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We propose a novel method called Adjacency Propagation Algorithm (APAL) which considers the notion that the adjacent vertices are the best candidates for detecting overlapping communities in an undirected, unweighted, nontrivial graph. This is a compact algorithm with a single threshold parameter used to filter the detected communities according to their intraconnectivity property . In this study, APAL was tested rigorously using synthetic generators, such as the widely accepted LFR benchmark, as well as real data sets of yeast and human protein interactions networks . It was compared against the foremost algorithms in the field; the Clique Percolation Method (CPM), Community Overlap Propagation Algorithm (COPRA) and Neighbourhood-Inflated Seed Expansion (NISE). The results show that APAL outperforms its competitors for networks with increases in the number of memberships of the overlapping vertices. Such conditions are often found in biological networks, where a particular protein subunit may form part of several complexes. We believe that this shows the value of the implementation of APAL for protein interaction and other biological networks.

Tartarus is a commonly used benchmark problem for genetic programming. However, it has never been fully explored for its difficulty tuning property. Using the data from a previous study in which we have executed millions of Tartarus instances, we contribute to the literature with an equation to estimate their difficulty. Our approach uses four metrics that are embedded into the equation. These metrics are related to the number of clusters and clusters sizes, the distances of boxes to the edges of the board grid, the number of boxes around the agent, and the minimum number of actions for the agent to reach the largest cluster. The coefficients of these metrics have been fit to the data using the general linear model and a mean residual error of ~0.1 has been achieved. This is the first study that can estimate the difficulty of a Tartarus board without modifying the problem in any way., Tartarus genetik programlamada sıkça kullanılan bir kıyaslama problemidir. Fakat zorluk ayarı özelliği henüz tam olarak araştırılmamıştır. Literatüre milyonlarca Tartarus örneği çalıştırdığımız önceki bir çalışmanın verilerini kullanarak zorluklarını tahmin edebilen bir denklemle katkıda bulunuyoruz. Yaklaşımımız denklemin içinde yer alan dört yeni metrik kullanıyor. Bu metrikler küme sayıları ve büyüklüklerine, kutuların kenarlardan uzaklığına, yazılım etmeninin etrafındaki kutuların sayısına ve etmenin en büyük kümeye varması için gereken hareket sayısına bağlıdır. Metriklerin katsayıları veriye genel doğrusal model ile uyarlanmış ve ortalama ~0.1 kadar bir hata başarısına ulaşılmıştır. Bu çalışma Tartarus probleminde bir değişiklik yapmadan problemin zorluğunu tahmin edebilen ilk çalışmadır.

The Tartarus Problem is one of the candidate benchmark problems in evolutionary algorithms. We take advantage of the graphical processing unit (GPU) to improve the results of the software agents that use finite state machines (FSMs) for this benchmark. While doing so we also contribute to the study of the problem on several grounds. Similar to existing studies we use genetic algorithms to evolve FSMs, but unlike most of them we use adaptive operators for controlling the parameters of the algorithm. We show that the actual number of valid boards is not 297,040, but 74,760, because the agent is indifferent to the rotations of the board. We also show that the agent can only come across 383 different combinations, rather than 6561 that is used in the current literature. A final contribution is that we report the first true scores for the agents by testing them with all available 74,760 boards. Our best solution has a mean score of 8.5379 on all boards.

Speech is the most natural way of expressing ourselves as humans. It is only natural then to extend this communication medium to computer applications. We define speech emotion recognition (SER) systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions. SER is not a new field, it has been around for over two decades, and has regained attention thanks to the recent advancements. These novel studies make use of the advances in all fields of computing and technology, making it necessary to have an update on the current methodologies and techniques that make SER possible. We have identified and discussed distinct areas of SER, provided a detailed survey of current literature of each, and also listed the current challenges.

We propose two contributions with novel approaches to fMRI activation analysis. The first is to apply confidence intervals to locate activations in real-time, and second is a new metric based on robust regression of fMRI signals. These contributions are implemented in our four proposed methods; Instantaneous Activation Method (TAM), Instantaneous Activation Method with Past Blocks (TAMP) for real-time analysis, Task Robust Regression Distance Method (TRRD) for the new metric with robust regression and Instantaneous Robust Regression Distance Method (IRRD) for both contributions. For comparison, a statistical offline method called Task Activation Method (TAM) and a correlation analysis method are also implemented. The methods are initially evaluated with synthetic data generated using two different approaches; first using varying hemodynamic response function signals to simulate a wide range of stimuli responses, along with a Gaussian white noise, and second using no activity state data of a real fMRI experiment, which removes the need to generate noise. The methods are also tested with real fMRI experiments and compared with the results obtained by the widely used SPM tool. The results show that instantaneous methods reveal activations that are lost statistically in an offline analysis. They also reveal further improvements by robust fitting application, which minimizes the outlier effect. TRRD has an area under the ROC curve of 0,7127 for very noisy synthetic images, is reaching up to 0,9608 as the noise decreases, while the instantaneous score is in the range of 0,6124 to 0,8019 in the same noise levels. (C) 2017 Elsevier B.V. All rights reserved.

