M
Mahdieh Ghazvini
Researcher at Shahid Bahonar University of Kerman
Publications - 28
Citations - 399
Mahdieh Ghazvini is an academic researcher from Shahid Bahonar University of Kerman. The author has contributed to research in topics: Communication channel & Network packet. The author has an hindex of 7, co-authored 25 publications receiving 228 citations. Previous affiliations of Mahdieh Ghazvini include University of Isfahan.
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Game Theory Applications in CSMA Methods
TL;DR: This research reviews different CSMA games presented for wireless MAC and classifies them, finding advantages and shortcomings of these games and some open research directions for future research, supported.
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A modified algorithm to improve security and performance of AODV protocol against black hole attack
TL;DR: A new algorithm is suggested which enhances the security of AODV routing protocol to encounter the black hole attacks and tries to identify malicious nodes according to nodes’ behaviours in an Ad Hoc network and delete them from routing.
Journal Article
Defect Detection of Tiles Using 2D-Wavelet Transform and Statistical Features
TL;DR: In this study, along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated.
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A modified method for image encryption based on chaotic map and genetic algorithm
TL;DR: The experimental results and several security analyses show that the proposed modified method provides an efficient scheme for image encryption and good robustness against frequent statistical and security attacks.
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Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features
Hesam Akbari,Muhammad Tariq Sadiq,Muhammad Tariq Sadiq,Ateeq Ur Rehman,Ateeq Ur Rehman,Mahdieh Ghazvini,Rizwan Ali Naqvi,Malih Payan,Hourieh Bagheri,Hamed Bagheri +9 more
TL;DR: RPS of EEG signals can be used as a biomarker for psychiatrists which are simpler than the EEG signals in visual depression diagnostics, and it is found that EEG signals from the right hemisphere are significant for depression detection than the left hemisphere.