Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "data mining" wg kryterium: Temat

  Lista filtrów

Dostawca treści

Podbaza

Wyrównaj
Wyświetlanie 1-15 z 285
Tytuł:
A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment
Autorzy:
Del Gallo, Mateo
Ciarapica, Filippo Emanuele
Mazzuto, Giovanni
Bevilacqua, Maurizio
Powiązania:
https://bibliotekanauki.pl/articles/27324213.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
data mining
association rules
optimization model
production scheduling
job-shop scheduling
flow shop scheduling
Opis:
Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.
Źródło:
Management and Production Engineering Review; 2023, 14, 4; 56--70
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Feature selection for the low industrial yield of cane sugar production based on rule learning algorithms
Autorzy:
Gil Rodríguez, Yohan
Socorro Llanes, Raisa
Rosete, Alejandro
Bravo Ilisástigui, Lisandra
Powiązania:
https://bibliotekanauki.pl/articles/27314245.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
feature selection
rule learning
data mining
CRISP-DM
industrial yield
Opis:
This article presents a model based on machine learning for the selection of the characteristics that most influence the low industrial yield of cane sugar production in Cuba. The set of data used in this work corresponds to a period of ten years of sugar harvests from 2010 to 2019. A pro‐ cess of understanding the business and of understand‐ ing and preparing the data is carried out. The accuracy of six rule learning algorithms is evaluated: CONJUNC‐ TIVERULE, DECISIONTABLE, RIDOR, FURIA, PART and JRIP. The results obtained allow us to identify: R417, R379, R378, R419a, R410, R613, R1427 and R380, as the indi‐ cators that most influence low industrial performance.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 1; 13--21
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
From business to clinical trials: a systematic review of the literature on fraud detection methods to be used in central statistical monitoring
Autorzy:
Fronc, Maciej
Jakubczyk, Michał
Powiązania:
https://bibliotekanauki.pl/articles/2176605.pdf
Data publikacji:
2023-02-28
Wydawca:
Główny Urząd Statystyczny
Tematy:
fraud detection
clinical trials
finance
data mining
big data
Opis:
Data-driven decisions can be suboptimal when the data are distorted by fraudulent behaviour. Fraud is a common occurrence in finance or other related industries, where large datasets are handled and motivation for financial gain may be high. In order to detect and the prevent fraud, quantitative methods are used. Fraud, however, is also committed in other circumstances, e.g. during clinical trials. The article aims to verify which analytical fraud-detection methods used in finance may be adopted in the field of clinical trials. We systematically reviewed papers published over the last five years in two databases (Scopus and the Web of Science) in the field of economics, finance, management and business in general. We considered a broad scope of data mining techniques including artificial intelligence algorithms. As a result, 37 quantitative methods were identified with the potential of being fit for application in clinical trials. The methods were grouped into three categories: pre-processing techniques, supervised learning and unsupervised learning. Our findings may enhance the future use of fraud-detection methods in clinical trials.
Źródło:
Przegląd Statystyczny; 2022, 69, 3; 1-22
0033-2372
Pojawia się w:
Przegląd Statystyczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Investigation of the parameters of the effective mixing design on bleeding asphalt and reducing the drivers’ safety in right lane of tropical roads
Autorzy:
Khabiri, Mohamad Mehdi
Ghafori Fard, Zohreh
Nik Farjam, Hossein
Powiązania:
https://bibliotekanauki.pl/articles/27311346.pdf
Data publikacji:
2023
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
bleeding
data mining
right lane
tropical region
safety
odpowietrzanie
eksploracja danych
prawy pas
region tropikalny
bezpieczeństwo
Opis:
Distresses are integral parts of pavement that occur during the life of the road. Bitumen distress is known as one of the most important problems of Iran's roads, especially in tropical areas and transit routes with heavy axes; so, identifying the effective factors in creating the bleeding phenomenon is very necessary and important. Therefore, this study was conducted to investigate the parameters of the mixing design in creation of bleeding phenomenon and its severity. The collected data were then analyzed and grouped using Design Expert and SPSS software. The results show that all five parameters of optimal bitumen percent, bitumen percent in asphalt mixture, void percent of Marshall Sample, percent void and filler to bitumen ratio are effective on bitumen and its intensity. Among the mentioned parameters, two parameters of percent of bitumen compared to asphalt mixture and the void percent in the Marshall sample have a greater effect on the severity of the bleeding phenomenon.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2023, 119; 19--35
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine Learning Algorithms for Data Enrichment: A Promising Solution for Enhancing Accuracy in Predicting Blast-Induced Ground Vibration in Open-Pit Mines
Autorzy:
Nguyen, Hoang
Bui, Xuan-Nam
Drebenstedt, Carsten
Powiązania:
https://bibliotekanauki.pl/articles/25212182.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
blast-induced ground vibration
data enrichment
sustainable and responsible mining
machine learning
open-pit mining
performance improvement
górnictwo odkrywkowe
sztuczna inteligencja
maszyny
Opis:
The issue of blast-induced ground vibration poses a significant environmental challenge in open-pit mines, necessitating precise prediction and control measures. While artificial intelligence and machine learning models hold promise in addressing this concern, their accuracy remains a notable issue due to constrained input variables, dataset size, and potential environmental impact. To mitigate these challenges, data enrichment emerges as a potential solution to enhance the efficacy of machine learning models, not only in blast-induced ground vibration prediction but also across various domains within the mining industry. This study explores the viability of utilizing machine learning for data enrichment, with the objective of generating an augmented dataset that offers enhanced insights based on existing data points for the prediction of blast-induced ground vibration. Leveraging the support vector machine (SVM), we uncover intrinsic relationships among input variables and subsequently integrate them as supplementary inputs. The enriched dataset is then harnessed to construct multiple machine learning models, including k-nearest neighbors (KNN), classification and regression trees (CART), and random forest (RF), all designed to predict blast-induced ground vibration. Comparative analysis between the enriched models and their original counterparts, established on the initial dataset, provides a foundation for extracting insights into optimizing the performance of machine learning models not only in the context of predicting blast-induced ground vibration but also in addressing broader challenges within the mining industry.
Źródło:
Inżynieria Mineralna; 2023, 2; 79--88
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning-based business rule engine data transformation over high-speed networks
Autorzy:
Neelima, Kenpi
Vasundra, S.
Powiązania:
https://bibliotekanauki.pl/articles/38700094.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
CRISP-DM
data mining algorithms
business rule
prediction
classification
machine learning
deep learning
AI design
algorytmy eksploracji danych
reguła biznesowa
prognoza
klasyfikacja
nauczanie maszynowe
uczenie głębokie
projekt Sztucznej Inteligencji
Opis:
Raw data processing is a key business operation. Business-specific rules determine howthe raw data should be transformed into business-required formats. When source datacontinuously changes its formats and has keying errors and invalid data, then the effectiveness of the data transformation is a big challenge. The conventional data extraction andtransformation technique produces a delay in handling such data because of continuousfluctuations in data formats and requires continuous development of a business rule engine.The best business rule engines require near real-time detection of business rule and datatransformation mechanisms utilizing machine learning classification models. Since data iscombined from numerous sources and older systems, it is challenging to categorize andcluster the data and apply suitable business rules to turn raw data into the business-required format. This paper proposes a methodology for designing ensemble machine learning techniques and approaches for classifying and segmenting registered numbersof registered title records to choose the most suitable business rule that can convert theregistered number into the format the business expects, allowing businesses to provide customers with the most recent data in less time. This study evaluates the suggested modelby gathering sample data and analyzing classification machine learning (ML) models todetermine the relevant business rule. Experimentation employed Python, R, SQL storedprocedures, Impala scripts, and Datameer tools.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 55-71
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Narzędzia Business Intelligence dedykowane do analityki big data
Business Intelligence tools dedicated to big data analytics
Autorzy:
Zabroń, Mariusz
Wołoszyn, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/28394709.pdf
Data publikacji:
2023-11
Wydawca:
Uniwersytet Rzeszowski
Tematy:
raportowanie
analiza danych
predykcja
eksploracja danych
sztuczna inteligencja
reporting
data analysis
prediction
data mining
artificial intelligence
Opis:
W dobie transformacji cyfrowej przedsiębiorstw, sprawna analityka biznesowa staje się koniecznością. Zastosowanie odpowiednich systemów informatycznych dedykowanych do tych operacji może sprawić, że podejmowanie decyzji biznesowych stanie się szybkie, proste i trafne. W niniejszym opracowaniu opisano szereg zagadnień związanych z terminem „Business Intelligence” (BI). Wyjaśniono kwestię samego pojęcia, a także przedstawiono dedykowane do tego narzędzia wraz z rozwiązaniami opartymi na elementach sztucznej inteligencji. Podstawowe rozważania dotyczą opisu narzędzi informatycznych dedykowanych analizie danych biznesowych, może być doskonałym punktem wyjścia do szczegółowych rozważań w tej dziedzinie.
In the era of digital transformation of enterprises, efficient business analytics is becoming a necessity. The use of appropriate IT systems dedicated to these operations can make making business decisions fast, simple and accurate. This paper describes a number of issues related to the term “Business Intelligence” (BI). The issue of the concept it self was explained, as well as dedicated tools were presented along with solutions based on elements of artificial intelligence. Basic consideration and description of IT tools dedicated to business data analysis can be an excellent starting point for detailed considerations in this field.
Źródło:
Dydaktyka informatyki; 2023, 18, 18; 185-193
2083-3156
2543-9847
Pojawia się w:
Dydaktyka informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Effect of Nanomaterial Type on Water Disinfection Using Data Mining
Autorzy:
Hamdan, Mohammad
Khalil, Rana Haj
Abdelhafez, Eman
Ajib, Salman
Powiązania:
https://bibliotekanauki.pl/articles/24201710.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
water disinfection
artificial neural network
nanotechnology
data mining
Opis:
Multiple linear regression and artificial neural network (ANN) models were utilized in this study to assess the type influence of nanomaterials on polluted water disinfection. This was accomplished by estimating E. coli (E.C) and the total coliform (TC) concentrations in contaminated water while nanoparticles were added at various concentrations as input variables, together with water temperature, PH, and turbidity. To achieve this objective, two approaches were implemented: data mining with two types of artificial neural networks (MLP and RBF), and multiple linear regression models (MLR). The simulation was conducted using SPSS software. Data mining was revealed after the estimated findings were checked against the measured data. It was found that MLP was the most promising model in the prediction of the TC and E.C concentration, s followed by the RBF and MLR models, respectively.
Źródło:
Journal of Ecological Engineering; 2023, 24, 4; 244--251
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vision-based biomechanical markerless motion classification
Autorzy:
Liew, Yu Liang
Chin, Jeng Feng
Powiązania:
https://bibliotekanauki.pl/articles/2204259.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
vision
single camera
markerless
stick model
human motion
motion classification
data mining
Opis:
This study used stick model augmentation on single-camera motion video to create a markerless motion classification model of manual operations. All videos were augmented with a stick model composed of keypoints and lines by using the programming model, which later incorporated the COCO dataset, OpenCV and OpenPose modules to estimate the coordinates and body joints. The stick model data included the initial velocity, cumulative velocity, and acceleration for each body joint. The extracted motion vector data were normalized using three different techniques, and the resulting datasets were subjected to eight classifiers. The experiment involved four distinct motion sequences performed by eight participants. The random forest classifier performed the best in terms of accuracy in recorded data classification in its min-max normalized dataset. This classifier also obtained a score of 81.80% for the dataset before random subsampling and a score of 92.37% for the resampled dataset. Meanwhile, the random subsampling method dramatically improved classification accuracy by removing noise data and replacing them with replicated instances to balance the class. This research advances methodological and applied knowledge on the capture and classification of human motion using a single camera view.
Źródło:
Machine Graphics & Vision; 2023, 32, 1; 3--24
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Brand position in the eyes of customers: assessment of selected airlines by the passengers online reviews
Autorzy:
Hoffmann, Natalia
Powiązania:
https://bibliotekanauki.pl/articles/16729698.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
data mining
text mining
branch
brand
opinion
R
client
airline
sentiment analysis
Opis:
Motivation: The motivation to write an article on airlines was the desire to rank them based on customer reviews and see how these reviews reflect the actual brand image. The opinions that companies collect about themselves have a very strong power when it comes to building its reputation. Aim: The aim of the study was to use digital transformation and transform raw data into specific information that expressed customer emotions to create a profile of selected airlines. A secondary goal of the article was also to check how the analyzed airlines perform in similar areas. Materials and methods: The data used for the analysis was collected from the eSky.com website and covers the 2019-2020 period. The airlines concerned by the customer reviews were LOT, Ryanair, Wizzair, Czarter, EasyJet, Lufthansa and Laudamotion. Their selection was dictated by the number of opinions necessary to conduct the analysis. The research based on the use of data mining techniques, but it should be noted that most of it uses text mining tools. Topic modelling was used to prepare the data properly and assign each word to groups with similar themes. In order to obtain information whether a given opinion has a positive, negative or neutral tenor, sentiment analysis was used. The final part of the analysis was based on the net sentiment score indicator. The entire analysis was carried out in the R-Studio. Results: The most common subjects of opinions written by customers were "delay", "service", "boarding" and "airline". It was confirmed that the opinions of each airline concern different topics, although some common topics were noticeable. Two topics were repeated among the 7 analyzed airlines: "service" and "delay". Based on the sentiment analysis, for the Ryanair airline the percentage of negative opinions was highest and equal to 35%, almost 40%, of neutral opinions fell on the WizzAir airline and the largest percentage of positive feedback, as much as 46%, was attributed to EasyJet. EasyJet line looks the best in the eyes of customers. The line that evoked uniformly positive, negative and neutral emotions in the opinions was Ryanair.
