Missing values handling in any collected data is one of the first issues that must be resolved to be able to use that data. This paper presents an approach used for missing values interpolation in PurpleAir particle pollution sensor data, based on a correlation of the measurements from the observed locations with the measurements from its neighboring locations, using KNIME Analytics Platform. Results of our experiments with data from five locations in Bosnia & Herzegovina, presented in this paper, show that this approach, which is relatively simple to implement, gives good results. All modeling and experiments were conducted using KNIME Analytics Platform.

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

Color vision deficiency is a surprisingly frequent vision impairment, but not considered to be a mayor eye disease due to being inherited condition and not progressive condition. However it poses serious restrictions on a visually impaired person because vision deficiency tests are commonly used to disqualify individuals affected by color vision deficiency from certain occupations. Color vision deficiency cannot be cured, thus it is important to develop suitable assistive technology to overcome the restrictions it poses. Virtual reality can project custom and separate images to both eyes in a real-time and thus enabling a new class of assistive technology that can deliver visual information in a highly customized manner. Virtual reality based assistive technology is promising for age-related macular degeneration, diabetic retinopathy and particularly for color vision deficiency. Virtual reality prototype is created based on a video see-through setup using commercial virtual reality headset and stereo camera. The prototype uses custom image processing to transform visual information from the camera to color vision deficiency friendly form. Time-domain color mapping real-time image processing is proposed to improve scores on standard color vision deficiency tests - Ishihara tests. Experiment is conducted to evaluate a protanope time-domain color mapping with sinusoidal envelope.

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas

Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.

Pavement cracks are the first signs of structural damage in the asphalt pavement surfaces. The oldest method for detection and estimation of the pavement cracks is human visual inspection, also known as manual visual inspection. However, using human inspectors is very time consuming, very expensive and poses a risk to human safety. Another negative side is the fact that the task generally requires road to be closed. Hence, automatic prevention and reparation of cracks on the asphalt surface pavements is an important task, especially because the advanced stages of road deformation lead to formation of potholes. This has negative impact on the total reparation cost. In this paper, we proposed a new unsupervised method for the detection of cracks with gray color based histogram and Ostu's thresholding method on 2D pavement image. At first, the method divides the input image into a four independent equally sized sub-images. Then, the search for cracks is based on the ratio between Ostu's threshold and the maximum histogram value for every sub-image. Finally, all sub-images are assembled into the resulting image. The method was tested on the dataset which contains different pavement images with very versatile types of cracks. The results showed that the proposed method achieves satisfactory performance, especially in the cases of low signal-to-noise ratio, and is very fast.

The objective of the paper is to present a case study about innovations of business processes related to bachelors' and masters' theses on the Faculty of Electrical Engineering of University Sarajevo by applying concepts of business process management (BPM) on those business processes. Theoretical context of the paper is created by presenting BPM concepts. Then, these concepts where applied on the case study of business processes of Faculty. Those processes are led through first phase of BPM lifecycle. In the phase of process design, current processes are analyzed, problems are recognized, and the new processes are proposed. Innovations of Faculty's information system are proposed and described.

Efficient detection of distresses on asphalt pavements has a great impact on safe driving, thus it has been very active research subject in recent years. High severity level distresses, such as potholes, are the most severe threat to safe driving, hence timely detecting and repairing potholes is crucial in ensuring safety and quality of driving. Existing methods often require sophisticated equipment and algorithms with high-computational pre-processing steps for analysis of substantial amount of existing data (images or videos). In this paper, a new unsupervised method for detection of high severity distresses on asphalt pavements was proposed. The method was tested on highly unstructured image data set captured from different cameras and angles, with different irregular shapes and number of potholes to demonstrate its capability. Results indicated that the method can be used for rough detection and estimation of damaged pavements.

Skin detection is a crucial pre-processing step for finding human faces in images. The challenging task is to find a reliable, yet efficient method for detection of skin region(s). In this paper, we proposed a new, simple and efficient method for skin detection based on image segmentation of different color spaces, and simple clustering technique (K-means) for clustering similar pixels on an image. Three K-means input features are used : a) two components from two different color spaces (Hue, Cr, Cb), b) positions of pixels on an image and c) rough estimation of skin pixels obtained from skin-color based detection. Our approach showed promising results on human images from different ethnicities, with simple background and high illumination. The computational cost of the method has been very low, since no training data is required. Results indicate that the method is suitable as a pre-processing step for some supervised method for advanced human skin segmentation and detection.

The software composition using high-granularity entities nowadays is a common practice. The process of software composition is supported by various CASE tools. First tools were made on the basis of very simple formalisms (e.g. intuitionistic propositional logic). During the years the tools evolved to more efficient ones, which are able to deal with concurrency, multiparty sessions and other advanced aspects of distributed software. Such tools often are based on the behavioral types (BT). This paper presents 3 logically related tools: CoCoViLa, SoCoSys and BSynth in order to expose such evolution. CoCoViLa and (partially) SoCoSys are based on the Structural Synthesis of Programs method while BSynth tool is more related to behavioral types. The focus of this paper is more on the BSynth, because it implements the Evolutionary prediction algorithm, enabling to predict what components are missing in initial repository. The possible future trends of automated component-based software development and its relation to selected tools are discussed also.