Farm land appraising, with essentials of farm management and agricultural engineering,

"Contains the complete series of 34 booklets which constitute the Farm land division of 'Real estate appraising' (with essentials of farm management and agricultural engineering). In binding, the sectional title pages and student answer pages have been deleted."

Preliminary training for drug evaluation and classification. The PRE-School student manual.

National Highway Traffic Safety Administration, Washington, D.C.

A system of modern geography, for schools, academies, and families, designed to answer the twofold purpose of a correct guide to the student, and of a geographical reading book.

Mode of access: Internet.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

DOES THE CHRONOTYPE CLASSIFICATION NEED TO BE UPDATED? PRELIMINARY FINDINGS

Traditionally, chronotype classification is based on the Morningness-Eveningness Questionnaire (MEQ). It is implicit in the classification that intermediate individuals get intermediate scores to most of the MEQ questions. However, a small group of individuals has a different pattern of answers. In some questions, they answer as ""morning-types"" and in some others they answer as ""evening-types,"" resulting in an intermediate total score. ""Evening-type"" and ""Morning-type"" answers were set as A(1) and A(4), respectively. Intermediate answers were set as A(2) and A(3). The following algorithm was applied: Bimodality Index = (Sigma A(1) x Sigma A(4))(2) - (Sigma A(2) x Sigma A(3))(2). Neither-types that had positive bimodality scores were classified as bimodal. If our hypothesis is validated by objective data, an update of chronotype classification will be required. (Author correspondence: brunojm@ymail.com)

Use of web-based formative assessment to support student learning in metabolism/nutrition unit

In 2002, an integrated basic science course was introduced into the Bachelor of Dental Sciences programme at the University of Queensland, Australia. Learning activities for the Metabolism and Nutrition unit within this integrated course included lectures, problem-based learning tutorials, computer-based self-directed learning exercises and practicals. To support student learning and assist students to develop the skills necessary to become lifelong learners, an extensive bank of formative assessment questions was set up using the commercially available package, WebCT®. Questions included short-answer, multiple-choice and extended matching questions. As significant staff time was involved in setting up the question database, the extent to which students used the formative assessment and their perceptions of its usefulness to their learning were evaluated to determine whether formative assessment should be extended to other units within the course. More than 90% of the class completed formative assessment tasks associated with learning activities scheduled in the first two weeks of the block, but this declined to less than 50% by the fourth and final week of the block. Patterns of usage of the formative assessment were also compared in students who scored in the top 10% for all assessment for the semester with those who scored in the lowest 10%. High-performing students accessed the Web-based formative assessment about twice as often as those who scored in the lowest band. However, marks for the formative assessment tests did not differ significantly between the two groups. In a questionnaire that was administered at the completion of the block, students rated the formative assessment highly, with 80% regarding it as being helpful for their learning. In conclusion, although substantial staff time was required to set up the question database, this appeared to be justified by the positive responses of the students.

Comparative evaluation of underlying causes of death processed by the Automated Classification of Medical Entities and the Underlying Cause of Death Selection Systems

INTRODUCTION: The correct identification of the underlying cause of death and its precise assignment to a code from the International Classification of Diseases are important issues to achieve accurate and universally comparable mortality statistics These factors, among other ones, led to the development of computer software programs in order to automatically identify the underlying cause of death. OBJECTIVE: This work was conceived to compare the underlying causes of death processed respectively by the Automated Classification of Medical Entities (ACME) and the "Sistema de Seleção de Causa Básica de Morte" (SCB) programs. MATERIAL AND METHOD: The comparative evaluation of the underlying causes of death processed respectively by ACME and SCB systems was performed using the input data file for the ACME system that included deaths which occurred in the State of S. Paulo from June to December 1993, totalling 129,104 records of the corresponding death certificates. The differences between underlying causes selected by ACME and SCB systems verified in the month of June, when considered as SCB errors, were used to correct and improve SCB processing logic and its decision tables. RESULTS: The processing of the underlying causes of death by the ACME and SCB systems resulted in 3,278 differences, that were analysed and ascribed to lack of answer to dialogue boxes during processing, to deaths due to human immunodeficiency virus [HIV] disease for which there was no specific provision in any of the systems, to coding and/or keying errors and to actual problems. The detailed analysis of these latter disclosed that the majority of the underlying causes of death processed by the SCB system were correct and that different interpretations were given to the mortality coding rules by each system, that some particular problems could not be explained with the available documentation and that a smaller proportion of problems were identified as SCB errors. CONCLUSION: These results, disclosing a very low and insignificant number of actual problems, guarantees the use of the version of the SCB system for the Ninth Revision of the International Classification of Diseases and assures the continuity of the work which is being undertaken for the Tenth Revision version.

