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This study aims to obtain a sustainable substitute for man-made fibers using Sambucus ebulus L. as a reinforcement in the polymer composites. With this purpose, an extensive characterization of Sambucus ebulus L. stem fiber was performed. The chemical composition was obtained approximately as 76 wt.% cellulose, 66 wt.% hemicellulose, and 4.47 wt.% lignin. Scanning electron microscope analysis proved that the fibers are nonuniform and have rough surface along the length. The elemental composition was found as 58.67% carbon and 40.34% oxygen with energy-dispersive X-ray analysis. Furthermore, the fiber diameter was estimated as 759.41 µm with image processing from scanning electron microscope graphs of fibers. X-ray diffraction analysis verified the existence of crystalline cellulose having 72% crystalline index with 3.9 nm crystalline size. Functional groups of the fiber were determined with Fourier transform infrared analysis. A single fiber tensile test found out that the fibers have the mean tensile strength, elongation at break, and Young’s modulus of 90.6 MPa, 6.45%, and 1.46 GPa, respectively. Moreover, the obtained fibers are thermally durable to 456.98 °C with thermogravimetric analysis. The results indicate that Sambucus ebulus L. stem fibers have the comparable properties to other natural cellulosic fibers. It is suggested to use Sambucus ebulus L. stem fiber in composites as reinforcement to substitute for man-made fibers.

Eyupoglu, Can/0000-0002-6133-8617 WOS: 000569971700001 In this study, the use of a polyester fabric produced from microfibers as a siding material for construction industry was investigated. In this context, an ultraviolet (UV) absorber was applied to the polyester fabric samples with and without dyeing process. These samples were treated with UV absorber at 1-4% concentration and then an accelerated aging test was carried out. The UV absorbance capacity of the samples was investigated before and after the accelerated aging test. Furthermore, the effects of dyeing process on UV absorbance capacity of samples were analyzed. Afterwards, a multilayer perceptron artificial neural network (MLP-ANN) model was proposed and utilized to predict the UV protection properties which are UV protection factor, UV-A and UV-B in polyester microfiber fabric. The MLP-ANN based results demonstrate that the regression (R) values are almost 1 for all UV protection properties. Accordingly, it was seen that the proposed MLP-ANN is correctly modeled and the prediction of UV protection properties is successfully performed.

In the colouring processes of textile products, more environmentally friendlychemicals and finishing methods should be used instead of conventional onesthat harm the environment every day, so that alternative realistic ways to pro-tect nature, both academically and industrially, could be possible. Due to someinconveniences caused by synthetic dyes that are widely used today, in thisstudy, ultrasonic dyeing of wool fabric withHibiscus sabdariffawas carried outafter environmental-friendly air vacuum plasma application which increasedthe absorption of the dyes into the textile material. According to the perfor-mance results, colour strengths of the wool fabrics were increased significantly.Surface morphology analysis was carried out and etching effects of air vacuumplasma treatment were clearly seen on the micrographs of the treated woolfabrics. An environmental-friendly green process was achieved through thisstudy and it was concluded that vacuum air plasma treatment could be analternative green-process as a pretreatment to increase the dye up-take of natu-ral dyeing treatment. Moreover, in this study, a feed-forward neural network(FFNN) model was presented and used for predicting the dyeing properties(L,a,bandK/S) of samples. The experimental results showed that the pre-sented model achieves the regression values greater than 0.9 for all dyeingproperties. Consequently, it was considered that the proposed FFNN was suc-cessfully modelled and could be efficiently utilised for dyeing characteristics ofwool fabrics dyed with natural dye extracted fromHibiscus sabdariffa

Studies on computer vision have gained momentum especially with the development of convolutional neural networks (CNN). In addition, the creation of better data sets in terms of quality and quantity, providing access to these data sets, developments in computational power and parallel programming are other issues that affect the achievement of high levels of accuracy. In this study, the concept of computer vision, the factors that make computer vision difficult, the studies that have been done in computer vision until today are summarized. The algorithms developed for this purpose are examined and the studies done in the last five years are analyzed and presented comparatively. With this study, it is aimed to provide a general perspective for future studies.

Cyber attacks are carried out by unauthorized access to a system connected to an internet network anywhere in the world or by taking control of this system. These attacks continue to increase exponentially with the introduction of artificial intelligence technology, which has been used effectively in the field of science and engineering. With the use of artificial intelligence in the cyber field, conventional cyber attacks have begun to emerge as smart cyber attacks. Software development with traditional algorithms is insufficient to defend against attacks. In this study, studies in the literature on artificial intelligence methods used in the field of cyber security were examined. On the subjects discussed from different perspectives, solutions in terms of defense and attack, the applications used, the contexts between different disciplines, analyzes and examples on the basis of countries will be presented.

