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About a discussion development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms, by Murat Albayrak and Novruz Allahverdi, 2011. Expert System with Applications, 38; 3, pp. 1313-1320
(2015) Allahverdi, Novruz
In the Short Communication published in Expert Systems with Application in volume 41 2014, (Comments on "Albayrak, M., & Allahverdi N. (2011). Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms. Expert Systems with Applications, 38(3), 1313-1320" [1]: A Proposal of Good Practice; E. Osaba, E. Onieva, F. Diaz, R. Carballedo, Volume: 41, Issue: 4, Pages: 1530-1531, Part: 1, March 2014) [4] the Osoba E. et al have discussed our method to solve the Traveling Salesman Problem pointing that we use our developed new algorithm to compare different versions of a classical genetic algorithm, each of one with a different mutation operator and they write that this can generate some controversy. Here we shortly analyze the comment of Osaba E. et al. to show that our comparing method has a chance of existence.
Algebraic Approach to Transformations on Hypercube System
(1996) Kahramanlı, Ş. Ş.; Allahverdi, Novruz
In this study some algebraic transformations on hypercube system are proposed. Tke presented transformations of forbidden subcubes provide defining set of all maximal and nonfaulty subcubes, and subset of minimal and nonfaulty subcubes included certain given vertices. It provides formal and simple findings of all the current sources and targets of information, and also the pathways between them. The correctness of all these transformations is proved by the aid of three theorems. In the proposed transformations there were three special operations of cube algebra applied, as subtraction, product and intersection on coordinates.
Application of fuzzy control approach in greenhouse automation
(2010) Ödük, Mehmet Nuri; Allahverdi, Novruz
The aim of the study is to design a Fuzzy Expert System (FES) which manupulates with more parameters, than the traditional systems. Temperature, air humidity, light intensity, soil moisture, wind speed and the amount of carbon dioxide were taken as the input parameters of the Fuzzy Control System whereas heating, cooling, shading, irrigation, lighting and ventilation values were taken as output parameters. By using the MNO-V1-2010 software developed with Delphi 7.0 in greenhouse automations which provides saving energy and time and more production, the simulation of the greenhouse automation with fuzzy control was carried out. The values of the input variables can either be entered by the user or can be received in real time through a data collection card. It can be seen that the performance indicators that were obtained from the simulation results were successful. One of the advantages of the design is that the system minimizes human related errors and the design makes the system perform the decision making process in a full automatic manner. It is considered that the approach can also be implemented in various areas of the industry. Copyright © 2010 ACM.
The Application of Fuzzy Expert Cooling System for Multi Core Microprocessors and Mainboards
(2009) Zühtüoğulları, Kürşat; Sarıtaş, İsmail; Allahverdi, Novruz
The aim of this study is to construct an effective fuzzy logic based control system for multi core (core2duo) microprocessors and mainboards. Fuzzy Expert System (FES) software was developed in C#.net programming language for controlling the cooling mechanisms of the microprocessor and the mainboard cooling systems. Fuzzy Expert Systems were constructed for controlling the power delivered to CPU and mainboard cooling fans. The CPU cooling FES consists of three inputs named as CPU temperature, CPU Frequency and CPU Core voltage. The output of this FES calculates the optimum speed of microprocessor fan and sends the speed value to the electronic fan driver circuit via serial port. The mainboard cooling FES consists of two inputs named as CPU temperature and the Mainboard temperature. This FES calculates the optimum fan speed and controls the power delivered to the chassis fans via electronic fan driver circuit connected to the serial port of the computer. Safe, effective and silent cooling systems were realised by using the FES. In addition to all of these, CPU can be used more efficiently and the effective performance (speed) of the computer can be raised.
Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Tasdemir, Sakir; Saritas, Ismail; Ciniviz, Murat; Allahverdi, Novruz
This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work. (C) 2011 Elsevier Ltd. All rights reserved.
