Unsupervised Fingerprint Classification with Directional Flow Filtering

1st International Informatics and Software Engineering Conference (2019 : Ankara, Turkey)In this study, an unsupervised neural network model is proposed for fingerprint classification. The proposed model uses directional flow or local ridge orientation (LRO) information and the relative locations of the fingerprint singular points as features for the neural network. The LRO is obtained by storing the directional data in a neighborhood from the raw fingerprint and this data is filtered to prevent the flow errors due to noisy sections of the scanned fingerprint. From the resulting LRO data, the singular points are extracted and the information is used as the input to the SimNet unsupervised neural network model and the fingerprint is classified accordingly. For identification, the raw image is passed through a band-pass filter and the minutiae are extracted from the processed and thinned fingerprint image. The fingerprint identification is performed within the obtained class by comparing the minutiae of the input fingerprint to the other existing prints in that class. The number of classes can be adjusted by the number of the singular points and the distance between these points. The results show that the model can generate a hierarchical classification model that can reduce the search space for identification which could be used in small to mid size fingerprint database applications. © 2019 IEEE

Hardgrove Grindability Index Estimation Using Neural Networks

In a previous study, different techniques for the estimation of coal HGI values were investigated (Özbayoğlu et.al, 2008). As continuation of that research, in this study a revised neural network methodology is used for estimating the HGI values using the same data from 163 sub-bituminous coals from Turkey. The parameter set used for estimating HGI consisted of moisture, ash, volatile matter and Rmax ratios. These 4 coal parameters were fed into different neural network topologies. The network parameters were optimized by genetic algorithms. The test results indicate that estimation rate was improved %10-15 over the previous results (Özbayoğlu et.al, 2008) by using this new parameter set and optimized neural network configurations

Comparison of Gross Calorific Value Estimation of Turkish Coals using Regression and Neural Networks Techniques

26th International Mineral Processing Congress, IMPC (2012 : New Delhi; India)Gross calorific value (GCV) of coals was estimated using artificial neural networks, linear and non- linear regression techniques. Proximate and ultimate analysis results were collected for 187 different coal samples. Different input data sets were compared, such as both proximate and ultimate analysis data, and only proximate analysis data and only ultimate analysis data. It was observed that the best results were obtained when both proximate analysis and ultimate analysis results were used for estimating the gross calorific value. When the performance of artificial neural networks and regression analysis techniques were compared, it was observed that both artificial neural networks and regression techniques were promisingly accurate in estimating gross calorific values. In general, most of the models estimated the gross calorific value within ±3% of the expected value

Dinamik Zaman Bükme ve Ayrık Dalgacık Dönüşümü ile Uydu Görüntülerinde Bant Eşleştirme

26th IEEE Signal Processing and Communications Applications Conference (2018 : İzmir; Türkiye)Due to the imperfect physical arrangement of camera sensors, spectral bands of ground observation satellite images are usually shifted relative to each other. In order to address this issue, we propose a computationally simple band registration method which is based on Dynamic Time Warping (DTW) and Discrete Wavelet Transform (DWT) algorithms. This method has been tested on 10 frames of GOKTURK-2 images and compared to a Scale-Invariant Feature Transform (SIFT) based method. In terms of quality, the proposed method have yielded very close results compared to SIFT. © 2018 IEEE.Yer gözlem uyduları tarafından çekilen görüntülerin spektral bantları arasında, mükemmel olmayan kamera sensör diziliminden kaynaklanan bazı kaymalar meydana gelebilmektedir. Bu çalışmada, bantlar arasındaki bu tip kaymaları düzeltmek için Dinamik Zaman Bükme (DZB) ve Ayrık Dalgacık Dönüşümü (ADD) algoritmalarına dayalı ve hesaplamsal olarak basit bir bant eşleştime yöntemi önerilmektedir. Bu yöntem 10 kare GÖKTÜRK-2 görüntüsü üzerinde test edilmiş ve Scale-Invariant Feature Transform (SIFT) tabanlı bir yöntemle karşılaştırılmıştır. Önerilen yöntemden elde edilen sonuçların SIFT’e oldukça yakın kalitede oldugu gözlemlenmiştir.Aselsan,et al.,Huawei,IEEE Signal Processing Society,IEEE Turkey Section,Neta

Computational intelligence models for PIV based particle (cuttings) direction and velocity estimation in multi-phase flows

