Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only An anomaly intrusion detection approach using Cellular Neural Networks(SPRINGER-VERLAG BERLIN, 2006) Yang, Zhongxue; Karahoca, Adem; Levi, A; Savas, E; Yenigun, H; Balcisory, S; Saygin, Y; Nanjing Xiaozhuang University; Bahcesehir UniversityThis paper presents an anomaly detection approach for the network intrusion detection based on Cellular Neural Networks (CNN) model. CNN has features with multi-dimensional array of neurons and local interconnections among cells. Recurrent Perceptron Learning Algorithm (RPLA) is used to learn the templates and bias in CNN classifier. Experiments with KDD Cup 1999 network traffic connections which have been preprocessed with methods of features selection and normalization have shown that CNN model is effective for intrusion detection. In contrast to back propagation neural network, CNN model exhibits an excellent performance owing to the higher attack detection rate with lower false positive rate.Publication Metadata only Network intrusion detection by using Cellular Neural Network with Tabu Search(IEEE COMPUTER SOC, 2008) Yang, Zhongxue; Karahoca, Adem; Yang, Ning; Aydin, Nizamettin; Stoica, A; Arslan, T; Howard, D; Higuchi, T; ElRayis, A; Nanjing Xiaozhuang University; Bahcesehir UniversityThis paper presents a novel Cellular Neural Network (CNN) templates learning approach based on Tabu Search (TS) for detecting network intrusions. The TS method was applied to CNN with symmetric templates and was verified by simulations. Simulation experiments on intrusion detection have shown that the TS-based template learning algorithm exhibits superior performance in computation time to find the optimal solution and in the solution quality in contrast to the algorithm of genetic algorithm (GA) and simulated annealing (SA).Publication Metadata only Fraud Detection Using an Adaptive Neuro-Fuzzy Inference System in Mobile Telecommunication Networks(OLD CITY PUBLISHING INC, 2009) Sanver, Mert; Karahoca, Adem; Stanford University; Bahcesehir UniversityGSM (Global Services of Mobile Communications) 1800 licenses were granted in the beginning of the 2000's in Turkey. Especially in the installation phase of the wireless telecom services, fraud usage can be an important source of revenue loss. Fraud can be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is the name of the activities to identify unauthorized usage and prevent losses for the mobile network operators'. Mobile phone user's intentions may be predicted by the call detail records (CDRs) by using data mining (DM) techniques. This study compares various data mining techniques to obtain the best practical solution for the telecom fraud detection and offers the Adaptive Neuro Fuzzy Inference (ANFIS) method as a means to efficient fraud detection. In the test run, shown that ANFIS has provided sensitivity of 97% and specificity of 99%, where it classified 98.33% of the instances correctly.Publication Metadata only Prediction of cyclosporine A blood levels(SPRINGER HEIDELBERG, 2008) Goeren, Sezer; Karahoca, Adem; Onat, Filiz Y.; Goeren, M. Zafer; Marmara University; Bahcesehir UniversityObjective Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. Methods We proposed the use of the Takagi and Sugeno-type adaptive-network-based fuzzy inference system (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. Results Fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB environment. Conclusions ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting.Publication Metadata only Churn Management of E-Banking Customers(ATLANTIS PRESS, 2009) Bilgen, Oytun; Karahoca, Adem; Karahoca, Dilek; Huang, C; Wiener, JB; Ni, J; Bahcesehir University; Bahcesehir UniversityAs the world goes through a paper-less environment, electronic banking solutions are more welcome by most of corporations. It is important that companies control their cash management electronically at all times. Banks, providing such solutions to their clients will need to perform analytics on their customers, to serve better and satisfy the customers. In this paper focuses on E-Banking usage data to identify the customer behavior.Publication Metadata only GSM churn management by using geno-anfis(WORLD SCIENTIFIC PUBL CO PTE LTD, 2008) Karahoca, Adem; Karahoca, Dilek; Ruan, D; Montero, J; Lu, J; Martinez, L; DHondt, P; Kerre, EE; Bahcesehir UniversityChum management and prediction systems' main goal is to determine churners who wish to switch another GSM (Global Services