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1.

001-es BibID:BIBFORM131912
035-os BibID:(WOS)001527570300001
Első szerző:Abuhayi, Biniyam Mulugeta
Cím:A Hybrid Compact Convolutional Transformer with Bilateral Filtering for Coffee Berry Disease Classification / Biniyam Mulugeta Abuhayi, Andras Hajdu
Dátum:2025
ISSN:1424-8220
Megjegyzések:Coffee berry disease (CBD), caused by Colletotrichum kahawae, significantly threatens global Coffee arabica production, leading to major yield losses. Traditional detection methods are often subjective and inefficient, particularly in resource-limited settings. While deep learning has advanced plant disease detection, most existing research targets leaf diseases, with limited focus on berry-specific infections like CBD. This study proposes a lightweight and accurate solution using a Compact Convolutional Transformer (CCT) for classifying healthy and CBD-affected coffee berries. The CCT model combines parallel convolutional branches for hierarchical feature extraction with a transformer encoder to capture long-range dependencies, enabling high performance on limited data. A dataset of 1737 coffee berry images was enhanced using bilateral filtering and color segmentation. The CCTmodel, integrated with a Multilayer Perceptron (MLP) classifier and optimized through early stopping and regularization, achieved a validation accuracy of 97.70% and a sensitivity of 100% for CBD detection. Additionally, CCT-extracted features performed well with traditional classifiers, including Support Vector Machine (SVM) (82.47% accuracy; AUC 0.91) and Decision Tree (82.76% accuracy; AUC 0.86). Compared to pretrained models, the proposed system delivered superior accuracy (97.5%) with only 0.408 million parameters and faster training (2.3 s/epoch), highlighting its potential for real-time, lowresource deployment in sustainable coffee production systems
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
compact convolution transformers
bilateral filtering
coffee berry disease
Megjelenés:Sensors. - 25 : 13 (2025), p. 1-23. -
További szerzők:Hajdu András (1973-) (matematikus, informatikus)
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2.

001-es BibID:BIBFORM131358
Első szerző:Al-Hamadani, Mokhaled N. A. (informatics)
Cím:Improving Cell Detection and Tracking in Microscopy Images Using YOLO and an Enhanced DeepSORT Algorithm / Mokhaled N. A. Al-Hamadani, Richard Poroszlay, Gabor Szeman-Nagy, Andras Hajdu, Stathis Hadjidemetriou, Luca Ferrarini, Balazs Harangi
Dátum:2025
ISSN:1424-8220
Megjegyzések:Accurate and automated detection and tracking of cells in microscopy images is a persistent challenge in biotechnology and biomedical research. Effective detection and tracking are crucial for understanding biological processes and extracting meaningful data for subsequent simulations. In this study, we present an integrated pipeline that leverages a fine-tuned YOLOv8x model for detecting cells and cell divisions across microscopy image series. While YOLOv8x exhibits strong detection capabilities, it occasionally misses certain cells, leading to gaps in data. To mitigate this, we incorporate the DeepSORT tracking algorithm, which enhances data association and reduces the cells' identity (ID) switches by utilizing a pre-trained convolutional network for robust multi-object tracking. This combination ensures continuous detection and compensates for missed detections, thereby improving overall recall. Our approach achieves a recall of 93.21% with the enhanced DeepSORT algorithm, compared to the 53.47% recall obtained by the original YOLOv8x model. The proposed pipeline effectively extracts detailed information from structured image datasets, providing a reliable approximation of cellular processes in culture environments.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
microscopy images
cell detection
YOLO model
cell tracking
DeepSORT algorithm
UKF
multi-scale ResNet50
Megjelenés:Sensors. - 25 : 14 (2025), p. 1-15. -
További szerzők:Poroszlay Richárd Szemán-Nagy Gábor (1975-) (biológia tanár-molekuláris biológus) Hajdu András (1973-) (matematikus, informatikus) Hadjidemetriou, Stathis Ferrarini, Luca Harangi Balázs (1986-) (programtervező matematikus)
Pályázati támogatás:TKP2021-NKTA-34
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3.

001-es BibID:BIBFORM121134
035-os BibID:(Scopus)85191427096 (WoS)001210747400001
Első szerző:Al-Hamadani, Mokhaled N. A. (informatics)
Cím:Reinforcement Learning Algorithms and Applications in Healthcare and Robotics : A Comprehensive and Systematic Review / Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Balazs Harangi
Dátum:2024
ISSN:1424-8220
Megjegyzések: Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.
Tárgyszavak:idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
reinforcement learning
dynamic programming
Monte Carlo
temporal difference
cell growth
object grasping and manipulation
Megjelenés:Sensors. - 24 : 8 (2024), p. 1-42. -
További szerzők:Fadhel, Mohammed A. Alzubaidi, Laith Harangi Balázs (1986-) (programtervező matematikus)
Pályázati támogatás:TKP2021-NKTA-34
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4.

