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1.

001-es BibID:BIBFORM099172
035-os BibID:(WoS)000646080100009 (Scopus)85107227644
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:Segmentation of MRI images to detect multiple sclerosis using non-parametric, non-uniform intensity normalization and support vector machine methods / Mohammad Moghadasi, Fazekas Gabor
Dátum:2021
ISSN:2061-2079
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Infocommunications Journal. - 13 : 1 (2021), p. 68-74. -
További szerzők:Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM086227
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:Cloud Computing Auditing : roadmap and process / Mohammad Moghadasi, Seyed Majid Mousavi, Gábor Fazekas
Dátum:2018
ISSN:2158-107X 2156-5570
Megjegyzések:Cloud Computing is a new form of IT system and infrastructure outsourcing as an alternative to traditional IT Outsourcing (ITO). Hence, migration to cloud computing is rapidly growing among organizations. Adopting this technology brings numerous positive aspects, although imposing different risks and concerns to organization. An organization which officially deputes its cloud computing services to outside (offshore or inshore) providers and implies that it outsources its functions and process of its IT to external BPO services providers. Therefore, customers of cloud must evaluate and manage the IT infrastructure construction and the organization's IT control environment of BPO vendors [25]. Since cloud is an internet-based technology, cloud auditing would be very critical and challengeable in such an environment. This paper focuses on practices related to auditing processes, methods, techniques, standards and frameworks in cloud computing environments.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:International Journal of Advanced Computer Science and Applications. - 9 : 12 (2018), p. 467-472. -
További szerzők:Mousavi, Seyedmajid (1982-) (informatika) Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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3.

001-es BibID:BIBFORM086226
035-os BibID:(WoS)000485682500027 (Scopus)85070095162
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:An Automatic Multiple Sclerosis Lesion Segmentation Approach based on Cellular Learning Automata / Moghadasi, Mohammad; Fazekas, Gabor
Dátum:2019
ISSN:2158-107X 2156-5570
Megjegyzések:Multiple Sclerosis (MS) is a demyelinating nerve disease which for an unknown reason assumes that the immune system of the body is affected, and the immune cells begin to destroy the myelin sheath of nerve cells. In Pathology, the areas of the distributed demyelination are called lesions that are pathologic characteristics of the Multiple Sclerosis (MS) disease. In this research, the segmentation of the lesions from one another is studied by using gray scale features and the dimensions of the lesions. The brain Magnetic Resonance Imaging (MRI) images in three planes (T1, T2, PD)1,2,3 containing MS disease lesions have been used. Cellular Learning Automata (CLA) is applied on the MRI images with a novel trial and error approach to set penalty and reward frames for each pixel. The images were analyzed in MATLAB and the results show the MS disease lesions in white and the brain anatomy in red on a black background. The proposed approach can be considered as a supplementary or superior method for other methods such as Graph Cuts (GC), fuzzy c-means, mean-shift, k-Nearest Neighbor (KNN), Support Vector Machines (SVM).
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:International Journal of Advanced Computer Science and Applications. - 10 : 7 (2019), p. 178-183. -
További szerzők:Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

4.

001-es BibID:BIBFORM086224
035-os BibID:(WoS)000582418600040 (Scopus)85085570624
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:Multiple Sclerosis Detection via Machine Learning Algorithm, Accurate Simulated Database 3D MRI to 2D Images, using value of Binary Pattern Classification : A Case Study / Mohammad Moghadasi, Gabor Fazekas
Dátum:2020
Megjegyzések:This paper is aiming to review some machine learning tools and to see if support vector machines (SVM) are accurate in 3D MRI images. We intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. According many research and database, 3D images are more informative than 2D ones, however working with 3D images is time consuming and requires specific programs and coding. In this research work and next which case study is under progress, we try to see the benefits of having a 3D database but to use 2D vectors only for comparison and more accurate results. This case study helps to gain better outcome. Support vector machines (SVM) can be a useful tool during the diagnosis process, however to be able to make better assumptions, more tests are needed. The technology can be viewed as detailed because it is usually applied in the discrimination of the blocks found in the areas of MS lesions and the regions which are not affected by the lesions. Primarily, to correctly segregate the different areas the textural background plays a crucial part in elevating the effectiveness of the imaging concept. In this case, the study of the slice blocks should be done comprehensively as it aims in ensuring that the type of results provided showcases the exact situation of the individual suffering from the ailment.
ISBN:9781728147932
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Megjelenés:Proceedings of the 10th IEEE International Conference on Cognitive Infocommunications : CogInfoCom 2019 / szerk. Péter Baranyi. - p. 233-240. -
További szerzők:Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

