Ismail Saritas

Prediction of diesel engine performance using biofuels with artificial neural network

Biodiesel, bioethanol and biogas are the most important alternative fuels produced by using biologic origin sources. Effect of biofuel on engine performance is one of the research subjects of today. The engine experiments to test the engines are many times are hard, time consuming and high cost. Additionally, it is impossible to perform the test outside of limiting values. In this study, an artificial neural network, an artificial intelligence technique, is developed to successfully apply on automotive sector as well as many different areas of technology aiming to overcome difficulties of the experiments, minimize the cost, time and workforce waste. Diesel fuel, biodiesel, B20 and bioethanol-diesel fuel having different percentages (5%, 10%, and 15%) and biodiesel were mixed together, to use in developed artificial neural network. Mixtures were also controlled for their fuel properties and motor experiments were performed to collect the reference values. Power, moment, hourly fuel consumption and specific fuel consumption were estimated by using the artificial neural network developed by using the reference values. Estimated values and experiment results are compared. As a result, from the performed statistical analyses, it is seen that realized artificial intelligence model is an appropriate model to estimate the performance of the engine used in the experiments. Reliability value is calculated as 99.94% (p=0.9994 and p>0.05) by using statistical analyses.

A fuzzy expert system design for diagnosis of prostate cancer

In this study a fuzzy expert system design for diagnosing, analyzing and learning purpose of the prostate cancer diseases was described. For this prostate was used prostate specific antigen (PSA), age and prostate volume (PV) as input parameters and prostate cancer risk (PCR) as output. This system allows determining if there is a need for the biopsy and it gives to user a range of the risk of the cancer diseases. There was observed that this system is rapid, economical, without risk than traditional diagnostic systems, has also a high reliability and can be used as learning system for medicine students.

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

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.

Design of a fuzzy expert system for determination of coronary heart disease risk

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.

Prognosis of prostate cancer by artificial neural networks

In this study, an artificial neural network has been devised that yields a prognostic result indicating whether patients have cancer or not using their free prostate-specific antigen, total prostate-specific antigen and age data. Though this system does not diagnose cancer conclusively, it helps the doctor in deciding whether a biopsy is necessary by providing information about whether the patient has prostate cancer or not. Data from 121 patients who were definitively diagnosed with cancer after biopsy were used in devising the system. The results of the definitive diagnoses of the patients and the results of the ANN that was performed were analysed using confusion matrix and ROC analyses. As a result of ANN, which was implemented on the basis of these analyses, success rates of 94.11% and 94.44% were achieved for prognosis of disease and validity, respectively. The ANN, which yielded these high rates of reliability, will help doctors make quick and reliable diagnoses without any risks and make it a better option to monitor patients with low prostate cancer risk on whom biopsies must not be carried out through a policy of wait and see.

Prediction of Breast Cancer Using Artificial Neural Networks

In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.

Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation

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.

Prediction of surface roughness using artificial neural network in lathe

In this study, the effect of tool geometry on surface roughness has been investigated in universal lathe. Machining process has been carried out on AISI 1040 steel in dry cutting condition using various insert geometry at depth of cut off 0.5 mm. At the end of the cutting operation, surface roughness has been measured using MAHR M1 perthometer. After experimental study, to predict the surface roughness, an ANN has been modelled using the data obtained. Modelling of ANN; tool nose radius (r), approach angle (K), rake angle (Y), tool overhang (L) have been used. In this study, surface roughness (Ra) is output data. The ANN has been designed on PC by using Matlab 6.5 software. Comparison of the experimental data and ANN results by means of statistically t test show that there is no significant difference and ANN has been used confidently.

Fuzzy expert system design for operating room air-condition control systems

In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, real operating rooms have been studied to see if there are more useful, reliable and comfortable ones. How a operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show systems advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, particle, humidity and oxygen are used as input parameters, and fresh air entrance and the fan circulation are chosen as output parameters. With the help of an expert, appropriate language expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. To transfer the data to the system simultaneously, an interface was written in C#. Whether it provides the most suitable control for the system prototype was determined by simulating the operation with varying number of personnel and duration of time. In these trials, input, output and the other necessary parameters were saved in the computer. Consequently, in this study we obtained very good results in prototype operating room control with FES. The results of analyses carried out indicated that the controls performed with FES provide more economical, comfortable, reliable and consistent controls and that they are feasible in real operating rooms.

A comparative study of ANN and FES for predicting of cutting forces and tool tip temperature in turning

In this study, fuzzy expert system (FES) and artificial neural network (ANN) models are designed for the estimation of cutting forces in turning operations. On designed models, cutting forces and experimental temperature data obtained from different cutting conditions were used in process of turning. Cutting forces at different cutting conditions and temperature values can be estimated with the help of developed models. The results obtained with these models, compared with the experimental data. The regression values were found as 0.99505 between the Experiment-FES and, 0.9888 between Experiment-ANN in the analysis. As a result, the both artificial intelligence (AI) methods have made successful modeling, but its seen that, realized FES model has more successful results than the ANN model in the process of estimation of cutting forces.