygiene among schoolchildren aged from 7 to 9 years in Araraquara, SP, Brazil. The methods tested were: indirect instruction using 'The Smiling Robot' (group I), indirect instruction through class presentation (group II) and direct instruction with macromodels (group III). A control group was also constituted, which received no kind of motivation (group IV). The O'Leary Plaque Index was used as the evaluation method, applied before the instruction and 30 days after application of the different methods. It was noted that the plaque index had not decreased in group IV only. In conclusion, all the motivation methods promoted significant decrease of plaque index and among these methods, the 'The Smiling Robot' was the one that provided the best results.

Fault identification in distribution lines using intelligent systems and statistical methods

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Comparison of Two Staining Methods for Pollen Viability Studies in Sugarcane

The present work aimed to compare two staining methods for pollen viability evaluation in sugarcane. Pollen from four sugarcane genotypes were collected at three different times (6.00, 8.00 and 9.00 a.m.) and tested for viability using two staining methods (iodine and lactophenol blue). Three anthers, of each genotype were crushed in a glass slide with a drop of the respective stain (iodine 0.1 N and lactophenol blue). The percentage of pollen viability was obtained with an optic microscope (250×) and compared with the pollen germination at culture media where one raquis of each genotype was gentle shaken in a petridish. Three replicates (petri dishes) was performed for each genotype which were maintained at the temperature of 25 °C and air humidity around 95 % for 30 min. The factors (staining methods, genotypes and times) and their interactions were evaluated by the analysis of variance, F test (P < 0.01) and the means compared by the t test (P < 0.05). The lactophenol blue staining was more sensible than the iodine staining method to detect the decrease of pollen viability which occurs naturally in sugarcane. The iodine staining method was more stable and easier than lactophenol to perform the inflorescence classification at any evaluated time (6.00, 8.00 and 9.00 a. m.). Both staining methods overestimated the viability obtained by the germination at culture media when performed at 6.00 a.m. © 2012 Society for Sugar Research & Promotion.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.