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1
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Texture recognition system based on the Deep Neural Network

100%
Kapela R., Kapela R.
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2020
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issue 6
2
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Manufacturing lead time prediction for extrusion tools with the use of neural networks

89%
Sajko N., Kovacic S., Ficko M., Palcic I., Klancnik S., Sajko N., Kovacic S., Ficko M., Palcic I., Klancnik S.
Management and Production Engineering Review
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2020
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vol. 11
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issue 3
3
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Deep learning in pharmacology: opportunities and threats

88%
Kocić I., Kocić M., Rusiecka I., Kocić A., Kocić E.
European Journal of Translational and Clinical Medicine
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2021
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vol. 5
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issue 2
88-94
EN
Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.
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Structural approach to medical image understanding

88%
Tadeusiewicz R., Ogiela M. R.
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issue 2
5
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Is medicine ready for ChatGPT – why not just ask ChatGPT?

88%
Rodigin A.
European Journal of Translational and Clinical Medicine
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2023
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vol. 6
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issue 1
5-8
EN
Vol. 6
6
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Intelligent energy management system of a smart microgrid using multiagent systems

75%
Azeroual M., Lamhamdi T., El Moussaoui H., El Markhi H., Azeroual M., Lamhamdi T., El Moussaoui H., El Markhi H.
Archives of Electrical Engineering
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2020
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vol. 69
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issue 1
7
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Study of the BA1044 Alloy Properties and Their Interpretation with the Use of Fuzzy Logic

75%
Kluska-Nawarecka S., Wilk-Kołodziejczyk D., Górny Z., Saja K.
Archives of Foundry Engineering
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2013
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issue 2
8
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Residual life estimation of fabricated humidity sensors using different artificial intelligence techniques

75%
Bhargava C., Aggarwal J., Sharma P. K.
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2019
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issue 1
9
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Evolutionary design and optimization of combinational digital circuits with respect to transistor count

75%
Słowik A., Białko M.
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2006
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vol. 54
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issue 4
10
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Design of IIR digital filters with non-standard characteristics using differential evolution algorithm

75%
Słowik A., Białko M.
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2007
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vol. 55
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issue 4
11
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Artificial intelligence for supervised classification purposes: Case of the surface water quality in the Moulouya River, Morocco

75%
Manssouri I., Talhaoui A., El Hmaidi A., Boudad B., Boudebbouz B., Sahbi H., Manssouri I., Talhaoui A., El Hmaidi A., Boudad B., Boudebbouz B., Sahbi H.
Journal of Water and Land Development
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2021
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issue 50
12
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Heuristic Model of the Mechanical Properties of a Hypoeutectic EN AC-42100 (EN AC-AlSi7Mg0.3) Silumin Alloy Subjected to Heat Treatment

75%
Kluska-Nawarecka S., Górny Z., Wilk-Kołodziejczyk D., Czekaj E.
Archives of Foundry Engineering
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2014
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issue 4
13
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A comparative study of artificial intelligence models for predicting monthly river suspended sediment load

75%
Rezaei K., Vadiati M., Rezaei K., Vadiati M.
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14
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Intelligent system supporting technological process planning for machining and 3D printing

75%
Rojek I., Mikołajewski D., Kotlarz P., Macko M., Kopowski J., Rojek I., Mikołajewski D., Kotlarz P., Macko M., Kopowski J.
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2021
15
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Coherent structures and flow control: genesis and prospect

51%
M G.-E. H.
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2019
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issue 3
16
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Evaluating concrete materials by application of automatic reasoning

51%
Alterman D., Kasperkiewicz J.
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2006
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vol. 54
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issue 4
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