Investigation of the effect of enzymatic and alkali treatments on the physico-chemical properties of Sambucus ebulus L. plant fiber.

The investigation aims to determine the effect of enzymatic and alkali treatments on Sambucus ebulus L. stem fiber. For this purpose, Sambucus ebulus L. stem fibers were treated with alkali, cellulase, and pectinase enzymes. An image processing technique was developed and implemented to calculate the average thicknesses of Sambucus ebulus L. fibers. The thickness of alkali, cellulase and pectinase enzyme treated fibers was determined as 478.62 µm, 808.28 µm and 478.20 µm, respectively. Scanning electron microscopy analysis illustrated that enzymatic and alkali treatments lead to the breakage of fiber structure. Furthermore, enzymatic and alkali treatments induce variations in elemental ingredients. All treatments increased the crystallinity index of Sambucus ebulus L. fiber from 72 % (raw fiber) to 83 % (alkali treated), 75.2 % (cellulase enzyme treated) and 86.3 % (pectinase enzyme treated) due to the hydrolysis of hemicellulose. Fourier transform infrared analysis indicated that there are no significant differences in functional groups. Thermogravimetric analysis shows that enzymatic and alkali treatments improve final degradation temperature of the fiber. Mechanical behaviors of cellulase enzyme-treated fiber decrease compared to raw fiber, while pectinase enzyme and alkali treatment cause to improve mechanical properties. Tensile strength of samples was determined as 76.4 MPa (cellulase enzyme treated fiber), 210 MPa (pectinase enzyme treated fiber) and 240 MPa (alkali treated fiber). Young's modules of cellulase enzyme, pectinase enzyme and alkali treated fibers were predicted as 5.5 GPa, 13.1 GPa and 16.6 GPa. Elongation at break of samples was calculated as 5.5 % (cellulase enzyme treated fiber), 6.5 % (pectinase enzyme treated fiber) and 6 % (alkali treated fiber). The results suggest that enzymatic and alkali treatments can modify the functional and structural attributes of Sambucus ebulus L. fiber.

An effective approach for breast cancer diagnosis based on routine blood analysis features.

Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstract.

A cepstrum analysis-based classification method for hand movement surface EMG signals.

It is of great importance to effectively process and interpret surface electromyogram (sEMG) signals to actuate a robotic and prosthetic exoskeleton hand needed by hand amputees. In this paper, we have proposed a cepstrum analysis-based method for classification of basic hand movement sEMG signals. Cepstral analysis technique primarily used for analyzing acoustic and seismological signals is effectively exploited to extract features of time-domain sEMG signals by computing mel-frequency cepstral coefficients (MFCCs). The extracted feature vector consisting of MFCCs is then forwarded to feed a generalized regression neural network (GRNN) so as to classify basic hand movements. The proposed method has been tested on sEMG for Basic Hand movements Data Set and achieved an average accuracy rate of 99.34% for the five individual subjects and an overall mean accuracy rate of 99.23% for the collective (mixed) dataset. The experimental results demonstrate that the proposed method surpasses most of the previous studies in point of classification accuracy. Discrimination ability of the cepstral features exploited in this study is quantified using Kruskal-Wallis statistical test. Evidenced by the experimental results, this study explores and establishes applicability and efficacy of cepstrum-based features in classifying sEMG signals of hand movements. Owing to the non-iterative training nature of the artificial neural network type adopted in the study, the proposed method does not demand much time to build up the model in the training phase. Graphical abstract.

An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques.

The topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals' sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback-Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback-Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing.