Analyzing the impact of activation functions on the performance of the data-driven gait model

Singh, Bharat; Patel, Suchit; Vijayvargiya, Ankit; Kumar, Rajesh

doi:10.1016/j.rineng.2023.101029

Cited by 18 publications

(3 citation statements)

References 39 publications

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“…x denotes input data in the function, while e denotes a constant value that has a value of 2.71828 [39]. As previously described, the sigmoid is suitable for binary classification cases, where the value range of the sigmoid activation function is from 0 to 1 [40].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

2023

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The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.

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Section: Deep Learning (Dl)mentioning

confidence: 99%

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

2023

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“…The different activation functions used include Sigmoid, Rectified Linear Unit (ReLU), hyperbolic tangent (Tanh), Leaky ReLU, Exponential Linear Unit ELU), and Softmax. ReLU is a commonly used activation function [42,43]. The output layer is the final layer of the artificial neural network (ANN) and computes the final prediction.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra

Pokhrel

Sirisomboon

Khurnpoon

et al. 2023

Sensors

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The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

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“…The dropout layer then follows a dense layer, which is composed of 256 neurons, and is activated with the SeLU activation function. SeLU is a simple yet effective activation function developed to address the dying ReLu problem [55]. The final layer consists of two and five neurons, with softmax activation for binary and multi-class classifications, respectively.…”

Section: Teacher Modelmentioning

confidence: 99%

Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings

Islam,

Jony,

Hasan

et al. 2023

Applied Sciences

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Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors to blindness. Effective intervention necessitates accurate classification of DR; this is a need that computer vision-based technologies address. However, using large-scale deep learning models for DR classification presents difficulties, especially when integrating them into devices with limited resources, particularly in places with poor technological infrastructure. In order to address this, our research presents a knowledge distillation-based approach, where we train a fusion model, composed of ResNet152V2 and Swin Transformer, as the teacher model. The knowledge learned from the heavy teacher model is transferred to the lightweight student model of 102 megabytes, which consists of Xception with a customized convolutional block attention module (CBAM). The system also integrates a four-stage image enhancement technique to improve the image quality. We compared the model against eight state-of-the-art classifiers on five evaluation metrics; the experiments show superior performance of the model over other methods on two datasets (APTOS and IDRiD). The model performed exceptionally well on the APTOS dataset, achieving 100% accuracy in binary classification and 99.04% accuracy in multi-class classification. On the IDRiD dataset, the results were 98.05% for binary classification accuracy and 94.17% for multi-class accuracy. The proposed approach shows promise for practical applications, enabling accessible DR assessment even in technologically underdeveloped environments.

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Analyzing the impact of activation functions on the performance of the data-driven gait model

Cited by 18 publications

References 39 publications

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra

Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings

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