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.

Tough, sensitive and sincere : how adolescent boys manage masculinities and emotions

This study aimed to explore adolescent boys’ views of masculinity and emotion management and their potential effects on well-being. Interviews with 33 adolescent boys aged 16–17 years in Sweden were analysed using grounded theory. We found two main categories of masculine conceptions in adolescent boys: gender-normative masculinity with emphasis on group-based values, and non-gender-normative masculinity based on personal values. Gender-normative masculinity comprised two seemingly opposite emotional masculinity orientations, one towards toughness and the other towards sensitivity, both of which were highly influenced by contextual and situational group norms and demands, despite their expressions contrasting each other. Non-gender-normative masculinity included an orientation towards sincerity emphasising the personal values of the boys; emotions were expressed more independently of peer group norms. Our findings suggest that different masculinities and the expression of emotions are strongly intertwined and that managing emotions is vital for well-being.

Topic- and mode-sensitive interaction strategies : Functions of ellipsis in oral communication

In this article, we discuss ellipsis as an interactive strategy by analysing the author’s textchat corpus and the VOICE corpus of English as a Lingua Franca. It is found that there were fewer repetitions in the textchat data, and this is explained as a consequence of the textchat mode. Textchat contributions are preserved as long as the chat is active or has been saved, and therefore users can scroll through and review the discussion, compared to the more fleeting nature of oral conversation. As a result, repetition is less necessary. The frequency of other functions identified could be attributed to the topic of discourse. Discussions involve much ellipsis used to develop discourse, although some were self-presentations with repetition used to confirm details. Back-channel support and comments were often low because speakers instead used forms like yeah as supportive utterances.