001-es BibID:BIBFORM072985
035-os BibID:(WoS)000428805300218 (Scopus)85044260496
Első szerző:Csiszkó Adrienn
Cím:Pressure Distribution during Negative Pressure Wound Therapy of Experimental Abdominal Compartment Syndrome in a Porcine Model / Csiszkó Adrienn, Balog Klaudia, Godó Zoltán Attila, Juhász Gyula, Pető Katalin, Deák Ádám, Berhés Mariann, Németh Norbert, Bodnár Zsolt, Szentkereszty Zsolt
Dátum:2018
ISSN:1424-8220
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Sensors. - 18 : 3 (2018), p. 1-11. -
További szerzők:Balog Klaudia (1988-) (sebész szakorvos) Godó Zoltán (1972-) (biológus, informatikus) Juhász Gyula Pető Katalin (1961-) (kutatóorvos) (Kísérletes orvostudomány) Deák Ádám (1974-) (állatorvos) Berhés Mariann (1975-) (orvos) Németh Norbert (1975-) (kutatóorvos) Bodnár Zsolt (1976-) (sebész) Szentkereszty Zsolt (1961-) (sebész)
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5.

001-es BibID:BIBFORM128684
035-os BibID:(WoS)001454481100001 (Scopus)105000949044
Első szerző:Gao, Tianyu
Cím:Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B / Tianyu Gao, Jozsef Suto
Dátum:2025
ISSN:1424-8220
Megjegyzések:This work investigates the efficiency and power consumption of using the Intel® (Santa Clara, CA, USA) Neural Compute Stick 2 (NCS2) on the Raspberry Pi 4B platform to accelerate image classification and object tracking. The motivation behind this study is to enable the real-time operation of complex neural networks in embedded systems, potentially reducing the cost of deep learning neural network deployment and expanding industrial applications. This study also supplements the OpenVINO? 2022.3.2 documentation by recording the application of the Raspberry Pi 4B combined with the NCS2inthelatest Europeansoftware repositories. Supported by OpenVINO?2022.3.2 and the Deep SORT algorithm, this study consists of two distinct tests: image recognition and real-time object tracking. A single model is used for image recognition, while two models are deployed for object tracking. These test cases evaluate the performance of the execution hardware by varying the different number of models in different application scenarios and evaluating the impact of NCS2 acceleration under various conditions. The results indicate that, for the specific models used in this experiment, the NCS2 increases image recognition performance by approximately 400% and real-time object tracking by around 1400% to 1200%. The results presented in this work indicate that the NCS2 can achieve more than 50 FPS (frames per second) in image recognition and more than 20 FPS in object tracking. The power efficiency obtained by using the NCS2 can vary from 200% to 400%. These findings highlight the significant performance gains NCS2 offers in constrained hardware environments.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Intel® Neural Compute Stick 2
Raspberry Pi
OpenVINO
object tracking
image recognition
power efficiency
Megjelenés:Sensors. - 25 : 6 (2025), p. 1-16. -
További szerzők:Sütő József (1990-) (programtervező informatikus)
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6.

001-es BibID:BIBFORM124974
035-os BibID:(Scopus)85205273669 (WoS)001323649100001
Első szerző:Godó Zoltán (biológus, informatikus)
Cím:A Custom-Developed Device for Testing Tensile Strength and Elasticity of Vascular and Intestinal Tissue Samples for Anastomosis Regeneration Research / Godo Zoltan Attila, Fazekas Laszlo Adam, Fritsch Gergo, Szabo Balazs, Nemeth Norbert
Dátum:2024
ISSN:1424-8220
Megjegyzések:Optimizing the regeneration process of surgically created anastomoses (blood vessels, intestines, nerves) is an important topic in surgical research. One of the most interesting parameter groups is related to the biomechanical properties of the anastomoses. Depending on the regeneration process and its influencing factors, tensile strength and other biomechanical features may change during the healing process. Related to the optimal specimen size, the range and accuracy of measurements, and applicability, we have developed a custom-tailored microcontroller-based device. In this paper, we describe the hardware and software configuration of the latest version of the device, including experiences and comparative measurements of tensile strength and elasticity of artificial materials and biopreparate tissue samples. The machine we developed was made up of easily obtainable parts and can be easily reproduced on a low budget. The basic device can apply a force of up to 40 newtons, and can grasp a 0.05?1 cm wide, 0.05?1 cm thick tissue. The length of the test piece on the rail should be between 0.3 and 5 cm. Low production cost, ease of use, and detailed data recording make it a useful tool for experimental surgical research.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
tensile strength
elasticity
vascular anastomosis
intestinal anastomosis
experimental model
Megjelenés:Sensors. - 24 : 18 (2024), p. 1-11. -
További szerzők:Fazekas László (1994-) (általános orvos) Fritsch Gergő Szabó Balázs (1991-) (általános orvos) Németh Norbert (1975-) (kutatóorvos)
Pályázati támogatás:K-139184
OTKA
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7.

