TY  - JOUR
AU  - Vukićević, Arso M.
AU  - Mačužić, Ivan
AU  - Mijailović, Nikola
AU  - Peulić, Aleksandar
AU  - Radović, Miloš
PY  - 2021
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1222
AB  - Pushing and pulling (P&P) are common and repetitive tasks in industry, which non-ergonomic execution is among major causes of musculoskeletal disorders (MSD). The current safety management of P&P assumes restrictions of maximal weight, distance, height – while variable individual parameters (such as the P&P pose ergonomic) remain difficult to account for with the standardized guides. Since manual detection of unsafe P&P acts is subjective and inefficient, the aim of this study was to utilize IoT force sensors and IP cameras to detect unsafe P&P acts timely and objectively. Briefly, after the IoT module detects moments with increased P&P forces, the assessment of pose ergonomics was performed from the employee pose reconstructed with the VIBE algorithm. The experiments showed that turn-points correspond to the high torsion of torso, and that in such moments poses are commonly non ergonomic (although P&P forces are below values defined as critical in previous studies – their momentum cause serious load on the human body). Moreover, the analysis revealed that the loading/unloading of a cargo are also moments of frequent unsafe P&P acts – although they are commonly neglected when studying P&P. The experimental validation of the solution showed good agreement with motion sensors and high potential for monitoring and improving P&P workplace safety. Accordingly, future research will be directed towards: 1) acquisition of P&P data sets for direct recognition and classification of unsafe P&P acts; 2) incorporation of wearable sensors (EMG and EEG) for detecting fatigue and decrease of physical abilities.
PB  - Elsevier
T2  - Expert Systems with Applications
T1  - Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors
VL  - 183
SP  - 115371
DO  - 10.1016/j.eswa.2021.115371
ER  - 

@article{
author = "Vukićević, Arso M. and Mačužić, Ivan and Mijailović, Nikola and Peulić, Aleksandar and Radović, Miloš",
year = "2021",
abstract = "Pushing and pulling (P&P) are common and repetitive tasks in industry, which non-ergonomic execution is among major causes of musculoskeletal disorders (MSD). The current safety management of P&P assumes restrictions of maximal weight, distance, height – while variable individual parameters (such as the P&P pose ergonomic) remain difficult to account for with the standardized guides. Since manual detection of unsafe P&P acts is subjective and inefficient, the aim of this study was to utilize IoT force sensors and IP cameras to detect unsafe P&P acts timely and objectively. Briefly, after the IoT module detects moments with increased P&P forces, the assessment of pose ergonomics was performed from the employee pose reconstructed with the VIBE algorithm. The experiments showed that turn-points correspond to the high torsion of torso, and that in such moments poses are commonly non ergonomic (although P&P forces are below values defined as critical in previous studies – their momentum cause serious load on the human body). Moreover, the analysis revealed that the loading/unloading of a cargo are also moments of frequent unsafe P&P acts – although they are commonly neglected when studying P&P. The experimental validation of the solution showed good agreement with motion sensors and high potential for monitoring and improving P&P workplace safety. Accordingly, future research will be directed towards: 1) acquisition of P&P data sets for direct recognition and classification of unsafe P&P acts; 2) incorporation of wearable sensors (EMG and EEG) for detecting fatigue and decrease of physical abilities.",
publisher = "Elsevier",
journal = "Expert Systems with Applications",
title = "Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors",
volume = "183",
pages = "115371",
doi = "10.1016/j.eswa.2021.115371"
}

Vukićević, A. M., Mačužić, I., Mijailović, N., Peulić, A.,& Radović, M.. (2021). Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors. in Expert Systems with Applications
Elsevier., 183, 115371.
https://doi.org/10.1016/j.eswa.2021.115371

Vukićević AM, Mačužić I, Mijailović N, Peulić A, Radović M. Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors. in Expert Systems with Applications. 2021;183:115371.
doi:10.1016/j.eswa.2021.115371 .