Epilepsy is a brain disorder that can be defined as a short-time and temporary occurrence of symptoms because of abnormal extreme or synchronous neuronal activity of the brain. Almost one percent of the world's population is struggling with epilepsy illness. The detection of epileptic seizures is mainly realized with reading the electroencephalogram (EEG) recordings by medical doctors due to the unpredictable and complex nature of the disease. This process takes much time and depends on the expert's experience. For this reason, automatic seizure detection using EEG recordings is necessary and of great importance for the comfort of medical doctors and patients. While detecting epileptic seizure automatically, machine learning techniques are used in the field of computer science. This chapter deals with the methods, approaches, models, and techniques which are utilized to detect epileptic seizures.

The healthcare sector has evolved from Healthcare 1.0 to 4.0 with revolutionary developments. While Healthcare 1.0 is a doctor-centric approach, in Healthcare 4.0, the latest technologies such as Internet of things (IoT), big data, fog computing and cloud computing are used to share data between several stakeholders. Although Healthcare 4.0 has brought many advantages, it has also caused some security and privacy problems. In this study, the security and privacy problems in Healthcare 4.0 are mentioned and the methods developed to solve these problems in the literature are examined.

Artificial intelligence is a branch of research that allows machines to solve intuitive problems by imitating the way the human brain works. Meaningful information can be extracted from image and video analysis using computer vision methods. Computer vision studies in areas such as digital image processing, object recognition, object tracking have been strengthened with artificial intelligence-based components developed in recent years. Thus, faster and more precise results can be obtained. The aim of this study is to reveal the artificial intelligence methods used in the field of computer vision, the principles and constraints of the methods in the light of the related literature.

Big data has attracted significant and increasing attention recently and has become a hot topic in the areas of IT industry, finance, business, academia, and scientific research. In the digital world, the amount of generated data has increased. According to the research of International Data Corporation (IDC), 33 zettabytes of data were created in 2018, and it is estimated that the amount of data will scale up more than five times from 2018 to 2025. In addition, the advertising sector, healthcare industry, biomedical companies, private firms, and governmental agencies have to make many investments in the collection, aggregation, and sharing of enormous amounts of data. To process this large-scale data, specific data processing techniques are used rather than conventional methodologies. This chapter deals with the concepts, architectures, technologies, and techniques that process big data.

In this study, to produce single-use bee-repellent fabrics, a variety of essential oils were encapsulated with gum arabic wall material at a 1:5 ratio of wall to the core substance. The following core substances were used: lavender oil, laurel oil, fennel oil, N, N-diethyl-3-methylbenzamide (DEET), lavender + laurel oil, lavender + fennel oil, laurel + fennel oil, lavender + fennel + laurel oil, lavender oil + DEET, fennel oil + DEET and laurel oil + DEET. Lavender, fennel and laurel oils were analyzed by high-performance liquid chromatography. In this context, 11 different microcapsules were produced. After the microencapsulation process, the microcapsules were analyzed with a light microscope and by Fourier transform infrared spectroscopy. Furthermore, an image processing application was developed and implemented to determine the particle size distribution of the microcapsules. After the analysis of the microcapsules, cotton fabric samples were treated with the microcapsules. In order to analyze the microcapsules on the fabric samples, scanning electron microscopy (SEM) was used. To analyze the bee-repellent abilities of the fabric samples, 12 different measurement cabinets made of pine tree and glass were produced. According to the results, lavender and fennel oils can be used as bee-repellent alternatives to DEET in beekeeping. © The Author(s) 2018. Ministry of Science, Technology and Space: 0897 -- The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science, Industry, and Technology in Turkey under Grant Number 0897.STZ.2015. --

This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature. © 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

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.

The topic of big data has attracted outstanding and increasing attention in recent years. Cloud computing and Internet of Things (IoT) are two hot topics and have become parts of our daily lives. Besides, these technologies allow to collect huge amounts of data nowadays. In this study, the relationship between big data, cloud computing and IoT is investigated. The main aim of this study is to provide general information to the researchers who will work in this field.

