Hamza Erol

Yüz ifadeleri, insanlar arası iletişimde ana bilgi kanallarından biridir. İnsanlar günlük hayatlarında psikolojik durumları ifade etmek için çok fazla yüz ifadesi oluşturmaktadır. Bu yüz ifadeleri temel ve karmaşık duygular olarak ayrılır. İnsanlar bu duygu ifadelerini tanımlamada hala zorlanırken makineler için de gelişmekte olan bir konudur. Bu sebeple son zamanlarda çok fazla ilgi görmektedir. Bu çalışmada Ohio Eyalet Üniversitesine ait Compound Emotion (CE) veri setindeki temel 7 duygu olan doğal, mutlu, üzgün, öfkeli, şaşırmış, korkulu ve iğrenme duyguları üzerinde durulmaktadır. Veri seti 1610 görüntüden oluşmaktadır. Başarımı arttırmak için veri çoğaltma işlemi uygulanarak 5478 görüntü elde edilmektedir. Eğitilmiş evrişimli sinir ağı modelleri ile sınıflandırma işleminde optimizasyon yöntemlerinin etkileri gösterilmektedir. VGG19 ve MobileNet modelleri ile birlikte Adadelta, Adagrad ve SGD optimizasyon yöntemlerinin duygu sınıflarında en başarılı sonuç tespit edilmektedir.

Ülkemizde ve Dünya genelinde hızla artan yaşlı nüfus oranı dikkat çekmektedir. Yaşlanan bu nüfus ile birlikte de bazı sorunlar ortaya çıkmaktadır. Yaşlanan insanların vücut dirençlerinin düşmesi, metabolik reaksiyonlarının yavaşlaması, hareket kabiliyetlerinin zayıflaması, düşünüp karar verebilme yetilerinin azalması gibi birçok sebepten bu insanlar bakıma muhtaç hale gelmektedir. Ayrıca muhtaç olma durumu sadece yaşlı insanlarda değil fiziksel veya zihinsel anlamda doğuştan ya da sonradan engelli olan insanlarda da görülmektedir. İnsanlar hasta, yaşlı veya engelli olmasalar dahi ameliyat, tedavi, muayene gibi sağlık hizmetlerinin arkasından sunulan sosyal bakım hizmetlerini evlerinde almak istemektedirler. Evde sunulan bu sağlık veya bakım hizmetleri ile ayrıca devletlerin sağlık kurumlarındaki mali giderler düşürülmekte ve bu kurum veya kuruluşlar fazladan iş yükünden de kurtulmaktadırlar. Bu çalışma da evde sunulan bakım hizmetleri kapsamında yer alan beslenme veya üç öğün yemek dağıtımında araçların en kısa mesafe ve dolayısıyla zamanda yönlendirilmesi, mevcut maliyetleri düşürmek için meta sezgisel yöntemlerden Karınca Koloni Algoritması ile iyileştirmelerin yapılması sağlanarak ihtiyaç sahibi insanlara ulaştırılan hizmetin kalitesinin arttırılmasına yardımcı olunmuştur. Bu yolla mobil hizmet sunan araç maliyetlerinin azaltılmasının yanı sıra hizmet veren ekibin tükenmişliği de azaltılarak hizmet veriminin artırılmasını hedeflenmiştir.

SPSS 15.0 Paket Programı ile İstatistiksel Veri Analizi Düzeltilmiş ve Genişletilmiş İkinci Baskı kitabı, araştırma veya deney sonucu elde edilen verilerin istatistiksel analizini gerçekleştirmek için kullanılan SPSS programının kullanımını anlatmaktadır. Kitap, verinin bilgisayar ortamına girilmesi, değişkenlerin tanımlanması, analiz yöntemlerinin belirlenmesi, analiz sonuçlarının yorumlanması ve raporlanması gibi adımları içermektedir. Kitap, temel istatistiksel veri analizi ve ileri istatistiksel veri analizi olmak üzere iki bölümden oluşmaktadır. Temel istatistiksel veri analizi, gözlem değerlerine dayalı olarak tanımlayıcı istatistikler elde etmeyi içermektedir. İleri istatistiksel veri analizi ise hipotez testleri, değişkenler arasındaki ilişkilerin incelenmesi ve istatistiksel modellerin oluşturulması gibi daha karmaşık analizleri içermektedir. Kitap, SPSS 15.0 paket programı kullanılarak bilgisayar ortamında istatistiksel veri analizi yöntemlerini örnekler üzerinde açıklamaktadır. Bu ikinci baskıda, önceki baskıdan kaynaklanan eksiklikler ve yanlışlıklar düzeltilmiş, bölüm sayısı arttırılmıştır. Kitabın içeriği yazarı bağlamaktadır. The book \"SPSS 15.0 Package Program for Statistical Data Analysis Corrected and Expanded Second Edition\" explains the use of the SPSS program for performing statistical analysis of research or experimental data. The book covers steps such as entering data into the computer, defining variables, determining analysis methods, interpreting analysis results, and reporting. It is divided into two sections: basic statistical data analysis and advanced statistical data analysis. The basic section focuses on obtaining descriptive statistics based on observed values, while the advanced section covers more complex analyses such as hypothesis testing, examining relationships between variables, and creating statistical models. The book uses examples to illustrate the application of statistical data analysis methods using the SPSS 15.0 package program. This second edition corrects any errors or deficiencies from the previous edition and includes additional chapters.

