Development of improved coyote optimization with deep neural network for intelligent skill knowledge transfer for human to robot interaction

Gawali, Mahendra Bhatu; Gawali, Swapnali Sunil

doi:10.1007/s41315-022-00236-0

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…The effectiveness of the developed arrhythmia classification approach was analyzed with the previously utilized detection models and heuristic algorithms to validate the effectiveness. Heuristic algorithms used for these analysis were Single Static Assignment (SSA) [30], Coyote Optimization Algorithm (COA) [31], ASO [27], and DSO [26], while the classification techniques used for this performance analysis was SVM [8], 3DCNN [28], LSTM [6] and 3DCNN-ResNet [29].…”

Section: Methodsmentioning

confidence: 99%

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Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Munawar¹,

Geetha²,

Srinvas³

2023

INDJCSE

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Arrhythmia is characterized by aberrant electrical activity of the heart, which can be identified the changes in the Electrocardiogram (ECG). Automatic ECG detection is needed because it is mainly used for detecting arrhythmias. Although several algorithms are implemented for the automatic classification of cardiac arrhythmias based on the characteristics of the ECG, their stratification rate is very less because of the unreliable features of signal characteristics or limited generalization capability of the classifier and it is still difficult to diagnose the arrhythmia disease automatically. At this work, they propose a new hybrid deep learning technique for the classification of arrhythmia from the ECG signal. Initially, the wanted ECG signal is collected from the standard websites and then it is assigned to the preprocessing technique. The preprocessing techniques includes the artifacts removal and peak detection techniques noise removal for the elimination of the unwanted distortions and the noise present in the signal then the resultant signal is fed to the Short-time Fourier transform (STFT) to achieve the spectrogram signals and then the spectrogram signal. Thus, the resultant spectrogram signal is given to the hybrid deep learning architecture that includes the 3DCNN-ResNet for diagnosing the arrhythmia disease. Here, the parameter optimizations take place using the hybrid Artificial Showering Dolphin Swarm Optimization (ASDSO) to increase the classification performance. It classifies the signal into five prominent classes that is Premature Ventricular Contraction (V), Right Bundle Branch Block (RBBB or R), Normal Sinus Rhythm (N), Left Bundle Branch Block (LBBB or L), and Atrial Premature Beat (A). The success of the proposed model is validated through diverse benchmark datasets with the performance validation like recall, precision, accuracy, f-measure and some negative measures.

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Section: Methodsmentioning

confidence: 99%

“…Predation phase: In the predation phase, the dolphin encircling radius is denoted by 2 r and the search radius is represented by 1 r and it is measured using Eq. (31).…”

mentioning

confidence: 99%

Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Munawar¹,

Geetha²,

Srinvas³

2023

INDJCSE

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“…Tested on 71 benchmark functions from various suites, OSCOA, along with seven optimizers, demonstrates efficacy and stability in real-world scenarios. Mahendra Bhatu Gawali et al (23) introduced modified RL with BWF-COA to minimize error differences in movement. Results indicate substantial information gains compared to traditional models.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancing Image Clarity: Feature Selection with Trickster Coyote Optimization in Noisy/Blurry Images

Jain,

Maan

2024

Salud, Ciencia y Tecnología

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This paper presents a novel method for recognizing human emotions from gait data collected in an unconstrained environment. The method uses a bi-directional long short-term memory (FL-BiLSTM) network that is optimized by an augmented trickster coyote algorithm for feature selection and classification. The study focuses on overcoming the limitations of existing gait recognition systems that struggle with changes in walking direction. The paper evaluates the performance of the proposed FL-BiLSTM classifier method on a dataset of gait sequences with different emotions and compares it with existing methods. The results show that the proposed method achieves high accuracy, sensitivity, and specificity in emotion recognition from gait.

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A collective approach to reach known and unknown target in multi agent environment using nature inspired algorithms

Sharma,

Yadav

2024

Cluster Comput

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Development of improved coyote optimization with deep neural network for intelligent skill knowledge transfer for human to robot interaction

Cited by 6 publications

References 39 publications

Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Enhancing Image Clarity: Feature Selection with Trickster Coyote Optimization in Noisy/Blurry Images

A collective approach to reach known and unknown target in multi agent environment using nature inspired algorithms

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