Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for increasing the block size. To overcome this problem with the aim of increasing the algorithm’s performance, the pyramidal residual networks gradually increasing hypothesis is utilized in the proposed approach, which is the main contribution and novelty of this study. Besides, by using the pyramidal residual networks gradually increasing hypothesis in the proposed approach, the parameter size of the models is also reduced, which affects the computational time. Two different models are proposed by considering addition and multiplication manners, and the proposed models are evaluated using well-known remote sensing datasets NWPU-RESISC45 and UC Merced. The results obtained by the proposed model are compared with the results of traditional image enhancement algorithms together with the EDSR itself, EDSR with deeper structure, Super-Resolution Generative Adversarial Networks approach, and Residual Local Feature Networks approach in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) metrics and showed that the proposed models present better quality images. Moreover, considering the computational time and complexity, it is shown that some proposed models achieve approximately 27% less output parameter having similar PSNR and SSIM values and computational time for EDSR itself and 65% less output parameter having better PSNR and SSIM values and 16% lower computational time for EDSR with deeper structure.

Land readjustment and reallocation (LR) applications are complex and difficult real-world problems involving many different criteria. By considering these criteria, it is very difficult and takes a long time to be solved manually by an expert. Since the search space of these problems is very large, solution of these problems requires meta-heuristic optimization algorithms instead of classical methods in order to acquire more robust, acceptable and qualified solutions. Considering the meta-heuristic approaches, the algorithm needs an objective function that can make the right decision and evaluate the solutions most reasonably among the candidate solutions. Using the proposed objective function, the quality of the distribution and subdivision plans will be automatically evaluated and compared without the need for an expert. In this study, an objective function which considers all the criteria in the LR problems is proposed. In addition, unlike the available crossover operators used in meta-heuristic algorithms in the literature, two different parcel-based crossover operators called Classical (CPC) and Intelligent (IPC) Parcel-Based Crossover Operators are proposed. While CPC performs the distribution of the owners to the predetermined parcel randomly, IPC makes this operation with a greedy approach rather than randomly. According to this approach, if the shareholder and distance values after the crossover operation would be better than the existing ones, the crossover operation is performed. Otherwise, this operation is cancelled. By using the proposed objective function and crossover operators, artificial bee colony (ABC), particle swarm optimization (PSO) and differential evolution (DE) algorithms are run under equal conditions on a real project site, and the obtained results are compared with the official results obtained by a technician in the study. In addition, since there will be so many zoning blocks of different sizes and shapes on a real project site, it is very possible to have gaps or overflows in the blocks of subdivision plans obtained from the algorithms. Therefore, the gaps and overflow areas in the blocks can be completely eliminated by utilizing an Expert System developed specifically for LR problems called LRES, and as a result, the solutions obtained from the algorithms can be directly applicable in real life by the LRES. It's clearly seen from the experimental studies that all of the results obtained by using the algorithms based on LRES are much more effective than the official results obtained by a technician in terms of both solution quality and speed. In addition, among the evaluated algorithms, it is observed that the PSO algorithm presents much more effective and robust results than results of the other algorithms. Moreover, as a consequence of the algorithms using the IPC presents much more successful results than the results of the algorithms using CPC, it can be used as a very effective alternative crossover operator for land use problems.

The planned development of urban areas depends on zoning applications. Although zoning practices are performed using different techniques, the parcelling operations that shape the future view of the city are the same. Preparing the parcelling plans is an important step that has a direct impact on ownership structure and reallocation. Parcelling operations are traditionally handled manually by a technician. This is a serious problem in terms of time and cost. In this study, by taking the zoning legislation, the production of a pre-land subdivision plan has been automatically performed for a region of Konya, which is one of the major cities in Turkey. The parcelling processes have been performed in three different ways: the first parcelling technique is parcelling with edge values, the second is parcelling with area values and the third is parcelling using both edge and area values together. For the entire parcelling process, the area of the parcel has been calculated using the Gauss method. Moreover, to effectively determine the boundaries and to calculate the parcel area in the parcelling process, the binary search technique has been used in all the methods. The experimental results show that the parcelling operations were carried out very quickly and successfully.

Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especially t2TSA outperforms all the algorithms in terms of performance and time.

Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn’t achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.

Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.

Land Readjustment and redistribution (LR) is a land management tool that helps regular urban development with the contribution of landowners. The main purpose of LR is to transform irregularly developed land parcels into suitable forms. Since it is necessary to handle many criteria simultaneously to solve LR problems, classical mathematical methods can be insufficient due to time limitation. Since LR problems are similar to traveling salesman problems and typical scheduling problems in terms of structure, they are kinds of NP-hard problems in combinatorial optimization. Therefore, metaheuristic algorithms are used in order to solve NP-hard problems instead of classical methods. At first, in this study, an effective problem-specific objective function is proposed to address the main criteria of the problem. In addition, a map-based crossover operator and three different mutation operators are proposed for the LR, and then a hybrid approach is implemented by utilizing those operators together. Furthermore, since the optimal value of the problem handled in real world cannot be exactly estimated, a synthetic dataset is proposed as a benchmarking set in LR which makes the success of algorithms can be objectively evaluated. This dataset consists of 5 different problems according to number of parcel which are 20, 40, 60, 80 and 100. Each problem set consists of 4 sub-problems in terms of number of landowners per-parcel which are 1, 2, 3 and 4. Therefore, the dataset consists of 20 kinds of problems. In this study, artificial bee colony, particle swarm optimization, differential evolution, genetic and tree seed algorithm are used. In the experimental studies, five algorithms are set to run under equal conditions using the proposed synthetic dataset. When the acquired experimental results are examined, genetic algorithm seems to be the most effective algorithm in terms of both speed and performance. Although artificial bee colony has better results from genetic algorithm in a few problems, artificial bee colony is the second most successful algorithm after genetic algorithm in terms of performance. However, in terms of time, artificial bee colony is an algorithm nearly as successful as genetic algorithm. On the other hand, the results of differential evolution, particle swarm optimization and tree seed algorithms are similar to each other in terms of solution quality. In conclusion, the statistical tests clearly show that genetic algorithm is the most effective technique in solving LR problems in terms of speed, performance and robustness.

