Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm

Tamilmani, G.; Devi, V. Brindha; Sujithra, T.; Shajin, Francis H.; Rajesh, P.

doi:10.1016/j.bspc.2022.103545

Cited by 19 publications

(13 citation statements)

References 29 publications

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“…Therefore, the proposed MOMF optimization method is applied in the phase factor of OFDM to improve the efficiency of the model, BER, and PAPR. Mayfly group behavior is mimicked to develop the Mayfly algorithm with mating behavior [23][24][25][26][27][28][29][30]. Female and male mayflies are randomly divided from the population at the initial stage of algorithm.…”

Section: Multi-objective Mayfly Algorithmmentioning

confidence: 99%

Multi-Objective Mayfly Optimization in Phase Optimization of OFDM

Azeez

Tarannum

2023

IIUMEJ

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Communication systems have been used tremendously in recent years which results in the need for high data transmission rates. Orthogonal Frequency Division Multiplexing (OFDM) provides robust performance in frequency selective fading due to high bandwidth efficiency and inter-symbol interference. Various optimization techniques were applied in existing research to increase the efficiency of OFDM in a communication system. The existing research has a limitation of considering a single objective to improve the efficiency of OFDM and also has a local optima trap. This research proposes a Multi-Objective Mayfly algorithm (MOMF) to consider multi-objective and provides a proper trade-off between exploration and exploitation. The Partial Transmit Sequence (PTS) is applied in the model to test the performance. The FFT sizes and modulation orders are varied to evaluate the performance of the MOMF technique in phase optimization. The MOMF technique effectively increases the performance of the model than other existing optimization techniques. The MOMF technique provides a non-dominated solution to escape from local optima trap. The MOMF model considers PAPR, BER, and SER in MIMO-OFDM system to increase the efficiency of the system. The exploration-exploitation trade-off helps to improve the convergence and overcome local optima trap. The MOMF in OFDM phase optimization was evaluated using BER, SER, and Peak-to-Average Power Ratio (PAPR) metrics. The MOMF method has PAPR of 3.95 dB and PSO-GWO method has 4.92 dB of PAPR. ABSTRAK: Sistem komunikasi telah digunakan secara meluas sejak beberapa tahun ini dan dapatan kajian menunjukkan keperluan pada kadar transmisi data yang tinggi. Pemultipleksan Bahagian Frekuensi Ortogon (OFDM) menyediakan prestasi berkesan dalam pemilihan pemudaran frekuensi berdasarkan keberkesanan lebar jalur tinggi dan gangguan antara-simbol. Pelbagai teknik optimum digunakan pada kajian sebelum ini bagi meningkatkan keberkesanan OFDM dalam sistem komunikasi. Kajian tersebut mempunyai kekurangan dalam memilih satu objektif bagi membaiki keberkesanan OFDM dan juga mempunyai perangkap optima setempat. Kajian ini mencadangkan algoritma Mayfly Objektif-Pelbagai (MOMF) bagi memilih objektif-pelbagai dan menyediakan keseimbangan yang wajar antara eksplorasi dan eksploitasi. Urutan Pancar Separa (PTS) telah digunakan dalam model ini bagi menguji prestasi. Saiz FFT dan turutan modulasi dipelbagaikan bagi menguji keberkesanan teknik MOMF pada fasa pengoptimuman. Teknik MOMF dengan berkesan menaikkan prestasi model ini berbanding teknik-teknik sedia ada yang lain. Teknik MOMF menyediakan solusi kepada teknik bukan-dominasi bagi mengelak perangkap optima setempat. Model MOMF ini mengambil kira PAPR, BER, dan SER dalam sistem MIMO-OFDM bagi meningkatkan kecekapan sistem. Keseimbangan yang wajar antara eksplorasi-eksploitasi membantu dalam membaiki penumpuan dan mengatasi perangkap optima setempat. MOMF dalam fasa optimanisasi OFDM telah dinilai menggunakan BER, SER, dan matrik Nisbah Kuasa Puncak-kepada-Purata (PAPR). Kaedah MOMF mempunyai nilai PAPR sebanyak 3.95 dB dan kaedah PSO-GWO mempunyai PAPR 4.92 dB.

