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.

International conference site selection criteria : And a case study of Stockholm as an international conference city

The purpose of this thesis is to identify the destination site selection criteria for internationalconferences from the perspectives of the three main players of the conference industry,conference buyers (organizers and delegates) and suppliers. Additionally, the researchidentifies the strengths and weaknesses of the congress cities of Stockholm and Vienna.Through a comparison with Vienna, the top city for hosting international conferences, a roadmap for Stockholm has been designed, to strengthen its congress tourism opportunities, thus,obtaining a higher status as an international congress city. This qualitative research hascombined both primary and secondary data methods, through semi-standardized expertinterviews and secondary studies respectively, to fulfil the study’s aim. The data have beenanalysed by applying the techniques of qualitative content analysis; the secondary dataadopting an inductive approach according to Mayring (2003) while the expert interviewsusing a deductive approach according to Meuser & Nagel (2009). The conclusions of thesecondary data have been further compared and contrasted with the outcomes of the primarydata, to propose fresh discoveries, clarifications, and concepts related to the site selectioncriteria for international conferences, and for the congress tourism industry of Stockholm. Theresearch discusses the discoveries of the site selection criteria, the implications of thestrengths and weaknesses of Stockholm in comparison to Vienna, recommendations forStockholm via a road map, and future research areas in detail. The findings andrecommendation, not only provide specific steps and inceptions that Stockholm as aninternational conference city can apply, but also propose findings, which can aid conferencebuyers and suppliers to cooperate, to strengthen their marketing strategies and developsuccessful international conferences and destinations to help achieve a greater competitiveadvantage.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.