TY - JOUR AU - Lakatos, Róbert AU - Pollner, Péter AU - Hajdu, András AU - Joó, Tamás TI - A multimodal deep learning architecture for smoking detection with a small data approach JF - FRONTIERS IN ARTIFICIAL INTELLIGENCE J2 - FRONTI ARTIF INTELL VL - 7 PY - 2024 PG - 8 SN - 2624-8212 DO - 10.3389/frai.2024.1326050 UR - https://m2.mtmt.hu/api/publication/34694205 ID - 34694205 AB - Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data. LA - English DB - MTMT ER - TY - JOUR AU - Lakatos, Róbert AU - Bogacsovics, Gergő AU - Harangi, Balázs AU - Lakatos, István AU - Tiba, Attila AU - Tóth, János AU - Szabó, Marianna AU - Hajdu, András TI - A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews JF - BIG DATA AND COGNITIVE COMPUTING J2 - BIG DATA COGN COMPUT VL - 8 PY - 2024 IS - 3 SP - 1 EP - 24 PG - 24 SN - 2504-2289 DO - 10.3390/bdcc8030020 UR - https://m2.mtmt.hu/api/publication/34676546 ID - 34676546 AB - The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task. LA - English DB - MTMT ER - TY - CHAP AU - Bouali, Kassem Anis AU - Hajdu, András ED - Kotecha, Ketan ED - Vakaj, Edlira ED - Mishra, Sashikala ED - Ortiz-Rodríguez, Fernando ED - Tiwari, Sanju TI - Real-Time Birds Shadow Detection for Autonomous UAVs T2 - Artificial Intelligence: Towards Sustainable Intelligence PB - Springer Nature Switzerland AG CY - Cham SN - 9783031479977 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1907. PY - 2023 SP - 169 EP - 177 PG - 9 DO - 10.1007/978-3-031-47997-7_13 UR - https://m2.mtmt.hu/api/publication/34560319 ID - 34560319 LA - English DB - MTMT ER - TY - JOUR AU - Pándy, Árpád AU - Kovács, László AU - Hajdu, András TI - Steering Angle Prediction From a Camera Image as a Backup Service JF - IEEE SENSORS LETTERS J2 - IEEE SENSORS LETTERS VL - 7 PY - 2023 IS - 11 SP - 1 EP - 4 PG - 4 SN - 2475-1472 DO - 10.1109/LSENS.2023.3326105 UR - https://m2.mtmt.hu/api/publication/34529262 ID - 34529262 LA - English DB - MTMT ER - TY - CHAP AU - Pándy, Árpád AU - Harangi, Balázs AU - Hajdu, András TI - Extracting Drug Names from Medical Reports T2 - 2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT) PB - IEEE SN - 9798350360462 PY - 2023 SP - 1 EP - 4 PG - 4 DO - 10.1109/CSIT61576.2023.10324071 UR - https://m2.mtmt.hu/api/publication/34523011 ID - 34523011 LA - English DB - MTMT ER - TY - JOUR AU - Gombos, Béla AU - Nagy, Zoltán AU - Hajdu, András AU - Nagy, János TI - Climate change in the Debrecen area in the last 50 years and its impact on maize production JF - IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE J2 - IDŐJÁRÁS VL - 127 PY - 2023 IS - 4 SP - 485 EP - 504 PG - 20 SN - 0324-6329 DO - 10.28974/idojaras.2023.4.5 UR - https://m2.mtmt.hu/api/publication/34417887 ID - 34417887 AB - The average yield of maize is significantly dependent on the meteorological conditions of the growing year. Both the most favorable weather conditions and the weather anomalies that tend to cause damage depend on the given phenophase. The aim of this research is to analyze the climatic changes that are important in maize production in the Hajdúság region. For the climatological study of the area, homogenized temperature and precipitation data from the Hungarian Meteorological Service was used for the Debrecen region, which are freely available for download from the data repository of the institution. Trend analysis was performed for the last 50-year (1973–2022) and 30-year (1993–2022) periods. In total, 40 meteorological data series matching the study objective were analyzed. Linear regression calculations were performed using the SPSS 27 statistical software. For the non-parametric procedure, the MAKESENS Excel application was used, based on the Mann-Kendall (MK) test and Sen's slope estimation. This research shows that the choice of the length of the study period affects the results of trend analysis. The numerical values of the trend slope for the 30-year vs. 50-year period differ, and for some parameters there are also substantial differences (e.g., trend sign). The results of the parametric and non-parametric trend analyses differed only marginally for the temperature variables included. Also, for precipitation data that do not follow a normal distribution (e.g., monthly), there were only a few significant differences. The trend in mean annual temperature shows an increase of 0.39 and 0.52 °C in 10 years, and an increase of around 2 °C in 50 years and 1.5 °C in 30 years. There is a significant warming in both the summer and winter half-years, with the summer half-year showing a steeper upward trend in the 50-year data series and the winter half-year in the 30-year data series. There is a clear pattern of large, highly significant warming in the summer months and less significant changes in the two spring and two autumn months that were observed. A negative, non-significant