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Full-Text Articles in Physical Sciences and Mathematics
Using Latent Semantic Analysis For Automated Keyword Extraction From Large Document Corpora, Tuğba Önal Süzek
Using Latent Semantic Analysis For Automated Keyword Extraction From Large Document Corpora, Tuğba Önal Süzek
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we describe a keyword extraction technique that uses latent semantic analysis (LSA) to identify semantically important single topic words or keywords. We compare our method against two other automated keyword extractors, Tf-idf (term frequency-inverse document frequency) and Metamap, using human-annotated keywords as a reference. Our results suggest that the LSA-based keyword extraction method performs comparably to the other techniques. Therefore, in an incremental update setting, the LSA-based keyword extraction method can be preferably used to extract keywords from text descriptions from big data when compared to existing keyword extraction methods.
Protein Fold Classification With Grow-And-Learn Network, Özlem Polat, Zümray Dokur
Protein Fold Classification With Grow-And-Learn Network, Özlem Polat, Zümray Dokur
Turkish Journal of Electrical Engineering and Computer Sciences
Protein fold classification is an important subject in computational biology and a compelling work from the point of machine learning. To deal with such a challenging problem, in this study, we propose a solution method for the classification of protein folds using Grow-and-Learn (GAL) neural network together with one-versus-others (OvO) method. To classify the most common 27 protein folds, 125 dimensional data, constituted by the physicochemical properties of amino acids, are used. The study is conducted on a database including 694 proteins: 311 of these proteins are used for training and 383 of them for testing. Overall, the classification system …
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …
System Designs To Perform Bioinformatics Sequence Alignment, Çağlar Yilmaz, Mustafa Gök
System Designs To Perform Bioinformatics Sequence Alignment, Çağlar Yilmaz, Mustafa Gök
Turkish Journal of Electrical Engineering and Computer Sciences
The emerging field of bioinformatics uses computing as a tool to understand biology. Biological data of organisms (nucleotide and amino acid sequences) are stored in databases that contain billions of records. In order to process the vast amount of data in a reasonable time, high-performance analysis systems are developed. The main operation shared by the analysis tools is the search for matching patterns between sequences of data (sequence alignment). In this paper, we present 2 systems that can perform pairwise and multiple sequence alignment operations. Through the optimized design methods, proposed systems achieve up to 3.6 times more performance compared …
An Automated Signal Alignment Algorithm Based On Dynamic Time Warping For Capillary Electrophoresis Data, Fethullah Karabi̇ber
An Automated Signal Alignment Algorithm Based On Dynamic Time Warping For Capillary Electrophoresis Data, Fethullah Karabi̇ber
Turkish Journal of Electrical Engineering and Computer Sciences
Correcting the retention time variation and measuring the similarity of time series is one of the most popular challenges in the area of analyzing capillary electrophoresis (CE) data. In this study, an automated signal alignment method is proposed by modifying the dynamic time warping (DTW) approach to align the time-series data. Preprocessing tools and further optimizations were developed to increase the performance of the algorithm. As a demonstrative case study, the developed algorithm is applied to the analysis of CE data from a selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) evaluation of the RNA secondary structure. The time-shift problem …