M
Mohammad GhasemiGol
Researcher at University of Birjand
Publications - 34
Citations - 484
Mohammad GhasemiGol is an academic researcher from University of Birjand. The author has contributed to research in topics: Support vector machine & Fuzzy clustering. The author has an hindex of 11, co-authored 29 publications receiving 309 citations. Previous affiliations of Mohammad GhasemiGol include Ferdowsi University of Mashhad & University of North Texas.
Papers
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Journal ArticleDOI
Coronary artery disease diagnosis; ranking the significant features using a random trees model
Javad Hassannataj Joloudari,Edris Hassannataj Joloudari,Hamid Saadatfar,Mohammad GhasemiGol,Seyyed Mohammad Razavi,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband,Laszlo Nadai +8 more
TL;DR: The proposed integrated method to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking shows promising results and the study confirms that the RTs model outperforms other models.
Journal ArticleDOI
Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning
Javad Hassannataj Joloudari,Mojtaba Haderbadi,Amir Mashmool,Mohammad GhasemiGol,Shahab S. Band,Amir Mosavi +5 more
TL;DR: The experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.
Inside the Mind of the Insider: Towards Insider Threat Detection Using Psychophysiological Signals.
TL;DR: The use of human bio-signals to detect the malicious activities and its applicability for insider threats detection are examined to show that human brain and heart signals can reveal valuable knowledge about the malicious behaviors and could be an effective solution for detecting insider threats.
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A comprehensive approach for network attack forecasting
TL;DR: The primary goal of this paper is to present an attack forecasting approach that can predict future network attacks with more precision and dynamically adapts to changes in the environment.
Journal ArticleDOI
Advanced damage detection technique by integration of unsupervised clustering into acoustic emission
Arash Behnia,Arash Behnia,Hwa Kian Chai,Mohammad GhasemiGol,Alireza Sepehrinezhad,Ahmad Mousa +5 more
TL;DR: An unsupervised kernel fuzzy c-means pattern recognition analysis and the principal component method were utilized to categorize various damage stages in plain and steel fiber reinforced concrete specimens monitored by AE technique.