001-es BibID:BIBFORM117871
035-os BibID:(Scopus)85183252226
Első szerző:Godó Zoltán (biológus, informatikus)
Cím:A Custom-Tailored Multichannel Pressure Monitoring System Designed for Experimental Surgical Model of Abdominal Compartment Syndrome / Godó Zoltán Attila, Pető Katalin, Balog Klaudia, Deák Ádám, Ványolos Erzsébet, Fazekas László Ádám, Szentkereszty Zsolt, Németh Norbert
Dátum:2024
ISSN:1424-8220
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Sensors. - 24 (2024), p. 1-12. -
További szerzők:Pető Katalin (1961-) (kutatóorvos) (Kísérletes orvostudomány) Balog Klaudia (1988-) (sebész szakorvos) Deák Ádám (1974-) (állatorvos) Ványolos Erzsébet (sebészeti műtéttan) Fazekas László (1994-) (általános orvos) Szentkereszty Zsolt (1961-) (sebész) Németh Norbert (1975-) (kutatóorvos)
Pályázati támogatás:K-139184
OTKA
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8.

001-es BibID:BIBFORM128340
035-os BibID:(WoS)001443468900001 (Scopus)86000531493
Első szerző:Lakatos István (programtervező informatikus)
Cím:AI-Driven Framework for Enhanced and Automated Behavioral Analysis in Morris Water Maze Studies / István Lakatos, Gergő Bogacsovics, Attila Tiba, Dániel Priksz, Béla Juhász, Rita Erdélyi, Zsuzsa Berényi, Ildikó Bácskay, Dóra Ujvárosy, Balázs Harangi
Dátum:2025
ISSN:1424-8220
Megjegyzések:The Morris Water Maze (MWM) is a widely used behavioral test to assess the spatial learning and memory of animals, particularly valuable in studying neurodegenerative disorders such as Alzheimer`s disease. Traditional methods for analyzing MWM experiments often face limitations in capturing the complexity of animal behaviors. In this study, we present a novel AI-based automated framework to process and evaluate MWM test videos, aiming to enhance behavioral analysis through machine learning. Our pipeline involves video preprocessing, animal detection using convolutional neural networks (CNNs), trajectory tracking, and postprocessing to derive detailed behavioral features. We propose concentric circle segmentation of the pool next to the quadrant-based division, and we extract 32 behavioral metrics for each zone, which are employed in classification tasks to differentiate between younger and older animals. Several machine learning classifiers, including random forest and neural networks, are evaluated, with feature selection techniques applied to improve the classification accuracy. Our results demonstrate a significant improvement in classification performance, particularly through the integration of feature sets derived from concentric zone analyses. This automated approach offers a robust solution for MWM data processing, providing enhanced precision and reliability, which is critical for the study of neurodegenerative disorders.
Tárgyszavak:idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
morris water maze
convolutional neural network
machine learning
behavioral classification
Megjelenés:Sensors. - 25 (2025), p. 1-16. -
További szerzők:Bogacsovics Gergő (1996-) (informatikus) Tiba Attila (1990-) (informatikus, matematikus) Priksz Dániel (1989-) (farmakológus) Juhász Béla (1978-) (kísérletes farmakológus) Erdélyi Rita Berényi Zsuzsa Bácskay Ildikó (1969-) (gyógyszerész, gyógyszertechnológus) Ujvárosy Dóra (1985-) Harangi Balázs (1986-) (programtervező matematikus)
Pályázati támogatás:TKP2021-NKTA-34
Egyéb
GINOP-2.3.4-15-2020-00008
GINOP
GINOP-2.3.3-15-2016-00021
GINOP
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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9.