2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2019 -- 28 January 2019 through 30 January 2019 -- -- 145781 Due to their efficient operation in small volumes, Thermoelectric Coolers (TECs) have a wide usage in cooling applications, and are preferable wherever usage of compressor-based cooling systems is hard. In this study, a control unit for thermoelectric cooling modules has been designed and implemented based on a microcontroller. The designed system allows users to specify the threshold values for thermoelectric coolers to operate on. The microcontroller drives the TEC using a pulse width modulation (PWM) technique via a MOSFET as a switching component. Adopting a PWM-based control technique makes it possible to manage the speed and energy consumption of the TEC system. © 2019 IEEE

In this paper, we investigate Network Coding for Video (NCV) which we apply for video streaming over wireless networks. NCV provides a basis for network coding. We use NCV algorithm to increase throughput and video quality. When designing NCV algorithm, we take the deadline as well as the decodability of the video packet at the receiver. In network coding, different flows of video packets are packed into a single packet at intermediate nodes and forwarded to other nodes over wireless networks. There are many problems that occur during transmission on the wireless channel. Network coding plays an important role in dealing with these problems. We observe the benefits of network coding for throughput increase thanks to applying broadcast operations on wireless networks. The aim of this study is to implement NCV algorithm using C programming language which takes the output of the H.264 video codec generating the video packets. In our experiments, we investigated improvements in terms of video quality and throughput at different scenarios.

2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2018 -- 29 January 2018 through 1 February 2018 -- -- 135266 Use of electricity in industry opened a new age which is dominated by industrial automation. In the beginning, automation systems were based on electro-mechanical systems consisting of relays and contactors. Developments in semiconductor technology inevitably transformed the existing structure and cause electronic-controlled Programmable Logic Controller (PLC) to prevail in the automation systems. PLCs consist of a microcontroller/microprocessor, relay or transistor for switching, isolating equipment such as opto-couplers and voltage converter/inverters. Industrial PLCs are quite expensive devices. In this study, design and implementation of a low-cost PLC using PIC18F4580 microcontroller are realized. Special software including a graphical user-interface is developed for programming the PLC and transferring code data to the device. Both ladder diagram and word instructions can be used for programming the PLC. © 2018 IEEE.

World Conference on Technology, Innovation and Entrepreneurship -- MAY 28-30, 2015 -- Istanbul, TURKEY WOS: 000380509900236 Multiplication is one of the most important operations in computer arithmetic. Multiplication is used in many operations such as squaring, division and computing reciprocal. Moreover, multiplication is a very significant arithmetic operation for lots of signal processing applications which are convolution, correlation, frequency analysis, image processing etc. The efficiency of multiplication operation is crucial for the process time of these applications. Nikhilam algorithm is one of the algorithms developed for increasing the efficiency and reducing the cost in order to simplify multiplication. In this work, the performance of Nikhilam multiplication algorithm is analyzed in terms of the number of multiplication and the total process time for different bit lengths. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

World Conference on Technology, Innovation and Entrepreneurship -- MAY 28-30, 2015 -- Istanbul, TURKEY WOS: 000380509900237 The topic of energy efficiency in backbone networks has recently gained an increasing interest and has become one of the important parts of networking research. The energy consumption of current electronic devices is increasing too fast with the continuous growth of the Internet traffic. In addition, Information and Communications Technologies (ICT) consume the worldwide energy ranging from 2% to 10% and the consumption of ICT will almost double by 2020. According to many studies, the Internet will use 50% of the world electricity in the near future. In this study, energy efficiency of backbone networks is examined. Besides, the techniques recently used for energy saving in backbone networks are analyzed. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

World Conference on Technology, Innovation and Entrepreneurship -- MAY 28-30, 2015 -- Istanbul, TURKEY WOS: 000380509900263 In this study, cotton fabric was treated with oxygen, nitrogen and argon plasma application. The plasma application was carried out for 10 minutes at low frequency. Firstly, contact angle of plasma treated samples was measured at the end of plasma treatment. The results showed that the plasma treatment causes to increase the hydrophility of samples. Moreover, it was seen that the type of gas affects the hydrophilic properties of samples. After then the samples were left during 1 month with different conditions which were under normal condition and covered with aluminum foil. Finally, the contact angle of plasma treated samples was measured after 1 month in order to determine the durability effect of plasma treatment on properties of cotton fabric. After the contact angle measurements, the results were evaluated with statistical analyses in terms of the type of gas and storage conditions of plasma treated samples. The results demonstrated that the storage conditions and type of gas affect the hydrophilic properties of plasma treated cotton fabric after 1 month. (C) 2015 The Authors. Published by Elsevier Ltd.

2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference, ElConRus 2017 -- 1 February 2017 through 3 February 2017 -- -- 127473 In daily life, human beings are exposed to disturbing environmental effects. Sound pollution is one of the most disturbing environmental effects for human. In recent years, researchers have evidenced that sound pollution affects human psychology and health negatively. In order to avoid negative effects of sound pollution, a variety of materials providing acoustic insulation are produced commercially. Furthermore, acoustic insulation properties of new materials have been investigated. Use of fibrous materials in acoustic insulation applications has been researched since fibrous materials are economic, light and they have high physical properties. In addition, porous structure of fibrous materials is an advantage to use these materials in acoustic insulation applications. © 2017 IEEE.

