Search results

1

Design of a Machine Learning Based Predictive Analytics System for Spam Problem

100%

Yüksel A., Çankaya Ş., Üncü İ.

Acta Physica Polonica A

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2017

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vol. 132

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issue 3

500-504

EN

Spamming is the act of abusing an electronic messaging system by sending unsolicited bulk messages. Filtering of these messages is merely another line of defence and does not prevent spam messages from circulating in email systems. This problem causes users to distrust email systems, suspect even legitimate emails and leads to substantial investment in technologies to counter the spam problem. Spammers threaten users by abusing the lack of accountability and verification features of communicating entities. To contribute to the fight against spamming, a cloud-based system that analyses the email server logs and uses predictive analytics with machine learning to build trust identities that model the email messaging behavior of spamming and legitimate servers has been designed. The system constructs trust models for servers, updating them regularly to tune the models. This study proposed that this approach will not only minimize the circulation of spam in email messaging systems, but will also be a novel step in the direction of trust identities and accountability in email infrastructure.

2

Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis

88%

Borzyszkowski B., Damaszke K., Romankiewicz J., Świniarski M., Moszyński M., Borzyszkowski B., Damaszke K., Romankiewicz J., Świniarski M., Moszyński M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2021

3

Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method

88%

Pajchrowski T., Siwek P., Wójcik A., Pajchrowski T., Siwek P., Wójcik A.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2020

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issue 5

4

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

88%

Fang Z., Yang H., Li C., Cheng L., Zhao M., Xie C., Fang Z., Yang H., Li C., Cheng L., Zhao M., Xie C.

Archives of Environmental Protection

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2021

5

Application of genetic programming for proton-proton interactions

88%

El-Dahshan E.-S.

Open Physics

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2011

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vol. 9

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issue 3

874-883

EN

The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P(n ch)), the average multiplicity (〈n ch〉) and the total cross section (σ tot) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy ($$ \sqrt s $$) and charged particles (n ch). Also, both the average multiplicity and the total cross section are obtained as a function of $$ \sqrt s $$. Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data.

6

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

88%

Kun Q., Janott C., Zixing Z., Jun D., Baird A., Clemens H., Hohenhorst W., Herzog M., Werner H., Schuller B.

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issue 3

7

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.

8

Automatic speech based emotion recognition using paralinguistics features

88%

Hook J., Noroozi F., Toygar O., Anbarjafari G.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2019

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issue 3

9

The identification method of the coal mill motor power model with the use of machine learning techniques

75%

Łabęda-Grudziak Z. M., Lipiński M., Łabęda-Grudziak Z. M., Lipiński M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2021

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issue 1

10

MIMO Beam Selection in 5G Using Neural Networks

75%

Ruseckas J., Molis G., Bogucka H., Ruseckas J., Molis G., Bogucka H.

International Journal of Electronics and Telecommunications

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2021

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vol. 67

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issue 4

11

Stationary supercapacitor energy storage operation algorithm based on neural network learning system

75%

Jefimowski W., Nikitenko A., Drążek Z., Wieczorek M., Jefimowski W., Nikitenko A., Drążek Z., Wieczorek M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2020

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issue 4

12

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

75%

Fuada S., Shiddieqy H. A., Adiono T., Fuada S., Shiddieqy H. A., Adiono T.

International Journal of Electronics and Telecommunications

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2020

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vol. 66

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issue 4

13

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

75%

Drzewiecki W.

Geodesy and Cartography

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2017

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vol. 66

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issue 2

14

A machine learning-based mobile robot visual homing approach

75%

Zhu Q., Ji X., Cai C., Wang J.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2018

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issue 5

15

Hybrid Predictions of the Homogenous Properties’ Market Value with the Use of Ann

75%

Anysz H., Podwórna M., Ibadov N., Lennerts K., Dikarev K., Anysz H., Podwórna M., Ibadov N., Lennerts K., Dikarev K.

Archives of Civil Engineering

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2021

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vol. 67

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issue 1

16

Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

75%

Wodziński M., Krzyżanowska A.

Metrology and Measurement Systems

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2017

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vol. 24

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issue 2

17

Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning

75%

Usman M., Zubair M., Ahmad Z., Zaidi M., Ijyas T., Parayangat M., Wajid M., Shiblee M., Ali J. A., Usman M., Zubair M., Ahmad Z., Zaidi M., Ijyas T., Parayangat M., Wajid M., Shiblee M., Ali J. A.

Archives of Acoustics

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2021

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vol. 46

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issue 1

18

An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment

75%

Tharani P., Kalpana A. M., Tharani P., Kalpana A. M.

Bulletin of the Polish Academy of Sciences: Technical Sciences

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2021

19

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

75%

Drzewiecki W.

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issue 2

20

ChatGPT – a tool for assisted studying or a source of misleading medical information? AI performance on Polish Medical Final Examination

75%

Żmudka K., Spychał A., Ochman B., Popowicz Ł., Piłat P., Jaroszewicz J.

