Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Linear time-varying systems analysis in wavelet domain

A new analysis method based on wavelet domain for linear time-varying systems is developed and introduced and it is called system analysis in wavelet domain (SAIWD). Linear time-varying systems described by a higher order differential equation or state-space representation are analyzed in wavelet domain. To solve system equations, they are transferred to wavelet domain by forming algebraic matrix-vector relations using the wavelet transform coefficients. These relations are achieved by defining operator matrices concerned with the commonly used time domain operators. Orthogonal and compact support wavelets provide a simple way to define these operator matrices. It is seen from the solved examples that the percentage error between the analytical and wavelet domain solutions is around 1% in total sampling points

Designing simulated annealing and subtractive clustering based fuzzy classifier

A new systematic way in order to obtain optimized fuzzy inference system for classification task is developed. The proposed algorithm, Simulated Annealing (SA) and Subtractive Clustering (SC) based Fuzzy Classifier (SASCFC) is a cooperation of the SA and the SC methods. The SA is used in order to optimize the SC parameters, feature subspace and output threshold value of fuzzy based classifier. A hybrid feature selection strategy which combines filter and wrapper type approaches is also proposed. In order to demonstrate the effects of these optimizations, we proposed four different SASCFC classifiers which are called as the SASCFC-Type1, Type2, Type3 and Type4. The performance and rule base complexity of proposed classifiers are compared with each other and also some classifier tools on some well known classification tasks. The results show that the proposed classifiers have a satisfactory performance in comparison with its counterparts. (C) 2010 Elsevier B. V. All rights reserved

Effects of parasitic elements in operational transconductance amplifier AM modulator

On the basis of spectral domain analysis a general method is described for the calculation of the noise effects of the parasitic elements in operational transconductance amplifier amplitude modulator. Parasitic elements are determined as a function of the controlling current of the operational transconductance amplifier. Output voltage waveform which is the AM modulated signal is derived in terms of the circuit parameters, and signal and noise powers are calculated. (C) 1997 The Franklin Institute

A New Fuzzy Classifier Based on Simulated Annealing and Subtractive Clustering

In this study, subtractive clustering method is used in order to obtain membership functions and rule base of fuzzy classifier system. Neighborhood radii of subtractive clustering method which directly effects the system dynamic and number of rules in the fuzzy classifier, is obtained by new simulated annealing optimization algorithm. The proposed classifier is firstly applied on pap-smear lest which is used in diagnosis of cervical cancer. The classifier is also tested with some well known biomedical and other classification problems in the literature. The results obtained by the offered hybrid classifier have better performance than the other methods

ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds

The modified embedded zero-tree wavelet (MEZW) compression algorithm for the one-dimensional signal was originally derived for image compression based on Shapiro's EZW algorithm. It is revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate. The EZW and MEZW algorithms apply the chosen threshold values or the expressions in order to specify that the significant transformed coefficients are greatly significant. Thus, two different threshold definitions, namely percentage and dyadic thresholds, are used, and they are applied for different wavelet types in biorthogonal and orthogonal classes. In detail, the MEZW and EZW algorithms results are quantitatively compared in terms of the compression ratio (CR) and percentage root mean square difference (PRD). Experiments are carried out on the selected records from the MIT-BIH arrhythmia database and an original ECG signal. It is observed that the MEZW algorithm shows a clear advantage in the CR achieved for a given PRD over the traditional EZW, and it gives better results for the biorthogonal wavelets than the orthogonal wavelets. (c) 2005 Elsevier Ltd. All rights reserved

Multi-iteration wavelet zero-tree coding for image compression

Here, we present a modification of Shapiro's embedded zerotree wavelet algorithm (EZW) for image codec. Shapiro's technique is based on the wavelet transform and on the self-similarity inherent in images. In the EZW, the wavelet transform (WT) coefficients, which provide a multiresolution representation of the image, are arranged according to their significance across scales using a small symbol set (zerotree (ZT) coding). An analysis of the symbol entropy shows that better compression rates can be obtained when two or more iterations of the original algorithm are combined. Consequently, we proposed a modification of Shapiro's original algorithm which we called multi-iteration EZW designed ro optimise the combination of ZT and Huffman coding. We studied the behaviour of the multi-iteration algorithm in terms of image quality and bit-rate for natural and medical images. Our findings show that for a given image quality the multi-iteration algorithms and particularly the two-iteration EZW produce lower bit-rates than Shapiro's. In addition, we suggest that the idea of multi-iteration can be generalised to other techniques based on ZT coding. (C) 2000 Elsevier Science B.V. All rights reserved

Formulation of low intensity direct current effects on wound healing of skin using genetic programming

This study has two main purposes: Firstly.. the effect of electrical stimulation was studied on experimental wound healing of skin. It investigates whether a triggering was possible on collagen synthesis in wound healing before the 3rd day Of wounding, via measurements of hydroxyproline, a measure of collagen concentration in the wound tissue. Wound tissue hydroxyproline contents were determined with the effects of two different electric current intensities of. 200 mu A and 400 mu A at 48 and 72 hours after wounding by Woessner's Method. For both of the current intensities, statistically significant increases were found in hydroxyproline contents of current applied wounds with respect to their controls for 72 hours whereas decreased hydroxyproline content was found for 400 mu A of current intensities at 48 hours. Secondly, it was aimed to model and formulate how electric current effects wound healing using Genetic Programming (GP) based on experimental results. The accuracy of the proposed GP formulation is defined quantiatively by standard deviation (SD=0.39) and correlation coefficient (R=0.85), which is found to be high

Formulation of ELF magnetic fields' effects on malondialdehyde level and myeloperoxidase activity in kidney using genetic programming

In vivo exposure effects of electromagnetic fields (EMFs) on various tissues of experiment animals have been investigated. In this sense, modeling and formulation of these biological effects have been of significant importance. In this study extremely low frequency (ELF) EMFs effects on malondialdehyde (MDA) level and myeloperoxidase (MPO) activity in kidney of guinea pigs exposed to 50 Hz magnetic fields of 1 mT, 2 mT and 3 mT have been presented. It has been planned to determine whether genetic programming (GP) is appropriate to analyze and formulate these biological effects. Consequently, it has been observed that GP can be effectively used to model MDA level and MPO activity. The performances of prediction of the proposed GP formulation versus actual experimental values are found to be quite satisfactory in terms of standard deviation and correlation coefficient. It is concluded that the GP application serves to form a database for the researchers in this field, without exposing tissues to EMF and without using too many guinea pigs. (c) 2006 Elsevier Ireland Ltd. All rights reserved