Hossam Faris

TL;DR: A novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks and the extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.

TL;DR: The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called Grey Wolf Optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnets problem.

TL;DR: It is argued that the exploitation tendency of WOA is limited and can be considered as one of the main drawbacks of this algorithm, and the exploitative and exploratory capabilities of modified WOA in conjunction with a learning mechanism are improved.

TL;DR: Results and analysis show that SVM-based feature selection technique with the proposed binary GWO optimizer with elite-based crossover scheme has enhanced efficacy in dealing with Arabic text classification problems compared to other peers.

TL;DR: The MTV approach is introduced to boost the performance of the MTDE and demonstrates its advantages in dealing with problems of different levels of complexity.