Mehmet Emin Tagluk

Effects of electromagnetic radiation from 3G mobile phone on heart rate, blood pressure and ECG parameters in rats

Effects of electromagnetic energy radiated from mobile phones (MPs) on heart is one of the research interests. The current study was designed to investigate the effects of electromagnetic radiation (EMR) from third-generation (3G) MP on the heart rate (HR), blood pressure (BP) and ECG parameters and also to investigate whether exogenous melatonin can exert any protective effect on these parameters. In this study 36 rats were randomized and evenly categorized into 4 groups: group 1 (3G-EMR exposed); group 2 (3G-EMR exposed + melatonin); group 3 (control) and group 4 (control + melatonin). The rats in groups 1 and 2 were exposed to 3G-specific MP’s EMR for 20 days (40 min/day; 20 min active (speech position) and 20 min passive (listening position)). Group 2 was also administered with melatonin for 20 days (5 mg/kg daily during the experimental period). ECG signals were recorded from cannulated carotid artery both before and after the experiment, and BP and HR were calculated on 1st, 3rd and 5th min of recordings. ECG signals were processed and statistically evaluated. In our experience, the obtained results did not show significant differences in the BP, HR and ECG parameters among the groups both before and after the experiment. Melatonin, also, did not exhibit any additional effects, neither beneficial nor hazardous, on the heart hemodynamics of rats. Therefore, the strategy (noncontact) of using a 3G MP could be the reason for ineffectiveness; and use of 3G MP, in this perspective, seems to be safer compared to the ones used in close contact with the head. However, further study is needed for standardization of such an assumption.

A novel machine learning method based on generalized behavioral learning theory

Learning is an important talent for understanding the nature and accordingly controlling behavioral characteristics. Behavioral learning theories are one of the popular learning theories which are built on experimental findings. These theories are widely applied in psychotherapy, psychology, neurology as well as in advertisements and robotics. There is an abundant literature associated with understanding learning mechanism, and various models have been proposed for the realization of learning theories. Nevertheless, none of those models are able to satisfactorily simulate the concept of classical conditioning. In this study, popular behavioral learning theories were firstly simplified and the contentious issues with them were clarified by conducting intuitive experiments. The experimental results and information available in the literature were evaluated, and behavioral learning theories were jointly generalized accordingly. The proposed model, to our knowledge, is the first one that possesses not only modeling all features of classical conditioning but also including all features with behavioral theories such as Pavlov, Watson, Guthrie, Thorndike and Skinner. Also, a microcontroller card (Arduino Mega 2560) was used to validate the applicability of the proposed model in robotics. Obtained results showed that this generalized model has a high capacity for modeling human learning. Then, the proposed learning model was further improved to be utilized as a machine learning method that can continuously learn similar to human being. The result obtained from the use of this method, in terms of computational cost and accuracy, showed that the proposed method can be successfully employed in machine learning, especially for time ordered datasets.

EMG signal classification by extreme learning machine

From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.

Beneficial effects of dexpanthenol on mesenteric ischemia and reperfusion injury in experimental rat model

ABSTRACT Background and aim It has been reported that intestinal ischemia–reperfusion (I/R) injury results from oxidative stress caused by increased reactive oxygen species. Dexpanthenol (Dxp) is an alcohol analogue with epitelization, anti-inflammatory, antioxidant, and increasing peristalsis activities. In the present study, the aim was to investigate protective and therapeutic effects of Dxp against intestinal I/R injury. Materials and methods Overall, 40 rats were assigned into five groups including one control, one alone Dxp, and three I/R groups (40-min ischemia; followed by 2-h reperfusion). In two I/R groups, Dxp (500 mg/kg, i.m.) was given before or during ischemia. The histopathological findings including apoptotic changes, and also tissue and serum biochemical parameters levels, were determined. Oxidative stress and ileum damage were assessed by biochemical and histological examination. In the control (n = 8) and alone Dxp (n = 8; 500 mg/kg, i.m. of Dxp was given at least 30 min before recording), groups were incised via laparotomy, and electrical activity was recorded from their intestines. In this experiment, the effect of Dxp on the motility of the intestine was examined by analyzing electrical activity. Results In ileum, oxidant levels were found to be higher, while antioxidant levels were found to be lower in I/R groups when compared with controls. Dxp approximated high levels of oxidants than those in the control group, while it increased antioxidant values compared with I/R groups. Histopathological changes caused by intestinal I/R injury and histological improvements were observed in both groups given Dxp. In the Dxp group, electrical signal activity markedly increased compared with the control group. Conclusions Here, it was seen that Dxp had protective and therapeutic effects on intestinal I/R injury and gastrointestinal system peristaltism.

