The spectacular evolution of information and communications technology in the last two decades and the growing obvious chase of utopian maximization of profit by economic organizations have made an impact on various fields, including that of methodological instruments regarding the lifecycle of software development. We will speak about the necessity of organizational ability to adapt to continuously changing market conditions, which leads undoubtedly to acquiring functional flexibility and, in the end, of business agility. Also, the agility of a business (not only in software development) is supported by the manifestation of agility on all three architectural levels: business, informational, technological. In this study we aim to identify the elements that impact on agility in developing software products, in a gradual approach, from the traditional waterfall model towards approaches like Scrumban. Additionally, we will understand the social dimension of using agile methodologies in software development projects and the main barriers in adopting such methodologies. 1984 includes many themes, like computers programming, software integration and hardware testing. The main domain of his last research activity is the new economydigital economy in information and knowledge society. Since 1998 he managed over 25 research projects like System methodology of distance learning and permanent education, The change and modernize of the economy and society in Romania, E-Romaniaan information society for all, Social and environmental impact of new forms of work and activities in information society.
One of the most visible domains of the last decade emerging technological explosion is education. In this paper we will analyze the educational field seen as an intelligent learning environment, in the context of a modern information and communication technology paradigm: fog & cloud computing. An intelligent educational environment built on the IoT (Internet of Things) ecosystem involves at least two dimensions: conceptual and functional. These aspects will be highlighted in this paper, identifying the intensity of cloud computing relations and fog computing-IoT, as global infrastructure for building an intelligent education environment. In the current economic, social and environmental conditions, developing an intelligent educational frame must take into account multiple aspects. Among them are critical factors, like legal frame, ecological dimension and quality insurance. Any intelligent educational frame must consider the environmental factors and converge towards and ecological structure, an eco-school.
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.
Many technological applications of our time rely on images captured by multiple cameras. Such applications include the detection and recognition of objects in captured images, the tracking of objects and analysis of their motion, and the detection of changes in appearance. The alignment of images captured at different times and/or from different angles is a key processing step in these applications. One of the most challenging tasks is to develop fast algorithms to accurately align images perturbed by various types of transformations. The paper reports a new method used to register images in the case of geometric perturbations that include rotations, translations, and non-uniform scaling. The input images can be monochrome or colored, and they are preprocessed by a noise-insensitive edge detector to obtain binarized versions. Isotropic scaling transformations are used to compute multi-scale representations of the binarized inputs. The algorithm is of memetic type and exploits the fact that the computation carried out in reduced representations usually produces promising initial solutions very fast. The proposed method combines bio-inspired and evolutionary computation techniques with clustered search and implements a procedure specially tailored to address the premature convergence issue in various scaled representations. A long series of tests on perturbed images were performed, evidencing the efficiency of our memetic multi-scale approach. In addition, a comparative analysis has proved that the proposed algorithm outperforms some well-known registration procedures both in terms of accuracy and runtime.
Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.
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