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Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review
Hydrological processes forecasting is an essential step for better water management and sustainability. Among several hydrological processes, lake...
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Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure
In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the...
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A reference spike train-based neurocomputing method for enhanced tactile discrimination of surface roughness
Spike trains (STs) induced by external stimuli are complex and challenging to decode the embedded spatiotemporal information. Although various...
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Using neurocomputing techniques to determine microstructural properties in a Li-ion battery
Current ab-initio approaches such as Quantum Mechanics (QM) calculations or Molecular Dynamics (MD) simulations to study the doped cathode structures...
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Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them,...
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Properties of the Weighted and Robust Implicitly Weighted Correlation Coefficients
Pearson product-moment correlation coefficient represents a fundamental measure of similarity between two data vectors. In various applications, it...
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A Bibliometric Analysis of the Last Ten Years of Fuzzy Min-Max Neural Networks
Neural networks have been widely used in many application areas such as power systems, weather forecasting, face recognition, behaviour analysis,...
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Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia
This paper proposes a new hybrid SVR-ANN model for water demand forecasting. Where an adaptation of the methodology proposed by Zhang (Neurocomputing...
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A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing
In industrial process control systems, there is overwhelming evidence corroborating the notion that economic or technical limitations result in some...
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A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device
Spiking Neural Networks (SNNs) have gained significant attention in the field of neuromorphic computing for their potential to mimic the brain’s...
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TransFGVC: transformer-based fine-grained visual classification
Fine-grained visual classification (FGVC) aims to identify subcategories of objects within the same superclass. This task is challenging owing to...
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Vehicle Re-Identification by Separating Representative Spatial Features
As a complex image classification problem, re-identification (ReID) requires the model to capture diverse representative features of vehicles through...
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Revival of Optical Computing
Optical computing is a general term for high-performance computing technologies that effectively use the physical properties of light. With the rapid...
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Efficient implicit Lagrangian twin parametric insensitive support vector regression via unconstrained minimization problems
In this paper, an efficient implicit Lagrangian twin parametric insensitive support vector regression is proposed which leads to a pair of...
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Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems
The main aim of this work is to design a non-fragile sampled data control (NFSDC) scheme for the asymptotic synchronization criteria for...
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Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function
In recent decades, the world has witnessed a great expansion in the world of technology and electronics, in addition to the tremendous development in...
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Impact of Autotuned Fully Connected Layers on Performance of Self-supervised Models for Image Classification
With the recent advancements of deep learning-based methods in image classification, the requirement of a huge amount of training data is inevitable...
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Scene-adaptive crowd counting method based on meta learning with dual-input network DMNet
Crowd counting is recently becoming a hot research topic, which aims to count the number of the people in different crowded scenes. Existing methods...
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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries
Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods...
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RGB oralscan video-based orthodontic treatment monitoring
Orthodontic treatment monitoring involves using current images and previous 3D models to estimate the relative position of individual teeth before...