of Mobile Communications) operator for getting more optimum benefits and services. Chum management systems determine patterns to promote the subscribers and prevent them to be lost to another operator. In this study, ANFIS (adaptive neuro fuzzy inference system) and genetic approach based systems are used to determine churners. First classification step starts with parallel Neuro fuzzy classifiers. After then, FIS takes neuro fuzzy classifiers' outputs as input to make a decision about churner activity. Optimization process can be provided by using genetic algorithms to make fine and tuning in fuzzy process.Publication Metadata only Designing an Early Warning System for Stock Market Crashes Based On Adaptive Neuro Fuzzy Inference System Forecasting(ATLANTIS PRESS, 2009) Acar, Murat; Karahoca, Adem; Huang, C; Wiener, JB; Ni, J; Bahcesehir UniversityIn this paper, we focus on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically in this study. Also, Istanbul Stock Exchange (ISE) National 100 Index data will be used to achieve a better results from the point of modeling purpose. A stock market crash risk indicator is computed to predict crashes and to give an early warning signal. Besides, adaptive neuro fuzzy inference system (ANFIS) will be used as a training tool for EWS. The empirical results show that the proposed adaptive neuro fuzzy model is successful thanks to the ANFIS that includes both artificial neural network learning ability and the fuzzy logic inference mechanism.Publication Metadata only GSM churn management using an adaptive neuro-fuzzy inference system(IEEE COMPUTER SOC, 2007) Karahoca, Adem; Karahoca, Dilek; Aydm, Nizamettin; Bahcesehir UniversityThe movement of subscribers from one operator to another operator is named as churn management for looking for better and cheaper products and services. As markets become saturated and competition intensifies, customers have more choices to take promotions from alternative telecom operators in Turkish GSM (Global Services of Mobile Communications) sector. This study compares various data mining techniques to obtain best practical solution for churning customer detection. Test results offer the Adaptive Neuro Fuzzy Inference System (ANFIS) as a means to efficient churn management methodology. The test bed results show that ANFIS provides 85% of sensitivity with 88% of specificity where it classified 80% of the instances correctly.Publication Metadata only Data mining to cluster human performance by using online self regulating clustering method(WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC, 2008) Karahoca, Adem; Karahoca, Dilek; Kaya, Osman; Demiralp, M; Mikhael, WB; Caballero, AA; Abatzoglou, N; Tabrizi, MN; Leandre, R; Bahcesehir UniversityHuman capital is critical and vital factor for banking sector. High technology usage and procedural job descriptions have been forced the staff to develop themselves. In this study, staff performance measurements and evaluations gathered by Human Resource department. For cluster evaluation, x-mean algorithm is used to find optimum clusters. Findings show that the usefulness of an innovative technique when applied to research so far conducted through traditional methodologies, and brings to the surface questions about the universal applicability of the widely accepted relationship between superior HRM and superior business performance.Publication Metadata only Data mining usage in emboli detection(IEEE COMPUTER SOC, 2007) Karahoca, Adem; Kucur, Turkalp; Aydin, Nizamettin; Stoica, A; Arslan, T; Howard, D; Kim, TH; ElRayis, A; Bahcesehir UniversityAsymptomatic circulating cerebral emboli, which are particles bigger than blood cells, can be detected by transcranial Doppler ultrasound. In certain conditions asymptomatic embolic signals (ES) appear to be markers of increased stroke risk. ES, reflected by an embolus, have usually larger amplitude than the signals from normal blood flow and show a transient characteristic. A number of methods to detect cerebral emboli have been studied in the literature. In this study, data mining techniques have been used in order to increase sensitivity and specificity of an embolic signal detection system. The classification results of different methods have been compared by using a data set including 100 ES, 100 speckle and 100 artifact. The ROC analysis results show that adaptive neuro fuzzy inference (ANFIS) system method appears to give better results.
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