Social media reflects the attitudes of the public towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities (NEs). This can define NEs as sentiment-bearing components. In this paper, we dive beyond NEs recognition to the exploitation of sentiment-annotated NEs in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of NEs based on the majority of attitudes towards them. This enabled tagging NEs with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that NEs have no considerable impact on the supervised model, while employing NEs in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.

Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 "Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)". We developed two hate speech detection model variants through Tw-StAR framework. While the first model adopted one-hot encoding ngrams to train an NB classifier, the second generated and learned n-gram embeddings within a feedforward neural network. For both models, specific terms, selected via MWT patterns, were tagged in the input data. With two feature types employed, we could investigate the ability of n-gram embeddings to rival one-hot n-grams. Our results showed that in English, n-gram embeddings outperformed one-hot ngrams. However, representing Spanish tweets by one-hot n-grams yielded a slightly better performance compared to that of n-gram embeddings. The official ranking indicated that Tw-StAR ranked 9th for English and 20th for Spanish.

Energy demand forecasting is an important issue whose solution is evaluated by policy makers in order to take the vital decisions that affect the economy of a country. In the last decade, several approaches including machine-learning techniques have been proposed to increase the success of this estimation. In this study, two different mathematical models are proposed to predict Turkey's energy demand by using the Grey Wolf Optimizer (GWO) algorithm. In the development of models, gross domestic product, population import and export are taken as parameters. Linear and quadratic mathematical models are used for energy demand forecast. From 1979 to 2011, 33 years of historical data are for training and testing phases of the models. The developed models are used to estimate energy demand of Turkey for a 29-year period from 2012 to 2040 in the three different possible scenarios. The GWO method is compared with other methods in the literature to prove its success for the energy demand problem. The results show that the proposed GWO method is more successful than the other methods, especially for the linear form.

ÖZÇoklu eşikleme, görüntü işleme ve örüntü tanıma için önemli bir görüntü işleme tekniğidir.Optimal bir eşik değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir.İki seviye segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir.Özellikle çok seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır.Zor problemler için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak bilinmektedir.Bu çalışmada, doğadaki gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son zamanlarda önerilmiş sürü

By providing great flexibility non-uniform rational B-spline (NURBS) curves and surfaces are reason of preferability on areas like computer aided design, medical imaging and computer graphics. Knots, control points and weights provide this flexibility. Computation of these parameters makes the problem as a non-linear combinational optimization problem on a process of reverse engineering. The ability of solving these problems using meta-heuristics instead of conventional methods attracts researchers. In this paper, NURBS curve estimation is carried out by a novel optimization method namely gravitational search algorithm. Both knots and knots together weights simultaneous optimization process is implemented by using research agents. The high performance of the proposed method on NURBS curve fitting is showed by obtained results.Keywords: Non-uniform rational B-spline, gravitational search algorithm, meta-heuristic

Multilevel thresholding is an important image process technique for image processing and pattern recognition. Selecting an optimal threshold value is one of the most crucial phase in image thresholding. While bi-level segmentation contains separating the original image into subdivided sections with help of a threshold value, multilevel segmentation involves multi threshold values. Especially in multilevel image tresholding, the computational time of detailed search increases exponentially with the number of preferred thresholds. For compelling problems, swarm intelligence is known as one of the successful and influential optimization methods. In this paper, the grey wolf optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates the social leadership and hunting behavior of gray wolves in nature is employed for solving the multilevel image thresholding problem. The experimental results on standard benchmark images indicate that the grey wolf optimizer algorithm is comparable with other state of the art algorithms.

In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 “Affect in Tweets”, subtask E-c “Detecting Emotions (multi-label classification)”. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets’ features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.

Social media platforms have been witnessinga significant increase in posts written in the Tunisiandialect since the uprising in Tunisia at the end of2010. Most of the posted tweets or comments reflectthe impressions of the Tunisian public towards social,economical and political major events. These opinionshave been tracked, analyzed and evaluated throughsentiment analysis systems. In the current study,we investigate the impact of several preprocessingtechniques on sentiment analysis using two sentimentclassification models: Supervised and lexicon-based.These models were trained on three Tunisian datasetsof different sizes and multiple domains. Our resultsemphasize the positive impact of preprocessing phaseon the evaluation measures of both sentiment classifiersas the baseline was significantly outperformed whenstemming, emoji recognition and negation detectiontasks were applied. Moreover, integrating namedentities with these tasks enhanced the lexicon-basedclassification performance in all datasets and that of thesupervised model in medium and small sized datasets.

This paper investigates the impact of preprocessing on sentiment classification of Turkish movies and products reviews. Input datasets were subjected to several combinations of preprocessing techniques. Later, the manipulated reviews were fed to a lexicon-based and a supervised machine learning sentiment classifiers. The achieved results emphasize the positive impact of preprocessing phase on the accuracy of both sentiment classifiers as the baseline was outperformed with a considerable margin especially when stemming, emoji recognition and negation detection tasks were applied.

This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other.Keywords: Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimization

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big BangBig Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested. (C) 2016 Elsevier B.V. All rights reserved.