Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

Manoeuvring between anxiety and control : Patients experience of learning to live with diabetes: a lifeworld phenomenological study

Research shows that people with diabetes want their lives to proceed as normally as possible, but some patients experience difficulty in reaching their desired goals with treatment. The learning process is a complex phenomenon interwoven into every facet of life. Patients and healthcare providers often have different perspectives in care which gives different expectations on what the patients need to learn and cope with. The aim of this study, therefore, is to describe the experience of learning to live with diabetes. Interviews were conducted with 12 patients afflicted with type 1 or type 2 diabetes. The interviews were then analysed with reference to the reflective lifeworld research approach. The analysis shows that when the afflicted realize that their bodies undergo changes and that blood sugar levels are not always balanced as earlier in life, they can adjust to their new conditions early. The afflicted must take responsibility for balancing their blood sugar levels and incorporating the illness into their lives. Achieving such goals necessitates knowledge. The search for knowledge and sensitivity to changes are constant requirements for people with diabetes. Learning is driven by the tension caused by the need for and dependence on safe blood sugar control, the fear of losing such control, and the fear of future complications. The most important responsibilities for these patients are aspiring to understand their bodies as lived bodies, ensuring safety and security, and acquiring the knowledge essential to making conscious choices.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The Importance of Teaching Students How to Read to Comprehend Mathematical Language

In this action research study of my classroom of 8th and 9th grade Algebra I students, I investigated if there are any benefits for the students in my class to learn how to read, translate, use, and understand the mathematical language found daily in their math lessons. I discovered that daily use and practice of the mathematical language in both written and verbal form, by not only me but by my students as well, improved their understanding of the textbook instructions, increased their vocabulary and also increased their understanding of their math lessons. I also found that my students remembered the mathematical material better with constant use of mathematical language and terms. As a result of this research, I plan to continue stressing the use of mathematical language and vocabulary in my classroom and will try to develop new ways to help students to read, understand, and remember mathematical language they find daily in their textbooks.

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.

The Dorsolateral Periaqueductal Gray and Its Role in Mediating Fear Learning to Life Threatening Events

The dorsolateral column of the periaqueductal gray (dlPAG) integrates aversive emotional experiences and represents an important site responding to life threatening situations, such as hypoxia, cardiac pain and predator threats. Previous studies have shown that the dorsal PAG also supports fear learning; and we have currently explored how the dlPAG influences associative learning. We have first shown that N-methyl-D-aspartate (NMDA) 100 pmol injection in the dlPAG works as a valuable unconditioned stimulus (US) for the acquisition of olfactory fear conditioning (OFC) using amyl acetate odor as conditioned stimulus (CS). Next, we revisited the ascending projections of the dlPAG to the thalamus and hypothalamus to reveal potential paths that could mediate associative learning during OFC. Accordingly, the most important ascending target of the dlPAG is the hypothalamic defensive circuit, and we were able to show that pharmacological inactivation using beta-adrenoceptor blockade of the dorsal premammillary nucleus, the main exit way for the hypothalamic defensive circuit to thalamo-cortical circuits involved in fear learning, impaired the acquisition of the OFC promoted by NMDA stimulation of the dlPAG. Moreover, our tracing study revealed multiple parallel paths from the dlPAG to several thalamic targets linked to cortical-hippocampal-amygdalar circuits involved in fear learning. Overall, the results point to a major role of the dlPAG in the mediation of aversive associative learning via ascending projections to the medial hypothalamic defensive circuit, and perhaps, to other thalamic targets, as well. These results provide interesting perspectives to understand how life threatening events impact on fear learning, and should be useful to understand pathological fear memory encoding in anxiety disorders.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Learning to recognize gender using experince

[EN]Automatic facial analysis abilities are commonly integrated in a system by a previous off-line learning stage. In this paper we argue that a facial analysis system would improve its facial analysis capabilities based on its own experience similarly to the way a biological system, i.e. the human system, does throughout the years. The approach described, focused on gender classification, updates its knowledge according to the classification results. The presented gender experiments suggestthatthisapproachispromising,evenwhenjustashort simulationofwhatforhumanswouldtakeyearsofacquisition experience was performed.

Learning to Live in a Therapeutic Community: A study of elderly inpatients.

Fifty-two elderly mental patients in a state hospital were transferred to a new milieu ward. In order to evaluate patient success in the unit, three outcome categories were defined nine months after the unit opened: discharge to the community, adjustment to the setting, and return to the previous ward. Despite the unit's emphasis on performance criteria for success, staff evaluations of the patients' personality rather than the patients' achievement of the behavioural criteria, accounted for success in the setting.

Creative Commons Olympics. How Big Media is Learning to License From Amateur Authors

NBC Universal’s decision to use Creative Commons-licensed photographs in an Olympic broadcast is an example of how media conglomerates are experimenting with collaboration with amateurs, but it also reveals potential problems of letting non-lawyers negotiate copyright licensing agreements. In the process, NBC’s producers nearly opened the door for a multimillion-dollar infringement law suit. To avoid such pitfalls, media companies need to adopt policies and best practices for using amateur licensed works. These guidelines should instruct how a production can attribute collaborating authors and how the Open Content licensing terms affect the licensing of the productions. The guidelines should also instruct how producers can seek alternative licensing arrangements with amateurs and contribute back to the Open Content community.

Learning to drive: learners' self-reported cognitive failure level predicts driving instructor's observation rating of driving performance

OBJECTIVES Evidence increases that cognitive failure may be used to screen for drivers at risk. Until now, most studies have relied on driving learners. This exploratory pilot study examines self-report of cognitive failure in driving beginners and error during real driving as observed by driving instructors. METHODS Forty-two driving learners of 14 driving instructors filled out a work-related cognitive failure questionnaire. Driving instructors observed driving errors during the next driving lesson. In multiple linear regression analysis, driving errors were regressed on cognitive failure with the number of driving lessons as an estimator of driving experience controlled. RESULTS Higher cognitive failure predicted more driving errors (p < .01) when age, gender and driving experience were controlled in analysis. CONCLUSIONS Cognitive failure was significantly associated with observed driving errors. Systematic research on cognitive failure in driving beginners is recommended.