In multi-phase flow, the gas phase, the liquid phase and the particles (cuttings) within the liquid have different flow behaviors. Particle velocity and particle direction are two of the important aspects for determining the drilling particle behavior in multi-phase flows. There exists a lack of information about particle behavior inside a drilling annular wellbore. This paper presents an approach for particle velocity and direction estimation based on data obtained through Particle Image Velocimetry (PIV) techniques fed into computational intelligence models, in particular Artificial Neural Networks (ANNs) and Support Vector Machines (SVM). In this work, feed forward neural networks, support vector machines, support vector regression, linear regression and nonlinear regression models are used for estimating both particle velocity and particle direction. The proposed system was trained and tested using the experimental data obtained from an eccentric pipe configuration. Experiments have been conducted at the Cuttings Transport and Multi-phase Flow Laboratory of the Department of Petroleum and Natural Gas Engineering at Middle East Technical University. A high speed digital camera was used for recording the flow at the laboratory. Collected experimental data set consisted of 1080 and 1235 data points for 15° inclined wellbores, 1087 and 1552 data points for 30° inclined wellbores and 885 and 1119 data points for horizontal (0°), wellbores respectively to use in estimation and classification problems. Results obtained from computational intelligence models are compared with each other through some performance metrics. The results showed that the SVM model was the best estimator for direction estimation, meanwhile the SVR model was the best estimator for velocity estimation. The direction and speed of the particles were estimated with a reasonable accuracy; hence the proposed model can be used in eccentric pipes in the field. © 2018 Elsevier B.V

Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing

Options are commonly used by traders and investors for hedging their investments. They also allow the traders to execute leveraged trading opportunities. Meanwhile accurately pricing the intended option is crucial to perform such tasks. The most common technique used in options pricing is Black-Scholes (BS) formula. However, there are slight differences between the BS model output and the actual options price due to the ambiguity in defining the volatility. In this study, we developed hybrid deep learning based options pricing models to achieve better pricing compared to BS. The results indicate that the proposed models can generate more accurate prices for all option classes. Compared with BS using annualized 20 intraday returns as volatility, 94.5% improvement is achieved in option pricing in terms of mean squared error

Anomaly detection in vehicle traffic with image processing and machine learning

Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning (2018 : United States)Anomaly detection is an important part of an Intelligent Transportation System. In this study, image processing and machine learning techniques are used to detect anomalies in vehicle movements. These anomalies include standing and traveling in reverse direction. Images are captured using CCTV cameras from front and rear side of the vehicle. This capability makes the results robust to the variations in operational and environmental conditions. Multiple consecutive frames are acquired for motion detection. Features such as edges and license plate corner locations are extracted for tracking purposes. Direction of the traffic flow is obtained from the trained classifier. K-nearest neighbor is chosen as the classifier model. The proposed method is evaluated on a public highway and promising detection results are achieved. © 2018 The Authors. Published by Elsevier B.V

Estimation of Cuttings Concentration and Frictional Pressure Losses using Data Driven Models

