@inproceedings{MTMT:34567948,
	title = {In vitro Dissolution Prediction using Deep Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34567948},
	author = {Knyihár, Gábor and Csorba, Kristóf},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23)},
	unique-id = {34567948},
	year = {2023},
	pages = {27-38}
}

@inproceedings{MTMT:34476285,
	title = {Droplet Based Prediction of Viscosity of Water-PVP Solutions Using Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34476285},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Nagy, Brigitta},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23)},
	unique-id = {34476285},
	abstract = {The viscosity of a liquid is the property that measures the liquid internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields including chemical, pharmaceutical, food, and energy-related industries. The most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for industries where accurate and real-time viscosity information is vital. In this work, we prepared thirteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a Convolutional neural network model to estimate the viscosity values of the WaterPVP solutions. The proposed model was able to predict the viscosity values of the samples using images of their droplets with a high accuracy on the test dataset.},
	year = {2023},
	pages = {1-15},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@article{MTMT:34045465,
	title = {Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34045465},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Charaf, Hassan},
	doi = {10.3390/pr11071917},
	journal-iso = {PROCESSES},
	journal = {PROCESSES},
	volume = {11},
	unique-id = {34045465},
	issn = {2227-9717},
	abstract = {The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required.},
	year = {2023},
	eissn = {2227-9717},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@inproceedings{MTMT:33605280,
	title = {Viscosity Estimation of Water-PVP Solutions from Droplets using Artificial Neural Networks and Image Processing},
	url = {https://m2.mtmt.hu/api/publication/33605280},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Nagy, Brigitta},
	booktitle = {Artificial Intelligence and Soft Computing},
	doi = {10.1007/978-3-031-42505-9_14},
	unique-id = {33605280},
	year = {2023},
	pages = {157-166},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@article{MTMT:33604474,
	title = {Evaluating the Accuracy of a Linear Regression Model in Predicting the Dissolution of Tablets based on Raman Maps},
	url = {https://m2.mtmt.hu/api/publication/33604474},
	author = {Knyihár, Gábor and Csorba, Kristóf and Charaf, Hassan},
	doi = {10.5121/sipij.2023.14102},
	journal-iso = {SIPIJ},
	journal = {SIGNAL & IMAGE PROCESSING: AN INTERNATIONAL JOURNAL},
	volume = {14},
	unique-id = {33604474},
	issn = {2229-3922},
	abstract = {Investigation of the dissolution of tablets is an important area of pharmaceutical research. Such research aims to predict the dissolution process as accurately as possible without destroying the tablets. Several methods have been published that can estimate dissolution with approximate accuracy, but they are primarily complex learning algorithms that are time-consuming and require many samples to train. This article seeks to answer whether these complex models are necessary or whether a similar result can be achieved with the help of more straightforward methods. Therefore, during this work, a simpler linear regression model was created and analysed its effectiveness in estimating the dissolution curves. The investigation concluded that the results are not as accurate as in the case of more complex methods, but the model is more robust and can be used in the case of fewer samples. Thus, by further developing these and combining the methods, we can achieve better results in the future.},
	keywords = {Linear regression; Principal component analysis (PCA); RAMAN spectroscopy; Dissolution curve},
	year = {2023},
	eissn = {0976-710X},
	pages = {23-33}
}

@inproceedings{MTMT:33604424,
	title = {Tabletták kioldódásának előrejelzése Raman térképek alapján lineáris regressziós modell segítségével},
	url = {https://m2.mtmt.hu/api/publication/33604424},
	author = {Knyihár, Gábor and Csorba, Kristóf and Charaf, Hassan},
	booktitle = {Hungarian Association for Image Analysis and Pattern Recognition - 14th Conference},
	unique-id = {33604424},
	abstract = {A tabletták oldódásának vizsgálata a gyógyszerészeti kutatások egyik fontos területe. Az ilyen kutatások célja az oldódás folyamatának előrejelzése a lehető legpontosabban a tabletták roncsolása nélkül. Számos olyan módszert publikáltak már, ami közelítő pontossággal meg tudja becsülni a kioldódást, de ezek többnyire olyan összetett tanuló algoritmusok, amelyek időigényesek és betanításukhoz nagy számú mintára van szükség. Ez a cikk arra a kérdésre keresi a választ, hogy szükségeseke ezek az összetett modellek, vagy egyszerűbb módszerek segítségével is elérhető hasonló eredmény. A munka során ezért egy egyszerűbb lineáris regressziós modellt készítettünk, és ennek hatékonyságát mértük a kioldódási görbék becslésében. A vizsgálattal arra a következtetésre jutottunk, hogy az eredmények ugyan nem annyira pontosak, mint a komplexebb módszerek esetében, ellenben a modell robosztusabb és kevesebb minta esetében is használható. Így a jövőben ezek továbbfejlesztésével és a módszerek kombinálásával akár jobb eredményeket is elérhetünk.},
	year = {2023}
}

