Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification

Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH.

Moderate Step Over Sensitive Move Towards Mental Stress

Data classification study is applied in a profound manner to find some statistical classifier to analyze the higher order and lower order priors, like a hood to perform the posterior using Bayesian classifiers. The rough set approaches planned with functional and to determine the existence of the attribute with the level to correlation among them. Approximations towards the set of some logical factors were filtered towards to detect the mental stress through data collection mainly from the working people sector. Significant analyses to quantify the features of mental stress in various diseases cataloging manner with its technical aspects with its classification types based on the class value. As a proposed system of suggested hypothesis planned to predict the classifier model to reduce the stress dependency for the working people as much as possible in a smoother way.

Moderate Step Over Sensitive Move Towards Mental Stress

Data classification study is applied in a profound manner to find some statistical classifier to analyze the higher order and lower order priors, like a hood to perform the posterior using Bayesian classifiers. The rough set approaches planned with functional and to determine the existence of the attribute with the level to correlation among them. Approximations towards the set of some logical factors were filtered towards to detect the mental stress through data collection mainly from the working people sector. Significant analyses to quantify the features of mental stress in various diseases cataloging manner with its technical aspects with its classification types based on the class value. As a proposed system of suggested hypothesis planned to predict the classifier model to reduce the stress dependency for the working people as much as possible in a smoother way

Construction, visualization and application of neutral zone classifiers

When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer.

Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC)

<p>Masyarakat modern dengan kesibukan sehari-harinya tentu akan mendapat tekanan emosional yang cukup tinggi. Hal yang dilakukan untuk meredakan emosi tersebut adalah salah satu dengan mendengarkan musik. MOODSIC merupakan sebuah aplikasi yang dapat memutar musik sesuai dengan ekspresi wajah pengguna. Aplikasi MOODSIC dibangun menggunakan mesin pengenalan ekspres wajah berbasis DCT dan LDA serta algoritma klasifikasi statistik. Berdasarkan hasil pengujian secara <em>off-line</em> mesin pengenalan ekspresi wajah berhasil memberikan performa yang baik, dengan akurasi sebesar 100% untuk data masukkan terdiri atas fitur DCT 144 elemen, 6 eigen vektor LDA dan klasifikasi statistik jenis LDA. Mesin pengenalan ekspresi wajah memerlukan waktu pengenalan yang pendek yaitu 1 milidetik. Secara <em>real-time</em> MOODSIC memberikan hasil yang cukup baik dengan akurasi pengenalan ekspresi sebesar 91.51% atau dengan tingkat kesalahan pengenalan 9.49%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><strong></strong></em><em>Modern society lifestyles face many activities every day, which make people receive a fairly high emotional stress. To reduce such kind of emotions can be treated by listening music. MOODSIC is an application that can play music according to the user's face expression. MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. The face expression recognition engine took shorter time to classification about 1 milliseconds. MOODSIC also give good performance with the accuracy of expression recognition about 91.51% or recognition error of 9,49% for real-time testing.</em></p>