The paper describes one of the methods of data acquisition in data mining models used to support decision-making. The study presents the possibilities of data collection using the phases of the CRISP-DM model for an organization and presents the possibility of adapting the model for analysis and management in the decisionmaking process. The first three phases of implementing the CRISP-DM model are described using data from an enterprise with small batch production as an example. The paper presents the CRISP-DM based model for data mining in the process of predicting assembly cycle time. The developed solution has been evaluated using real industrial data and will be a part of methodology that allows to estimate the assembly time of a finished product at the quotation stage, i.e., without the detailed technology of the product being known.

3D human pose estimation (3D-HPE) has emerged as a prominent research area with diverse applications. This work focuses on enhancing the accuracy of 3D-HPE by proposing a two-stage model with a multi-feature fusion approach. The proposed model utilizes convolutional kernels of different sizes to extract feature maps with diverse resolutions and dimensions. These feature maps, along with the 2D coordinates of key joint points from the input frame, are fused in the first stage. In the second stage, the fused feature map is combined with the feature points of 2D key joints to jointly predict the key joints in 3D space. Experimental evaluations demonstrate the superiority of the proposed model over representative methods. It achieves significant improvements of 9.47% and 8.55% in average MPJPE and average P-MPJPE, respectively, which are critical metrics for evaluating pose estimation accuracy. The proposed two-stage model with multi-feature fusion offers a comprehensive and accurate approach to 3D-HPE. It outperforms existing methods and showcases its effectiveness in capturing the intricate details of human poses. The results validate the significance of the proposed model in advancing the field of 3D-HPE.

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation

Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly.

The quality dimensions of an information system, such as system, information, and service qualities, play a crucial role in determining the overall performance of an organization. These quality dimensions are significant as they can impact employee outcomes, which are key factors in determining whether an organization is able to achieve a competitive advantage in the market. The aim of this study is to explore the impact of quality dimensions on employee outcomes such as learning ability, adaptability, and job satisfaction. The research was conducted by distributing a structured survey questionnaire to 300 employees of 8 commercial banks at different management levels. The measurement and structural models were analyzed using Smart PLS. This study employed descriptive analysis to present a comprehensive demographic profile of both the organizations and the participants. Out of the nine hypotheses tested, seven were found to be significant. The findings of this study show that while all three quality dimensions (system, information, and service) of information systems positively affect employee learning, only system and information qualities positively affect employee learning, and as for job satisfaction, only system and service qualities play an important role. Therefore, implementing suitable information systems to improve employee outcomes in an organization, especially a financial organization, is paramount in this information age. This research contributes to understanding information systems, their implementation, and employee outcomes in an organization.

In the paper, the authors are presenting the analysis of implementation of IoT system of road quality analysis. The proposed system has been prepared with edge, on-device processing in mind, allowing for reduction of amount of data being sent to cloud computing aggregation subsystem, sending only 2.5% of the original data. Several algorithms for road quality analysis has been implemented on a real device and tested in a real-world conditions. The system has been compared to the state-of-the-art offline processing approach and shown very similar results.

Maintenance has a key impact on the efficiency of the production processes because the efficiency of the machines determines the ability of the system to produce in accordance with the assumed schedule. The key element of the system performance assessment remains the availability of technological equipment, which directly translates into the efficiency and effectiveness of the performed production tasks. Taking into account the dynamic nature of manufacturing processes, the proper selection of machinery and equipment for the implementation of specific production tasks becomes an issue of particular importance. The purpose of this research was to determine the impact of technical and non-technical factors on the material selection of machine tools for production tasks and to develop a method of supporting the selection of production resources using the AHP and Fuzzy AHP methods. The research was carried out in a manufacturing company from the automotive industry.

The paper presents a comparative analysis of the operation of two variants of centrifugal pump rotors, a description of the main parameters, and the influence of the blade geometry on the performance characteristics obtained. Rotors have been designed using the arc and point method. Based on the developed 3D CAD models, the rotors were printed using the rapid prototyping method on a 3D printer in FFF (Fused Filament Fabrication) technology, in order to experimentally verify the performance, by placing them on the Armfield FM50 test stand. The analysis part of the CFD includes a fluid flow in Ansys Fluent. The process of creating a flow domain and generating a structural mesh was described, along with the definition of boundary conditions, the definition of physical conditions and the turbulence model. The distribution of pressures and velocities in the meridional sections is shown graphically. The chapter with the experimental analysis contains a description of the measuring stand and the methodology used. The results obtained made it possible to generate the characteristics, making it possible to compare the results received. The results allowed to note the influence of geometry on the behavior of the rotors during operation in the system and to indicate that the arc rotor gets a 7% higher head and 2% higher efficiency than the point method rotor, which gives the basis for its commercial use in industry.

Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.

Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.