RETRACTED ARTICLE: Hybrid FOW—a novel whale optimized firefly feature selector for gait analysis

Monica, K. M.; Parvathi, R.

doi:10.1007/s00779-021-01525-4

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Because of its straightforward design with limited control parameters and an information-sharing mechanism, SI algorithms are recognized as widespread and most-used optimizers for hybridizing with other algorithms. Accordingly, many hybrid WOA variants have been developed by hybridizing with SI algorithms such as particle swarm optimization (PSO) [ 115 ], artificial bee colony algorithm (ABC) [ 116 ], grey wolf optimizer (GWO) [ 117 ], firefly algorithm (FA) [ 118 ], slime mould algorithm (SMA) [ 119 ], dragonfly algorithm (DA) [ 120 ], seagull optimization algorithm (SOA) [ 121 ], woodpecker mating algorithm (WMA) [ 105 ], and salp swarm algorithm (SSA) [ 122 ].…”

Section: Different Approaches To Developing Woamentioning

confidence: 99%

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A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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Section: Different Approaches To Developing Woamentioning

confidence: 99%

“…Monica et al [ 118 ] proposed the FOW algorithm by hybridizing binary WOA and the firefly algorithm (FA) to select optimal clinical treatment and diagnosis based on gait features. The FOW algorithm merges fireflies’ attraction strategy and the exploration phases of WOA.…”

Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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“…Cadence (steps per minute), Gait cycle duration (s), swing as well as stance ratio, and doublesupport were used in this study; stance was separated into 3 stages: foot flat, load, and push [36][37][38].…”

Section: Lstm Modelmentioning

confidence: 99%

Efficient Gait Analysis Using Deep Learning Techniques

Monica¹,

Parvathi²

2023

Computers, Materials &Amp; Continua

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Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used for the classification. It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate, with almost all activities accurately getting recognized over 90% of the time. Furthermore, because all the examples are generated from only 20 s of data, these actions can be recognised fast. Apart from classification, the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy (CP) for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory. Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg. Optical Motion Capture (OMC) equipment was used as the referral method to assess the functionality and quality of the foot-worn device. The average accuracy ± precision for stride length, cadence, and step length was 3.5 ± 4.3, 4.1 ± 3.8, and 0.6 ± 2.7 cm respectively. For cadence, stride length, swing, and step length, people with CP had considerably high inter-stride variables. Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

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Determining the severity of Parkinson’s disease in patients using a multi task neural network

García-Ordás

Benítez-Andrades

Aveleira‐Mata

et al. 2023

Multimed Tools Appl

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Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.

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RETRACTED ARTICLE: Hybrid FOW—a novel whale optimized firefly feature selector for gait analysis

Cited by 4 publications

References 31 publications

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Efficient Gait Analysis Using Deep Learning Techniques

Determining the severity of Parkinson’s disease in patients using a multi task neural network

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