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Article: On Approximating the

Baressi Šegota, Sandi / Lorencin, Ivan / Kovač, Zoran / Car, Zlatan

2023 Volume 11, Issue 2

Abstract: In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is ...

Abstract	In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested-modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.
Language	English
Publishing date	2023-01-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines11020284
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms.

Đuranović, Daniel / Baressi Šegota, Sandi / Lorencin, Ivan / Car, Zlatan

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 3

Abstract: Imaging is one of the main tools of modern astronomy-many images are collected each day, and they must be processed. Processing such a large amount of images can be complex, time-consuming, and may require advanced tools. One of the techniques that may ... ...

Abstract	Imaging is one of the main tools of modern astronomy-many images are collected each day, and they must be processed. Processing such a large amount of images can be complex, time-consuming, and may require advanced tools. One of the techniques that may be employed is artificial intelligence (AI)-based image detection and classification. In this paper, the research is focused on developing such a system for the problem of the Magellan dataset, which contains 134 satellite images of Venus's surface with individual volcanoes marked with circular labels. Volcanoes are classified into four classes depending on their features. In this paper, the authors apply the You-Only-Look-Once (YOLO) algorithm, which is based on a convolutional neural network (CNN). To apply this technique, the original labels are first converted into a suitable YOLO format. Then, due to the relatively small number of images in the dataset, deterministic augmentation techniques are applied. Hyperparameters of the YOLO network are tuned to achieve the best results, which are evaluated as mean average precision (mAP@0.5) for localization accuracy and F1 score for classification accuracy. The experimental results using cross-vallidation indicate that the proposed method achieved 0.835 mAP@0.5 and 0.826 F1 scores, respectively.
Language	English
Publishing date	2023-01-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23031224
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Article ; Online: Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier

Nikola Anđelić / Sandi Baressi Šegota / Matko Glučina / Ivan Lorencin

Applied Sciences, Vol 13, Iss 1962, p

2023 Volume 1962

Abstract: In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of ... ...

Abstract	In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross-validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation); however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy ( <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover><mrow><mi>A</mi><mi>C</mi><mi>C</mi></mrow><mo>¯</mo></mover></semantics></math> ), area under receiving operating characteristics curve ( <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover><mrow><mi>A</mi><mi>U</mi><mi>C</mi></mrow><mo>¯</mo></mover></semantics></math> ), <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover><mrow><mi>P</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow><mo>¯</mo></mover></semantics></math> , <math xmlns="http://www.w3.org/1998/Math/MathML" ...<br />
Keywords	genetic programming ; oversampling methods ; robot fault operation ; random oversampling ; symbolic classifier ; SMOTE ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Subepidermal Basal Cell carcinoma Following Laser Treatment of Congenital Capillary Malformation: A Case Report.

Bulić, Krešimir / Ilić, Ivana / Brenner, Eva / Bulić, Luka / Lorencin Bulić, Mia

Acta dermatovenerologica Croatica : ADC

2024 Volume 31, Issue 4, Page(s) 220–222

Abstract: While basal cell carcinoma is the most common type of skin cancer in humans, its subepidermal presentation is extremely rare. The risk factors for basal cell carcinoma development are well-known, but it remains unclear in which setting the tumor ... ...

Abstract	While basal cell carcinoma is the most common type of skin cancer in humans, its subepidermal presentation is extremely rare. The risk factors for basal cell carcinoma development are well-known, but it remains unclear in which setting the tumor restricts itself to the dermal compartment. We present the fifth known case of subepidermal basal cell carcinoma. However, this particular presentation is unique due to arising beneath a capillary malformation. The patient had previously undergone multiple laser treatments which yielded no success. Initially, the vascular malformation was removed and sent for histopathological diagnosis. After the discovery of basal cell carcinoma, wide surgical resection was performed. The patient had no recurrence up to the last follow-up at 18 months postoperatively. This case demonstrates a new presentation of a very rare condition, but also highlights the importance of histopathological examination and the need for future research on any possible association between laser therapy and carcinogenesis.
MeSH term(s)	Humans ; Skin Neoplasms/pathology ; Skin Neoplasms/surgery ; Carcinoma, Basal Cell/surgery ; Carcinoma, Basal Cell/pathology ; Laser Therapy/adverse effects ; Capillaries/pathology ; Capillaries/abnormalities ; Vascular Malformations/surgery ; Vascular Malformations/diagnosis ; Male ; Female
Language	English
Publishing date	2024-04-23
Publishing country	Croatia
Document type	Case Reports ; Journal Article
ZDB-ID	1180727-1
ISSN	1847-6538 ; 1330-027X
ISSN (online)	1847-6538
ISSN	1330-027X
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Zs.A 3934: Show issues

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Article ; Online: The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm

Nikola Anđelić / Ivan Lorencin / Sandi Baressi Šegota / Zlatan Car

Applied Sciences, Vol 13, Iss 1, p

2022 Volume 574

Abstract: Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to ...