2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference, ElConRus 2017 -- 1 February 2017 through 3 February 2017 -- -- 127473 Use of automation technologies has received significant attention in recent years and has become one of the crucial parts of industry. The inspection of outputs in a production process is of great importance for companies to produce faultless products. This is vital for companies to sustain credibility and reliability as well. In this study, it is aimed to detect faulty packaging of bottles. Mineral water is bottled in factories and packaged to produce six-bottle and twenty-bottle packs transported on a conveyor belt. Faulty packaged products are rarely observed in this production process. However, it is not acceptable for these faulty products to leave the production line. In this work, an image processing application is implemented to detect and label faulty bottle packages for impeccable production. Sobel filter is utilized for edge detection and Hough transform is used for calculation of number of bottles in packages. The ones labelled as faulty packages are eliminated from conveyor belt by means of a boxer motor. © 2017 IEEE.

2nd International Conference on Computer Science and Engineering, UBMK 2017 -- 5 October 2017 through 8 October 2017 -- -- 132116 Since breast cancer is a common disease in society all over the world, early diagnosis is of vital importance in order to treat patients before it reaches an irreversible phase. Expert systems are being developed to make it easier to diagnose the disease. In this study, an ensemble of neural networks named radial basis function network (RBFN), generalized regression neural network (GRNN) and feed forward neural network (FFNN) is implemented to separate breast cancer data samples into benign/malignant classes. The utilities of these common methods and the proposed hybrid model which is a combination of these methods are explored and their performances are comparatively presented. The experimental results on Wisconsin Diagnostic Breast Cancer (WDBC) dataset have proven that the proposed method presents a promise for diagnosis of breast cancer. The proposed model can be used as a tool to assist medical specialists in making their decision on the disease. © 2017 IEEE.

In this study, polyester, i.e., Poly(ethylene terephthalate) (PET) nonwoven fabric, was coated with white tuff, perlite, and volcanic stone powder at rates of 10, 20, 30 and 40 % so as to increase the thermal insulation properties of PET nonwoven fabric. In order to apply white tuff, perlite, and volcanic stone powder to PET nonwoven fabric, polyurethane-based coating material was used as a cross-linking agent. The porosity and thermal conductivity coefficients of samples were then investigated as regards the type and concentration of stone powder. Furthermore, three-layer feed-forward artificial neural network (3FFNN) was used to estimate and verify the accuracy of the thermal conductivity coefficient of PET nonwovens coated with white tuff, perlite, and volcanic stone powder. The results showed that perlite stone powder provided higher thermal insulation compared to white tuff and volcanic stone powder. Moreover, thermal insulation coefficient of samples increased with the rise in concentration of white tuff, perlite, and volcanic stone powder. Besides, the accuracy of 3FFNN was 99 %. Artificial neural network (ANN)-based results showed that the thermal conductivity coefficients of samples with four different concentrations obtained from white tuff, perlite, and volcanic stone powder were almost the same for experimental and ANN-trained models. According to the results, it was seen that 3FFNN was correctly modeled, and the prediction of the thermal conductivity coefficients was successfully realized. Copyright © 2019 by ASTM International

IEEE North West Russia Section Young Researchers in Electrical and Electronic Engineering Conference, EIConRusNW 2016 -- 2 February 2016 through 3 February 2016 -- -- 121142 The topic of color face recognition has received significant attention in recent years and has become one of the important parts of image analysis and pattern recognition research. Furthermore, it is used in various applications related to person identification, video surveillance, access control, smart card, passport, information and social security, etc. In this study, k-Nearest Neighbors (k-NN) is used in order to classify color face images. Firstly, the classification is performed using only k-NN classifier. After that Principal Component Analysis (PCA) and k-NN classifier are used together. In addition, these two methods are implemented for different color space models and k values. Finally, the experiment results are compared with each other. © 2016 IEEE.

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. © 2018 by the authors.

World Conference on Technology, Innovation and Entrepreneurship -- MAY 28-30, 2015 -- Istanbul, TURKEY WOS: 000380509900224 In computer arithmetic, multiplication is one of the most significant operations. Multiplication is used in many operations such as division, squaring and computing reciprocal. In addition, the efficiency of multiplication is crucial due to the use of digital signal processing applications such as correlation, filtering, frequency analysis and image processing. Karatsuba algorithm is one of the algorithms developed for increasing the efficiency and reducing the cost in order to simplify multiplication. In this study, the performance of Karatsuba algorithm is analyzed in terms of the number of multiplication and the total process time for different bit lengths. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).