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Öğe 3D ELECTRONIC BRAIN ATLAS MODEL FOR THE DETECTION OF NEUROLOGICAL DISORDERS(ST JOHN PATRICK PUBL, 2017) Ozic, Muhammet Usame; Ozsen, Seral[Abstract not Available]Öğe Atlas-Based Segmentation Pipelines on 3D Brain MR Images: A Preliminary Study(EDUSOFT PUBLISHING, 2018) Ozic, Muhammet Usame; Ekmekci, Ahmet Hakan; Ozsen, SeralThree dimensional structural MR imaging is a high-resolution imaging technique used in the detection and follow up of neurological disorders. Rigid changes in the brain are usually interpreted and reported manually by radiologists using MR images. The results of manual interpretation may vary with respect to the experts. At the same time, measurement and segmentation of the brain regions and the manual evaluation of the volume changes are a difficult process. With the increase of numerical methods, automated and semi-automated package programs have been developed for the analysis of brain measurements. These programs use electronic brain atlases or tissue probability maps. However, since the package programs have a lot of analysis time and give only certain outputs, they may be disadvantaged in the use of segmentation and measurement of brain regions. Hence, special pipelines are needed especially to obtain valuable features for artificial intelligence and classification studies. In this study, we propose pipelines to segment 3D certain brain regions, which will help to find the basic features such as volume changes, intensity variations, symmetry deteriorations, and tissue changes. With these pipelines, 3D segmentation of the brain regions defined in the atlas can be performed and normalized. It is aimed to use these studies as a preliminary study in order to quantitatively determine the basic changes in the brain by performing the volume of interest methods and to formulate a decision support system.Öğe Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Ozsen, Seral; Gunes, SalihAn increasing number of algorithms and applications have coming into scene in the field of artificial immune systems (AIS) day by day. Whereas this increase is bringing successful studies, still, AIS is not an effective problem solver in some problem fields such as classification, regression, pattern recognition, etc. So far, many of the developed AIS algorithms have used a distance or similarity measure as the case in instance based learning (IBL) algorithms. The efficiency of IBL algorithms lies mainly in the weighting scheme they used. This weighting idea was taken as the objective of our study in that we used genetic algorithms to determine the weights of attributes and then used these weights in our previously developed Artificial Immune System (AWAIS). We evaluated the performance of new configuration (GA-AWAIS) on two medical datasets which were Statlog Heart Disease and BUPA Liver Disorders dataset. We also compared it with AWAIS for those problems. The obtained classification accuracy was very good with respect to both AWAIS and other common classifiers in literature. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods(SPRINGER LONDON LTD, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemSleep staging is a significant process to diagnose sleep disorders. Like other stages, several parameters are required for the determination of N-REM2 stage. Sleep spindles (SSs) are among them. In this study, a methodology was presented to automatically determine starting and ending positions of SSs. To accomplish this, short-time Fourier transform-artificial neural networks (STFT-ANN), empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were used. Two considerable methods which were determination envelope and thresholding of the decomposed signals by EMD and DWT were also presented in this study. A database from the EEG signals of nine healthy subjects-which consisted of 100 epochs including 172 SSs in total-was prepared. According to the test results, the highest sensitivity rate was obtained as 100 and 99.42 % for EMD and DWT methods. However, the sensitivity rate for the STFT-ANN method was recorded as 55.93 %. The results indicated that the EMD method could be confidently used in the automatic determination of SSs. Thanks to this study, the sleep experts will be able to reliably find out the epochs with SSs and also know the places of SSs in these epochs, automatically. Another important point of the study was that the sleep staging process-tiring, time-consuming and high fallibility for the experts-could be performed in less time and at higher accuracy rates.