Vukićević, Arso M., Mačužić, Ivan, Mijailović, Nikola, Peulić, Aleksandar, Radović, Miloš, "Assessment of the handcart pushing and pulling safety by using deep learning 3D pose estimation and IoT force sensors" in Expert Systems with Applications, 183 (2021):115371,
https://doi.org/10.1016/j.eswa.2021.115371 . .

TY  - JOUR
AU  - Radović, Miloš
AU  - Milošević, Marina
AU  - Ninković, Srđan
AU  - Filipović, Nenad
AU  - Peulić, Aleksandar
PY  - 2015
UR  - https://gery.gef.bg.ac.rs/handle/123456789/710
AB  - BACKGROUND: Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. OBJECTIVE: We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. METHODS: Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. RESULTS: By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. CONCLUSION: By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.
PB  - IOS Press, Amsterdam
T2  - Technology and Health Care
T1  - Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms
VL  - 23
IS  - 6
SP  - 757
EP  - 774
DO  - 10.3233/THC-151034
UR  - https://hdl.handle.net/21.15107/rcub_gery_710
ER  - 

@article{
author = "Radović, Miloš and Milošević, Marina and Ninković, Srđan and Filipović, Nenad and Peulić, Aleksandar",
year = "2015",
abstract = "BACKGROUND: Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. OBJECTIVE: We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. METHODS: Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. RESULTS: By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. CONCLUSION: By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.",
publisher = "IOS Press, Amsterdam",
journal = "Technology and Health Care",
title = "Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms",
volume = "23",
number = "6",
pages = "757-774",
doi = "10.3233/THC-151034",
url = "https://hdl.handle.net/21.15107/rcub_gery_710"
}

Radović, M., Milošević, M., Ninković, S., Filipović, N.,& Peulić, A.. (2015). Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms. in Technology and Health Care
IOS Press, Amsterdam., 23(6), 757-774.
https://doi.org/10.3233/THC-151034
https://hdl.handle.net/21.15107/rcub_gery_710

Radović M, Milošević M, Ninković S, Filipović N, Peulić A. Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms. in Technology and Health Care. 2015;23(6):757-774.
doi:10.3233/THC-151034
https://hdl.handle.net/21.15107/rcub_gery_710 .

Radović, Miloš, Milošević, Marina, Ninković, Srđan, Filipović, Nenad, Peulić, Aleksandar, "Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms" in Technology and Health Care, 23, no. 6 (2015):757-774,
https://doi.org/10.3233/THC-151034 .,
https://hdl.handle.net/21.15107/rcub_gery_710 .

TY  - JOUR
AU  - Filipović, Nenad
AU  - Đukić, Tijana
AU  - Radović, Miloš
AU  - Cvetković, Danijela
AU  - Ćurčić, Milena
AU  - Marković, Snežana
AU  - Peulić, Aleksandar
AU  - Jeremić, Branislav
PY  - 2014
UR  - https://gery.gef.bg.ac.rs/handle/123456789/605
AB  - Background: There is a strong interest in the investigation of extremely low frequency Electromagnetic Fields (EMF) in the clinic. While evidence about anticancer effects exists, the mechanism explaining this effect is still unknown. Methods: We investigated in vitro, and with computer simulation, the influence of a 50 Hz EMF on three cancer cell lines: breast cancer MDA-MB-231, and colon cancer SW-480 and HCT-116. After 24 h preincubation, cells were exposed to 50 Hz extremely low frequency (ELF) radiofrequency EMF using in vitro exposure systems for 24 and 72 h. A computer reaction-diffusion model with the net rate of cell proliferation and effect of EMF in time was developed. The fitting procedure for estimation of the computer model parameters was implemented. Results: Experimental results clearly showed disintegration of cells treated with a 50 Hz EMF, compared to untreated control cells. A large percentage of treated cells resulted in increased early apoptosis after 24 h and 72 h, compared to the controls. Computer model have shown good comparison with experimental data. Conclusion: Using EMF at specific frequencies may represent a new approach in controlling the growth of cancer cells, while computer modelling could be used to predict such effects and make optimisation for complex experimental design. Further studies are required before testing this approach in humans.
PB  - BMC, London
T2  - Cancer Cell International
T1  - Electromagnetic field investigation on different cancer cell lines
VL  - 14
DO  - 10.1186/s12935-014-0084-x
UR  - https://hdl.handle.net/21.15107/rcub_gery_605
ER  - 

