Browsing by Subject "Discriminant analysis"

Now showing 1 - 5 of 5

Open Access
Contaminating factors in university students' evaluation of instructors
(Turk Egitim Dernegi, 2011) Kalender, İlker
The present study seeks to determine the variables explaining differences between the scores of student ratings given to instructors within the context of the university through discriminant analysis. Ratings given by students were grouped into two groups based on their means and instructors were labeled as low-rated and high-rated. Predictors identified by discriminant analysis are (i) class size, (ii) credit, (iii) grade level, (iv) mean grade, and (v) number of sections. Results of the study suggested that low rated instructors are those who teach courses with smaller number of students, lower credits, higher grade levels, higher mean grades, and one section. Identification of source of differences between ratings may provide invaluable information for those who are interested in assessment of instructional effectiveness.
Open Access
High-resolution magic anglespinning ¹H nuclear magnetic resonance spectroscopy metabolomics of hyperfunctioning parathyroid glands
(Mosby, Inc., 2016) Battini, S.; Imperiale, A.; Taïeb, D.; Elbayed, K.; Cicek, A. E.; Sebag, F.; Brunaud, L.; Namer, Izzie-Jacques
Background Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis. Methods The method used was 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study. Results Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R2Y = 0.85, Q2 = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R2Y = 0.96, Q2 = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease. Conclusion This study shows for the first time that 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies.
Open Access
İki durumlu bir beyin bilgisayar arayüzünde özellik çıkarımı ve sınıflandırma
(IEEE, 2017-10) Altındiş, Fatih; Yılmaz, B.
Beyin bilgisayar arayüzü (BBA) teknolojisi motor nöronlarının özelliğini kaybeden ve hareket kabiliyeti kısıtlanmış ALS ve felçli hastalar gibi birçok kişinin dış dünya ile iletişimini sağlamaya yönelik kullanılmaktadır. Bu çalışmada, Avusturya’daki Graz Üniversitesi’nde alınmış EEG veri seti kullanılarak gerçek zamanlı EEG işleme simülasyonu ile motor hayal etme sınıflandırılması amaçlanmıştır. Bu veri setinde sağ el ya da sol elin hareket ettirilme hayali esnasında 8 kişiden alınmış iki kanallı EEG sinyalleri bulunmaktadır. Her katılımcıdan 60 sağ ve 60 sol olmak üzere toplamda 120 adet yaklaşık 9 saniyelik motor hayal etme deneme sinyali kayıt edilmiştir. Bu sinyaller filtrelemeye tabi tutulmuştur. Yirmi dört, 32 ve 40 elemanlı özellik vektörü bant geçiren filtreler kullanarak elde edilen göreceli güç değişim değerleridir (GGDD). Bu çalışmada, lineer diskriminant analizi (LDA), k en yakın komşular (KNN) ve destek vektör makinaları (SVM) ile sınıflandırma yapılmış, en iyi sınıflandırma performansının 24 değerli özellik vektörüyle ve LDA sınıflandırma yöntemiyle elde edildiği gösterilmiştir.
Open Access
Searching video for complex activities with finite state models
(IEEE, 2007-06) İkizler, Nazlı; Forsyth, D.
We describe a method of representing human activities that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across space and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. We show results for a large range of queries applied to a collection of complex motion and activity. Our models of short time scale limb behaviour are built using labelled motion capture set. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by the changes of clothing. © 2007 IEEE.
Open Access
Sparsity Based Image Retrieval using relevance feedback
(IEEE, 2012) Günay, Osman; Çetin, A. Enis
In this paper, a Content Based Image Retrieval (CBIR) algorithm employing relevance feedback is developed. After each round of user feedback Biased Discriminant Analysis (BDA) is utilized to find a transformation that best separates the positive samples from negative samples. The algorithm determines a sparse set of eigenvectors by L1 based optimization of the generalized eigenvalue problem arising in BDA for each feedback round. In this way, a transformation matrix is constructed using the sparse set of eigenvectors and a new feature space is formed by projecting the current features using the transformation matrix. Transformations developed using the sparse signal processing method provide better CBIR results and computational efficiency. Experimental results are presented. © 2012 IEEE.