One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of cancers should be studied properly prior to the diagnosis to save patients life. Thus, an automatic prediction system for classifying...
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One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of cancers should be studied properly prior to the diagnosis to save patients life. Thus, an automatic prediction system for classifying cancer using gene expression data is needed. This paper develops a cancer classification and detection method by proposing the rider Chicken optimisationalgorithm dependent Recurrent Neural Network (RCO-RNN) classifier. At first, pre-processing is done on the gene expression data to fit for the further processes of classification. In gene selection, the genes are selected based on entropy for reducing the dimension. Finally, the selected genes are classified using Recurrent Neural Network (RNN), which is trained by using the proposed rider Chicken optimisation (RCO) algorithm, which is the integration of Chicken Swarm optimisation (CSO), and rider optimisation algorithm (ROA). The experimentation is carried out using the Leukaemia database, Small Blue Round Cell Tumour (SBRCT) dataset and Lung Cancer Dataset. The performance of the RCO-RNN is evaluated based on specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. The proposed method produces the maximal accuracy, sensitivity, PPV, NPV and specificty upto 95%. Which indicates the superiority of the proposed method.
Resource allocation in wireless communication systems is of great concern in order to guarantee the efficient utilisation of scary resources. In space-time block codes-based multiple-input multiple-output with orthogo...
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Resource allocation in wireless communication systems is of great concern in order to guarantee the efficient utilisation of scary resources. In space-time block codes-based multiple-input multiple-output with orthogonal frequency division multiplexing (STBC-MIMO-OFDM) system, scheduling the appropriate user to the available antenna is the major challenge. To overcome the challenge, this study proposes a priority-based scheduling mechanism using the hybrid optimisation, Dolphin-rideroptimisation (DRO), which is a combination of Dolphin Echolocation algorithm and rider optimisation algorithm. The prioritisation follows the power and quality of service constraints of the users in such a way that the energy efficiency is enabled in the system. The performance of the system is analysed using the Rayleigh and Rician channels with the transmission media as, text, audio, and image. Moreover, the comparative analysis of the proposed DRO is enabled using three modulation schemes, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying, and quadrature amplitude modulation. The proposed user scheduling mechanism with the BPSK modulation is found to be effective with a minimal bit error rate and maximal throughput of 3.78x10(-7) and 0.875.
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