Functional magnetic resonance imaging aims to correlate brain functions with the regions of the brain. To find out the region responsible for a brain function, the brain should be exposed to stimuli related to the function, and the changes in time should be monitored. To acquire images that change over time makes it more complex and difficult to extract deductions from functional images than structural images. To get the design right, it is necessary to ask the right questions, to give the correct stimuli at the right times, and to analyze the obtained data according to the design of the experiment. As a result, the design of the experiment affects the whole process. This paper presents a wide view of the complete process in the design, execution and the analysis of fMRI experiments, along with the related software and hardware requirements, as well as mentioning the challenges involved.

Three Dimensional magnetic resonance imaging (3D-MRI) has been utilized to classify patients with neuroanatomical abnormalities apart from healthy controls (HCs). The studies on the diagnosis of Bipolar Disorder (BD) focuses also on the unaffected relatives of BD patients in order to examine the heritable resistance factors associated with the disorder. Hence, the comparison of Healthy Siblings of Bipolar Disorder patients (HSBDs) and HCs is also required owing to the high heritability of BD. In this paper, the classification of 27HSBDs from 38 HCs has been studied by using 3D-MRI and Computer-Aided Detection (CAD). The pre-processing of 3D-MRI data is performed by taking advantage of Voxel-Based Morphometry (VBM) and the structural deformations in the Gray Matter (GM) and White Matter (WM) are obtained by using a general linear model. The model is configured by using a two sample t-test technique and Total Intracranial Volume (TIV) as a covariate. The altered voxels between data groups are considered as Voxel of Interests (VOIs) and the 3D masks are generated for GM and WM tissue probability maps. The Relief-F algorithm is utilized to rank the features and a Fisher Criterion (FC) method is considered to determine the number of top-ranked discriminative features. The performances of Support Vector Machines (SVM) and the Naive Bayes (NB) algorithms are compared on the classification of HSBD and HC. The experiments are performed for GM-only, WM-only, and their combinations. The experimental results indicate that the changes between the brain regions of HSBD and HC might provide information on the heritable factors associated with the BD. Additionally, it is concluded that using the combination of GM and WM tissue probability map provides better results than considering them, separately. Finally, it is obtained that the classification accuracy of SVM on HSBD and HC comparison is better than that of NB.

Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection and diagnosis of neuroanatomical abnormalities, including bipolar disorder (BD). Pre-processing of 3D-MRI scans plays an important role in post-processing. In this study, Voxel-Based Morphometry (VBM) is used to compare the morphological differences at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex, and their combinations) on classification of BDs from HCs have been investigated for GM-only, WM-only, and their combination. 3D masks for GM and WM are generated separately by using local differences between BPs and HCs and the two sample t-test method. Principle component analysis based dimensionality reduction and support vector machine with Gaussian kernel are employed for classification of 26 BDs and 38 HCs obtained from Ege University, School of Medicine, Department of Psychiatry. The results indicate that using only TIV as a covariate provides more robust results for BD classification compared to other covariate combinations. Furthermore, the combination of GM and WM improves classification performance. The highest classification accuracies obtained for GM, WM, and their combination are 70.30%, 79.70% and 82.80% respectively.

EgeUn###, Detection of Bipolar Disease (BD), one of the most common neuroanatomical abnormalities, using machine learning algorithms together with Magnetic Resonance Imaging (MRI) data has been widely studied. BD is a highly heritable disease, yet not all siblings tend to have it despite they might have similar genetic and environmental risk factors. In this paper, the classifications of two self-acquired data groups, namely 26BD patients and 38 unrelated Healthy Controls (HCs) as well as 27 Healthy Siblings of BD (BDHSs) and 38HCs are examined. Voxel-Based Morphometry (VBM) is utilized to segment and pre-process the MRI data. In order to obtain the morphological alterations in the Gray Matter (GM) and White Matter (WM) of data groups separately, a general linear model is configured and a two sample t-test based statistical method is used. The obtained differentiated voxels are considered as Voxel of Interests (VOIs) and using VOIs reduces the dimension of the original data into the number of VOIs. The effects of using different covariates (i.e. total intracranial volume (TIV), age, and sex) on classification of the two data groups have been studied for GM-only, WM-only, and their combination. Principle Component Analysis (PCA) is used to reduce the dimension of the extracted VOIs data and Support Vector Machine (SVM) with Gaussian kernel is taken into account as a classifier. The experimental results indicate that among three covariates, TIV provides better results for both data groups and the classification accuracies of the combination of GM and WM maps is higher than that of GM-only and WM- only for both groups. In BD and HC comparison, the highest classification accuracies of 70.3% for GM, 79.7% for WM, and 82.8% for fusion of extracted GM as well as WM are obtained. In BDHS and HC comparison, the highest classification accuracies of 72.3% for GM, 76.9% for WM, and 78.5% for fusion of extracted GM as well as WM are obtained. © 2019 IEEE.

