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Huffman coding is widely used in data compression. Canonical Huffman coding is a subset of Huffman coding and has some advantages, such as shorter header and less memory usage. Therefore, studies on this coding have been continuing. The algorithm producing the lengths of Algebraic Canonical Huffman Codes (ACHC) is one of these studies and this algorithm obtains the code lengths nearest to optimum value. In this study, an algorithm obtaining the code lengths that are the basis for the production of canonical Huffman codes using Evolutionary Strategies (ESs) is proposed, and this algorithm means that the ACHC algorithm is optimized by the ESs method. ESs is an evolutionary algorithm that mostly uses the mutation. Replication creates many copies of a single ancestor. The copies are mutated to produce offspring. Among the offspring and the ancestor, the individual with the best fitness value is chosen as the ancestor of the next generation. This cycle continues until the stop criteria is met. In this study, the length array obtained by ACHC was used as the first ancestor. The optimum value was achieved as a result of the evolution of this ancestor by mutation. As a result of the tests, the required number of cycles to reach the optimum value was determined as a fixed number. In this case, the time complexity is O (n²), where n is the number of symbols used. The amount of memory used is O (n²) because of the usage of multiple individuals. [ABSTRACT FROM AUTHOR]

Recent studies show that the performance of Evolutionary Algorithms often depends on choosing appropriate parameter configurations. Thus, researchers have generally tuned these parameters either looking at the similar research areas in the literature or manually, e.g. Grid Search. However, searching the parameter manually is laborious and timeconsuming; therefore, meta-optimization techniques have become commonly used methods to adjust parameters of an algorithm. These techniques can be classified in two widespread manners: off-line, tuning parameters of an algorithm before the algorithm initiates, and on-line, tuning the parameters while it is working. In this paper, Bipolar Matching Tendency (BMT) algorithm has been chosen as the selection method of a Genetic Algorithm (GA). The new obtained algorithm is named GA-BMT and has been used for the first time as an online metaoptimizer. In addition, the paper utilizes two search algorithms (Grid Search, Coarse to Fine Search) with three meta-optimization methods (Standard GA, Particle Swarm Optimization, GA-BMT) to investigate the best parameter settings of the Standard GA for 17 test functions, and offers a comparative work by comparing their results. Furthermore, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank, were performed to demonstrate the significance of the results. Based on the all results that achieved, GA-BMT presents a reasonable achievement. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) are successfully employed to solve complex optimization problems such as network design problems, path finding problems, scheduling problems and social and economic planning. By using intelligent strategies, EAs iteratively improve the initial solution to produce new solutions. Particularly in genetic algorithms (GAs), one of the most known EAs, selection mechanism, has crucial importance due to its effect on the diversity of a population and convergence ability to optimal solution. As most of the selection methods of GAs favor the good solutions for reproductions, average or poor solutions are overlooked. Recently, we proposed a new selection method, called Bipolar Mating Tendency (BMT), that is based on Standard Tournament (ST) selection. Unlike the state-of-the-art selection methods of GAs, BMT diversifies the mating process with the inclusion of less favorable solutions. In this paper, the performance of BMT was compared with the performance of ST and prevalent selection methods that are also based on ST: restricted tournament selection, unbiased tournament selection, fine-grained tournament selection and cooperative selection. For the comparison, twenty-one well-known benchmark optimization functions and sixteen IEEE CEC 2018 test functions were utilized. Also, nonparametric statistical tests were performed to demonstrate the significance of the results. In order to test the effectiveness of GA-BMT (GA that uses BMT as a selection method), three real-world engineering problems were used. In addition to showing predominant results comparing to the other methods in test functions, BMT has also provided successful results in the problems. [ABSTRACT FROM AUTHOR]

