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The objective of this paper is to integrate a multi-hop transmission between WLAN 802.11n and 802.11g IEEE standards, so as to enhance the throughput and reduce the data dropped packet and media access delay which is used in IOT & multimedia applications. In the past decades, Wireless Local Area Network standards were used only for transmission of low bandwidth traffic, but with the ongoing standards such as 802.11n and 802.11g, there is a possibility to transmit high bandwidth traffic, such as video traffic and voice traffic. This paper also analyzes the performance of these IEEE standards with a setup of four different scenarios under the Riverbed modeler 17.5. The network performance for each scenario has been evaluated in terms of data drop packet, MAC delay and throughput using a novel of integrated network. [ABSTRACT FROM AUTHOR]

Recently, the artificial bee colony (ABC) algorithm has been developed to efficiently and effectively solve a wide range of optimization problems. In this work, the standard ABC algorithm is extended by incorporating a cooperation approach, and an algorithm called cooperative ABC (CABC) is proposed to solve the optimal power flow (OPF) problem. CABC aims at improving the performance of the standard ABC algorithm by using multiple artificial bee colonies to optimized different components of the solution vector cooperatively. With six well known benchmarks, CABC is proved to have significant better performance improvement on the standard ABC. CABC is then applied to the real-world OPF problem on an IEEE 30-bus test system. The simulation results showed that the proposed CABC outperforms other algorithms investigated in this paper in terms of optimization accuracy and computation robustness. [ABSTRACT FROM AUTHOR]

The imbalanced datasets are common in real-world application and the problem of imbalanced dataset affect classification performance of many standard learning approaches. To address imbalanced datasets, a weighted extreme learning machine (WELM) solving the L 2 -regularized weighted least squares problem is presented to avoid the generation of an over-fitting model and obtain better generalization ability compared with ELM. However, the weight generated according to class distribution of training data leads to lack of finding optimal weight with good generalization performance and the randomness of input weight and hidden biases of network makes the algorithm produce suboptimal classification model. In this paper, a weighted extreme learning machine based on hybrid artificial bee colony (HABC) is proposed to obtain better performance than WELM, in which input weights and hidden bias of WELM and the weight assigned to training samples are optimized by the hybrid artificial bee colony algorithm. HABC combines the diversities of the perturbed parameter vectors of differential evolution with the best solution information of the artificial bee colony effectively. In the empirical study, different class imbalance data handling methods including four WELM-based methods, weighted support vector machine, four ensemble methods which combine data sampling and the Bagging or Boosting are compared with our method. The experimental results on 15 imbalanced datasets show that the proposed method outperforms most methods, which indicates its superiority. [ABSTRACT FROM AUTHOR]