Yazar "Feizi-Derakhshi, Mohammad-Reza" seçeneğine göre listele

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    C-y: Chaotic yolo for user intended image encryption and sharing in social media
    (Elsevier Science Inc, 2021) Asgari-Chenaghlu, Meysam; Feizi-Derakhshi, Mohammad-Reza; Nikzad-Khasmakhi, Narjes; Feizi-Derakhshi, Ali-Reza; Ramezani, Majid; Jahanbakhsh-Nagadeh, Zoleikha; Balafar, Mohammad-Ali; Rahkar-Farshi, Taymaz
    Social media is an inseparable part of our daily life where we post and share photos and media related to our life and in some cases we intend to share them between specific people. This intended and cherry picked sharing of media needs a better solution rather than simply picking users. Some social media platforms do not restrict other users from sharing timeline posts of others; meaning, one can simply forward a post from another person to a third one and no data preservation has been applied. In most cases we do not intend to secure the whole media and only important parts of it are intended to be secured. In this work we propose a novel method based on YoloV3 object detection and chaotic image encryption to overcome the issue of user intended data preservation in social media platforms. Our proposed method is capable of both automatic image encryption on full or user selected regions. Statistical and cryptographic analysis show superiority of our method compared to other state-of-the-art methods while it keeps the speed as high as possible for online and realtime use cases. (C) 2020 Elsevier Inc. All rights reserved.
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    Multi-modal forest optimization algorithm
    (Springer London Ltd, 2020) Orujpour, Mohanna; Feizi-Derakhshi, Mohammad-Reza; Rahkar-Farshi, Taymaz
    Multi-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Therefore, during the search process, most of the points should be detected by the algorithm. The forest optimization algorithm has been recently introduced as a new evolutionary algorithm with the capability of solving unimodal problems. This paper presents the multi-modal forest optimization algorithm (MMFOA), which is constructed by applying a clustering technique, based on niching methods, to the unimodal forest optimization algorithm. The MMFOA operates by dividing the population of the forest into subpopulations to locate existing local and global optima. Subpopulations are generated by the Basic Sequential Algorithmic Scheme with a radius neighborhood. As population size is self-adaptive in MMFOA, population size can be increased in functions with too many local and global optima. The proposed algorithm is evaluated by a set of multi-modal benchmark functions. The experiment results show that not only is the population size low, but also that the convergence speed is high, and that the algorithm is efficient in solving multi-modal problems.