Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data

Piri, Jayashree; Mohapatra, Puspanjali; Acharya, Biswaranjan; Gharehchopogh, Farhad Soleimanian; Gerogiannis, Vassilis C.; Kanavos, Andreas; Manika, Stella

doi:10.3390/math10152742

Cited by 65 publications

(16 citation statements)

References 71 publications

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“…The HLBDA aims to forecast death and recovery conditions. Piri et al [ 60 ] used a discrete gorilla troop optimizer (DAGTO) for medical sector feature selection. Based on the number and type of objective functions for identifying relevant features, four variants of the proposed method were proposed in this study: (1) single objective, (2) bi-objective (wrapper), (3) bi-objective (filter wrapper hybrid), and (4) tri-objective (filter wrapper hybrid).…”

Section: Related Workmentioning

confidence: 99%

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

2023

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Section: Related Workmentioning

confidence: 99%

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

2023

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“…GTO was employed by Ahmed Ginidi et al [ 25 ] for photovoltaic model parameter extraction, while Abdel-Basset et al [ 26 ] presented memory-based improved GTO (MIGTO) for the same problem. To handle feature selection in biological data, Piri et al [ 27 ] introduced discrete artificial GTO (DAGTO). Liang et al [ 28 ] updated GTO with opposition-based learning and parallel methods, offering OPGTO to decrease errors in the wireless sensor network’s node location.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Hamdy

Ismail

Awad

et al. 2023

Sensors

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MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

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“…Many recent DL and ML techniques have been introduced to allocate various resources and power in the system. In addition, the traditional GTO technique 31,32 requires many iterations to avoid falling in local optima. However, these techniques show high computational complexity and error within the system architecture.…”

Section: Pa and Ra Using Hgtlo Techniquementioning

confidence: 99%

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

Rameshchandra

Borugadda

2023

Trans Emerging Tel Tech

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Multiple input multiple output (MIMO) plays an integral role in improving energy efficiency for enhancing all users' quality of service (QoS). For 5G wireless communication (WC), the NOMA strategy is used with the MIMO system to improve the system's sum rate and computational complexity. In this, terahertz (THz) band communication is introduced for future demand to carry more users and obtains high multiplexing gain. Multiple antenna arrays are used in the MIMO‐NOMA system to achieve high signal transmission. However, the use of antenna arrays increases as the number of users increases. A beam‐forming‐based signal transmission is introduced in this work to overcome this issue. For performing digital and Analog beam‐forming, a hybrid minimum mean square error (MMSE) with zero‐forcing (ZF) pre‐coder is proposed to eliminate the inter‐cluster interference. Initially, user clustering is undertaken using a fuzzy k‐means clustering (FKM) algorithm, and beam‐forming is performed among the user clustering. The joint power allocation (PA) and resource allocation (RA) is done using a hybrid artificial gorilla troop with leader optimization (HGTLO) technique to achieve a maximum sum rate of the RF channel. The performance of a proposed method is analyzed using the MATLAB tool. In the experimental scenario, the performance of energy efficiency (EE), spectral efficiency (SE), achievable sum rate, throughput, bit error rate (BER), and mean square error (MSE) attains the value of 35.82 bits/Hz/Joules, 198.11 bps/Hz, 116.78 Mbps, 93.06%, .003 and .011 respectively. The performance measures prove the efficacy of a proposed method.

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Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data

Cited by 65 publications

References 71 publications

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

Joint resource allocation and power allocation scheme forMIMOassistedNOMAsystem

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