Bayram Akdemir

Computer aided diagnosis of ECG data on the least square support vector machine

In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system.

Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals

OBJECTIVE This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed. METHODS AND MATERIAL The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition. RESULTS The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean. CONCLUSION These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals.

Prediction of Aortic Diameter Values in Healthy Turkish Infants, Children, and Adolescents by Using Artificial Neural Network

The aorta is the largest vessel in the systemic circuit. Its diameter is very important to guess for child before adult age, due to growing up body. Aortic diameter, one of the cardiac values, changes in time. Evaluation of the cardiac structures and generating a valid regional curve requires a large study group experience for accurate data on normal values. In this study, our aim is to estimate aortic diameter values without curve of charts. Using real sample of the all groups has been predicted using a hybrid system based on combination of Line Based Normalization Method (LBNM) and Artificial Neural Network (ANN) with Levenberg–Marquardt (LM) algorithm. In this study, aortic diameter values dataset divided into two groups as 50% training–50% testing of whole dataset. In order to show the performance of the proposed method, two fold cross validation and prevalent performance measuring methods, Mean Square Error (MSE), Absolute Deviation (AD), Root Mean Square Error (RMSE), statistical relation factor T and R2, have been used. The obtained MSE results from combination of Min–Max normalization and ANN, combination of Decimal Scaling and ANN, combination of Z-score and ANN, and combination of LBNM and ANN (the proposed method) are 0.00517, 0.001299, 0.006196, and 0.000145, respectively. For the suggested method, error’s results have been given discretely for every age up to adult age. The results are compared to real aortic diameter values by expert with nine year experiences in medical area. These results have shown that the proposed method can be confidently used in the prediction of aortic diameter values in healthy Turkish infants, children and adolescents.

Prediction of cardiac end-systolic and end-diastolic diameters in m-mode values using adaptive neural fuzzy inference system

The cardiac, end-systolic and end-diastolic diameters values are very important m-mode cardiac parameters for infant, children, and adolescents, due to growing up body. These parameters, belonging to heart, must be known in order to make a decision about the subject. The expert decision occurs after comparing measured value to hard-copied charts. Hard-copied charts were prepared previously as a result of long statistical studies and these charts depend on a certain region. Our proposed method presents a valid virtual chart for the experts. The proposed method comprises of two stages: (i) data normalization based on euclidean distance (ii) normalized cardiac parameters predicting using adaptive neural fuzzy system. In order to present performance of the proposed method, mean absolute error, absolute deviation and two-fold cross-validation were used. In addition to performance criteria, different common normalization methods, z-score, decimal scaling and minimum-maximum normalization methods were used to compare. In this study, the aim is to create a valid virtual chart which helps the expert during making the decision about predicting end-systolic and end-diastolic cardiac m-mode values. The results were compared with real cardiac parameters by expert with 10years of medical experience.

Elliot Waves Predicting for Stock Marketing Using Euclidean Based Normalization Method Merged with Artificial Neural Network

Financial Marketing is very common in the world to make money or to control the company strategy. Nearly all events trigger to each other and moreover countries. Some predicting methods, on guessing the marketing depends on natural behavior of the events. When, have a scrutinize to backwards, it can be evaluated that some upfront events occur periodically and trigger to each others and may lead to next known moving. Elliot is one of the famous estimating methods on stock marketing. Elliot waves let to time to think and analyze the next moving not in hurry. The proposed method consist of three stages (i) arranging the real time data (ii) data normalization according to Euclidean distance named Euclidean Based Normalization Method and (iii) performing artificial neural network to predict the next swing of the stock or financial marketing. The results compared raw data results and minimum maximum normalization methods to EBNM. Mean squared error and Average Deviation and R2 statistical value were used as performance criteria. According to MSE, the obtained results were 0.000484, 0.205069 and 0.003178 minimum maximum normalization, raw data set and EBNM method respectively. The performance of the proposed method has more accurate than the other two methods.

Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces

Automatic defect detection on reflective surfaces is a compelling process. In particular, detection of tiny defects is almost impossible for human eye or simple machine vision methods. Therefore, the need for fast and sensitive machine vision methods has gained importance. In this study, an effective defect detection method is presented for reflective surfaces such as glass, tile, and steel. Defects on the surface of the product are determined automatically without the need for human intervention. The proposed system involves illumination unit, digital camera, and defect detection algorithm. Firstly, color image is taken by digital camera. Then, properties of taken image are selected. At this stage, ambient condition of lighting devices is very important. Reflections are minimized thanks to the true lighting. Selected properties are: red, green, and blue values, and luminance value. These properties are applied to fuzzy inputs. Information from the inputs is evaluated according to determined rules. Finally, each pixel is classified as black or white. Thirty-two glass pieces are tested using the proposed system. The proposed method was compared with commonly used methods. The success rate of the proposed algorithm is 83.5% and is higher than that of other algorithms .

Importance of Holidays for Short Term Load Forecasting Using Adaptive Neural Fuzzy Inference System

In distributing systems, load forecasting is one of the major management problems to carry on energy flowing; protect the systems, and economic management. In order to manage the system, next step of the load characteristics must be inform from historical data sets. For the forecasting, not only historical parameters are used but also external parameters such as weather conditions, seasons and populations and etc. have much importance to forecast the next behavior of the load characteristic. Holidays and week days have different affects on energy consumption in any country. In this study, target is to forecast the peak energy level the next an hour and to compare affects of week days and holidays on peak energy needs. Energy consumption data sets have nonlinear characteristics and it is not easy to fit any curve due to its nonlinearity and lots of parameters. In order to forecast peak energy level, Adaptive neural fuzzy inference system is used for hourly affects of holidays and week days on peak energy level is argued. The obtained values from output of the artificial intelligence are evaluated two fold cross validation and mean absolute percentage error. The obtained two fold cross validation error as mean absolute percentage error is 3.51 and included holidays data set has more accuracy than the data set without holiday. Total success increased 2.4%.

Microcontroller Compatible Sealed Lead Acid Battery Remaining Energy Prediction Using Adaptive Neural Fuzzy Inference System

All over the world, many portable devices need battery to run. Every expert has to use efficient hardware and software documentation to make battery last longer and make a correlation between microcontrollers’ duties and the remaining energy of batteries. In order to make battery last longer, battery information must be evaluated continuously. In many devices, fluctuating current is used due to its own load so alternating current makes it hard to compute the remaining battery level. For many devices, there could be battery level indicator as solution. This solution gives clue about the remaining time for user but it does not give any hint for microcontroller about battery situation. For low cost devices, it could be very difficult to estimate the remaining storage energy in the battery. In this study, microcontroller compatible sealed lead acid battery remaining energy predictor based on adaptive neural fuzzy inference system has been designed and proposed. In order to test proposed method, mean absolute error and leave one out have been used to measure proposed system performance. The obtained mean absolute error results for leave one out is 10.55, epoch error is 11.72. Through the study, low adaptive neural fuzzy inference system rules and low microcontroller memory consumption were aimed.

Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity:

Abstract In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg—Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8—13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum—maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent performance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.

A convolutional neural network model for semantic segmentation of mitotic events in microscopy images

Mitosis, which has important effects such as healing and growing for human body, has attracted considerable attention in recent years. Especially, cell division characteristics contain useful information for regenerative medicine. However, the analysis of this complex structure is very challenging process for experts, because many cells are scattered at random times and at different speeds. Therefore, we propose an automatic mitosis event detection method using convolutional neural network (CNN). In the proposed method, semantic segmentation has been applied with the help of CNN in order to make the complex mitosis images more easily understandable. The CNN structure consists of four convolution layers, four pooling layers, one rectified linear unit layer and softmax layer. Generally, the aim of CNN structure is to reduce the image size, but in this study, the image size is preserved for the semantic segmentation which provides high-level information. For this, the size of the images at each layer output is calculated and updated with the appropriate padding parameters. Thus, real-size images presented at the network output can be easily understood. BAEC and C2C12 phase-contrast microscopy image sequences are used for experiments. The precision, recall and F-score parameters are used for evaluating the success of the proposed method and compared with the other methods using the same datasets.