Medical image retrieval and automatic annotation: VPA-SABANCI at ImageCLEF 2009

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Competition, the proposed solutions are still far from being su±ciently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation task. We use a direct and two hierarchical classification schemes that employ support vector machines and local binary patterns, which are recently developed low-cost texture descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed hierarchi-cal schemes divide the classification task into sub-problems. The first hierarchical scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that hier-archical annotation of images by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme

Adapted Infinite Kernel Learning by Multi-Local Algorithm

The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV

Editorial of the special issue data analysis and intelligent optimization with applications

Artículo de publicación IS

A Novel Hybrid House Price Prediction Model

The real estate sector is evolving and changing rapidly with the increase in housing demand, and new luxury housing projects appear every day. The reliability of housing market investments is largely dependent on accurate pricing.The aim of this study is to introduce a dynamic pricing procedure that estimates house prices using the most important characteristics of a house. For this purpose, a hybrid algorithm using linear regression, clustering analysis, nearest neighbor classification and Support Vector Regression (SVR) method is proposed. Our hybrid algorithm involves using the output of one method as the input of another method for home price prediction to deal with the heteroscedastic nature of the housing data. In other words, the aim of this study is to present a hybrid algorithm that will create different housing clusters from the available data set, classify the houses to which the cluster is unknown, and make price predictions by creating separate prediction models for each class. Housing data collected through manual web scraping of Kadikoy district in Istanbul were used for training and validation of the proposed algorithm. In addition to these data, we validated our algorithm on the KAGGLE house dataset, which covers a wide range of features. The results of the hybrid algorithm were compared using multiple linear regression, Lasso, ridge regression, Support Vector Regression (SVR), AdaBoost, decision tree, random forest and XGBoost regression. Experimental results show that the proposed hybrid model is superior in terms of both Residual Mean Square Error (RMSE), Mean Absolute Value Percent Error (MAPE) and adjusted Rsquare measures for both Kadikoy and KAGGLE housing dataset

Neurological effects of long-term diet on obese and overweight individuals: An electroencephalogram and event-related potential study

The rise of overweight and obesity across all age groups can be considered as an epidemic. A high body mass index not only has effects on longevity and quality of life of people who suffer from it but also has cognitive and neurological consequences. Executive function (EF)-or the neural regions that support it might act as causes of obesity. The aim of this study is to compare the differences between healthy and obese/overweight individuals and to understand how a prolonged diet of 2 months can affect EF event-related potential (ERP) components. Among the ERP metrics, N1, N2, and P3 are measured. Subjects underwent a diet program and their electroencephalogram was recorded every 4-6 weeks using a Stroop paradigm experiment. As the diet progressed, it was observed that perhaps more neural networks and executive control are engaged as the diet begins to take effect. Attention to nonfood increases and more control is exhibited when participants are presented with food images and food word stimuli. Increase in the N2 and P3 components highlight how the diet affects the brain during various stages of diet plans