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Heart Rate Variability-Based Mental Stress Detection: An Explainable Machine Learning Approach
Stress may be identified by examining changes in everyone’s physiological reactions. Due to its usefulness and non-intrusive appearance, wearable...
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Detecting deception using machine learning with facial expressions and pulse rate
Given the ongoing COVID-19 pandemic, remote interviews have become an increasingly popular approach in many fields. For example, a survey by the HR...
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Cognizable crime rate prediction and analysis under Indian penal code using deep learning with novel optimization approach
The exacerbation of high crime rate has become a critical impediment to the country’s economy, therefore necessitating the involvement of data...
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Model hybridization & learning rate annealing for skin cancer detection
The increasing frequency of skin tumour across the globe and their timely diagnosis is one of the most promising research directions in the...
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A deep learning-based nonlinear ensemble approach with biphasic feature selection for multivariate exchange rate forecasting
Exchange rate prediction is a challenging task for investors and policymakers due to its nonstationary and nonlinear characteristics. This study...
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Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems
To further improve the performance of optimization spiking neural P system (OSNPS), a multi-learning rate optimization spiking neural P system...
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Cyclic learning rate based HybridSN model for hyperspectral image classification
Classification of remotely sensed hyperspectral images (HSI) is a challenging task due to the presence of a large number of spectral bands and due to...
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Case Study: Tuning CNN Learning Rate with BoTorch
By now, we have established a good foundation regarding the theoretical inner workings of a typical Bayesian optimization process: a surrogate...
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Fast Server Learning Rate Tuning for Coded Federated Dropout
In Federated Learning (FL), clients with low computational power train a common machine model by exchanging parameters via updates instead of...
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Your heart rate betrays you: multimodal learning with spatio-temporal fusion networks for micro-expression recognition
Micro-expressions can convey feelings that people are trying to hide. At present, some studies on micro-expression, most of which only use the...
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Forecasting of river water flow rate with machine learning
Today, the estimation of physical parameters has become very important; for instance, the water flow rate (RWFR) estimation is one of the types that...
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Achieving generalization of deep learning models in a quick way by adapting T-HTR learning rate scheduler
Deep neural network training involves multiplfe hyperparameters which have an impact on the prediction or classification accuracy of the model. Among...
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Predicting the level of autism and improvement rate from assessment dataset using machine learning techniques
Children with Autism Spectrum Disorder (ASD) is increasing rapidly worldwide and is a major concern nowadays. Considering the growing number of...
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Dynamic Adjustment of the Learning Rate Using Gradient
Gradient descent method is the preferred method to optimize neural networks and many other machine learning algorithms. Especially with the wide use...
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Feed-forward ANN and traditional machine learning-based prediction of biogas generation rate from meteorological and organic waste parameters
This study presents a comprehensive investigation into the prediction of biogas production (BP) using meteorological parameters and organic waste...
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Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systems
In face recognition systems, light direction, reflection, and emotional and physical changes on the face are some of the main factors that make...
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An Adaptive Learning Rate Schedule for SIGNSGD Optimizer in Neural Networks
SIGNSGD is able to dramatically improve the performance of training large neural networks by transmitting the sign of each minibatch stochastic...
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Learning Time and Recognition Rate Improvement of CNNs Through Transfer Learning for BMI Systems
Brain-Machine Interface (BMI) is a control paradigm involving using brain signal to generate control commands for other devices. A non-invasive...
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A Fast and Robust Photometric Redshift Forecasting Method Using Lipschitz Adaptive Learning Rate
With the recent large astronomical survey experiments using high-resolution cameras and telescopes, there has been a tsunami of astronomical data...
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Human emotion recognition by analyzing facial expressions, heart rate and blogs using deep learning method
Development of automated systems to recognize human emotions can enhance the quality of delivery of public health service to a great extent. Due to...