5.

001-es BibID:BIBFORM086222
035-os BibID:(WoS)000575043800007 (Scopus)85086004244
Első szerző:Moghadasi, Mohammad (informatikus)
Cím:Multiple sclerosis Lesion Detection via Machine Learning Algorithm based on converting 3D to 2D MRI images / Mohammad Moghadasi, Gabor Fazekas
Dátum:2020
ISSN:2061-2079
Megjegyzések:In the twenty first century, there have been various scientific discoveries which have helped in addressing some of the fundamental health issues. Specifically, the discovery of machines which are able to assess the internal conditions of individuals has been a significant boost in the medical field. This paper or case study is the continuation of a previous research which aimed to create artificial models using support vector machines (SVM) to classify MS and normal brain MRI images, analyze the effectiveness of these models and their potential to use them in Multiple Sclerosis (MS) diagnosis. In the previous study presented at the Cognitive InfoCommunication (CogInfoCom 2019) conference, we intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. The previous paper concluded that SVM is a potential method which can be involved during MS diagnosis, however, in order to confirm this statement more research and other potentially effective methods should be included in the research and need to be tested. First, this study continues the research of SVM used for classification and Cellular Learning Automata (CLA), then it expands the research to other method such as Artificial Neural Networks (ANN) and k-Nearest Neighbor (k-NN) and then compares the results of these.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény hazai lapban
folyóiratcikk
Megjelenés:Infocommunications Journal. - 12 : 1 (2020), p. 38-44. -
További szerzők:Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

6.

001-es BibID:BIBFORM070550
035-os BibID:(WoS)000428151900012 (Scopus)85047228414
Első szerző:Mousavi, Seyedmajid (informatika)
Cím:Dynamic resource allocation using combinatorial methods in Cloud : A case study / Seyed Majid Mousavi, Mohammad Moghadasi, Gabor Fazekas
Dátum:2017
Megjegyzések:Utilizing dynamic resource allocation for loadbalancing is considered as an important optimization process oftask scheduling in cloud computing. A poor scheduling policymay overload certain virtual machines while remaining virtualmachines are idle. Accordingly, this paper proposes a hybrid loadbalancing algorithm with combination of Teaching-Learning-Based Optimization (TLBO) and Grey Wolves Optimizationalgorithms, which can well contribute in maximizing thethroughput using well balanced load across virtual machines andovercome the problem of trap into local optimum. The hybridalgorithm is benchmarked on eleven test functions and acomparative study is conducted to verify the results with particleswarm optimization (PSO), Biogeography-based optimization(BBO), and GWO. To evaluate the performance of the proposedalgorithm for load balancing, the hybrid algorithm is simulatedand the experimental results are presented.
ISBN:978-1-5386-1264-4
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
cloud computing
resource allocation
optimization
Megjelenés:8th IEEE International Conference on Cognitive Infocommunications: CogInfoCom 2017 : Proceedings : September 11-14, 2017 Debrecen, Hungary. - p. 73-78. -
További szerzők:Moghadasi, Mohammad (1985-) (informatikus) Fazekas Gábor (1952-) (informatikus, matematikus)
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
DOI
Borító:
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