Bilgisayar destekli inşaat maliyet analizleri
(Selçuk Üniversitesi Teknoloji Fakültesi, 2004) Altın, Mustafa; Allahverdi, Novruz
Bu çalışmada; inşaatı meydana getiren malzemelerin miktarlarını ve günün şartlarına göre maliyetlerini hesap eden bir program tasarlanmıştır. Devlet ihale kanunlarına uygun bir yazılım olması hedeflenmiştir. Yapılan örnek uygulamalarda yazılımın istenen hedeflere ulaştığı gözlenmiş ve açılan yeni bir ihaleye katılacak olan firmaların işlerini doğru ve hızlı yaptıkları saptanmıştır. İlgili kurumlar tarafından yayınlanmış olan yaklaşık 60.000 adet birim fiyatın analizlerinin çıkarılması, bu analizlere göre malzeme miktarlarının hesaplanması ve bu malzeme miktarlarının fiyatlandırılarak toplam maliyetin bulunması hedeflenmiştir.Geliştirilen program ile bir inşaatın tüm maliyetinin %100 doğrulukta hesaplandığı, bu hesaplamalar yapılırken güncel değerlerin kullanıldığı gözlenmiştir.
Bulanık mantıkla sıcaklık ve nemin kontrolu ve sistemin gerçekleştirilmesi
(Selçuk Üniversitesi Teknik Bilimler Meslek Yüksekokulu, 2010) Özkan, Ali Osman; Allahverdi, Novruz
Bu çalışmada, bulanık mantık yöntemiyle sıcaklık ve nem parametreleri kontrol edilmiş ve sistem gerçekleştirilmiştir. Çalışmada bulanık mantığın, bulanık koşullu çıkarım mekanizması kullanılmış ve bu yöntemin sıcaklık ve nem gibi parametreleri daha esnek olarak nasıl kontrol edebildiği gösterilmiştir. Sistem donanım olarak bilgisayar, analog-dijital dönüştürücü kartı, sıcaklık ve nem sensörleri ve sıcaklık ve nem ölçme devrelerinden oluşmaktadır. Sıcaklık sensörü olarak LM335 ve nem sensörü olarak ta kapasitif bir sensör kullanılmıştır. Bu işlemi gerçekleştirecek olan sistemin algoritması geliştirilmiş ve kontrol programı QBASIC programlama dilinde yazılmıştır.
Comparision of numerical technique and artificiali intelligence techniques for performance modelling of a diesel engine
(2010) Tütüncü, Kemal; Allahverdi, Novruz
Comparision of numerical tehnique and AI techniques for determination of performance and emission characteristics of a diesel engine has been done in this study . Three different techniques namely multiple regression analysis, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) were used for modeling aims. Engine torque (Tq), power (P), specific fuel consumption (Sfc), emission values such as HC, CO2 and NOx have been investigated. R2 values of Tq, P, Sfc, HC, CO2 and NOx were obtained as 99.9, 99.45, 99.32, 99.84, 99.71 and 99.26 respectively when ANN was used. Main contribution of this study includes; 1) First study that makes comperision between a numerical technique and AI tehniques. 2) Dynamic load value was used as input parameter. So that both engine performance modeling and emission characteristic determination were done regarding to changing load. 3) Highest prediction for values of output parameters were reached. Copyright © 2010 ACM.
Design of a Fuzzy Expert System for determination of coronary heart disease risk
(2007) Allahverdi, Novruz; Torun, Serhat; Sarıtaş, İsmail
The aim of this study is to design a Fuzzy Expert System to determine coronary heart disease (CHD) risk of patient for the next ten-years. The designed system gives the user the ratio of the risk and may recommend using one of three results; (1) normal live; (2) diet; (3) drug treatment. The data (risk ratio) obtained from designed system are compared with the data in the literature [4] and better results are observed in the designed system. The system can be viewed as an alternative for existing methods to determine CHD risk. © 2007 ACM.