trend in annual precipitation is observed. The decreases of 17 mm and 24 mm/10 years obtained for the 50- and 30-year time series are not negligible from a practical point of view. For the summer half-year, the precipitation amount is decreasing, with a slope of -27 mm/10 years for the last 30 years, but even this value is not significant due to the high variability. There is no significant change in the amount of precipitation in the winter half-year over the last decades. Significant trends cannot be detected from monthly or even semi-annual or annual precipitation data. The Mann-Kendall test showed a trend decrease only in the 30-year April data series at the p=0.1 significance level. Overall, the changes are negative for maize production. It should be highlighted that the obvious warming, combined with a slight decrease in precipitation, is leading to a deterioration in crop water availability and a reduction in crop yields. The impact of the identified adverse climatic changes can be compensated to a significant extent by the proposed agrotechnical responses. LA - English DB - MTMT ER - TY - JOUR AU - Harangi, Balázs AU - Baran, Ágnes AU - Beregi-Kovács, Marcell AU - Hajdu, András TI - Composing Diverse Ensembles of Convolutional Neural Networks by Penalization JF - MATHEMATICS J2 - MATHEMATICS-BASEL VL - 11 PY - 2023 IS - 23 SP - 4730 SN - 2227-7390 DO - 10.3390/math11234730 UR - https://m2.mtmt.hu/api/publication/34395482 ID - 34395482 AB - Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNNs) can be created by forcing them to adjust their parameters during the training process to increase diversity in their decisions. As a new theoretical approach to reach this aim, we join the member neural architectures via a fully connected layer and insert a new correlation penalty term in the loss function to obstruct their similar operation. With this complementary term, we implement the standard guideline of ensemble creation to increase the members’ diversity for CNNs in a more detailed and flexible way than similar existing techniques. As for applicability, we show that our approach can be efficiently used in various classification tasks. More specifically, we demonstrate its performance in challenging medical image analysis and natural image classification problems. Besides the theoretical considerations and foundations, our experimental findings suggest that the proposed technique is competitive. Namely, on the one hand, the classification rate of the ensemble trained in this way outperformed all the individual accuracies of the state-of-the-art member CNNs according to the standard error functions of these application domains. On the other hand, it is also validated that the ensemble members get more diverse and their accuracies are raised by adding the penalization term. Moreover, we performed a full comparative analysis, including other state-of-the-art ensemble-based approaches recommended for the same classification tasks. This comparative study also confirmed the superiority of our method, as it overcame the current solutions. LA - English DB - MTMT ER - TY - CHAP AU - Bogacsovics, Gergő AU - Harangi, Balázs AU - Hajdu, András ED - Sicilia, R. ED - Kane, B. ED - Almeida, J.R. ED - Spiliopoulou, M. ED - Andrades, J.A.B. ED - Placidi, G. ED - Gonzalez, A.R. TI - Increasing the diversity of ensemble members for accurate brain tumor classification T2 - 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023 PB - Institute of Electrical and Electronics Engineers (IEEE) SN - 9798350312249 T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems, ISSN 1063-7125 ; 2023-June. PY - 2023 SP - 529 EP - 534 PG - 6 DO - 10.1109/CBMS58004.2023.00274 UR - https://m2.mtmt.hu/api/publication/34216921 ID - 34216921 LA - English DB - MTMT ER - TY - JOUR AU - Tóth, János AU - Tomán, Henrietta AU - Hajdu, Gabriella AU - Hajdu, András TI - Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles JF - MATHEMATICS J2 - MATHEMATICS-BASEL VL - 11 PY - 2023 IS - 18 PG - 17 SN - 2227-7390 DO - 10.3390/math11183992 UR - https://m2.mtmt.hu/api/publication/34182540 ID - 34182540 AB - An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F1 score of 0.9397 and an associated MCC of 0.7757. Our proposed method maintained the solution quality with an F1 score of 0.9395 and MCC of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold. LA - English DB - MTMT ER - TY - JOUR AU - Liu, Ying AU - Zhou, Shujing AU - Wang, Longbin AU - Xu, Ming AU - Huang, Xufeng AU - Li, Zhengrui AU - Hajdu, András AU - Zhang, Ling TI - Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction JF - FRONTIERS IN MICROBIOLOGY J2 - FRONT MICROBIOL VL - 14 PY - 2023 PG - 6 SN - 1664-302X DO - 10.3389/fmicb.2023.1153106 UR - https://m2.mtmt.hu/api/publication/33731626 ID - 33731626 LA - English DB - MTMT ER -