Semih Ergin

A new feature extraction framework based on wavelets for breast cancer diagnosis

This paper investigates a pattern recognition framework in order to determine and classify breast cancer cases. Initially, a two-class separation study classifying normal and abnormal (cancerous) breast tissues is achieved. The Histogram of Oriented Gradients (HOG), Dense Scale Invariant Feature Transform (DSIFT), and Local Configuration Pattern (LCP) methods are used to extract the rotation- and scale-invariant features for all tissue patches. A classification is made utilizing Support Vector Machine (SVM), k-Nearest Neighborhood (k-NN), Decision Tree, and Fisher Linear Discriminant Analysis (FLDA) via 10-fold cross validation. Then, a three-class study (normal, benign, and malignant cancerous cases) is carried out using similar procedures in a two-class case; however, the attained classification accuracies are not sufficiently satisfied. Therefore, a new feature extraction framework is proposed. The feature vectors are again extracted with this new framework, and more satisfactory results are obtained. Our new framework achieved a remarkable increase in recognition performance for the three-class study.

On feature extraction for spam e-mail detection

Electronic mail is an important communication method for most computer users. Spam e-mails however consume bandwidth resource, fill-up server storage and are also a waste of time to tackle.The general way to label an e-mail as spam or non-spam is to set up a finite set of discriminative features and use a classifier for the detection. In most cases, the selection of such features is empirically verified. In this paper, two different methods are proposed to select the most discriminative features among a set of reasonably arbitrary features for spam e-mail detection. The selection methods are developed using the Common Vector Approach (CVA) which is actually a subspace-based pattern classifier.Experimental results indicate that the proposed feature selection methods give considerable reduction on the number of features without affecting recognition rates.

A novel framework for SMS spam filtering

A novel framework for SMS spam filtering is introduced in this paper to prevent mobile phone users from unsolicited SMS messages. The framework makes use of two distinct feature selection approaches based on information gain and chi-square metrics to find out discriminative features representing SMS messages. The discriminative feature subsets are then employed in two different Bayesian-based classifiers, so that SMS messages are categorized as either spam or legitimate. Moreover, the paper introduces a real-time mobile application for Android™ based mobile phones utilizing the proposed spam filtering scheme, as well. Hence, SMS spam messages are silently filtered out without disturbing phone users. Effectiveness of the filtering framework is evaluated on a large SMS message collection including legitimate and spam messages. Following the evaluation, remarkably accurate classification results are obtained for both spam and legitimate messages.

A new classification scheme of plastic wastes based upon recycling labels

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fishers Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

ECG based biometric authentication using ensemble of features

In this work, the efficacy of various features on electrocardiogram (ECG) based biometric authentication process is thoroughly examined. In particular, the features acquired from temporal analysis, wavelet transformation, power spectral density estimation and QRS-complex detection over ECG signals are considered. These features are employed with two distinct classification algorithms, namely decision tree and Bayes network, specifically for gender, age and identity recognition problems. The biometric authentication framework is evaluated on a benchmark dataset that contains ECG records of 18 healthy people including 5 men, aged 26 to 45, and 13 women, aged 20 to 50. The results of the experimental analysis reveal that if all those features are used in combination rather than individually, better performance is attained for all classifiers in each recognition problem.

Detection of SMS spam messages on mobile phones

In this study, a novel “SMS spam message filter” utilizing effective feature selection and pattern classification techniques is proposed. The proposed filter detects and filters out SMS spam messages in a smart manner rather than black/white list approaches that require intervention of phone users. In the study, Gini index based approach is preferred as the feature selection method. The feature vectors consisting of the selected discriminative features are then fed into two well-known pattern classifiers, namely Naive Bayes and k-Nearest Neighbor, for recognition process. Furthermore, a mobile application, which exploits the proposed detection scheme, is developed particularly for the mobile phones with Android™ operating system. Thus, SMS spam messages are automatically filtered out without disturbing the phone user. The proposed detection scheme is evaluated on a large SMS message dataset consisting of spam and legitimate messages. The results of the experimental work reveal that the proposed system is considerably successful in filtering SMS spam messages.

ECG classification using ensemble of features

In the literature, countless efforts have been made to analyze and classify electrocardiogram (ECG) signals belonging to various heart problems. In all these efforts, many feature extraction strategies have been used to expose discriminative information from ECG signals. In this paper, the contributions of widely used features to the classification performance and the required processing times to extract those features are comparatively analyzed. The utilized features can be briefly listed as time domain (TD), wavelet transform (WT), and power spectral density (PSD) based features. These feature sets are employed individually and in combination within well-known pattern classifiers, namely decision tree and artificial neural network, to assess classification performance in each case. Later, a wrapper-based feature selection strategy is used to reveal the most discriminative feature subset among the entire feature set containing all the three previously mentioned feature sets. The proposed framework is assessed considering four classes of heart conditions including normal, congestive heart failure, ventricular tachyarrhythmia and atrial fibrillation. The results of the experiments conducted on a large dataset reveal that appropriate subset of TD, WT, and PSD features rather than individual features offer higher classification performance. On the other hand, if the processing time is of concern, TD features come out on top with moderate classification performance.

A survey on ECG analysis

Abstract The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the heart. In the literature, the ECG signal has been analyzed and utilized for various purposes, such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion recognition and biometric identification. ECG analysis (depending on the type of the analysis) can contain several steps, such as preprocessing, feature extraction, feature selection, feature transformation and classification. Performing each step is crucial for the sake of the related analysis. In addition, the employed success measures and appropriate constitution of the ECG signal database play important roles in the analysis as well. In this work, the literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above. Each step in ECG analysis is briefly described, and the related studies are provided.

DWT-based fault diagnosis in induction motors by using CVA

In this paper, faults in induction motors were diagnosed by using the Common Vector Approach (CVA). CVA is a well-known subspace-based pattern recognition method that is widely used in speech recognition, speaker recognition, and image recognition problems. In order to analyze the performance of CVA, a database including the current signals of six identical induction motors were used. One of these induction motors was healthy motor whereas the remaining five induction motors were exposed to different synthetic faults. The multi-step One-Dimensional Discrete Wavelet Transform (1D-DWT) is applied on the current signals in order to construct feature vectors of each faulty class in the database. While performing CVA, the leave-20-out strategy was followed in order to test all feature vectors in the database. Highly satisfactory classification results were obtained.

The assessment of feature selection methods on agglutinative language for spam email detection: A special case for Turkish

In this study, the assessment of three different feature selection methods including Information Gain (IG), Gini Index (GI), and CHI square (CHI2) is made by utilizing two popular pattern classifiers, namely Artificial Neural Network (ANN) and Decision Tree (DT), on the classification of Turkish e-mails. The feature vectors are constructed by the bag-of-words feature extraction method. This paper is focused on the Turkish language since it is one of the widely used agglutinative languages all around the world. The results obviously reveal that CHI2 and GI feature selection methods are more efficacious than IG method for Turkish language.