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Section: Multi-objective Mayfly Algorithmmentioning

confidence: 99%

Multi-Objective Mayfly Optimization in Phase Optimization of OFDM

Azeez

Tarannum

2023

IIUMEJ

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“…The Mayfly optimization Algorithm (MA) ) is a newly optimization algorithm which has already been used by researchers, to address their non-deterministic polynomial-time hardness problems (Alblehai et al, 2022;Bhattacharyya et al, 2020;Guo et al, 2021;Gupta & Gehlawat, 2022;Liu et al, 2021;Roni et al, 2022;Tamilmani et al, 2022;Zhao & Gao, 2020). The main reason behind the widely application of the particular algorithm, is that it combines major advantages of both evolutionary and swarm intelligence optimization algorithms.…”

Section: Mayfly Optimization Algorithmmentioning

confidence: 99%

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

et al. 2023

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Theatrical performance constitutes a complicated way for students to express and to communicate with each other, since it targets both various artistic and educational goals. Even though it constitutes a top moment of students’ expression, several students do not feel comfortable when participating in such cultural activities, as performance anxiety, a negative emotional experience stemming from the public audience exposure, affects them. The aim of this research is to apply and evaluate a student segmentation technique with the help of bio-inspired computational intelligence, for identifying high levels of performance anxiety at schoolchildren. A Mayfly-based clustering optimization algorithm is applied on a dataset with 774 instances of students to classify them according to their levels of emotions and performance anxiety that are developed during the event. A comparison with a genetic algorithm as well as particle swarm optimization shows that the proposed method is distinguished by superior categorization capabilities. The findings demonstrate the effective dissimilar student groups formation, with the members of each being distinguished by similar characteristics in terms of emotions and performance anxiety, highlighting the ones with unmanageable emotional experiences. Therefore, the drama educator is able to effortlessly detect, manage students and develop coping practices in those at risk, by acknowledging each group’s characteristics.

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“…15 Existing prediction methods such as WRS-OPUN, WRS-DBNS may struggle to adapt to these dynamic changes. 16,17 The authors propose a method called WRS-ResNetCNN-ZOA to assess the scalability and dependability of composite web services that use Business Process Execution Language (BPEL). 18 Existing prediction methods may rely on limited data sources or features to make predictions.…”

Section: Introductionmentioning

confidence: 99%

“…The internet is a dynamic environment where the performance and reliability of web services can fluctuate due to various factors such as network congestion, server failures, or changes in service providers 15 . Existing prediction methods such as WRS‐OPUN, WRS‐DBNS may struggle to adapt to these dynamic changes 16,17 . The authors propose a method called WRS‐ResNetCNN‐ZOA to assess the scalability and dependability of composite web services that use Business Process Execution Language (BPEL) 18 .…”

Section: Introductionmentioning

confidence: 99%

An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

Gokulakrishan,

Ramakrishnan,

Saritha

et al. 2024

Trans Emerging Tel Tech

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Web service reliability and scalability is an important mission that keeps web services running normally. Within web service, the web services invoked by users not only depend on the service itself, but also on web load condition. Due to the features of web dynamics, traditional reliability and scalability methods have become inappropriate; at the same time, the web condition parameter sparsity problem will cause inaccurate reliability prediction. To address these challenges, Web Service Reliability and Scalability Determination Using ResNet Convolutional Neural Network optimized with Zero Optimization Algorithm (WRS‐ResNetCNN‐ZOA) is proposed in this manuscript. Initially, the input data is collected from WSRec dataset. The ResNet convolutional neural network (ResNetCNN) with Business Process Execution Language (BPEL) specification is introduced to forecast the reliability and scalability of web service. The results are categorized as right and wrong based on ResNetCNN. The weight parameters of the ResNetCNN is optimized by Zebra Optimization Algorithm to improve accuracy of the prediction. The performance of the proposed method is examined under some performance metrics, like F‐measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed technique attains 15.36%, 35.39%, 23.87%, 20.67% better reliability, 42.39%, 11.39%, 34.16%, 25.78% better accuracy when analyzed to the existing methods, like Web Reliability based on K‐clustering, (WRS‐KClustering), Web Reliability prediction based on AdaBoostM1 and J48 (WRS‐AdaM1‐J48), Web Reliability prediction based on Online service Reliability (WRS‐OPUN), and Web Reliability prediction based on Dynamic Bayesian Network (WRS‐DBNS), respectively.

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Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm

Cited by 19 publications

References 29 publications

Multi-Objective Mayfly Optimization in Phase Optimization of OFDM

Multi-Objective Mayfly Optimization in Phase Optimization of OFDM

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

An advancing method for web service reliability and scalability using ResNet convolution neural network optimized with Zebra Optimization Algorithm

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