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Article Citation - WoS: 7Citation - Scopus: 9Ann-Assisted Forecasting of Adsorption Efficiency To Remove Heavy Metals(Tubitak Scientific & Technological Research Council Turkey, 2019) Buaısha, Magdi; Balku, Şaziye; Yaman, Şeniz ÖzalpIn wastewater treatment, scientific and practical models utilizing numerical computational techniques suchas artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems.In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transferfunction (purelin) in the output layer. Furthermore, the Levenberg–Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R2 . In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.Article Citation - WoS: 12Citation - Scopus: 12A Polarity Calculation Approach for Lexicon-Based Turkish Sentiment Analysis(Tubitak Scientific & Technological Research Council Turkey, 2019) Yurtalan, Gökhan; Koyuncu, Murat; Turhan, ÇiğdemSentiment analysis attempts to resolve the senses or emotions that a writer or speaker intends to send across tothe people about an object or event. It generally uses natural language processing and/or artificial intelligence techniquesfor processing electronic documents and mining the opinion specified in the content. In recent years, researchers haveconducted many successful sentiment analysis studies for the English language which consider many words and wordgroups that set emotion polarities arising from the English grammar structure, and then use datasets to test theirperformance. However, there are only a limited number of studies for the Turkish language, and these studies have lowerperformance results compared to those studies for English. The reasons for this can be incorrect translation of datasetsfrom English into Turkish and ignoring the special grammar structures in the latter. In this study, special Turkish wordsand linguistic constructs which affect the polarity of a sentence are determined with the aid of a Turkish linguist, and anappropriate lexicon-based polarity determination and calculation approach is introduced for this language. The proposedmethodology is tested using different datasets collected from Twitter, and the test results show that the proposed systemachieves better accuracy than the previously developed lexical-based sentiment analysis systems for Turkish. The authorsconclude that especially analysis of word groups increases the overall performance of the system significantly.Article Fitting a Recurrent Dynamical Neural Network To Neural Spiking Data: Tackling the Sigmoidal Gain Function Issues(Tubitak Scientific & Technological Research Council Turkey, 2019) Doruk, Reşat ÖzgürThis is a continuation of a recent study (Doruk RO, Zhang K. Fitting of dynamic recurrent neural networkmodels to sensory stimulus-response data. J Biol Phys 2018; 44: 449-469), where a continuous time dynamical recurrentneural network is fitted to neural spiking data. In this research, we address the issues arising from the inclusion ofsigmoidal gain function parameters to the estimation algorithm. The neural spiking data will be obtained from the samemodel as that of Doruk and Zhang, but we propose a different model for identification. This will also be a continuoustime recurrent neural network, but with generic sigmoidal gains. The simulation framework and estimation algorithmsare kept similar to that of Doruk and Zhang so that we can have a solid base to compare the results. We evaluatethe estimation performance in two different ways. First, we compare the firing rate responses of the original and theestimated model. We find that responses of both models to the same stimuli are similar. Secondly, we evaluate variationsof the standard deviations of the estimates against a number of samples and stimulus parameters. They show a similarpattern to that of Doruk and Zhang. We thus conclude that our model serves as a reasonable alternative provided thatfiring rate is the response of interest (to any stimulus).Article Selective Word Encoding for Effective Text Representation(Tubitak Scientific & Technological Research Council Turkey, 2019) Özkan, Savaş; Özkan, AkınDetermining the category of a text document from its semantic content is highly motivated in the literatureand it has been extensively studied in various applications. Also, the compact representation of the text is a fundamental step in achieving precise results for the applications and the studies are generously concentrated to improve itsperformance. In particular, the studies which exploit the aggregation of word-level representations are the mainstreamtechniques used in the problem. In this paper, we tackle text representation to achieve high performance in differenttext classification tasks. Throughout the paper, three critical contributions are presented. First, to encode the wordlevel representations for each text, we adapt a trainable orderless aggregation algorithm to obtain a more discriminativeabstract representation by transforming word vectors to the text-level representation. Second, we propose an effectiveterm-weighting scheme to compute the relative importance of words from the context based on their conjunction with theproblem in an end-to-end learning manner. Third, we present a weighted loss function to mitigate the class-imbalanceproblem between the categories. To evaluate the performance, we collect two distinct datasets as Turkish parliamentrecords (i.e. written speeches of four major political parties including 30731/7683 train and test documents) and newspaper articles (i.e. daily articles of the columnists including 16000/3200 train and test documents) whose data is availableon the web. From the results, the proposed method introduces significant performance improvements to the baselinetechniques (i.e. VLAD and Fisher Vector) and achieves 0.823% and 0.878% true prediction accuracies for the partymembership and the estimation of the category of articles respectively. The performance validates that the proposed contributions (i.e. trainable word-encoding model, trainable term-weighting scheme and weighted loss function) significantlyoutperform the baselines.Article Gold-Assembled Silica-Coated Cobalt Nanoparticles as Efficient Magnetic Separation Units and Surface-Enhanced Raman Scattering Substrate Lütfiye Sezen Yildirim1,, Murat Kaya2,∗,, Mürvet Volkan(Tubitak Scientific & Technological Research Council Turkey, 2019) Yıldırım, Lütfiye Sezen; Kaya, Murat; Volkan, MürvetMagnetic and optical bifunctional nanoparticles that combine easy separation, preconcentration, and efficientSERS capabilities have been fabricated with high sensitivity and reproducibility through a low-cost method. Thesegold nanoparticles attached on magnetic silica-coated cobalt nanospheres (Co@SiO2 /AuNPs) display the advantageof strong resonance absorption due to gaps at nanoscale between neighboring metal nanoparticles bringing large fieldenhancements, known as “hot spots”. The prepared particles can be controlled by using an external magnetic field,which makes them very promising candidates in biological applications and Raman spectroscopic analysis of dissolvedorganic species. The magnetic property of the prepared particles lowers the detection limits through preconcentrationwith solid-phase extraction in SERS analysis. The performance of the prepared nanostructures was evaluated as a SERSsubstrate using brilliant cresyl blue (BCB) and rhodamine 6G (R6G) as model compounds. The solid-phase affinityextraction of 4-mercapto benzoic acid (4-MBA) using bifunctional Co@SiO2 /AuNPs nanoparticles followed by magneticseparation and the measurement of the SERS signal on the same magnetic particles without elution were investigated.Approximately 50-fold increase in SERS intensity was achieved through solid-phase extraction of 8.3 × 10 −6 M 4-MBAin 10 min.