@article{
author = "Filipović, Nenad and Đukić, Tijana and Radović, Miloš and Cvetković, Danijela and Ćurčić, Milena and Marković, Snežana and Peulić, Aleksandar and Jeremić, Branislav",
year = "2014",
abstract = "Background: There is a strong interest in the investigation of extremely low frequency Electromagnetic Fields (EMF) in the clinic. While evidence about anticancer effects exists, the mechanism explaining this effect is still unknown. Methods: We investigated in vitro, and with computer simulation, the influence of a 50 Hz EMF on three cancer cell lines: breast cancer MDA-MB-231, and colon cancer SW-480 and HCT-116. After 24 h preincubation, cells were exposed to 50 Hz extremely low frequency (ELF) radiofrequency EMF using in vitro exposure systems for 24 and 72 h. A computer reaction-diffusion model with the net rate of cell proliferation and effect of EMF in time was developed. The fitting procedure for estimation of the computer model parameters was implemented. Results: Experimental results clearly showed disintegration of cells treated with a 50 Hz EMF, compared to untreated control cells. A large percentage of treated cells resulted in increased early apoptosis after 24 h and 72 h, compared to the controls. Computer model have shown good comparison with experimental data. Conclusion: Using EMF at specific frequencies may represent a new approach in controlling the growth of cancer cells, while computer modelling could be used to predict such effects and make optimisation for complex experimental design. Further studies are required before testing this approach in humans.",
publisher = "BMC, London",
journal = "Cancer Cell International",
title = "Electromagnetic field investigation on different cancer cell lines",
volume = "14",
doi = "10.1186/s12935-014-0084-x",
url = "https://hdl.handle.net/21.15107/rcub_gery_605"
}

Filipović, N., Đukić, T., Radović, M., Cvetković, D., Ćurčić, M., Marković, S., Peulić, A.,& Jeremić, B.. (2014). Electromagnetic field investigation on different cancer cell lines. in Cancer Cell International
BMC, London., 14.
https://doi.org/10.1186/s12935-014-0084-x
https://hdl.handle.net/21.15107/rcub_gery_605

Filipović N, Đukić T, Radović M, Cvetković D, Ćurčić M, Marković S, Peulić A, Jeremić B. Electromagnetic field investigation on different cancer cell lines. in Cancer Cell International. 2014;14.
doi:10.1186/s12935-014-0084-x
https://hdl.handle.net/21.15107/rcub_gery_605 .

Filipović, Nenad, Đukić, Tijana, Radović, Miloš, Cvetković, Danijela, Ćurčić, Milena, Marković, Snežana, Peulić, Aleksandar, Jeremić, Branislav, "Electromagnetic field investigation on different cancer cell lines" in Cancer Cell International, 14 (2014),
https://doi.org/10.1186/s12935-014-0084-x .,
https://hdl.handle.net/21.15107/rcub_gery_605 .

TY  - JOUR
AU  - Peulić, Aleksandar
AU  - Milojević, Natasa
AU  - Jovanov, Emil
AU  - Radović, Miloš
AU  - Saveljić, Igor
AU  - Zdravković, Nebojša
AU  - Filipović, Nenad
PY  - 2013
UR  - https://gery.gef.bg.ac.rs/handle/123456789/550
AB  - In this paper, a finite element (FE) modeling is used to model effects of the arterial stiffness on the different signal patterns of the pulse transit time (PTT). Several different breathing patterns of the three subjects are measured with PTT signal and corresponding finite element model of the straight elastic artery is applied. The computational fluid-structure model provides arterial elastic behavior and fitting procedure was applied in order to estimate Young's module of stiffness of the artery. It was found that approximately same elastic Young's module can be fitted for specific subject with different breathing patterns which validate this methodology for possible noninvasive determination of the arterial stiffness.
PB  - ComSIS Consortium
T2  - Computer Science and Information Systems
T1  - Modeling of Arterial Stiffness using Variations of Pulse Transit Time
VL  - 10
IS  - 1
SP  - 547
EP  - 565
DO  - 10.2298/CSIS120531015P
UR  - https://hdl.handle.net/21.15107/rcub_gery_550
ER  - 

