Hakan Işik

A compression-based text steganography method

Highlights? In the present study, LZW data compression based text steganography method has been proposed. ? Capacity, one of the main problems of steganography, has been increased to 7.042%. ? By employing stego keys and Combinatorics-based coding security of the proposed method has been supported. ? One advantage of the proposed method is not being language specific and protecting the originality of cover text. ? Cover texts have been generated naturally, so they do not raise suspicion and the method is resilient against the possible attacks. In this study, capacity and security issues of text steganography have been considered to improve by proposing a novel approach. For this purpose, a text steganography method that employs data compression has been proposed. Because of using textual data in steganography, the employed data compression algorithm has to be lossless. Accordingly, LZW data compression algorithm has been chosen due to its frequent use in the literature and significant compression ratio. The proposed method constructs - uses stego keys and employs Combinatorics-based coding in order to increase security. Secret information has been hidden in the chosen text from the previously constructed text base that consists of naturally generated texts. Email has been chosen as communication channel between the two parties, so the stego cover has been arranged as a forward mail platform. By means of the proposed scheme, capacity has been reached to 7.042% for the secret message containing 300 characters (or 300?8 bits). Finally, comparison of the proposed scheme with the other contemporary methods in the literature has been carried out. Experimental results show that the proposed scheme provided a significant increment in terms of capacity.

Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from EEG Signals

In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals’ automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.

In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.

A Takagi-Sugeno type neuro-fuzzy network for determining child anemia

Research highlights? Decision-making is a difficult and quite responsible task for doctors. Some of the computer decision models assisted the doctor with some computer decision models. In this study, Takagi-Sugeno type neuro-fuzzy network has been designed to determine anemia level of a child. First the input values have been fuzzified. The triangular membership function has been used for fuzzification. Algebraic product has been used as Tnorm in rule layer. The performance analyses have been obtained by leaving- one-out cross-validation. After statistical measurements, it was found that MPE=_0.0018, MAE=0.2090, MAPE=0.0511, RMSE=0.2743 and R2=0.9957 of this developed system. According to these results, the designed neuro-fuzzy network may be considered as adequate close to traditional decision-making methods and thus the designed network can be used effectively for child anemia prediction. Decision-making is a difficult and quite responsible task for doctors. Some of the computer decision models assisted the doctor with some computer decision models. In this study, neuro-fuzzy network has been designed to determine anemia level of a child. The performance analyses have been obtained by leaving-one-out cross-validation. After statistical measurements, it was found that MPE=-0.0018, MAE=0.2090, MAPE=0.0511, RMSE=0.2743 and R2=0.9957 of this developed system. According to these results, the designed neuro-fuzzy network may be considered as adequate close to traditional decision-making methods and thus the designed network can be used effectively for child anemia prediction.

Design and Construction of Thermoelectric Footwear Heating System for Illness Feet

In this study, a Thermoelectric Footwear Heating System is developed to use in cold weather conditions. The temperature is controlled by an analog electronic control system. Thermoelectric module is used to heat the bottom of the foot. A negative temperature coefficient (NTC) temperature sensor is used to sense the temperature and the temperature is controlled by an electronic circuit proportionally. A 3.5 V, 5000 mAh rechargeable battery is used as the power source. The temperature range of the system is between +15∘C and +50∘C. Developed footwear heating system is tested against various temperature conditions, and offer better results in the case of heating the illness feet.

The Design of Thermoelectric Footwear Heating System Via Fuzzy Logic

In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc.

An Artificial Neural Network Classification Approach for use the Ultrasound in Physiotherapy

In this study, a classification to be used in physiotherapy was realized by means of Artificial Neural Network (ANN). The aim of the classification was to determine the treatment length and appropriate ultrasound value for the age of physiotherapy patients, the area on which ultrasound will be applied, the fat rate in tissue and related factors. For this purpose, the patient information obtained from Selçuk University, Meram School of Medicine Hospital, Physiotherapy Department was used. In order to identify the appropriate ultrasound value and treatment length for the patient, the ultrasound therapy device realized with ANN was placed together in an embedded system. The results obtained by means of the designed and realized embedded system were compared with data gathered from an expert. As a result, the data obtained from the designed system were found out to be in line with the existing data.

Design of Fuzzy Logic Controlled Thermoelectric Renal Hypothermia System

Abstract In this study, a fuzzy logic controlled Thermoelectric Renal Hypothermia System has been implemented for use in renal surgery. Herein, temperature control of the Thermoelectric Renal Hypothermia System has been carried out via a Fuzzy Logic Controller. Since the most important issues in long‐lasting parenchymatous renal surgery are to provide an operation medium that is free of blood and to prevent renal dysfunction in the postoperative period, control of the temperature has become very important in renal surgery. The final result is seriously affected by changes in temperature; therefore, it is necessary to reach some desired temperature points quickly and to avoid a large overshoot. The PIC16F877 microcontroller has been used as the fuzzy logic controller. The designed system is energy saving, decreases temperature swings to a minimum level; so, it offers efficient results. Also, the designed system takes into consideration appropriate parameters that can reflect very small changes in the kidney and can be applied to people safely in the future.

Design and Construction of a Microcontroller-Based Ventilator Synchronized with Pulse Oximeter

This study aims to introduce a novel device with which mechanical ventilator and pulse oximeter work in synchronization. Serial communication technique was used to enable communication between the pulse oximeter and the ventilator. The SpO2 value and the pulse rate read on the pulse oximeter were transmitted to the mechanical ventilator through transmitter (Tx) and receiver (Rx) lines. The fuzzy-logic-based software developed for the mechanical ventilator interprets these values and calculates the percentage of oxygen (FiO2) and Positive End-Expiratory Pressure (PEEP) to be delivered to the patient. The fuzzy-logic-based software was developed to check the changing medical states of patients and to produce new results (FiO2 ve PEEP) according to each new state. FiO2 and PEEP values delivered from the ventilator to the patient can be calculated in this way without requiring any arterial blood gas analysis. Our experiments and the feedbacks from physicians show that this device makes it possible to obtain more successful results when compared to the current practices.

A New Microcontroller Supervised Thermoelectric Renal Hypothermia System

In the present study, a thermoelectric system controlled by a microcontroller is developed to induce renal hypothermia. Temperature value was managed by 8-byte microcontroller, PIC16F877, and was programmed using microcontroller MPASM package. In order to ensure hypothermia in the kidney 1–4 modules and sensors perceiving temperature of the area can be selected. Temperature values are arranged proportionately for the selected area and the determined temperature values can be monitored from an Liquid Crystal Display (LCD) screen. The temperature range of the system is between −50 and +50∘C. Renal hypothermia system was tried under in vivo conditions on the kidney of a dog.