Search Results - adoption _ differential ((selection algorithm) OR (encryption algorithm))

A new KD-3D-CA block cipher with dynamic S boxes based on 3D cellular automata by Hamdi, Ayman Majid

“…Deductively, the proposed KD-3D-CA block cipher algorithm is more secure than other block cipher algorithms and can be implemented for data encryption and decryption.…”
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Performance comparison of differential evolution and particle swarm optimization in constrained optimization by Iwan, Mahmud, Akmeliawati, Rini, Faisal, Tarig, Al-Assadi, Hayder M.A.A.

“…Here, the constrained optimization is represented by some selected standard benchmark functions. A new constraint handling and stopping criterion technique is also adopted in the optimization algorithm. …”
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Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization by Kamal Z., Zamli, Abdul Kader, ., Fakhrud Din, ., Alhadawi, Hussam S.

“…This paper introduces a new variant of a metaheuristic algorithm based on Tiki-Taka algorithm, called selective chaotic maps Tiki-Taka algorithm (SCMTTA). …”
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Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Md. Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

“…The spectral features were performed by employing the relative spectral powers of delta (δRP), theta (θRP), alpha (αRP), beta (βRP), and gamma (γRP). The differential evolution-based channel selection algorithm (DEFS_Ch) was computed to find the most suitable EEG channels that have the greatest efficacy for identifying the various emotional states of the brain regions. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…However, the learning complexity of classification is increased due to the expansion number of learning model. Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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Prediction of Alzheimer disease using improved MMSE ensemble regressor based on magnetic resonance images by Farzan, Ali

“…A minimal set of feature who passed the above criteria and can differentiate all of cognitive score pairs is selected by using a genetic search algorithm. …”
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Resource allocation based on interference coordination for Multicast-Broadcast Single Frequency Network (MBSFN) / Mohd Fahmi Azman by Mohd Fahmi , Azman

“…The receiver will receive multiple signals and interpret it either it is useful or destructive so that it can differentiate which signal to process. Thus, this approach will improve the spectral efficiency of the signal by only selecting the constructive signal. …”
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Agent-Based Model of Virtual Community Cohesion (S/O: 13443) by Yusop, Nor Iadah, Mat Aji, Zahurin, Ab. Aziz, Azizi, Md Dahalin, Zulkhairi

“…The formal specifications (differential equations) equations form the basis of algorithm development, which preceded the development of a virtual community cohesion prediction simulator. …”
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Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel by Amin, A. K. M. Nurul, Hafiz, A.M. Khalid, Lajis, M. A.

“…Shunmugam et al. [7] also combined RSM with differential evolution and genetic algorithms to draw a comparison between these methods. …”
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Optical Packet Switching Contention Resolution Based On A Hybrid Wavelength Conversion-Fiber Delay Line Scheme by Go, Yun Ii

“…Furthermore, the obtained simulation results show that by classifying packet priority, the proposed scheduling scheme and architecture are able to offer differentiated class of service.…”
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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. …”
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