@inproceedings{MTMT:33595245,
	title = {Spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Random Decision Forests},
	url = {https://m2.mtmt.hu/api/publication/33595245},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Nagy, Brigitta},
	booktitle = {Artificial Intelligence and Soft Computing},
	doi = {10.1007/978-3-031-23492-7_35},
	unique-id = {33595245},
	year = {2023},
	pages = {411-422},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@inproceedings{MTMT:33543170,
	title = {Predicting the Dissolution of Tablets based on Raman Maps using a Linear Regression Model},
	url = {https://m2.mtmt.hu/api/publication/33543170},
	author = {Knyihár, Gábor and Csorba, Kristóf and Charaf, Hassan},
	booktitle = {Computer Science and Machine Learning Trends 2023},
	doi = {10.5121/csit.2023.130107},
	unique-id = {33543170},
	abstract = {Investigation of the dissolution of tablets is an important area of pharmaceutical research. Such research aims to predict the dissolution process as accurately as possible without destroying the tablets. Several methods have been published that can estimate dissolution with approximate accuracy, but they are mostly complex and time-consuming. This article seeks to answer whether these complex models are necessary or whether a similar result can be achieved with the help of more straightforward methods. Therefore, during this work, a simpler linear regression model was created and analysed its effectiveness in estimating the dissolution curves. The investigation concluded that the results are not as accurate as in the case of more complex methods, but they are not far behind. Thus, even similar results may be achieved by fine-tuning and possibly developing these methods.},
	keywords = {Linear regression; Principal component analysis (PCA); RAMAN spectroscopy; Dissolution curve},
	year = {2023},
	pages = {79-85}
}

@inproceedings{MTMT:34476278,
	title = {Comparing Measurements in the Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34476278},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Nagy, Brigitta},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2022 (AACS'22)},
	unique-id = {34476278},
	abstract = {In the pharmaceutical industry, dissolution testing is part of the target
		product quality that are essentials in the approval of new products. The prediction
		of the dissolution profile based on spectroscopic data is an alternative to the current
		destructive and time-consuming method. Raman and near infrared (NIR) spectroscopies are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help in predicting their dissolution profiles. This work aims to compare the information collected by these methods to
		support the decision of which measurements should be used so that the accuracy requirement of the industry is met . Artificial neural network models were created, in
		which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution
		profiles. It was found that , using only the NIR transmission method along with the
		compression force data or the Raman and NIR reflection methods , the dissolution
		profile was estimated within the acceptance limits of the f2 similarity factor. Adding
		further spectroscopy methods increased the prediction accuracy.},
	year = {2022},
	pages = {1-11},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@inproceedings{MTMT:33127355,
	title = {Image Processing Methods for Detecting Voids in the Solder Joints of Surface-Mounted Components},
	url = {https://m2.mtmt.hu/api/publication/33127355},
	author = {Krammer, Olivér and Varga, Arpad and Géczy, Attila and Csorba, Kristóf},
	booktitle = {2022 45th International Spring Seminar on Electronics Technology (ISSE)},
	doi = {10.1109/ISSE54558.2022.9812775},
	unique-id = {33127355},
	abstract = {Void formation in the lead-free solder joints of electrical components can significantly affect their mechanical properties and thermal performance. Void-detection methods have been investigated in this paper, which are able to identify and measure voids in meniscus-shaped solder joints of surface mounted components automatically. In the experiment, 0603 size resistors (1.5 x 0.75 mm) were soldered onto a testboard, and X-ray images were acquired about the samples then. The image processing methods for the void detection were implemented in OpenCV-Python. At first, the properties of the images were analyzed (for example, calculating the histogram), and then the area, including the resistor and solder pads, was extracted by masking. Several methods were investigated for identifying voids in the images; Canny edge detection, global- and adaptive thresholding, and blob detection algorithm. The accuracy of the extraction methods was evaluated by identifying voids on a test image manually and comparing the results to that provided by the different methods. Canny edge detection was the best in our case, but global thresholding and blob detection are also promising solutions.},
	year = {2022},
	pages = {1-4}
}