Öğe Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal(PERGAMON-ELSEVIER SCIENCE LTD, 2018) Yucelbas, Sule; Yucelbas, Cuneyt; Tezel, Guley; Ozsen, Seral; Yosunkaya, SebnemElectroencephalogram (EEG) signals, which are among the primary polysomnography (PSG) signals used for sleep staging, are difficult to obtain and interpret. However, it is much easier to obtain and interpret electrocardiogram (ECG) signals. The use of ECG signals for automatic sleep staging systems could bring practicality to these systems. In this study, ECG signals were used to identify the wake (W), non-rapid eye movement (NREM) and rapid eye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea (OSA) patients) in total. Four different methods were used to extract features from these signals: Singular Value Decomposition (SVD), Variational Mode Decomposition (VMD), Hilbert Huang Transform (HHT), and Morphological method. As a result of applying the methods separately, four different data sets were obtained. The four different datasets were given to the Wrapper Subset Evaluation system with the best-first search algorithm. After the feature selection procedure, the datasets were separately classified by using the Random Forest classifier. The results were interpreted by using the essential statistical criteria. Among the methods, morphological method was the most successful and it was followed by SVD in terms of success rate for both two databases. For the first database, the mean classification accuracy rate, Kappa coefficient and mean F-measure value of the Morphological method were found as 87.11%, 0.7369, 0.869 for the healthy and 78.08%, 0.5715, 0.782 for the patient, respectively. For the second database, the same statistical measures were determined as 77.02%, 0.4308, 0.755 for the healthy and 76.79%, 0.5227, 0.759 for the patient, respectively. The performance results of the study, which is consistent with real life applications, were compared with the previous studies in this field listed in the literature. (C) 2018 Elsevier Ltd. All rights reserved.Öğe Classification of sleep stages using class-dependent sequential feature selection and artificial neural network(SPRINGER, 2013) Ozsen, SeralSeveral studies have been conducted for automatic classification of sleep stages to ease time-consuming manual scoring process that can involve a high degree of experience and subjectivity. But none of them has found a practical usage in medical area so far because of their under acceptable success rates. In this study, a different classification scheme is proposed to increase the success rate in automatic sleep stage scoring in which sleep stages were classified as Awake, Non-REM1, Non-REM2, Non-REM3 and REM stages. Using EEG, EMG and EOG recordings of five healthy subjects, a modified version of sequential feature selection method was applied to the sleep epochs in class by class basis and different artificial neural network (ANN) architectures were trained for each class. That is to say, sleep stages were classified with five ANN architectures each of which uses different features and different network parameters for classification. The highest classification accuracy was obtained for REM sleep as 95.13 % in addition to the lowest classification accuracy of 86.42 % for Non-REM3 sleep. The overall accuracy, on the other hand, was recorded as 90.93 %, which is a comparatively good result when the other studies using all stages are taken into account.Öğe Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Ozsen, Seral; Ceylan, RahimeData reduction is an indispensable part of pattern classification processes in many cases. If the number of samples is excessive, sample reduction or data reduction algorithms can be used for an effective processing time and reliable successive results. Many methods have been used for data reduction. Fuzzy c-means is one of these methods and it is widely used in such applications as clustering algorithms. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for the data reduction process. We realized the performance evaluation experiments on the standard Chain link and Iris datasets, while the main application was conducted using the Wisconsin Breast Cancer and Pima Indian datasets, which were taken from the University of California, Irvine Machine Learning Repository. For these datasets, the performance of the AIS in the data reduction process was compared with the fuzzy c-means clustering algorithm, in which a multilayer perceptron artificial neural network was used as a classifier after the data reduction processes. The obtained results show that the maximum classification accuracies were obtained as 73.96% for the Pima Indian Diabetes dataset and 97.80% for the Wisconsin Breast Cancer dataset with the AIS and the compression rates were 80% and 40% for these results. For fuzzy c-means clustering, however, the aforementioned accuracies were obtained as 63% and 86.69% for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets, respectively. Moreover, the compression rates for these results for fuzzy c-means were 90% and 70%. When the mean classification accuracy values over the experimented compression rates were taken into consideration, the AIS reached a mean classification accuracy of 70.07% for the Pima Indian Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this dataset. For the Wisconsin Breast Cancer dataset, however, the mean classification accuracies of the AIS and fuzzy c-means methods were recorded as 94.90% and 75.43%, respectively.Öğe Comparison of Some Spectral Analysis Methods in Detection of Sleep Spindles Using YSA(IEEE, 2015) Ozsen, Seral; Dursun, Mehmet; Yosunkaya, SebnemSleep spindle is a very determinant factor for detection of Non-REM2 stage in sleep staging studies. When it is considered that about half of the sleep consists of Non-REM2 stage, the importance of automatic sleep spindle detection stands out. In this study, three different spectral analysis method- FFT, Welch and AR have been used to estimate the frequency spectrum of sleep EEG signal and feature extraction from this spectrum has been realized. Obtained features have been used in ANN to classify EEG epochs as epochs with spindle and epochs without spindle. It has been observed that least classification error was obtained with FFT as 15.16%.