Źródło:
Catallaxy; 2022, 7, 1; 7-21
2544-090X
Pojawia się w:
Catallaxy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data mining approach in diagnosis and treatment of chronic kidney disease
Autorzy:
Turiac, Andreea S.
Zdrodowska, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/2105985.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
feature selection
classification
classification rules
action rules
data mining
chronic kidney disease
Opis:
Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their deci-sions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.
Źródło:
Acta Mechanica et Automatica; 2022, 16, 3; 180--188
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dozwolony użytek w zakresie eksploracji tekstów i danych w świetle Dyrektywy Parlamentu Europejskiego i Rady (UE) 2019/790
Autorzy:
Bagieńska-Masiota, Aleksandra
Powiązania:
https://bibliotekanauki.pl/articles/2056883.pdf
Data publikacji:
2022-06-09
Wydawca:
Uniwersytet Pedagogiczny im. Komisji Edukacji Narodowej w Krakowie
Tematy:
dozwolony użytek
eksploracja tekstów i danych
Text and Data Mining
Dyrektywa DSM
fair use
DSM Directive
Opis:
W artykule przeprowadzono analizę prawodawstwa europejskiego w zakresie eksploracji tekstów i danych na podstawie Dyrektywy Parlamentu Europejskiego i Rady (UE) 2019/790 w sprawie praw autorskich i pokrewnych na jednolitym rynku cyfrowym (Dyrektywa DSM). Przedmiotowa Dyrektywa wprowadziła dwa obligatoryjne wyjątki od istniejących na gruncie prawa europejskiego praw wyłącznych, na potrzeby eksploracji tekstów i danych (art. 3 i 4). Ponadto artykuł odpowiada na pytanie, czy i w jakim stopniu przepisy polskiego prawa autorskiego wymagają zmian dostosowawczych do porządku europejskiego w przedmiotowym zakresie.
The paper analyzes European legislation on text and data mining, based on Directive 2019/790 of the European Parliament and of the Council on Copyright and related rights in the Digital Single Market (DSM Directive). The Directive has introduced two mandatory exceptions to existing exclusive rights under European law for the purpose of text and data mining (Articles 3 and 4). Moreover, the article answers the question whether and to what extent the provisions of Polish copyright law require adjustment to the European order in this respect.
Źródło:
Annales Universitatis Paedagogicae Cracoviensis. Studia de Cultura; 2022, 14, 1; 118-128
2083-7275
Pojawia się w:
Annales Universitatis Paedagogicae Cracoviensis. Studia de Cultura
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fundamentals of a recommendation system for the aluminum extrusion process based on data-driven modeling
Autorzy:
Perzyk, Marcin
Kochański, Andrzej
Kozłowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/29520062.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
aluminum extrusion
advisory system
product defect
data mining
neural networks
system doradczy
wada produktu
eksploracja danych
sieci neuronowe
Opis:
The aluminum profile extrusion process is briefly characterized in the paper, together with the presentation of historical, automatically recorded data. The initial selection of the important, widely understood, process parameters was made using statistical methods such as correlation analysis for continuous and categorical (discrete) variables and ‘inverse’ ANOVA and Kruskal–Wallis methods. These selected process variables were used as inputs for MLP-type neural models with two main product defects as the numerical outputs with values 0 and 1. A multi-variant development program was applied for the neural networks and the best neural models were utilized for finding the characteristic influence of the process parameters on the product quality. The final result of the research is the basis of a recommendation system for the significant process parameters that uses a combination of information from previous cases and neural models.