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PL

WSTĘP: ChatGPT jest modelem językowym stworzonym przez OpenAI, który może udzielać odpowiedzi na zapytania użytkownika, generując tekst na podstawie otrzymanych danych. Celem pracy była ocena wyników działania ChatGPT na polskim Lekarskim Egzaminie Końcowym (LEK) oraz czynników wpływających na odsetek prawidłowych odpowiedzi. Ponadto zbadano zdolność chatbota do podawania poprawnego i wnikliwego wyjaśnienia. MATERIAŁ I METODY: Wprowadzono 591 pytań z dystraktorami z bazy LEK do interfejsu ChatGPT (wersja 13 lutego – 14 marca). Porównano wyniki z kluczem odpowiedzi i przeanalizowano podane wyjaśnienia pod kątem logicznego uzasadnienia. Dla poprawnych odpowiedzi przeanalizowano spójność logiczną wyjaśnienia, natomiast w przypadku odpowiedzi błędnej obserwowano zdolność do poprawy. Wybrane czynniki zostały przeanalizowane pod kątem wpływu na zdolność chatbota do udzielenia poprawnej odpowiedzi. WYNIKI: ChatGPT osiągnął imponujące wyniki poprawnych odpowiedzi na poziomie: 58,16%, 60,91% i 67,86%, przekraczając oficjalny próg 56% w trzech ostatnich egzaminach. W przypadku poprawnie udzielonych odpowiedzi ponad 70% pytań zostało popartych logicznie spójnym wyjaśnieniem. W przypadku błędnych odpowiedzi w 66% przypadków chatbot podał pozornie poprawne wyjaśnienie dla nieprawidłowych od-powiedzi. Czynniki takie jak konstrukcja logiczna (p < 0,05) i wskaźnik trudności zadania (p < 0,05) miały wpływ na ogólną ocenę, podczas gdy liczba znaków (p = 0,46) i język (p = 0,14) takiego wpływu nie miały. WNIOSKI: Mimo iż ChatGPT osiągnął wystarczającą liczbę punktów, aby zaliczyć LEK, w wielu przypadkach podawał wprowadzające w błąd informacje poparte pozornie przekonującym wyjaśnieniem. Chatboty mogą być szczególnym zagrożeniem dla użytkownika niemającego wiedzy medycznej, ponieważ w porównaniu z wyszukiwarką internetową dają natychmiastowe, przekonujące wyjaśnienie, co może stanowić zagrożenie dla zdrowia publicznego. Z tych samych przyczyn ChatGPT powinien być ostrożnie stosowany jako pomoc naukowa.

EN

INTRODUCTION: ChatGPT is a language model created by OpenAI that can engage in human-like conversations and generate text based on the input it receives. The aim of the study was to assess the overall performance of ChatGPT on the Polish Medical Final Examination (Lekarski Egzamin Końcowy – LEK) the factors influencing the percentage of correct answers. Secondly, investigate the capabilities of chatbot to provide explanations was examined. MATERIAL AND METHODS: We entered 591 questions with distractors from the LEK database into ChatGPT (version 13th February – 14th March). We compared the results with the answer key and analyzed the provided explanation for logical justification. For the correct answers we analyzed the logical consistency of the explanation, while for the incorrect answers, the ability to provide a correction was observed. Selected factors were analyzed for an influence on the chatbot’s performance. RESULTS: ChatGPT achieved impressive scores of 58.16%, 60.91% and 67.86% allowing it pass the official threshold of 56% in all instances. For the properly answered questions, more than 70% were backed by a logically coherent explanation. In the case of the wrongly answered questions the chatbot provided a seemingly correct explanation for false information in 66% of the cases. Factors such as logical construction (p < 0.05) and difficulty (p < 0.05) had an influence on the overall score, meanwhile the length (p = 0.46) and language (p = 0.14) did not. CONCLUSIONS: Although achieving a sufficient score to pass LEK, ChatGPT in many cases provides misleading information backed by a seemingly compelling explanation. The chatbot can be especially misleading for non-medical users as compared to a web search because it can provide instant compelling explanations. Thus, if used improperly, it could pose a danger to public health. This makes it a problematic recommendation for assisted studying.

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Design of a Machine Learning Based Predictive Analytics System for Spam Problem

Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis

Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

Application of genetic programming for proton-proton interactions

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

Deep learning in pharmacology: opportunities and threats

Automatic speech based emotion recognition using paralinguistics features

The identification method of the coal mill motor power model with the use of machine learning techniques

MIMO Beam Selection in 5G Using Neural Networks

Stationary supercapacitor energy storage operation algorithm based on neural network learning system

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

A machine learning-based mobile robot visual homing approach

Hybrid Predictions of the Homogenous Properties’ Market Value with the Use of Ann

Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning

An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

ChatGPT – a tool for assisted studying or a source of misleading medical information? AI performance on Polish Medical Final Examination