Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems

Sahoo, Karam Kumar; Ijaz, Muhammad Fazal; Mallik, Saurav; Roy, Arup; Singh, Pawan Kumar; Zhao, Zhongming

doi:10.1038/s41598-022-27192-w

Cited by 16 publications

(5 citation statements)

References 53 publications

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“…Furthermore, the Gaussian map is imbued with better ergodicity and mixing properties, enabling it to cover the search space thoroughly and uniformly. Thus, employing the Gaussian chaotic map in COWOA can augment its ability to locate the global optima and optimize its overall performance [39–41] …”

Section: Methodsmentioning

confidence: 99%

“…Thus, employing the Gaussian chaotic map in COWOA can augment its ability to locate the global optima and optimize its overall performance. [39][40][41] Step 1: A given network structure is first constructed using a given hidden layer, a given number of nodes, and an absurd measure of search agents. The input values, the quantity of hidden layer nodes, and number of output layers all affect how many weights and biases are applied to the control parameters.…”

Section: Chaotic Oppositional Based Whale Optimization Algorithmmentioning

confidence: 99%

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CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

Mahto

Das

Aziz

et al. 2023

Chemistry & Biodiversity

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The most significant groupings of cold‐blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting‐edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO‐WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG‐19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting‐edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group‐19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.

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Section: Methodsmentioning

confidence: 99%

Section: Chaotic Oppositional Based Whale Optimization Algorithmmentioning

confidence: 99%

CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

Mahto

Das

Aziz

et al. 2023

Chemistry & Biodiversity

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“…Some regularly occurring wrapper method examples are backward features elimination, recursive feature elimination, forward feature selection, and so forth (Maldonado & Weber, 2009). A wrapper-based feature selection technique generates and calculates the prediction performance of a subset of features iteratively (Chen et al, 2020;Sahoo et al, 2023).…”

Section: Wrapper Methodsmentioning

confidence: 99%

Feature Selection, Clustering and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Islam¹,

Seth²,

Bhadra³

et al. 2023

JDSIS

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In this era, feature clustering is a prominent technique in data mining. Features clustering have also huge application in biomedical research for multiple purpose including grouping, features reduction and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effect and human reaction have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structure in both labeled and unlabeled data sets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we are used five different type of feature selection like Minimum Redundancy - Maximum Relevance (mRMR), Random Forest, Normalized Mutual Information Feature Selection (NMIFS), F-Test and Chi-Square and five different type of Clustering algorithms like Hierarchical Clustering, Density-based spatial clustering of applications with noise (DBSCAN) Clustering, K-Means Clustering, Shrinkage Clustering, and Fuzzy C-Means Clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society.

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“…The Harris Hawks Optimizer [27] was implemented for tuning the parameters of a PI-based LFC, incorporated into a multi-interconnected system with RESs. The Wrapper-based deep feature optimization algorithm, Mimetic algorithm, and harmonic search algorithms, which are used to solve large and complex problems have been used in [28] and [29].…”

Section: List Of Abbreviationsmentioning

confidence: 99%

Performance Analysis of Student Psychology-Based Optimization for the Frequency Control of Hybrid-Power System

Ganguly,

Mudi,

Mukherjee

et al. 2023

IEEE Access

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Access to reliable electricity is crucial for rural development and improving the quality of life in remote areas. Standalone photovoltaic PV systems and hybrid power systems HPS are promising solutions for rural electrification. However, frequency stabilization is a critical challenge in such systems, and conventional control techniques are inadequate. This work proposes a novel approach to optimize the PID controller gains for the standalone PV-based isolated HPS (IHPS). The student psychology-based optimization algorithm (SPBOA) and quasi-oppositional-based whale optimization algorithm (QOWOA) are employed to enhance the performance of the IHPS models separately. The objective function considered is the integral of time absolute error (ITAE), and the frequency response profile is studied in the presence of the proposed SPBOA and QOWOA-based PID controllers. The results show that the proposed SPBOA outperforms the QOWOA in terms of reducing the ITAE by nearly 5% and minimizing the peak and settling time of the frequency response by 5%-7% in different scenarios. The transient responses of the systems to different input conditions are also analyzed, indicating that both the IHPS-I and IHPS-II models are feasible for remote rural electrification. Overall, the proposed approach offers an efficient solution to optimize the PID controller gains for frequency stabilization in standalone PV and HPS. The results demonstrate the benefits of using the SPBOA and QOWOA algorithms to enhance the performance of the systems, making them suitable for remote rural electrification.INDEX TERMS Diesel engine generator; Isolated hybrid power system; Photovoltaic; Proportionalintegral-derivative; Wind turbine generator; quasi-oppositional-based whale optimization algorithm; student psychology-based optimization algorithm.

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Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems

Cited by 16 publications

References 53 publications

CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

Feature Selection, Clustering and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Performance Analysis of Student Psychology-Based Optimization for the Frequency Control of Hybrid-Power System

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