Sondaj operasyonlarında kuyu dibi basıncının hassas bir şekilde tespiti, operasyonların emniyeti ve operasyonlarla doğrudan ilişkili birtakım mühendislik tasarımları açısından kritik bir öneme sahiptir. Kuyu dibi basıncı, “eşdeğer sirkülasyon yoğunluğu” (equivalent circulating density) değerinin doğru bir şekilde hesaplanması suretiyle tespit edilebilir. Ancak, eşdeğer sirkülasyon yoğunluğu, içiçe borulardaki halkasal yapının geometrik özelliklerine, dizinin kuyu içerisindeki pozisyonuna, dizinin dönüş hızına, akışkanın özelliklerine, dizinin burkulma nedeniyle meydana gelen geometrik değişimine bağlı davranmaktadır. O halde, eşdeğer sirkülasyon yoğunluğunun hassas ve doğru bir şekilde hesaplanabilmesi için yukarıda bahsi geçen unsurların gözönüne alındığı bir model veya yöntem gerekmektedir. Bu çalışmada, eşdeğer sirkülasyon yoğunluğunu hesaplamak için üç parametreli reolojik akışkan modeli temel alınarak; dizinin dönüş hızı, dizinin eksantrik pozisyonu ve dizinin burkulma-bükülme davranışı da dikkate alınarak bir model oluşturulmuştur. Bunun yanında, problemin karmaşıklığı da gözönünde bulundurularak, iki farklı yapay zeka modeli (yapay sinir ağları (neural networks) ve Rastgele Orman (Random Forest) oluşturulmuştur. Böylece, fiziksel ve mekanistik temele dayalı bir modelle, tamamen veriye dayalı iki modeli karşılaştırma imkanı doğmuştur. Ayrıca, literatürde bulunan ve konu ile doğrudan ilgili deneysel sonuçlar derlenmiştir, ki bu verilerin bir kısmı yapay zeka modellerinin “eğitilmesi” (training) amacıyla kullanılmıştır. Deneysel sonuçlar, modellerin performanslarının karşılaştırılması açısından da kritik bir öneme sahiptir. Yapay Sinir ağları ve Rastgele Orman modelleri, hem verilerin doğrudan kullanılması, hem de boyutsal analiz (dimensional analysis) tekniği ile elde edilen boyutsuz grupların kullanılması yöntemiyle eğitilmiştir, ve her iki yöntem de birbiriyle kıyaslanmıştır. Yapılan analizde, verilerin doğrudan kullanıldığı modeller, boyutsal analiz yöntemi kullanılarak eğitilen modellerden daha iyi performans göstermiştir. Ayrıca, deneysel sonuçlarla kıyaslandığında, yapay zeka modelleri kullanılarak elde edilen sonuçların, mekanistik model kullanılarak elde edilen sonuçlara göre daha iyi performans gösterdiği saptanmıştır. Son yıllarda veriye dayalı modellerin kullanımının yaygınlaşması, teknolojik gelişmelerin veriye dayalı yöntemlerin daha hızlı ve başarılı şekilde uygulanabilirliğini sağlaması, birçok alanda mekanistik veya analitik modellere kıyasla daha başarılı performans göstermeleri de dikkate alındığında, bu çalışmadan elde edilen sonuçlar da benzer bir yönü işaret etmiştir.Petrol Mühendisleri Odas

Comparison of Bayesian Estimation and Neural Network Model in Stock Market Trading

In this study, a decision support system for stock market prediction is proposed. This model uses the historical data of 180K data points obtained from the 215 highest volume ETFs that are open for trade in NYSE. The data is analyzed with several different criteria such as next 1,2,3,4,5 days percent increase/decrease, percent moves with respect to 50/200 day Moving Averages, changes in RSI, MACD values, direction of movement within Bollinger Bands, etc. The next day prediction is made by statistical analysis on the data using a Bayesian Maximum Likelihood decision model and the best course of action (which ETF is most likely to increase its value) is identified. The training data for the model is the historical data of these ETFs between 1999 and 2006. With the trained network, 2007 data has been tested and the results are analyzed. In order to compare the performance of the model, a multilayer perceptron neural network is developed using the same training and testing data and the results are compared. For performance evaluation, both models analyze which ETF is most likely to create the best short term (1 day – 5 days) rate of return and perform buy/sell decisions accordingly. The results indicate that both models can be used in stock/ETF selection in short term stock market trading, however neural network model provided better results

High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm

3rd IEEE International Conference on Big Data, IEEE Big Data (2015 : Santa Clara; United States)Achieving high quality clustering is one of the most well-known problems in data mining. k-means is by far the most commonly used clustering algorithm. It converges fairly quickly, but achieving a good solution is not guaranteed. The clustering quality is highly dependent on the selection of the initial centroid selections. Moreover, when the number of clusters increases, it starts to suffer from "empty clustering". The motivation in this study is two-fold. We not only aim at improving the k-means clustering quality, but at the same time not being effected by the empty cluster issue. For achieving this purpose, we developed a hybrid model, H(EC)S-2, Hybrid Evolutionary Clustering with Empty Clustering Solution. Firstly, it selects representative points to eliminate Empty Clustering problem. Then, the hybrid algorithm uses only these points during centroid selection. The proposed model combines Fireworks and Cuckoo-search based evolutionary algorithm with some centroid-calculation heuristics. The model is implemented using a Hadoop Mapreduce algorithm for achieving scalability when faced with a Big Data clustering problem. The advantages of the developed model is particularly attractive when the amount, dimensionality and number of cluster parameters tend to increase. The results indicate that considerable clustering quality performance improvement is achieved using the proposed model.CCF,et al.,Huawi,IEEE Computer Society,National Science Foundation (NSF),Springe