Correcção automática de respostas de texto livre para a aplicação UNI_NET-Classroom

Ao longo dos tempos foi possível constatar que uma grande parte do tempo dos professores é gasta na componente de avaliação. Por esse facto, há já algumas décadas que a correcção automática de texto livre é alvo de investigação. Sendo a correcção de exercícios efectuada pelo computador permite que o professor dedique o seu tempo em tarefas que melhorem a aprendizagem dos alunos. Para além disso, cada vez mais as novas tecnologias permitem o uso de ferramentas com bastante utilidade no ensino, pois para além de facilitarem a exposição do conhecimento também permitem uma maior retenção da informação. Logo, associar ferramentas de gestão de sala de aula à correcção automática de respostas de texto livre é um desafio bastante interessante. O objectivo desta dissertação foi a realização de um estudo relativamente à área de avaliação assistida por computador em que este trabalho se insere. Inicialmente, foram analisados alguns correctores ortográficos para seleccionar aquele que seria integrado no módulo proposto. De seguida, foram estudadas as técnicas mais relevantes e as ferramentas que mais se enquadram no âmbito deste trabalho. Neste contexto, a ideia foi partir da existência de uma ferramenta de gestão de sala de aula e desenvolver um módulo para a correcção de exercícios. A aplicação UNI_NET-Classroom, que foi a ferramenta para a qual o módulo foi desenvolvido, já continha um componente de gestão de exercícios que apenas efectuava a correcção para as respostas de escolha múltipla. Com este trabalho pretendeu-se acrescentar mais uma funcionalidade a esse componente, cujo intuito é dar apoio ao professor através da correcção de exercícios e sugestão da cotação a atribuir. Por último, foram realizadas várias experiências sobre o módulo desenvolvido, de forma a ser possível retirar algumas conclusões para o presente trabalho. A conclusão mais importante foi que as ferramentas de correcção automática são uma mais-valia para os professores e escolas.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.

Contribution à l'étude de la construction des concepts scientifiques au cours de l'apprentissage par problèmes en médecine

L’approche d’apprentissage par problèmes (APP) a vu le jour, dans sa forme contemporaine, à la Faculté de médecine de l’Université MacMaster en Ontario (Canada) à la fin des années 1960. Très rapidement cette nouvelle approche pédagogique active, centrée sur l’étudiant et basée sur les problèmes biomédicaux, va être adoptée par de nombreuses facultés de médecine de par le monde et gagner d’autres disciplines. Cependant, malgré ce succès apparent, l’APP est aussi une approche controversée, notamment en éducation médicale, où elle a été accusée de favoriser un apprentissage superficiel. Par ailleurs, les étudiants formés par cette approche réussiraient moins bien que les autres aux tests évaluant l’acquisition des concepts scientifiques de base, et il n’a jamais été prouvé que les médecins formés par l’APP seraient meilleurs que les autres. Pour mieux comprendre ces résultats, la présente recherche a voulu explorer l’apprentissage de ces concepts scientifiques, en tant que processus de construction, chez des étudiants formés par l’APP, à la Faculté de médecine de l’Université de Montréal, en nous appuyant sur le cadre théorique socioconstructivisme de Vygotski. Pour cet auteur, la formation des concepts est un processus complexe de construction de sens, en plusieurs étapes, qui ne peut se concevoir que dans le cadre d’une résolution de problèmes. Nous avons réalisé une étude de cas, multicas, intrasite, les cas étant deux groupes de neuf étudiants en médecine avec leur tuteur, que nous avons suivi pendant une session complète de la mi-novembre à la mi-décembre 2007. Deux grands objectifs étaient poursuivis: premièrement, fournir des analyses détaillées et des matériaux réflectifs et théoriques susceptibles de rendre compte du phénomène de construction des concepts scientifiques de base par des étudiants en médecine dans le contexte de l’APP. Deuxièmement, explorer, les approches de travail personnel des étudiants, lors de la phase de travail individuel, afin de répondre à la question de recherche suivante : Comment la dynamique pédagogique de l’APP en médecine permet-elle de rendre compte de l’apprentissage des concepts scientifiques de base? Il s’agissait d’une étude qualitative et les données ont été recueillies par différents moyens : observation non participante et enregistrement vidéo des tutoriaux d’APP, interview semi-structuré des étudiants, discussion avec les tuteurs et consultation de leurs manuels, puis traitées par diverses opérations: transcription des enregistrements, regroupement, classification. L’analyse a porté sur des collections de verbatim issus des transcriptions, sur le suivi de la construction des concepts à travers le temps et les sessions, sur le role du tuteur pour aider au développement de ces concepts Les analyses suggèrent que l’approche d’APP est, en général, bien accueillie, et les débats sont soutenus, avec en moyenne entre trois et quatre échanges par minute. Par rapport au premier objectif, nous avons effectivement fourni des explications détaillées sur la dynamique de construction des concepts qui s'étend lors des trois phases de l'APP, à savoir la phase aller, la phase de recherche individuelle et la phase retour. Pour chaque cas étudié, nous avons mis en évidence les représentations conceptuelles initiales à la phase aller, co-constructions des étudiants, sous la guidance du tuteur et nous avons suivi la transformation de ces concepts spontanés naïfs, lors des discussions de la phase retour. Le choix du cadre théorique socio constructiviste de Vygotski nous a permis de réfléchir sur le rôle de médiation joué par les composantes du système interactif de l'APP, que nous avons considéré comme une zone proximale de développement (ZPD) au sens élargi, qui sont le problème, le tuteur, l'étudiant et ses pairs, les ressources, notamment l'artefact graphique carte conceptuelle utilisée de façon intensive lors des tutoriaux aller et retour, pour arriver à la construction des concepts scientifiques. Notre recherche a montré qu'en revenant de leurs recherches, les étudiants avaient trois genres de représentations conceptuelles: des concepts corrects, des concepts incomplets et des concepts erronés. Il faut donc que les concepts scientifiques théoriques soient à leur tour confrontés au problème concret, dans l'interaction sociale pour une validation des attributs qui les caractérisent. Dans cette interaction, le tuteur joue un rôle clé complexe de facilitateur, de médiateur, essentiellement par le langage. L'analyse thématique de ses interventions a permis d'en distinguer cinq types: la gestion du groupe, l'argumentation, les questions de différents types, le modelling et les conclusions. Nous avons montré le lien entre les questions du tuteur et le type de réponses des étudiants, pour recommander un meilleur équilibre entre les différents types de questions. Les étudiants, également par les échanges verbaux, mais aussi par la construction collective des cartes conceptuelles initiales et définitives, participent à une co-construction de ces concepts. L'analyse de leurs interactions nous a permis de relever différentes fonctions du langage, pour souligner l'intérêt des interactions argumentatives, marqueurs d'un travail collaboratif en profondeur pour la co-construction des concepts Nous avons aussi montré l'intérêt des cartes conceptuelles non seulement pour visualiser les concepts, mais aussi en tant qu'artefact, outil de médiation psychique à double fonction communicative et sémiotique. Concernant le second objectif, l’exploration du travail personnel des étudiants, on constate que les étudiants de première année font un travail plus approfondi de recherche, et utilisent plus souvent des stratégies de lecture plus efficaces que leurs collègues de deuxième année. Ceux-ci se contentent, en général, des ouvrages de référence, font de simples lectures et s’appuient beaucoup sur les résumés faits par leurs prédécesseurs. Le recours aux ouvrages de référence essentiellement comme source d'information apporte une certaine pauvreté au débat à la phase retour avec peu d'échanges de type argumentatif, témoins d'un travail profond. Ainsi donc, par tout ce soutien qu'elle permet d'apporter aux étudiants pour la construction de leurs connaissances, pour le type d'apprentissage qu'elle offre, l’APP reste une approche unique, digne d’intérêt. Cependant, elle nécessite d'être améliorée par des interventions au niveau du tuteur et des étudiants.

Indexation et classification de la musique : approches d'analyse musicale, descripteurs et plateformes de recherche.

Travail réalisé à l'École de bibliothéconomie et des sciences de l'information (EBSI), Université de Montréal, sous la direction de Mme Audrey Laplante dans le cadre du cours SCI6850 Recherche individuelle, à l'automne 2012.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

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.