Browsing by Author "Kalayci, TE"

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    An ant colony optimisation approach for optimising SPARQL queries by reordering triple patterns
    Kalayci, EG; Kalayci, TE; Birant, D
    Processing the excessive volumes of information on the Web is an important issue. The Semantic Web paradigm has been proposed as the solution. However, this approach generates several challenges, such as query processing and optimisation. This paper proposes a novel approach for optimising SPARQL queries with different graph shapes. This new method reorders the triple patterns using Ant Colony Optimisation (ACO) algorithms. Reordering the triple patterns is a way of decreasing the execution times of the SPARQL queries. The proposed approach is focused on in-memory models of RDF data, and it optimises the SPARQL queries by means of Ant System, Elitist Ant System and MAX-MIN Ant System algorithms. The approach is implemented in the Apache Jena ARQ query engine, which is used for the experimentation, and the new method is compared with Normal Execution, Jena Reorder Algorithms, and the Stocker et al. Algorithms. All of the experiments are performed using the LUBM dataset for various shapes of queries, such as chain, star, cyclic, and chain-star. The first contribution is the real-time optimisation of SPARQL query triple pattern orders using ACO algorithms, and the second contribution is the concrete implementation for the ARQ query engine, which is a component of the widely used Semantic Web framework Apache Jena. The experiments demonstrate that the proposed method reduces the execution time of the queries significantly. (C) 2015 Elsevier Ltd. All rights reserved.
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    Area-priority-based sensor deployment optimisation with priority estimation using K-means
    Ates, E; Kalayci, TE; Ugur, A
    Deployment in wireless sensor networks (WSN) addresses maximising the coverage of sensors and reducing the total cost of deployment. The area-priority concept for WSN deployment that the authors contributed to the literature recently allows environments with regions that have different importance or priority levels. In this study, the authors propose the first priority-estimation method for area-priority-based WSN deployments. First, a satellite image of the environment that will be used in the deployment of the sensors is clustered by a K-means algorithm using the colour features of the regions. In the sensor deployment phase, this cluster information is used to determine the priorities of the sensor coverage areas on positions of the image. Sensors are initially deployed quickly using a priority queue-based technique. Then, a simulated annealing algorithm is used to maximise the total covered area priority and to minimise the gaps between the sensors. Various experiments are performed for different scenarios (land, sea, and forest) on images captured from Google Maps using different parameter values. The experiments confirm that the proposed approach performs well and outperforms the random deployment of sensors.
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    A reactive self-tuning scheme for multilevel graph partitioning
    Kalayci, TE; Battiti, R
    We propose a new multilevel graph bi-partitioning approach (M-RRTS) using greedy construction and reactive-randomized tabu search (RRTS). RRTS builds upon local search by adding prohibitions (to enforce diversification) and self-tuning mechanisms to adapt metaparameters in an online manner to the instance being solved. The novel M-RRTS approach adds a multi-scale structure to the previous method. The original graph is summarized through a hierarchy of coarser graphs. At each step, more densely-interconnected nodes at a given level of the hierarchy are coalesced together. The coarsest graph is then partitioned, and uncoarsening phases followed by refinement steps build solutions at finer levels until the original graph is partitioned. A variation of RRTS is applied for the refinement of partitions after each uncoarsening phase. We investigate various building blocks of the proposed multilevel scheme, such as different initial greedy constructions, different tie-breaking options and various matching mechanisms to build the coarser levels. Detailed experimental results are presented on the benchmark graphs from Walshaw's graph partitioning repository and potentially hard graphs. The proposed approach produces the record results for 14 of 34 graphs from the repository in lower CPU times with respect to competing approaches. These results confirm the value of the new self-tuning and multilevel strategy to rapidly adapt to new instances. (C) 2017 Elsevier Inc. All rights reserved.
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    Can wireless sensor networks be emotional? A survey of computational models of emotions and their applications for wireless sensor networks
    Kalayci, TE; Bahrepour, M; Meratnia, N; Havinga, PJM
    Advances in psychology have revealed that emotions and rationality are interlinked and emotions are essential for rational behaviour and decision making. Therefore, integration of emotions with intelligent systems has become an important topic in engineering. The integration of emotions into intelligent systems requires computational models to generate emotions from external and internal sources. This paper first provides a survey of current computational models of emotion and their applications in engineering. Finally, it assesses potential of integrating emotions in wireless sensor networks (WSNs) by listing some use scenarios and by giving one model application. In this model application performance of a neural network for event detection has been improved using brain emotional learning based intelligent controller (BELBIC).
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    Comparison of machine learning techniques for classification of phishing web sites
    Kalayci, TE
    Today, machine learning approaches are used to make computers act more accurately for various purpose. In this manner, one area in which the machine learning approaches are used is the detection of phishing web sites. Phishing is an online threat which depends on creating a fake web site that mimics a trustworthy web site to steal important personal information. It is important to predict whether a website is a phishing website in order to avoid this danger before it happens. In this study, AdaBoost multilayer perceptron, support vector machine, decision tree, k-nearest neighbors, Naive Bayes and random forest machine learning techniques are compared to predict the purpose of a website. This comparison is performed by experimenting over a dataset containing 1353 instances with 9 different features. The experimental evaluation is performed in two different settings. The first setting based on splitting the data into training and test sets. In this setting the evaluation results show that the random forest algorithm, which is an ensemble learning approach based on decision trees, outperforms other compared approaches. On the other hand, in the second setting based on cross validation, multilayer perceptron shows a better performance.
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    Genetic Algorithm-Based Sensor Deployment with Area Priority
    Kalayci, TE; Ugur, A
    We are introducing a new design goal called area priority to determine optimal sensor node distribution. The environment in which the wireless sensor network (WSN) will be placed is divided into parts and priorities are attached to these parts. Priorities make the deployment problem adaptable to nonhomogeneous environments with regions that have different importance levels such as forests. Various tree/animal types and densities, residential in the forest can be classified by the area priority concept that we propose. We also develop a genetic algorithm-based method to optimize the total importance in a fully connected WSN. Experimental results obtained for different priorities are presented and discussed.