Abstract-In this paper a new hierarchical age estimation method based on decision level fusion of global and local features is proposed. The shape and appearance information of human faces which are extracted with active appearance models (AAM) are used as global facial features. The local facial features are the wrinkle features extracted with Gabor filters and skin features extracted with local binary patterns (LBP). Then feature classification is performed using a hierarchical classifier which is the combination of an age group classification and detailed age estimation. In the age group classification phase, three distinct support vector machines (SVM) classifiers are trained using each feature vector. Then decision level fusion is performed to combine the results of these classifiers. The detailed age of the classified image is then estimated in that age group, using the aging functions modeled with global and local features, separately. Aging functions are modeled with multiple linear regressions. To make a final decision, the results of these aging functions are also fused in decision level. Experimental results on the FG-NET and PAL aging databases have shown that the age estimation accuracy of the proposed method is better than the previous methods.
Ö N E Ç I K A N L A R Konuşmacıları yaş ve cinsiyetlerine göre sınıflandıran yeni bir sistem önerilmiştir 954 kişinin 47 saatlik telefon konuşmaları kullanılmıştır Önerilen sistem için en uygun bileşen sayısı ve konuşma süresi belirlenmiştir Makale Bilgileri ÖZET Geliş: 26.11.201426.11. Kabul: 24.05.2016 DOI:Bu çalışmada konuşmacıları yaş ve/veya cinsiyet özelliklerine göre otomatik olarak sınıflandıran bir sistem önerilmiştir. Açık ve kapalı mekanlarda cep telefonu ve karasal bağlantılarla yapılan telefon konuşmalarının giriş olarak kullanıldığı bu sistemde konuşmacıların cinsiyetlerine göre üç (erkek, bayan, çocuk), yaşlarına göre dört (çocuk, genç yetişkin ve yaşlı) ve her iki özelliğine göre ise yedi sınıfa ayrılması amaçlanmıştır. Bu amaçla konuşmaların yalnızca sesli bölümlerinden elde edilen MFCC katsayıları ile oluşturulan GKM modelleri süpervektörlere dönüştürülerek DVM sınıflandırıcısına uygulanmıştır. Çalışmada konuşmaların ses içeren bölümlerinin belirlenmesinde sinyalin enerji özelliği kullanılırken GKM modellerinin eğitiminde ise geniş bir veritabanı ile eğitilen genel arka plan modelinin (GAM) uyarlanması yaklaşımı tercih edilmiştir. Çalışmada ayrıca farklı sayıda bileşenle oluşturulan GKM modelleri farklı uzunluklu konuşmalarla test edilerek GKM bileşen sayısı ve konuşma süresinin yaş ve cinsiyet tespiti üzerindeki etkisi de araştırılmıştır. Yapılan testlerde en yüksek sınıflandırma başarıları 16 saniyelik konuşmaların 64 bileşenli GKM'lerle modellenmesi sonucunda elde edilmiştir. Bu oranlar cinsiyet kategorisinde %92,42, yaş kategorisinde %60,10 ve yaş&cinsiyet kategorisinde ise %60,02 olarak ölçülmüştür. DOIIn this study, a system classifying speakers according to their age and/or genders is proposed. In this system phone conversations including mobile calls that took place indoor or outdoor are used as inputs. It is aimed to classify the speakers according to their genders into three classes as male, female and child, according to their ages into four classes as child, youth, adult and senior, and finally according to both gender and age into seven classes. For this aim, GMM models that are created with MFCC coefficients obtained by the voiced parts of the conversations are transformed into supervectors. These supervectors are applied to SVM classifier. Signal energy is used for determining the voiced parts of conversations. For the training of GMM models, the adaptation approach of UBM is preferred. Also, by testing GMM models that are created with different number of components and different length conversations, the impact of GMM components number and speech duration on the age and gender identification is investigated. At the end of these tests, the highest classification success rates are obtained by modeling 16-second speeches with 64-component GMMs. The rates obtained from these tests are measured as 92.42% for gender category, 60.10% for age category and 60.02% for age&gender category.
The plenary aim of mathematics education is to bring in mathematical knowledge and skills that are required by daily life to the individual, to teach him problem solving and to bring in him a way of thinking that handles incidents including s problem-solving approach. For this reason, problem solving skills have an important place among the mathematical skills. That problem solving keeps an important place in the overall objectives of mathematics course has carried this issue to the centre of mathematics curriculum at multiple levels starting from primary school. Indeed, NCTM standards, as well, indicate that problem solving skills are needed to be primarily in mathematics teaching (NCTM, 2000). For the solution process of problems, Polya (1957) recommends a framework that contains the stages of understanding the problem, selecting a strategy for the solution, the implementation of the strategy and the evaluation of the solution. The purpose of this study is to evaluate the intelligent tutoring system called as ARTIMAT with the opinions of teachers in terms of contribution to problem solving skills and academic achievements of the students. ARTIMAT, which has been prepared according to Pólya's problem solving steps. In this study case study design which is one of the qualitative research methods was adopted. The implementation, which was conducted in order to evaluate the system, has been performed with 5 teachers in an Anatolian High School. ARTIMAT system has been implemented for three weeks for two hours in each week in computer lab and in a way that each teacher has used his/her own computer. Data was collected administering a questionnaire contained open-ended questions. Descriptive analyses technique was administered on the collected data. In this study presents findings and results of the interviews collected from the participant teachers.
An automated computer aided diagnosis system has been proposed for detection of microcalcification (MC) clusters in mammograms. The proposed system is a whole system including suspicious regions identification, MCs detection, false positive reduction and benign/malign classification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP) neural network was used with grey level co-occurrence matrix (GLCM) and statistical features. Then to decrease the false positive classification ratio, we used cascade correlation neural network (CCNN) with grey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis and support vector machine (SVM) methods were used with GLRLM features for benign/malign classification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS) database was used for the study. Experimental results show that the proposed algorithm obtained 86% sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, the obtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficulty of MC clusters, the novel system provides very satisfactory results. Furthermore, the developed system is fully automatic whole system which gives outputs as percentages and transformed assessment categories.
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