Abstract	Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient’s blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve ( <semantics> A U C </semantics> ), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are <semantics> A C C ¯ ± S D ( A C C ) </semantics> , <semantics> A U C ¯ ± S D ( A U C ) </semantics> , <semantics> P r e c i s i o n ¯ ± S D ( P r e c i s i o n ) </semantics> , <semantics> R e c a l l ¯ ± S D ( R e c a l l ) </semantics> , and <semantics> F 1 − s c o r e ¯ ± S D ( F 1 − s c o r e ) </semantics> equal to <semantics> 0.99 ± 5.8 × 10 − 3 </semantics> , <semantics> 0.99 ± 5.4 × 10 − 3 </semantics> , ...
Keywords	ADASYN ; borderline SMOTE ; genetic programming-symbolic classifier ; Hepatitis C ; fibrosis ; cirrhosis ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	511
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis.

Lorencin, Ivan / Anđelić, Nikola / Španjol, Josip / Car, Zlatan

Artificial intelligence in medicine

2019 Volume 102, Page(s) 101746

Abstract: In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer ... ...

Abstract	In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.
MeSH term(s)	Algorithms ; Area Under Curve ; Artificial Intelligence ; Cystoscopy ; Databases, Factual ; Deep Learning ; Humans ; Image Interpretation, Computer-Assisted ; Neural Networks, Computer ; Urinary Bladder/diagnostic imaging ; Urinary Bladder Neoplasms/diagnosis ; Urinary Bladder Neoplasms/diagnostic imaging
Language	English
Publishing date	2019-11-13
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2019.101746
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Article ; Online: Estimation of COVID-19 epidemic curves using genetic programming algorithm.

Anđelić, Nikola / Baressi Šegota, Sandi / Lorencin, Ivan / Mrzljak, Vedran / Car, Zlatan

Health informatics journal

2021 Volume 27, Issue 1, Page(s) 1460458220976728

Abstract: This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered ...

Abstract	This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved
MeSH term(s)	Algorithms ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19/mortality ; Epidemics ; Epidemiologic Methods ; Humans ; Machine Learning ; Models, Theoretical ; SARS-CoV-2
Language	English
Publishing date	2021-02-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/1460458220976728
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Article ; Online: Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Sandi Baressi Šegota / Nikola Anđelić / Vedran Mrzljak / Ivan Lorencin / Ivan Kuric / Zlatan Car

International Journal of Advanced Robotic Systems, Vol

2021 Volume 18

Abstract: Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, ...

Abstract	Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.
Keywords	Electronics ; TK7800-8360 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	629
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
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Article: On Urinary Bladder Cancer Diagnosis: Utilization of Deep Convolutional Generative Adversarial Networks for Data Augmentation

Lorencin, Ivan / Baressi Šegota, Sandi / Anđelić, Nikola / Mrzljak, Vedran / Ćabov, Tomislav / Španjol, Josip / Car, Zlatan

Biology. 2021 Feb. 26, v. 10, no. 3

2021

Abstract: Urinary bladder cancer is one of the most common urinary tract cancers. Standard diagnosis procedure can be invasive and time-consuming. For these reasons, procedure called optical biopsy is introduced. This procedure allows in-vivo evaluation of bladder ...

Abstract	Urinary bladder cancer is one of the most common urinary tract cancers. Standard diagnosis procedure can be invasive and time-consuming. For these reasons, procedure called optical biopsy is introduced. This procedure allows in-vivo evaluation of bladder mucosa without the need for biopsy. Although less invasive and faster, accuracy is often lower. For this reason, machine learning (ML) algorithms are used to increase its accuracy. The issue with ML algorithms is their sensitivity to the amount of input data. In medicine, collection can be time-consuming due to a potentially low number of patients. For these reasons, data augmentation is performed, usually through a series of geometric variations of original images. While such images improve classification performance, the number of new data points and the insight they provide is limited. These issues are a motivation for the application of novel augmentation methods. Authors demonstrate the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for the generation of images. Augmented datasets used for training of commonly used Convolutional Neural Network-based (CNN) architectures (AlexNet and VGG-16) show a significcan performance increase for AlexNet, where AUCmicro reaches values up to 0.99. Average and median results of networks used in grid-search increases. These results point towards the conclusion that GAN-based augmentation has decreased the networks sensitivity to hyperparemeter change.
Keywords	biopsy ; bladder ; data collection ; geometry ; medicine ; motivation ; mucosa ; urinary bladder neoplasms
Language	English
Dates of publication	2021-0226
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology10030175
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.

Car, Zlatan / Baressi Šegota, Sandi / Anđelić, Nikola / Lorencin, Ivan / Mrzljak, Vedran

Computational and mathematical methods in medicine

2020 Volume 2020, Page(s) 5714714

Abstract: Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide ... ...

Abstract	Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group-deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (
MeSH term(s)	Algorithms ; COVID-19 ; Computational Biology ; Coronavirus Infections/epidemiology ; Coronavirus Infections/transmission ; Databases, Factual ; Humans ; Mathematical Concepts ; Models, Biological ; Neural Networks, Computer ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/transmission ; Regression Analysis
Keywords	covid19
Language	English
Publishing date	2020-05-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2252430-7
ISSN	1748-6718 ; 1748-670X ; 1027-3662
ISSN (online)	1748-6718
ISSN	1748-670X ; 1027-3662
DOI	10.1155/2020/5714714
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