Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 1 (2019), pp. 73–90
Abstract
Integration of algorithms of investment theory and artificial intelligence allows one to create a support system for investors in exchange markets based on the ensemble of long-short-term-memory (LSTM) based recurrent neural networks (RNN). The proposed support system contains five stages: preparation of historical data, prediction by an ensemble of LSTM RNNs, assessment of prediction distributions, investment portfolio formation and verification. The prediction process outputs a multi-modal distribution, which provides useful information for investors. The research compares four different strategies based on a combination of distribution forecasting models. The high-low strategy helps decision-makers in exchange markets to recognize signals of transactions and fix limits for expectations. A combination of high-low-daily-weekly predictions helps investors to make daily transactions with knowing distribution of exchange rates during the week. The shift in time of five hours between London and New York inspired us to create a UK-NY strategy, which allows investors to recognize the signals of the market in a very short time. The joined high-low-UK-NY strategy increases the possibility of recognizing the signals of transactions in a very short time and of fixing the limits for day trading. So, this support system for investors is verified as a profitable tool for speculators in the relatively risky currency market.
Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 1 (2019), pp. 53–72
Abstract
Saliency detection has been deeply studied in the last few years and the number of the designed computational models is increasing. Starting from the assumption that spatial and temporal information of an input video frame can provide better saliency results than using each information alone, we propose a spatio-temporal saliency model for detecting salient objects in videos. First, spatial saliency is measured at patch-level by fusing local contrasts with spatial priors to label each patch as a foreground or a background one. Then, the newly proposed motion distinctiveness feature and gradient flow field measure are used to obtain the temporal saliency maps. Finally, spatial and temporal saliency maps are fused together into one final saliency map.
On the challenging SegTrack v2 and Fukuchi benchmark datasets we significantly outperform the state-of-the-art methods.
Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 1 (2019), pp. 33–52
Abstract
The raw trajectories contain large amounts of redundant data that bring challenges to storage, transmission and processing. Trajectory compression algorithms can reduce the number of positioning points while minimizing the loss of information. This paper proposes a heading maintaining oriented trajectory compression algorithm, which takes into account both position information and direction information. By setting an angle threshold, the algorithm can achieve a more accurate approximation of trajectories than traditional position-preserving trajectory compression algorithms. The experimental results show that the algorithm can ensure certain effect on the direction information and is more flexible.
Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 1 (2019), pp. 21–32
Abstract
In Computer Vision and Pattern Recognition, surveillance-video crowded scenes have been analysed according to their structure, where the detection of distinguishable people groups is an essential step. In this paper, we are interested in detecting F-Formations (i.e. free standing conversational groups) on video, which are formed by people social relations. We proposed a new method based on fuzzy relations, where a new social representation for computing relation between individuals, fusion for search consensus in multiple frame and clustering are introduced. Finally, our proposal was tested in a real-world dataset, improving the already reported scores from literature.
Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 1 (2019), pp. 1–19
Abstract
Medical Ultrasound is a diagnostic imaging technique based on the application of ultrasound in various branches of medical sciences. It can facilitate the observation of structures of internal body, such as tendons, muscles, vessels and internal organs such as male and female reproductive system. However, these images usually degrade by a special kind of multiplicative noise called speckle. The main effects of speckle noise in the ultrasound images appear in the edges and fine details which lead to reduce their resolution and consequently make difficulties in medical diagnosing. Therefore, reducing of speckle noise seriously plays an important role in image diagnosing. Among the various methods that have been proposed to reduce the speckle noise, there exists a class of approaches that firstly convert multiplicative speckle noise into additive noise via log-transform and secondly perform the despeckling process via a directional filter. Usually, the additive noises are mutually uncorrelated and obey a Gaussian distribution. On the other hand, non-subsampled shearlet transform (NSST), as a multi scale method, is one of the effective methods in image processing, specially, denoising. Since NSST is shift invariant, it diminishes the effect of pseudo-Gibbs phenomena in the denoising. In this paper, we describe a simple image despeckling algorithm which combines the log-transform as a pre-processing step with the non-subsampled shearlet transform for strong numerical and visual performance on a broad class of images. To illustrate the efficiency of the proposed approach, it is applied on a sample image and two real ultrasound images. Numerical results illustrate that the proposed approach can obtain better performance in term of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index rather than existing state-of-the-art methods.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 4 (2018), pp. 801–824
Abstract
In this paper, we investigate green supplier evaluation and selection problems within the interval 2-tuple linguistic environment. Based on the operational laws and comparison rule of interval 2-tuple linguistic variables, we develop some new aggregation operators, such as the interval 2-tuple hybrid averaging (ITHA) operator, the interval 2-tuple ordered weighted averaging-weighted averaging (ITOWAWA) operator and the interval 2-tuple hybrid geometric (ITHG) operator. Then, an approach for green supplier evaluation and selection under the context of interval 2-tuple linguistic variables is proposed based on the developed interval 2-tuple linguistic hybrid aggregation operators. Finally, a practical application to the green supplier selection problem of an automobile manufacturer is presented to reveal the potentiality and aptness of the proposed green supplier selection approach. According to the findings, the supplier number ‘five’ got the highest rank, out of the five alternative green suppliers. The approach proposed in this paper may help managers and business professionals to evaluate and select the optimal green supplier by considering the importance degrees of both the given arguments and their ordered positions. Furthermore, it is able to take different scenarios into account and provide a more complete picture to the decision maker by using different hybrid aggregation operators.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 4 (2018), pp. 773–800
Abstract
Green supplier selection has recently become one of the key strategic considerations in green supply chain management, due to regulatory requirements and market trends. It can be regarded as a multi-criteria group decision-making (MCGDM) problem, in which a set of alternatives are evaluated with respect to multiple criteria. MCGDM methods based on Analytic Hierarchy Process (AHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are widely used in solving green supplier selection problems. However, the classic AHP must conduct large amounts of pairwise comparisons to derive a consistent result due to its complex structure. Meanwhile, the classic TOPSIS only considers one single negative idea solution in selecting suppliers, which is insufficiently cautious. In this study, an improved TOPSIS integrated with Best-Worst Method (BWM) is developed to solve MCGDM problems with intuitionistic fuzzy information in the context of green supplier selection. The BWM is investigated to derive criterion weights, and the improved TOPSIS method is proposed to obtain decision makers’ weights in terms of different criteria. Moreover, the developed TOPSIS-based coefficient is used to rank alternatives. Finally, a green supplier selection problem in the agri-food industry is presented to validate the proposed approach followed by sensitivity and comparative analyses.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 4 (2018), pp. 757–771
Abstract
Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio ($AVR$). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera Smartscope M5 PRO is used for image gathering.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 4 (2018), pp. 733–756
Abstract
In this work, the discrete time risk model with two seasons is considered. In such model, the claims repeat with time periods of two units, i.e. claim distributions coincide at all even instants and at all odd instants. Our purpose is to derive an algorithm for calculating the values of the particular case of the Gerber–Shiu discounted penalty function $\mathbb{E}({\mathrm{e}^{-\delta T}}{\mathbb{1}_{\{T<\infty \}}})$, where T is the time of ruin, and δ is a constant nonnegative force of interest. Theoretical results are illustrated by some numerical examples.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 4 (2018), pp. 711–732
Abstract
Neutrosophic linguistic numbers (NLNs) can depict the uncertain and imperfect information by linguistic variables (LVs). As the classical aggregation operator, the Maclaurin symmetric mean (MSM) operator has its prominent characteristic that reflects the interactions among multiple attributes. Considering such circumstance: there are interrelationship among the attributes which take the forms of NLNs and the attribute weights are fully unknown in multiple attribute group decision making (MAGDM) problems, we propose a novel MAGDM methods with NLNs. Firstly, the MSM is extended to NLNs, that is, aggregating neutrosophic linguistic information by two new operators – the NLN Maclaurin symmetric mean (NLNMSM) operator and the weighted NLN Maclaurin symmetric mean (WNLNMSM) operator. Then, we discuss some characteristics and detail some special examples of the developed operators. Further, we develop an information entropy measure under NLNs to assign the objective weights of the attributes. Based on the entropy weights and the proposed operators, an approach to MAGDM problems with NLNs is introduced. Finally, a manufacturing industry example is given to demonstrate the effectiveness and superiority of the proposed method.