A joint generalized exemplar method for classification of massive datasets

The proposed method depends on human learning. Therefore it is a natural way of classification.The main upgrade of JGE, which is derived from NGE, is to have adaptive boundaries.The obtained classification accuracies and speeds were acceptable depending upon NGE and other popular ML methods.The proposed method can be used with huge datasets and also can be used in real time application depending upon its simplicity and speed. Due to technological improvements, the number and volume of datasets are considerably increasing and bring about the need for additional memory and computational complexity. To work with massive datasets in an efficient way; feature selection, data reduction, rule based and exemplar based methods have been introduced. This study presents a method, which may be called joint generalized exemplar (JGE), for classification of massive datasets. This method aims to enhance the computational performance of NGE by working against nesting and overlapping of hyper-rectangles with reassessing the overlapping parts with the same procedure repeatedly and joining non-overlapped hyper-rectangle sections that falling within the same class. This provides an opportunity to have adaptive decision boundaries, and also employing batch data searching instead of incremental searching. Later, the classification was done in accordance with the distance between each particular query and generalized exemplars. The accuracy and time requirements for classification of synthetic datasets and a benchmark dataset obtained by JGE, NGE and other popular machine learning methods were compared and the achieved results by JGE found acceptable.

Learning with classical conditioning

Behavioral learning theory evaluates humans learning process in terms of observable stimulus and responses. One of the behavioral learning methods is the classical conditioning. The classical conditioning theory proposed by Pavlov concerns the analyses of conditioning a response with a neutral stimulus, inspiring from the relation between natural stimulus and response. In this study the classical conditioning theory is modeled in real-time. The viability of the proposed method to basic principles of classical conditioning, based on stimulus-response relations was achieved and compared to the available computational methods.

Forecasting financial indicators by generalized behavioral learning method

Forecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.

Effects of small-world rewiring probability and noisy synaptic conductivity on slow waves: Cortical network

Physiological rhythms play a critical role in the functional development of living beings. Many biological functions are executed with an interaction of rhythms produced by internal characteristics of scores of cells. While synchronized oscillations may be associated with normal brain functions, anomalies in these oscillations may cause or relate the emergence of some neurological or neuropsychological pathologies. This study was designed to investigate the effects of topological structure and synaptic conductivity noise on the spatial synchronization and temporal rhythmicity of the waves generated by cells in the network. Because of holding the ability of clustering and randomizing with change of parameters, small-world (SW) network topology was chosen. The oscillatory activity of network was tried out by manipulating an insulated SW, cortical network model whose morphology is very close to real world. According to the obtained results, it was observed that at the optimal probabilistic rates of conductivity noise and rewiring of SW, powerful synchronized oscillatory small waves are generated in relation to the internal dynamics of cells, which are in line with the network’s input. These two parameters were observed to be quite effective on the excitation-inhibition balance of the network. Accordingly, it may be suggested that the topological dynamics of SW and noisy synaptic conductivity may be associated with the normal and abnormal development of neurobiological structure.

A preliminary investigation of receiver models in molecular communication via diffusion

Molecular Communication (MC) is a new multidisciplinary subject concerning medicine, biology, and communication engineering. MC concept is introduced for modeling of communication of nano/micro scale devices. In MC systems, chemical signals carrying information in gaseous or liquid media are used. Similar to other communication systems, in MC sending information from transmitter to receiver with minimum error is one of the most important goals. In MC systems due to physical characteristics of medium, higher rates of inter symbol interference (ISI) and noise increase error probability. Figures of receiver mechanisms and signal detection techniques are therefore the main factors to be tuned for decreasing error probability. In this view, so far, many receiver models such as reversible adsorption and desorption (A&D), protrusion method, ligand receptor, and linear catalytic or CAT receiver models have been introduced. In this study, these models and the results obtained through their implementation are investigated and briefly reviewed.

Classification of hand opening/closing and fingers by using two channel surface EMG signal

In this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.