Design of a hybrid system for the diabetes and heart diseases
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Kahramanli, Humar; Allahverdi, Novruz
Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites. (C) 2007 Published by Elsevier Ltd.
Design of an embedded fuzzy PD controller for thermal comfort applications
(2010) Soy, H.; Yılmaz, Ersen; Allahverdi, Novruz
In this paper, a reasoning-based intelligent system makes use of fuzzy control approach, which is designed for thermal comfort applications by using an embedded microcontroller system. In general, it is not possible to implement the mathematical thermal comfort models in actual environment. Thermal comfort usually depends on four environmental parameters and two personal parameters. Normally, only air temperature and humidity could be controlled in conventional heating ventilation and air conditioning (HVAC) systems. Managing and controlling the temperature and humidity parameters are very important for creating healthy living and comfortable working places. As known, fuzzy logic allows complex control system design directly from human experience. Therefore, preferring fuzzy model-based control systems to traditional ones may provide valuable advantage in applications requiring different control strategies for personal choices like thermal comfort control in buildings. So, in this paper, a comfortable fuzzy control system is investigated and performance of this suggested system is tested by Proteus virtual system modelling software. As a result, success of system is founded acceptable from view of the thermal comfort. © 2010 Inderscience Enterprises Ltd.
Determination of Classification Rules for Heart Diseases
(IEEE, 2008) Kahramanli, Humar; Allahverdi, Novruz
Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In this study for the purpose of extracting rules from ANN which has been trained for classification has been used OptaiNET that is an Artificial Immune Algorithm (AIS) and a set of rules has been formed for heart diseases. Me proposed method is named as OPTBP.
Determination of classification rules for heart diseases [Kalp hastaliklari veri tabani için siniflandirma kurallarinin bulunmasi]
(2008) Kahramanlı, Humar; Allahverdi, Novruz
Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In this study for the purpose of extracting rules from ANN which has been trained for classification has been used OptaiNET that is an Artificial Immune Algorithm (AIS) and a set of rules has been formed for heart diseases. The proposed method is named as OPTBP. ©2008 IEEE.
Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation
(SPRINGER, 2009) Saritas, Ismail; Ozkan, Ilker A.; Allahverdi, Novruz; Argindogan, Mustafa
In this study, chronic intestine illness symptoms such as sedimentation and prostate specific antigen are used for the design of fuzzy expert system to determine the drug (salazopyrine) dose. Suitable drug dose for patients is obtained by using data of ten patients. The results of some patients are compared with the doses recommended to them by the physician. As a result, it has been seen that proposed system is helped to shorten the treatment duration and minimize or remove the negative effects of determination of drug dose for helping physicians.
Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms
(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Albayrak, Murat; Allahverdi, Novruz
In this study, a new mutation operator has been developed to increase Genetic Algorithm (GA) performance to find the shortest distance in the known Traveling Salesman Problem (TSP). We called this method as Greedy Sub Tour Mutation (GSTM). There exist two different greedy search methods and a component that provides a distortion in this new operator. The developed GSTM operator was tested with simple GA mutation operators in 14 different TSP examples selected from TSPLIB. The application of this GSTM operator gives much more effective results regarding to the best and average error values. The GSTM operator used with simple GAs decreases the best error values according to the other mutation operators with the ratio of between 74.24% and 88.32% and average error values between 59.42% and 79.51%. (C) 2010 Elsevier Ltd. All rights reserved.