001-es BibID:BIBFORM118284
035-os BibID:(WoS)001097548900005 (Scopus)85174839332
Első szerző:Pándy Árpád (szoftverfejlesztő rendszermérnök)
Cím:Steering Angle Prediction From a Camera Image as a Backup Service / Pandy, Arpad; Kovacs, Laszlo; Kovacs, Adam; Hajdu, Andras
Dátum:2023
ISSN:2475-1472
Megjegyzések:In this paper, we describe a method to predict the steering angle of the front wheels of a small-sized vehicle from a rear-view camera image. Our approach can be applied to give backup feedback for controlling units of autonomous vehicles or to detect anomalies in the running characteristics of the wheels. We determine how the emitted control (pulse-width modulation, PWM) signal correlates with the position of the wheel edges in the camera image. Thus, we can use the PWM values in a supervised learning scenario to train an appropriate convolutional neural network-based architecture to predict the steering angle. In our experiments, we found an approximately 95\% prediction accuracy for this task; we also made our data and source codes publicly available.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Autonomous Vehicles
Vehicle Control
Vision Sensors for Intelligent Vehicles
Video/Image Processing
Steering
Megjelenés:IEEE Sensors Letters. - 7 : 11 (2023), p. 1-4. -
További szerzők:Kovács László (1984-) (informatikus) Kovács Ádám Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:OTKA-143540
OTKA
TKP2020-NKA-04
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10.

001-es BibID:BIBFORM126074
035-os BibID:(WOS)001323342700001 (Scopus)85205239003
Első szerző:Pap, Iuliu Alexandru
Cím:eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication / Iuliu Alexandru Pap, Stefan Oniga
Dátum:2024
ISSN:1424-8220
Megjegyzések:In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most humanmedical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled.
Tárgyszavak:Műszaki tudományok Informatikai tudományok magyar nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
eHealth
mHealth
telehealth
telemedicine
remote patient monitoring
Internet of Things
artificial intelligence
large language model
Matrix open communication protocol
Megjelenés:Sensors. - 24 : 18 (2024), p. 1-16. -
További szerzők:Oniga István László (1960-) (villamosmérnök)
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11.

001-es BibID:BIBFORM121132
035-os BibID:(Scopus)85192703507 (WoS)001219905200001
Első szerző:Serban Norbert
Cím:Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks / Norbert Serban, David Kupas, Andras Hajdu, Peter Török, Balazs Harangi
Dátum:2024
ISSN:1424-8220
Megjegyzések:Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only visual information is available and they cannot use their tactile senses during keyhole surgeries. This is the case with laparoscopic hysterectomy since some organs are also difficult to distinguish based on visual information, making laparoscope-based hysterectomy challenging. In this paper, we propose a solution based on semantic segmentation, which can create pixel-accurate predictions of surgical images and differentiate the uterine arteries, ureters, and nerves. We trained three binary semantic segmentation models based on the U-Net architecture with the EfficientNet-b3 encoder; then, we developed two ensemble techniques that enhanced the segmentation performance. Our pixel-wise ensemble examines the segmentation mapof the binary networks on the lowest level of pixels. The other algorithm developed is a regionbased ensemble technique that takes this examination to a higher level and makes the ensemble based on every connected component detected by the binary segmentation networks. We also introduced and trained a classic multi-class semantic segmentation model as a reference and compared it to the ensemble-based approaches. We used 586 manually annotated images from 38 surgical videos for this research and published this dataset.
Tárgyszavak:idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
laparoscopic hysterectomy
semantic segmentation
ensemble model
Megjelenés:Sensors. - 24 : 9 (2024), p. 1-12. -
További szerzők:Kupás Dávid (1996-) (programtervező informatikus) Hajdu András (1973-) (matematikus, informatikus) Török Péter (1975-) (szülész-nőgyógyász) Harangi Balázs (1986-) (programtervező matematikus)
Pályázati támogatás:TKP2021-NKTA-34
Egyéb
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12.

001-es BibID:BIBFORM122678
035-os BibID:(Scopus)85199753784 (WoS)001277272700001
Első szerző:Sütő József (programtervező informatikus)
Cím:Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images / Jozsef Suto
Dátum:2024
ISSN:1424-8220
Megjegyzések:Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable progress in insect pest detector models, their practical application is hindered by the shortage of insect trap images. To attenuate the "lack of data" issue, the literature proposes data augmentation. However, our knowledge about data augmentation is still quite limited, especially in the field of insect pest detection. The aim of this experimental study was to investigate the effect of several widely used augmentation techniques and their combinations on remote-sensed trap images with the YOLOv5 (small) object detector model. This study was carried out systematically on two different datasets starting from the single geometric and photometric transformation toward their combinations. Our results show that the model`s mean average precision value (mAP50) could be increased from 0.844 to 0.992 and from 0.421 to 0.727 on the two datasets using the appropriate augmentation methods combination. In addition, this study also points out that the integration of photometric image transformations into the mosaic augmentation can be more efficient than the native combination of augmentation techniques because this approach further improved the model`s mAP50values to 0.999 and 0.756 on the two test sets, respectively.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
automated trap
insect counting
data augmentation
YOLOv5
Megjelenés:Sensors. - 24 : 14 (2024), p. 1-12. -
Pályázati támogatás:MAEO-2023-24 / 183910
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