Źródło:
Computer Methods in Materials Science; 2022, 22, 4; 173-188
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Industry 4.0: selected aspects of algorithmization of work environment
Autorzy:
Osika, Grażyna
Powiązania:
https://bibliotekanauki.pl/articles/27313534.pdf
Data publikacji:
2022
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
Industry 4.0
HRM
e-HRM
algorithmization
data mining
data science
Przemysł 4.0
algorytmizacja
eksploracja danych
nauka o danych
Opis:
Purpose: The aim of the article is to describe and forecast possible difficulties related to the development of cognitive technologies and the progressing of algorithmization of HRM processes as a part of Industry 4.0. Design/methodology/approach: While most of the studies to date related to the phenomenon of Industry 4.0 and Big Data are concerned with the level of efficiency of cyber-physical systems and the improvement of algorithmic tools, this study proposes a different perspective. It is an attempt to foresee the possible difficulties connected with algorithmization HRM processes, which understanding could help to "prepare" or even eliminate the harmful effects we may face which will affect decisions made in the field of the managing organizations, especially regarding human resources management, in era of Industry 4.0. Findings: The research of cognitive technologies in the broadest sense is primarily associated with a focus of thinking on their effectiveness, which can result in a one-sided view and ultimately a lack of objective assessment of that effectiveness. Therefore, conducting a parallel critical reflection seems even necessary. This reflection has the potential to lead to a more balanced assessment of what is undoubtedly "for", but also of what may be "against". The proposed point of view may contribute to a more informed use of algorithm-based cognitive technologies in the human resource management process, and thus to improve their real-world effectiveness. Social implications: The article can have an educational function, helps to develop critical thinking about cognitive technologies, and directs attention to areas of knowledge by which future skills should be extended. Originality/value: This article is addressed to all those who use algorithms and data-driven decision-making processes in HRM. Crucial in these considerations is the to draw attention to the dangers of unreflective use of technical solutions supporting HRM processes. The novelty of the proposed approach is the identification of three potential risk areas that may result in faulty HR decisions. These include the risk of "technological proof of equity", overconfidence in the objective character of algorithms and the existence of a real danger resulting from the so-called algorithm overfitting. Recognition of these difficulties ultimately contributed to real improvements in productivity by combining human performance with technology effectiveness.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2022, 155; 431--450
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Osiadanie powierzchni terenu w budownictwie na terenach górniczych
Subsidence of the land surface in construction in mining areas
Autorzy:
Paleczek, Witold
Powiązania:
https://bibliotekanauki.pl/articles/2202707.pdf
Data publikacji:
2022
Wydawca:
Polski Związek Inżynierów i Techników Budownictwa
Tematy:
osiadanie powierzchni
tereny górnicze
aproksymacja
masyw skalny
dane empiryczne
obliczenia
surface subsidence
mining areas
approximation
rock massif
empirical data
calculations
Opis:
Przedstawiono wyniki aproksymacji funkcji osiadania dwóch znanych teorii, tj. teorii Knothego-Budryka oraz teorii Chudka-Stefańskiego. Dotyczą one wpływów podziemnej eksploatacji górniczej na powierzchnię i górotwór. Funkcję obniżeń, określoną w tych teoriach wzorami całkowymi, aproksymowano do postaci algebraicznej, w taki sposób, aby nie było konieczności stosowania rachunku całkowego, uwzględniając jednocześnie średnie wartości geomechaniczne masywu skalnego. Dane empiryczne pozyskane z 34 rodzajów skał pozyskanych z 16 otworów wiertniczo-badawczych. Uzyskane zależności matematyczne umożliwiają obliczanie obniżeń powierzchni terenu na podstawie znanej geometrii wyrobisk i ich głębokości zalegania oraz wymienionych tu parametrów górotworu. Zaproponowano wzory empiryczne do oszacowania obrzeża eksploatacyjnego w funkcji zadanych parametrów geomechanicznych. Porównano różnice wyników otrzymywanych ze wzorów całkowych i otrzymanych wzorów aproksymujących.
The results of the approximation of the settlement function of two known theories, i.e. the Knothe-Budryk theory and the Chudek-Stefański theory, are presented. They concern the impact of underground mining on the surface and rock mass. The subsidence function, defined in these Theories by integral formulas, was approximated to an algebraic form in such a way that there was no need to use integral calculus, while taking into account the average geomechanical values of the rock mass. Empirical data obtained from 34 types of rocks obtained from 16 boreholes. The obtained mathematical dependencies enable the calculation of land surface depressions based on the known geometry of excavations and their depth, as well as the rock mass parameters listed here. Empirical formulas for the estimation of the exploitation margin as a function of the given geomechanical parameters were proposed.
Źródło:
Przegląd Budowlany; 2022, 93, 11-12; 86--89
0033-2038
Pojawia się w:
Przegląd Budowlany
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 285

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies