İhsan Ömür Bucak

Diagnosis of liver disease by using CMAC neural network approach

Liver performs several numbers of metabolic functions that are essential to human life. These functions make the liver one of the most important organs in the human body. There are diseases that occur in the liver in short time (acute) and long time (chronic). These diseases could occur because of medications, alcohol, viruses, or excessive fat accumulation or deposit in the liver. Some of these diseases are the inflammation of the liver, insufficient liver performance, Hepatitis A, B, C, D, and liver cirrhosis. If the liver malfunctions in anyway, people know that they are putting their life at risk. For this reason, diagnosing any disease in the liver is important and sometimes difficult. It is also important to notice the diagnosis of the patient at an early stage as the symptoms arise so that the patient might be able to carry on a normal life. The objective of this article is to diagnose the liver disease using an application of the CMAC (Cerebellar Model Articulation Controller) neural network so that it can shorten the medical diagnostic process and help the physician in the complex cases which are otherwise difficult to perceive.

Clustering-based spot segmentation of cDNA microarray images

Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background. The experiments show that fuzzy c-means have segmented spots of the microarray image more accurately than the k-means.

Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.

An analysis of sequence alignment: Heuristic algorithms

Sequence alignment becomes challenging with an increase in size and number of sequences. Finding optimal or near optimal solutions for sequence alignment is one of the most important operations in bioinformatics. This study aims to survey heuristics applied for the sequence alignment problem summarized in a time line.

Searching the Effects of Image Scaling for Underground Object Detection Using KMeans and KNN

With the increase in conflicts between countries, underground object detection has become a serious problem today. One of the commonly used technology is Ground penetrating Radar (GPR). There are different variants of GPR devices, but usually they have an array of sensors, which emits electromagnetic waves, and then, collect the reflecting data through its sensors. The signals travel with different speeds in different mediums which yield some beams together and forms holes or peaks in the signal. According to the properties of searching object, depth of the object, or the soil properties, GPR produces different signal signatures. These signals are used to detect searching underground objects. In order to have a detailed view, more sensors are used, and the frequency is changed to be able to detect deeper objects. Increasing the signal quality causes many algorithms to fail or slow down seriously. On the other hand, underground object detection needs fast and accurate detection. In this paper, we have analyzed the effects of image scaling on object detection using KMeans and k-Nearest Neighbor algorithms (kNN). According to our experiments, even after serious image scaling, the results have not change much while increasing the runtime performance and memory efficiency significantly.

Welfare classification using CMAC neural networks

The aim of this study is to create an Artificial Neural Network (ANN) for the prediction of welfare classification of world countries using an application of the Cerebellar Model Articulation Controller (CMAC). Firstly, welfare ranking and its importance in the economy and the usage of the CMAC algorithm are introduced. In the methodology part, the application that uses CMAC is described and implemented. Trained data are put into the application before predicting the results. Finally, the results are discussed and some suggestions are supplied to be used for further studies.