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.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Solar energy studies and extramural learning

Extramural learning refers to the educational process that takes place outside the walls of the school (or the university). Extramural learning that takes place in a science center is characterized by hands-on and interactivity. Interactive solar energy exhibits are particularly well suited for out-door science centers. The paper presents some solar energy hands-on exhibits and extramural activities that the author has initiated and participated in.

Representation and interpretation : textbooks, teachers, and historical culture

This article presents a study of how contemporary Swedish lower secondary school textbooks present the emergence of the Cold War and how 10 active lower secondary school history teachers interpreted a quotation that was ambiguous in relation to the general narrative in the studied Swedish textbooks, seeking to analyse textbooks both from the perspectives of content and reception. Applying a theoretical framework of uses of history, the study finds that the narratives presented in the studied textbooks are what could be called traditional in the sense that they do not acknowledge perspective and representation in history. While the interviewed teachers generally acknowledged that textbook narratives are representations of history and contingent on perspective, few teachers extended this to include how their own views affect their interpretations, suggesting an intermediary appreciation of the contextual contingency of historical narratives.

Medical students’ knowledge, attitudes, and perceptions on contraceptive use and counselling : a cross-sectional survey in Maharashtra, India

Objectives. This study aimed to investigate the knowledge, attitudes and perceptionstowards contraceptive use and counselling among medical students in Maharashtra, India. Setting. Considerable global maternal mortality and morbidity could be avoided through theuse of effective contraception. In India, contraception services are frequently unavailable or there are obstacles to obtaining modern, reversible contraceptives. Participants. A cross-sectional descriptive study using a self-administered questionnaire was conducted among 1996 medical students in their fifth year of study at 27 medical colleges in the state of Maharashtra, India. Descriptive and analytical statistics interpreted the survey instrument and significant results were presented with 95% CI. Results. Respondents expressed a desire to provide contraceptive services. A few studentshad experienced training in abortion care. There were misconceptions about moderncontraceptive methods and the impact of sex education. Attitudes towards contraceptionwere mainly positive, premarital counselling was supported and the influence of traditional values and negative provider attitudes on services was recognised. Gender, area of upbringing and type of medical college did not change the results. Conclusions. Despite mostly positive attitudes towards modern contraceptives, sex education and family planning counselling, medical students in Maharashtra have misconceptions about modern methods of contraception. Preservice and in-service training in contraceptive counselling should be implemented in order to increase women's access to evidence-based maternal healthcare services.