Öğe Detection of the Electrode Disconnection in Sleep Signals(IEEE, 2015) Yucelbas, Cuneyt; Ozsen, Seral; Yucelbas, Sule; Tezel, Gulay; Dursun, Mehmet; Yosunkaya, Sebnem; Kuccukturk, SerkanSleep staging process that is performed in sleep laboratories in hospitals has an important role in diagnosing some of the sleep disorders and disturbances which are seen in sleep. And also it is an indispensable method. It is usually performed by a sleep expert through examining during the night of the patients (6-8 hours) recorded Electroencephalogram (EEG), Electrooculogram (FOG), Electromyogram (EMG), electrocardiogram (ECG) and other some signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring studies has come to the fore. However, none of the so far made automatic sleep staging was not accepted by the experts. The most important reason is that the results of the automated systems are not desired accuracy. There are many factors that affecting the accuracy of the systems, such as noise, the inter-channel interference, excessive body movements and disconnection of electrodes. In this study, we examined the written an algorithm to be able to determine to what extent the disconnection of electrodes in EEG signal that obtained one healthy person at the sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University. According to the obtained application results, the electrodes disconnection in EEG signal could be detected maximum of 100% and minimum of 99.12% accuracy. Accordingly, based on the success achieved in the study, this algorithm is thought to contribute positively to the researchers that the work on and will work on sleep staging problems and increase the success of automatic sleep staging systems.Öğe DETERMINATION OF COMBUSTION DEGREE OF SOME COAL SAMPLES FROM THE SHORT AND SULPHUR ANALSIS RESULTS BY USING ARTIFICIAL NEURAL NETWORKS(INT SCIENTIFIC CONFERENCE SGEM, 2011) Ozsen, Seral; Ozsen, Hakan; Sensogut, CemCoal is the most consumed fossil fuel in the world. Determination of the thermal properties of coal is a very important matter and it is not straightforward because of the heterogeneous structure of the coal. The short and elementary analysis results of coals with different carbonization degrees are different. The mineral composition of a coal also affects the thermal behavior. To detect thermal properties of coals, thermal analysis devices are generally used in many widespread methods. The most widely used methods in thermal analysis of coals are Differential Thermal Analysis (DTA) and Thermogravimetry (TG). In this study however, a different analysis method to determine combustion degree of coals was applied. By utilizing from some properties of coals obtained by short analysis and sulphur analysis, an Artificial Neural Network (ANN) was trained to predict the combustion degrees of coals. For this application 84 coal samples were prepared from 28 different locations in TURKEY. Among these, 67 samples were used in training ANN and the remaining 17 were used in test procedure. For the test samples, the trained ANN was used to predict the combustion degrees of them by presenting 8 different properties obtained from short and Sulphur analysis results. Then the mean squared error (mse) was calculated between the real combustion degrees which were also determined from the TG method and predicted combustion degrees of ANN. The test mse was found to be 2.9x10(-4). This result means that the trained ANN could predict combustion degree of a coal sample with a mean error of 2.9x10(-4). When the time and effort spend on determining thermal property of a coal sample with a classical method is considered, this gives another alternative to the experimenter for determining combustion degree of that sample in more short and effortless manner.Öğe Effect of the Hilbert-Huang Transform Method on Sleep Staging(IEEE, 2017) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Yosunkaya, SebnemSleep scoring is performed by examining the recorded electroencephalogram (EEC) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method.Öğe Elimination of EMG Artifacts From EEG Signal in Sleep Staging(IEEE, 2016) Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Yosunkaya, Sebnem; Kuccukturk, SerkanSleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy.Öğe A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification(SPRINGER, 2017) Dursun, Mehmet; Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemInterference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification studies these effects are generally ignored. Thus, the objective of this study was to eliminate EOG artifacts from the EEG signals and to see the effects of this process. We proposed a new scheme in which EOG artifacts are separated from electrode or other line artifacts by a correlation and discrete wavelet transform-based rule. Also, to discriminate the situation of EEG contamination to EOG from EOG contamination to EEG, we introduced another rule and integrated this rule to our proposed method. The proposed method was also evaluated under two different circumstances: EOG-EEG elimination along the whole 0.3-35 Hz power spectrum and EOG-EEG elimination with discrete wavelet transform in 0-4 Hz frequency range. To see the consequences of EOG-EEG elimination in these circumstances, we classified pure EEG and artifact-eliminated EEG signals for each situation with artificial neural networks. The results on 11 subjects showed that pure EEG signals gave a mean classification accuracy of 60.12 %. The proposed EOG elimination process performed in 0-35 Hz frequency range resulted in a classification accuracy of 63.75 %. Furthermore, conducting EOG elimination process by using 0-4 Hz DWT detail coefficients caused this accuracy to be raised to 68.15 %. By comparing the results obtained from all applications, we concluded that an improvement about 8.03 % in classification accuracy with regard to the uncleaned EEG signals was achieved.Öğe A new denoising method for fMRI based on weighted three-dimensional wavelet transform(SPRINGER LONDON LTD, 2018) Ozmen, Guzin; Ozsen, SeralThis study presents a new three-dimensional discrete wavelet transform (3D-DWT)-based denoising method for functional magnetic resonance images (fMRI). This method is called weighted three-dimensional discrete wavelet transform (w-3D-DWT), and it is based on the principle of weighting the volume subbands which are obtained by 3D-DWT. Briefly, classical DWT denoising consists of wavelet decomposition, thresholding, and image reconstruction steps. In the thresholding algorithm, the thresholding value for each image cannot be chosen exclusively. Namely, a specific thresholding value is chosen and it is used for all images. The proposed algorithm in this study can be considered as a data-driven denoising model for fMRI. It consists of three-dimensional wavelet decomposition, subband weighting, and image reconstruction. The purposes of subband weighting algorithm are to increase the effect of the subband which represents the image better and to decrease the effect of the subband which represents the image in the worst way and thus to reduce the noises of the image adaptively. fMRI is one of the popular methods used to understand brain functions which are often corrupted by noises from various sources. The traditional denoising method used in fMRI is smoothing images with a Gaussian kernel. This study suggests an adaptive approach for fMRI filtering different from Gaussian smoothing and 3D-DWT thresholding. In this study, w-3D-DWT denoising results were evaluated with mean-square error (MSE), peak signal/noise ratio (PSNR), and structural similarity (SSIM) metrics, and the results were compared with Gaussian smoothing and 3D-DWT thresholding methods. According to this comparison, w-3D-DWT gave low-MSE and high-PSNR results for fMRI data.Öğe A New Model to Determine Asymmetry Coefficients on MR Images using PSNR and SSIM(IEEE, 2017) Ozic, Muhammet Usame; Ozsen, SeralThe human brain consists of two hemispheres, right and left. These two hemispheres are almost symmetrical, not perfectly. However, in neurological diseases, the volumetric losses in the brain begin to deteriorate asymmetrically between the two hemispheres. This deterioration can be local or global in the brain. Symmetry deterioration can be a biomarker in the early stage diagnosis and the following of neurological diseases. However, it has been stated that the analysis of asymmetry in the brain by numerical methods is problematic. In this study, a new approach is proposed to analyze the brain symmetry deterioration numerically. In order to perform asymmetry analysis in MR images, two hemispheres must be separated from each other by finding the midsagittal plane which are known symmetry axis. The PSNR and SSIM coefficients are often used for quality measurements between two images. In the study, these coefficients were tested for asymmetry measurement. Statistical analysis was performed by determining PSNR-SSIM coefficients between 70 Control and 70 Alzheimer Disease MR images from the OASIS database. It was determined that the use of PSNR and SSIM coefficients in the asymmetry analysis of MR images gave meaningful results.Öğe A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis(ELSEVIER IRELAND LTD, 2007) Ozsen, Seral; Kara, Sadik; Latifoglu, Fatma; Gunes, SalihBecause of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed. (C) 2007 Elsevier Ireland Ltd. All rights reserved.Öğe A Novel Feature Extraction Approach with VBM 3D ROI Masks on MRI(SPRINGER-VERLAG SINGAPORE PTE LTD, 2017) Ozic, Muhammet Usame; Ozsen, Seral; Ekmekci, Ahmet HakanAlzheimer's disease is a neurological disorder that usually starts with aging. Alzheimer's disease is a serious health and economic burden on governments, along with an increase in elderly population in developed and developing countries. There is no known cause of this disease and there is no treatment. For this reason, early diagnosis of the disease, socioeconomic and psychological outputs and medical treatments are still a hot topic investigated in the world. Magnetic Resonance Imaging is one of the medical imaging techniques that show the progression of Alzhiemer in brain. Brain deterioration and volume loss of the disease first begins with memory regions and then spreads to other brain