Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

New Meanings to Old Rituals : The Emergence of Mourning Rituals in Shiʿite Islam

This paper will discuss the emergence of Shiʿite mourning rituals around the grave of Husayn b. ʿAli. After the killing of Husayn at Karbala’ in 61/680, a number of men in Kufa feel deep regret for their neglect to come to the help of the grand­son of the Prophet. They gather and discuss how they can best make penitence for this crime. Eventually, they decide to take to arms and go against the Umayyad army – to kill those that killed Husayn, or be killed them­selves in the attempt to find revenge for him. Thus, they are called the Penitents (Ar. Tawwābūn). On their way to the battlefield they stop at Husayn’s tomb at Karbala’, dedicat­ing themselves to remorseful prayer, crying and wailing over the fate of Husayn and their own sin. When the Penitents perform certain ritual acts, such as weeping and wailing over the death of Husayn, visiting his grave, asking for God’s mercy upon him on the Day of Judgment, demand blood revenge for him etc., they enter into already existing rituals in the pre-Islamic Arab and early Muslim context. That is, they enter into rituals that were traditionally performed at the death of a person. What is new is that the rituals that the Penitents perform have partially received a new content. As described, the rituals are performed out of loyalty towards Husayn and the family of the Prophet. The lack of loyalty in connection with the death of Husayn is conceived of as a sin that has to be atoned. Blood revenge thus be­comes not only a pure action of revenge to restore honor, but equally an expression for true religious conversion and penitence. Humphrey and Laidlaw argue that ritual actions in themselves are not bearers of meaning, but that they are filled with mean­ing by the performer. Accord­ing to them, ritual actions are apprehensible, i.e. they can be, and should be filled with meaning, and the people who perform them try to do so within the context where the ritual is performed. The story of the Penitents is a clear example of mourning rituals as actions that survive from earlier times, but that are now filled with new meaning when they are performed in a new and developing move­ment with a different ideology. In later Shiʿism, these rituals are elaborated and become a main tenet of this form of Islam.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.