An efficient iris recognition system based on Modular Neural Networks
(WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC, 2008) Kocer, H. Erdinc; Allahverdi, Novruz
In this paper, we propose a neural network based iris recognition approach by analyzing iris patterns. The iris recognition system consists of iris localization, feature extraction and classification of the iris images. Hough transforms were used for localizing the iris region; Cartesian to polar coordinate transform was used for transforming the ring shaped iris image to the rectangular shape. Then, histogram equalization was applied to the iris image for making the shapes in image more distinctive. Average absolute deviation (AAD) algorithm was used for feature extraction in this approach. In matching process, Multi-Layered Perceptron (MLP) and Modular Neural Networks (MNN) are applied to the iris feature vector for classifying the iris images. In fact, this research is focused on measuring the performance of MNN in iris recognition system compared with Multi-Layered Perceptron (MLP) neural network. The gray-level iris images, experimented in this work, were obtained from Institute of Automation Chinese Academy of Science (CASIA) iris images database and Departments of Informatics University of Beira Interior (UBIRIS) iris images database. Experimental results are given in the last stage of this paper.
EVOLVING RULES FROM NEURAL NETWORKS TRAINED ON BINARY AND CONTINUOUS DATA
(NOVA SCIENCE PUBLISHERS, INC, 2010) Kahramanli, Humar; Allahverdi, Novruz
Although an Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from an ANN need to be formed to solve this problem and various methods have been improved to extract these rules. In this study, a new method that uses an Artificial Immune Systems (AIS) algorithm has been presented to extract rules from a trained ANN. The suggested algorithm does not depend on the ANN training algorithms; also, it does not modify the training results. This algorithm takes all input attributes into consideration and extracts rules from a trained neural network efficiently. This study demonstrates the use of AIS algorithms for extracting rules from trained neural networks. The approach consists of three phases: 1. data coding 2. classification of the coded data 3. rule extraction Continuous and noncontinuous values are used together in medical data. Regarding this, two methods are used for data coding and two methods (binary optimisation and real optimisation) are implemented for rule extraction. First, all data are coded binary and the optimal vectors are decoded and used to obtain rules. Then nominal data are coded binary and real data are normalized. After optimization, various intervals for continuous data are obtained and classification accuracy is increased.
Extension of Non-faulty Subcubes Set In a Faulty Hypercube Multiprocessor
(1998) Allahverdi, Novruz; Erciyes, Kayhan
In this study we considered and analyzed the different cases of node- and link-faults in a hypercube multiprocessor. We revealed that, the direct use of sharp product operation is not sufficient to discard only computational part (processor and memory), when only this part of node is faulty. We also showed that in case when some links in communication part (router) incident to a healthy node are faulty, the sharp product operation does not allow to leave the healthy links and the node incident to these links in the set of healthy subcubes. In order to remove this defficiency, we propose formal procedures with aid of which we can subtract first only the faulty node, excluding the healthy links incident to this node and second only the faulty links, excluding also the healthy nodes incident to these links. The procedures are independent from the number and scattering of faulty elements in hypercube. Thus, the proposed procedures allow to obtain a set of extended fault-free subcubes, that is a beginning set for further manipulation in hypercube multiprocessors and increase the reliability of such systems.
Extracting rules for classification problems: AIS based approach
(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Kahramanli, Humar; Allahverdi, Novruz
Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results in most cases may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In our previous work, a hybrid neural network was presented for classification (Kahramanli & Allahverdi, 2008). In this study a method that uses Artificial Immune Systems (AIS) algorithm has been presented to extract rules from trained hybrid neural network. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Cleveland heart disease and Hepatitis data. The proposed method achieved accuracy values 96.4% and 96.8% for Cleveland heart disease dataset and Hepatitis dataset respectively. It is been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites. (C) 2009 Published by Elsevier Ltd.
A Fault Tolerant Routing Algorithm Based on Cube Algebra for Hypercube Systems
(Elsevier Science Bv, 2000) Allahverdi, Novruz; Kahramanlı, Şirzad Ş.; Erciyes, Kayhan
We propose an approach to determine the shortest path between the source and the destination nodes in a faulty or a non-faulty hypercube. The number of faulty nodes and links may be rather large and if any path between the nodes exists, the developed algorithm determines it. To construct this algorithm, some properties of the cube algebra are considered and some transformations based on this algebra are developed.

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