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image flies were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed are taken into account; and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

In recent years, the analysis of medical images using deep learning techniques has become an area of increasing popularity. Advances in this area have been particularly evident after the discovery of deep artificial neural network models and achieving more successful performance results than other traditional models. In this study, the performance comparison of different deep learning models used to efficiently diagnose pneumonia on chest x-ray images was performed. The data set used in the study consists of a total of 5840 chest x-ray images of individuals. In order to classify these data, three different deep learning models are used: Convolutional Neural Network, Convolutional Neural Network with Data Augmentation and Transfer Learning. The images in the data set were classified into two categories as pneumonia and healthy people using these three deep learning models. The performances of these three deep learning models used in classification were compared in terms of loss and accuracy. In the comparison of three different deep learning models with two different performance values, 5216 chest x-ray images in the data set were used to train the deep learning model and the remaining 624 were used to test the model. At the end of the study, the most successful performance result was obtained by convolutional neural network model applied with data augmentation technique. According to the best results of this study, this model was able to accurately predict the class of 93.4% of the test data.

Aims/Objectives: Effective and efficient heart disease prediction via nonparametric mixture regression models. Data Source: Data used in this paper is from the UCI database of the Cleveland Clinic Foundation for heart disease. The original data source contains 76 raw attributes with 303 observations each. For the purpose of this paper only 14 attributes were used as explained in section 4. Methodology: Cluster analysis was applied via mixture models in the form of Nonparametric Density-based models. The clusters were identified using a graph theory based technique. Voronoi diagrams were used and and their distributions were estimated nonparametrically through a mixture model with Gaussian kernels. The optimal number of clusters and components of the identified clusters were determined, analysed and diagnosed using a density based silhouette information criteria. All the data analysis and model diagnosis were performed in R using the PdfCluster package. Results: Different number of components resulted in different number of clusters when nonparametric mixture are used on heart disease. However, the optimal number of clusters under heart disease risks were found to be represented by two clusters with two components using density based silhouette information criteria. These were both well separated and classi ed as indicated by lack of spurious clusters and high positive density based silhouette values (See Figs. 2 and 4). Their properties are given in Table 2. The result is irregardless of the flexible conditions which are assumptions free on: shape of the distribution, number of components and number of clusters. Conclusion: When nonparametric mixture models are used, individuals under risks of heart diseases can be diagnosed either under high or low risk depending on the dominant characteristics on a given individual. Those under high risk have attributes that makes them progress to heart diseases faster compared to those under low risk. Therefore by classifying individuals into these categories, medical personnel can quickly diagonise heart disease and efficiently identify characteristics associated with each category.

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

A new artificial intelligence algorithm for logical circuit design using orientation of clusters in multivariate data is developed for decision making in robotic studies. The reliability and risk of decision making based on logical circuit design can be predicted. Clusters in multivariate data is obtained by the method of mixture model clustering based on model selection using the segmentations of heterogeneous variables. The segments of heterogeneous variables forms the number and determines the structures of clusters. The orientations of clusters is used to construct the logical circuit design. The number of all cases for cluster centers determined according to the segmentations of heterogeneous variables. Some of all cases which the assumptions are not satisfied eliminated. The rest cases gives the number of possible cluster centers which the assumptions satisfied. Candidate mixture models are established to determine the number and structures of clusters for possible cluster centers using the partitions of heterogeneous variables. Logical circuit designs are established for possible cluster centers using the orientations of partitions of heterogeneous variables. The best mixture model is chosen among candidate mixture models for data clustering using information criterions. The best mixture model determines the number and the structure of clusters in data. The number of components in the best mixture model corresponds to the number of clusters in data. The components of the best mixture model corresponds to the structure of clusters in multivariate data. Logical circuit design of the best mixture model is used in computations of reliability and risk prediction for decision making in robotic studies.

A new data mining method is proposed for determining the number and structure of clusters, and refining groups in multivariate heterogeneous data set including groups, partly and completely overlapped group structures by using dynamic model based clustering. It is called dynamic model based clustering since the structure of model changes at each stage of refinement process dynamically. The proposed data mining method works without data reduction for high dimensional data in which some of variables including completely overlapped situations.

A model selection algorithm is developed for finding the best model among a set of mixture of normal densities fitted to heterogeneous multivariate data. Model selection algorithm proposed first finds total number of mixture of normal densities then selects possible number of mixture of normal densities and finally searches the best model among them in mixture model clustering of heterogeneous multivariate data. Log-likelihood function, Akaike's information criteria and Bayesian information criteria values are computed and graphically ploted for each mixture of normal densities. The best model is chosen according to the values of these information criterions.