Generalized Regression Neural Network (GRNN), is a radial basis function based supervised learning type Artificial Neural Network (ANN) which is commonly used for data predictions. In addition to its easy modelling structure, being fast and producing accurate results are the other strong features of it. On the other hand, GRNN employs a neuron in pattern layer for each data sample in training data set. Therefore, for huge data sets pattern layer size increases proportional to the number of samples in training data set, memory requirement and computational time also increase excessively. In this study, in order to reduce space and time complexity of GRNN, k-means clustering algorithm which had been used as pre-processor in the literature is utilized and outlier data emergence which affects the performances of previous studies negatively, is prevented by identifying test data located between clusters. Hence, while memory requirement in pattern layer and number of calculations are reduced, negative effect on the performance emerged by the use of clustering algorithm is significantly removed and almost the same prediction performances to that of standard GRNN are achieved by using 90% less training samples. [ABSTRACT FROM AUTHOR]

There was a colorful and multi-dimension relations between Turkey and Ukraine because of Desti Kıpchak country. During the phase of the Ukrainian nation formation, the relations between Turkey and Ukraine soils are in different dimensions due to Turkic elements and deep ties. One of the reason was Russia which helped two countries come closer to each other. Russia on the other hand were rising on Ukraine, at the same time they were holding the Ottoman country under threat through Carpathians, the Caucasus and the Black Sea whereas oppressed Ukrainian nation under the Czarist regime, they wanted to establish their own state in the historical era of nationalism. The most important factor in front of this goal was the Moscow-based Russian despot policies. Even though Ukraine nationalist movement at the beginning of XXst century gained some significant progress and popularity, they lost their rights for a while because of their scattered power and organization. Liberation Committee of Ukraine is an important step towards establishing an independent Ukrainian state in the days of leading to World War II. And to accomplish this goal, Liberation Committee of Ukraine organized in Istanbul and Vienna had a activity program. The Liberation Committee of Ukraine's goals and objectives titled as Istanbul, Ukraine, Turkey and Russia article magazine published in Istanbul in December of 1914 were could explained in this article. [ABSTRACT FROM AUTHOR]

Abstract: Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity. [Copyright &y& Elsevier]

The self-organizing map (SOM), which is widely used in economics and engineering applications, is a type of artificial neural network trained without supervision. SOM is used to represent multidimensional data in much lower dimensional spaces-usually in two dimensions (2D)-while preserving the topological properties of the input space. In this study, 2D maps were produced by using SOM to display the relationship between seasons, length, weight, and isopod infestation of goldblotch grouper ( Epinephelus costae Staindahner, 1878). This is first study of gnathiid isopod praniza larvae infesting goldblotch grouper ( E. costae Staindahner, 1878) in the northeast Mediterranean Sea (36°36′ N-36°07′ E, 35°52′ N-36°25′ E) in Iskenderun Bay. Fish were sampled monthly from Iskenderun Bay for a period of 12 months from 2006 May to 2007 April (Nt = 331, $$ {\hbox{Wt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {392}.{92}\pm {72}.{76}\,{\hbox{g}}\left( {{169} - {927}\,{\hbox{g}}} \right) $$, $$ {\hbox{TLt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {3}0.{85}\pm {3}.{88}\,{\hbox{cm}}\left( {{17} - {48}.{3}\,{\hbox{cm}}} \right) $$). Gnathia sp. was only extracted from the epithelium of the buccal cavity and internal side of the gills arch. The monthly patterns in infested fish samples (Np = 109, $$ {\hbox{Wp}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {349}.{25}\pm {182}.{79}\,{\hbox{g}}\left( {{169} - {853}\,{\hbox{g}}} \right) $$, $$ {\hbox{TLt}}\pm {\hbox{SD}}\left( {\hbox{range}} \right) = {26}.0{5}\pm {12}.{68}\,{\hbox{cm}}\left( {{18}.{2} - {45}.0\,{\hbox{cm}}} \right) $$) infestation rates (mean prevalence, P = 28.12% (0-60) and mean intensity $$ \left( {{\hbox{MI}}\pm {\hbox{SD}} = {23}.{69}\pm {14}.{78}\left( {{4} - {82}} \right)} \right. $$, the relationship between length-weight and infested/non-infested fish were calculated. Although the gnathiid parasite high intensities were observed in fish, there was no significant effect on the growth and general health condition of infested fish. [ABSTRACT FROM AUTHOR]