Medical Technologies Congress (TIPTEKNO) -- OCT 03-05, 2019 -- Izmir, TURKEY, Oguz, Kaya/0000-0002-1860-9127, WOS:000516830900117, Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection of bipolar disorder (BD). in this study, the structural alterations at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs) have been compared using Voxel-Based Morphometry (VBM). in order to obtain 3D GM and WM masks, the two sample t-test method and total intracranial volumes of BD and HC as a covariate have been utilized. in addition to analyzing effects of GM and WM tissue maps separately in the detection of BD, impacts of both GM and WM ones are studied by concatenating them in a matrix. The correlation-based feature selection (CFS) feature ranking method is applied to the obtained 3D masks to rank the features, the number of selected top-ranked features are determined using a Fisher criterion (FC) approach, and different classification algorithms are used to classify BD apart from HCs. in this study, 26 BDs and 38 HCs data are used. The experimental results indicate that the classification accuracy of Naive Bayes outperforms the other four classification algorithms used in this study. Additionally, concatenation of GM and WM tissue maps enhances the classification performances of using GM-only and WM-only ones. The classification accuracies obtained for GM, WM, and their concatenation are 72.92%, 78.33%, and 80.00% respectively., Biyomedikal Klinik Muhendisligi Dernegi, Izmir Katip Celebi Univ, Biyomedikal Muhendisligi Bolumu

26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY, Oguz, Kaya/0000-0002-1860-9127, WOS:000511448500126, Change point analysis is an efficient method for understanding the unexpected behavior of the data used in many different disciplines including medical imaging. It is important to find the instances the activations occur as much as finding the activation areas in the analysis of functional magnetic resonance imaging (fMRI). Change point detection algorithms can be used to find the activation instances. in this study, a regression based point detection method is proposed to find the activation instances in fMRI experiments. The proposed method is applied to a fMRI experiment which includes a motor task. A linear based evaluation method is also proposed. The analyses show that the activations are in accordance with the established methods in the literature., IEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univ

Dementia affects the lives of millions of people. Digital recognition and evaluation of the Clock Drawing Test that is used for diagnosis will remove subjective manual evaluation and will increase the awareness and early diagnosis. The proposed system evaluates the clocks drawn to the paper and loaded to it by extracting their graphical features. Besides image processing methods, artificial neural networks are used to detect the numbers. The results of the system are in accordance with the manual evaluation for 35 clocks drawn by healthy subjects.

Corpus callosum (CC) is an important structure for medical image registration. We propose three novel fully automated for the extraction of CC. Our first algorithm, Valley matching (VM), is based on fixed searched range in histogram processing and uses prior anatomical information for locating CC. The second one, Evolutionary CC Detection (ECD), based on genetic algorithm presents a new fitness function that uses anatomical ratios, instead of fixed prior knowledge without the need for preprocessing. The final one, called Evolutionary Valley Matching (EVM), takes advantages of the strong points of the first and second algorithms. The search space defined for ECD is reduced by VM which uses crowding method to find the peaks in the multi-modal histogram. Another important contribution of this study is that there is no existing method using genetic algorithm for extracting CC. Our proposed algorithms perform with the success rates up to 95.5%.

Disease diagnosis has been made by experts examining the images obtained by magnetic resonance imaging (MRI) technique, the disease process is observed using images taken at different times. Brain MR images are registered to the standard brain atlases because the human brain has a complex structure and varies from person to person. Corpus Callosum (CC) has a big importance for medical image registration because it can be easily distinguished on T1-weighted structural brain MR images and does not vary prominently between individuals. In this study, from the midsagittal brain MR image that belongs to the patient CC is detected fully automatically via Valley Matching (VM) Algorithm. The contribution of this study is registration of patient's MR image onto the Montreal Neurological Institute (MNI) image space by using automatically detected reference points.