regions. If atrophy is observed and detected by manual methods, it may not be seen due to user dependency, operator error and inexperience. For these reasons, automatic, numerical and atlas-based methods are being developed for the observation and capture of neurological diseases. In this study, 99 Alzheimer patients and 99 normal control MR images were analyzed using Voxel Based Morphometry, one of the numerical methods of atrophy observations in Magnetic Resonance Imaging. Losses in the brain were then produced as three-dimensional binary masks. Using these masks, normalized segmented, modulated normalized segmented, and normalized images that were stripped from the non-brain structures were masked. Histogram based first order statistical features were extracted in the masked areas. The efficany of this technique was statistically compared between Alzheimer's and normal control. MR images have been downloaded freely from the OASIS database.Öğe A novel system for automatic detection of K-complexes in sleep EEG(SPRINGER LONDON LTD, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemSleep staging process is applied to diagnose sleep-related disorders by sleep experts through analyzing sleep signals such as electroencephalogram (EEG), electrooculogram and electromyogram of subjects and determining the stages in 30-s-length time parts of sleep named as epochs. Subjects enter several stages during the sleep, and N-REM2 is one of them which has also the highest duration among the other stages. Approximately half of the sleep consists of N-REM2. One of the important parameters in determining N-REM2 stage is K-complex (Kc). In this study, some time and frequency analysis methods were used to determine the locations of Kcs, automatically. These are singular value decomposition (SVD), variational mode decomposition and discrete wavelet transform. The performance of them in detecting Kcs was compared. Furthermore, systems with combinations of these methods were presented with logic AND operations. The EEG recordings of seven subjects were obtained from the Sleep Research Laboratory of Necmettin Erbakan University. A database with total 359 Kcs in 320 epochs was prepared from the recordings. According to the results, the highest average recognition rate was found as 92.29% for the SVD method. Thanks to this study, the sleep experts can find out whether there were Kcs in related epochs and also know their locations in these epochs, automatically. Also, it will help automatic sleep stage classification systems.Öğe On the evolution of ellipsoidal recognition regions in Artificial Immune Systems(ELSEVIER SCIENCE BV, 2015) Ozsen, Seral; Yucelbas, CuneytUsing different shapes of recognition regions in Artificial Immune Systems (AIS) are not a new issue. Especially, ellipsoidal shapes seem to be more intriguing as they have also been used very effectively in other shape space-based classification methods. Some studies have done in AIS through generating ellipsoidal detectors but they are restricted in their detector generating scheme - Genetic Algorithms (GA). In this study, an AIS was developed with ellipsoidal recognition regions by inspiring from the clonal selection principle and an effective search procedure for ellipsoidal regions was applied. Performance evaluation tests were conducted as well as application results on some real-world classification problems taken from UCI machine learning repository were obtained. Comparison with GA was also done in some of these problems. Very effective and comparatively good classification ratios were recorded. (C) 2015 Elsevier B.V. All rights reserved.Öğe Performance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-response(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2013) Ozsen, Seral; Gunes, SalihIn this study, we have developed a nonlinear recognition system in the artificial immune systems (AIS) field named 'AaA-response (artificial neural network (ANN)-aided AIS-response)', which is different from previous AIS methods in that it uses a different modeling strategy in the formation of the memory response. Because it also uses ANNs in the determination of the correct output, it can be seen as a hybrid system that involves AIS and ANN approaches. Unlike the other AIS methods, AaA-response uses multiple system units (or antibodies) to form an output for a presented input. This property gives the proposed system the ability of producing the desired output values, other than just being a classification algorithm. That is, AaA-response can also be used as a regression method, like ANNs, by producing any output value for the given inputs. The parameter analyses of the system were conducted on an artificially generated dataset, the Chainlink dataset, and the important points in the parameter selection were emphasized. To investigate the performance of the system for real-world problems, the Iris dataset and Statlog Heart Disease dataset, taken from the University of California Irvine machine learning repository, were used. The system, which obtained 99.33% classification accuracy on the Iris dataset, has shown an important performance superiority with regard to the classification accuracy to other methods in the literature by reaching 90.37% classification accuracy for the Statlog Heart Disease dataset.