Abstract: A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models. [Copyright &y& Elsevier]

This paper describes a novel method, centre of mass model, to detect moving objects in a dynamic scene based on background subtraction. Any displacement of the position of centre of mass (CoMs) in two consecutive frames is the indicator of a moving object in a scene. Dividing a scene into subregions and modelling them as individual masses allow segmentation of the moving object(s). In the proposed scheme, an image is divided into blocks that are called super-pixels and each super-pixel is represented with the x and y components of CoM of a block. The segmentation is achieved by taking the absolute difference between CoM of current super-pixel and the mean of CoMs of previous corresponding super-pixels, and thresholding the difference with a dynamically updated value. A comparative work has been carried out to evaluate the performance of the proposed model and the previously reported seven different methods. The model produced consistent outputs for the images taken in different environmental conditions. The moving objects were successfully segmented with no post-processing operations. Centre of mass model demonstrated better overall performance than the methods previously reported. Its output was superior for auto-focused video images. [Copyright &y& Elsevier]

Abstract: The genetic and morphological variation of Pomatomus saltatrix were studied based on morphometric and meristic analyses of samples collected throughout the Black Seas, Marmara, Aegean and eastern Mediterranean Seas. In discriminant function analysis, plotting first and second discriminant functions explained 61 and 77% of the between-group variation for morphometric and meristic analyses, respectively, and indicated existence of three morphologically differentiated groups of P. saltatrix. Consistent differences in the summer and winter samples of the eastern Black Sea, the adjacent sea populations of western Black Sea, Marmara Sea and Aegean Sea, which were overlapping and northeastern Mediterranean samples were observed. The detected pattern of morphological differentiation also reflects their geographic isolation. The overall random assignment of individuals into their original population was moderate for morphometric (54%) and meristic (64%) characters. Both for morphometric and meristic analysis, the highest reclassification rate was observed for the eastern Black Sea and northeastern Mediterranean Sea samples, the most clearly isolated groups. Examination of the contribution of each morphometric and meristic character to the first and second discriminant functions revealed that the observed differences were mainly from head measurements, body depth, lateral scale and vertebrate numbers, indicating these characters to be important in the description of population characteristics. Mantel''s test showed that the Mahalanobis distance between the populations both for morphometric (r =−0.282; P =0.226) and meristic (r =0.632; P =0.119) were not significantly associated with their geographical distances. [Copyright &y& Elsevier]

Abstract: Broadcast-based DSM multiprocessors are nowadays an attractive platform for parallel computing due to their advantages in terms of scalability and programmability. In order to obtain high performance out of these systems, network latency reduction techniques should be developed, which requires the knowledge of the relationship between latency and other important DSM parameters. In this paper, self organizing maps (SOM) are used to investigate the effect of DSM parameters on network latency for a multiprocessor architecture interconnected by the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus). An event-based discrete simulator using OPNET Modeler is developed for simulating a SOME-Bus system containing 64-nodes. Two thousand data points have been collected in order to create the dataset used in this study. 2-dimensional (2D) maps are produced by using SOM to display the relationship between network latency and other parameters such as the miss rate to a modified block (P m), fraction of write misses (P w), probability of having an upgrade ownership request message (P uor), probability of having a cache full (P cf) and ratio of the mean thread run time to mean message transfer time (T/R). The results show that the most dominant DSM parameter effecting network latency is T/R, while P cf has the minimum affect on network latency among other parameters. The individual effect of P m, P w and P uor on network latency depends on the values of all other parameters. [Copyright &y& Elsevier]