Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Proposal of Critical Success Factors for eHealth Services Deployment
Get PDFeHealth is widely recognized as the application of information and communication technologies (ICT) in health. However, eHealth initiatives are still incipient in Latin America and the Caribbean, and, in many cases, restrictions in terms of access have been reported, also the necessary infrastructure, interoperability and scalability of these. This work proposes a set of critical success factors (CSFs) for the implementation of eHealth, which allow the identification of gaps, and the proposal of alternatives for the optimization of eHealth. It starts with the establishment of an eHealth domain, its scope and contributions, prior to the identification of key topics, and the establishment of every CSF, with support in guiding questions and metrics. The CSFs can facilitate the planning of projects or activities in eHealth, promoting strengths, either in ICT, management, or another of the involved topics. The CSFs must be employed with a criterion of flexibility and adequacy regarding the case in which they are applied. Finally, opportunities to evaluate and apply the CSFs in a specific context are set out
Early Warning Signals in Open Source Intelligence: Two Use Cases of the 2019 Iraqi and 2020 Indian Farmers' Protests
Get PDFEarly warning signals methods have been introduced in the field of ecological sciences and widely used in other domains. However, while these methods have proven effective for deterministic dynamics governed by differential equations or smooth maps – both on synthetic and real data – their application in the social sciences is more complex. A series of protests started in Iraq on 1 October 2019 and farmers' protests in India in September 2020. We investigate in this work how these waves could have been anticipated using early warning signals for the time series of daily occurrences of protests. We use to this end metric-based indicators (autocorrelation at-lag-1, standard deviation and skewness), analyse trends using Kendall rank correlation and use bootstrapping methods to implement a statistical test exhibiting a regime shift (tipping points) in the dynamics of protests. We moreover highlight the importance of the standard deviation as an indicator
Analyzing Terrific Traffic in Urban Areas: A Small Step Towards Bringing Order into City Roads
Get PDFAccurate travel time information enables travellers to plan their journey more wisely and efficiently. This in turn, lessens traffic congestion and improves people's travel experience, particularly in urban areas. Open-source traffic data available from different sources and Google Map API have raised opportunities for analyzing and predicting the traffic more accurately. The purpose of this research work is to analyze bus or car travel time data and showcase different insight and aspect of a society from its traffic pattern. Google Distance Matrix API, Python programming language and machine learning algorithms have been applied in this study to automatically extract, analyze, and visualize traffic data and showcase analysis methodology to improve people's travel experience in Dhaka City and the City of New York. In particular, we apply data analytics to develop an oracle that will give answers to different queries about traffic, such as least congested period and/or least congested route within a day/week/month etc., which in turn would enable people to make informed decisions for travel arrangements. The experimental results and detailed analyses show that there exists a wide fluctuation of travel time during the day in both cities. Furthermore, unlike other works, we accomplish various socio-cultural aspects and behaviour from traffic patterns in those two cities, perform the accessibility analysis and provide recommendations for further research
Efficient Density-Based Partitional Clustering Algorithm
Get PDFClustering is an important data mining technique that helps to detect hidden structures and patterns in the data. K-means algorithm is one of the most popular and widely used partitional clustering algorithms. It is a simple and efficient method but has several shortcomings. One major drawback of traditional K-means is that it selects initial centroids randomly, resulting in low-quality clusters. Various K-means extensions are designed to solve the issue of the random initial centroid. A novel density-based K-means (DK-means) algorithm is recently proposed that uses density-parameters for selecting initial centroids. It outperforms K-means in terms of accuracy at the cost of time. In this research, we present an efficient density-based K-means algorithm (EDK-means) that uses advance data structures and significantly reduces the DK-means algorithm's execution time. Furthermore, we rigorously evaluated the performance of density-based K-means on different challenging real-world datasets and compared it with traditional K-means. The experimental results are promising and show that density-based K-means outperforms K-means. It converges more rapidly than basic K-means, and it works well for the datasets with different cluster sizes.
Image Enhancement Algorithm Based on Image Spatial Domain Segmentation
Get PDFThis paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat+bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details
UML4NoSQL: A Novel Approach for Modeling NoSQL Document-Oriented Databases Based on UML
Get PDFThe adoption of Big Data systems by the companies is relatively new, although the data modeling and system design are ages old. Despite the fact that traditional databases are built on solid foundations, they cannot handle the swift and massive flow of data coming from multiple different sources. Herein, NoSQL databases are an inevitable alternative. However, these systems are schemaless compared to traditional databases. It is important to emphasize that schemaless does not mean no-schema which would mean that NoSQL databases do not need modeling. Hence, there is a need for conceptual models to define the data structure in these databases. This paper sheds a light on the importance of the UML in showing how to store Big Data described through meta-models within NoSQL databases. We propose a novel Big Data modeling methodology for NoSQL databases called UML4NoSQL, which is independent of the target system, and taking into account the four Big Data characteristics: Variety, Volume, Velocity, and Veracity (4 V's). The approach relies on the UML blocks with a data-up technique; it starts with a use-case and the class diagram resulting from the understanding of the data at hand and the definition of the developer's strategies while focusing on the user's needs. To illustrate our approach, we take a case study from health care domain. We show that our approach produces designs that can be implemented on NoSQL document-oriented system with respect to Big Data 4 V's
Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism
Get PDFThis paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach
Automating the Dataset Generation and Annotation for a Deep Learning Based Robot Trajectory Adjustment Application for Welding Processes in the Automotive Industry
Get PDFIndustrial companies are more and more interested in the use of artificial intelligence (AI) in the control and monitoring of their processes. They try to take advantage of the power of this technology in order to increase the level of automation and to build smarter machines with new capabilities of self-adaptation and self-control. Especially, the automotive industry, with their high requirements in productivity and diversity management, are eager to adapt AI concepts to their processes. However, the training of Deep Learning (DL) models requires an important effort of data preparation, providing a dataset of all possible configurations. Indeed, this dataset must be collected and then annotated. Considering the fact that automotive industry deals with a huge number of references and that it often and quickly needs to modify their products, it is very difficult, if not impossible, to gather sufficient datasets for each produced reference and to have the time to train DL models in the plants with the traditional methods. This paper presents an innovative methodology to prepare the dataset by creating virtual images instead of collecting real ones and then automatically annotating them. It will demonstrate that this method will reduce the efforts and the time of the preparation of the dataset significantly. The paper will also present how this method was deployed for the quality control of welding operations in the automotive industry