@article{
author = "Peulić, Aleksandar and Milojević, Natasa and Jovanov, Emil and Radović, Miloš and Saveljić, Igor and Zdravković, Nebojša and Filipović, Nenad",
year = "2013",
abstract = "In this paper, a finite element (FE) modeling is used to model effects of the arterial stiffness on the different signal patterns of the pulse transit time (PTT). Several different breathing patterns of the three subjects are measured with PTT signal and corresponding finite element model of the straight elastic artery is applied. The computational fluid-structure model provides arterial elastic behavior and fitting procedure was applied in order to estimate Young's module of stiffness of the artery. It was found that approximately same elastic Young's module can be fitted for specific subject with different breathing patterns which validate this methodology for possible noninvasive determination of the arterial stiffness.",
publisher = "ComSIS Consortium",
journal = "Computer Science and Information Systems",
title = "Modeling of Arterial Stiffness using Variations of Pulse Transit Time",
volume = "10",
number = "1",
pages = "547-565",
doi = "10.2298/CSIS120531015P",
url = "https://hdl.handle.net/21.15107/rcub_gery_550"
}

Peulić, A., Milojević, N., Jovanov, E., Radović, M., Saveljić, I., Zdravković, N.,& Filipović, N.. (2013). Modeling of Arterial Stiffness using Variations of Pulse Transit Time. in Computer Science and Information Systems
ComSIS Consortium., 10(1), 547-565.
https://doi.org/10.2298/CSIS120531015P
https://hdl.handle.net/21.15107/rcub_gery_550

Peulić A, Milojević N, Jovanov E, Radović M, Saveljić I, Zdravković N, Filipović N. Modeling of Arterial Stiffness using Variations of Pulse Transit Time. in Computer Science and Information Systems. 2013;10(1):547-565.
doi:10.2298/CSIS120531015P
https://hdl.handle.net/21.15107/rcub_gery_550 .

Peulić, Aleksandar, Milojević, Natasa, Jovanov, Emil, Radović, Miloš, Saveljić, Igor, Zdravković, Nebojša, Filipović, Nenad, "Modeling of Arterial Stiffness using Variations of Pulse Transit Time" in Computer Science and Information Systems, 10, no. 1 (2013):547-565,
https://doi.org/10.2298/CSIS120531015P .,
https://hdl.handle.net/21.15107/rcub_gery_550 .

TY  - CONF
AU  - Radović, Miloš
AU  - Đoković, Marina
AU  - Peulić, Aleksandar
AU  - Filipović, Nenad
PY  - 2013
UR  - https://gery.gef.bg.ac.rs/handle/123456789/603
AB  - One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.
PB  - IEEE, New York
C3  - 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
T1  - Application of Data Mining Algorithms for Mammogram Classification
UR  - https://hdl.handle.net/21.15107/rcub_gery_603
ER  - 

@conference{
author = "Radović, Miloš and Đoković, Marina and Peulić, Aleksandar and Filipović, Nenad",
year = "2013",
abstract = "One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.",
publisher = "IEEE, New York",
journal = "2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)",
title = "Application of Data Mining Algorithms for Mammogram Classification",
url = "https://hdl.handle.net/21.15107/rcub_gery_603"
}

Radović, M., Đoković, M., Peulić, A.,& Filipović, N.. (2013). Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
IEEE, New York..
https://hdl.handle.net/21.15107/rcub_gery_603

Radović M, Đoković M, Peulić A, Filipović N. Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). 2013;.
https://hdl.handle.net/21.15107/rcub_gery_603 .

Radović, Miloš, Đoković, Marina, Peulić, Aleksandar, Filipović, Nenad, "Application of Data Mining Algorithms for Mammogram Classification" in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE) (2013),
https://hdl.handle.net/21.15107/rcub_gery_603 .