[HTML][HTML] Improved handwritten digit recognition using convolutional neural networks (CNN)
Traditional systems of handwriting recognition have relied on handcrafted features and a
large amount of prior knowledge. Training an Optical character recognition (OCR) system …
large amount of prior knowledge. Training an Optical character recognition (OCR) system …
[HTML][HTML] Multiparametric programming in process systems engineering: Recent developments and path forward
The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …
An efficient approach for crops pests recognition and classification based on novel DeepPestNet deep learning model
Crop pests are to blame for significant economic, social, and environmental losses
worldwide. Various pests have different control strategies, and precisely identifying pests …
worldwide. Various pests have different control strategies, and precisely identifying pests …
An improved faster-RCNN model for handwritten character recognition
Existing techniques for hand-written digit recognition (HDR) rely heavily on the hand-coded
key points and requires prior knowledge. Training an efficient HDR network with these …
key points and requires prior knowledge. Training an efficient HDR network with these …
[HTML][HTML] Convolutional-neural-network-based handwritten character recognition: an approach with massive multisource data
Neural networks have made big strides in image classification. Convolutional neural
networks (CNN) work successfully to run neural networks on direct images. Handwritten …
networks (CNN) work successfully to run neural networks on direct images. Handwritten …
Better wind forecasting using evolutionary neural architecture search driven green deep learning
Climate Change heavily impacts global cities, the downsides of which can be minimized by
adopting renewables like wind energy. However, despite its advantages, the nonlinear …
adopting renewables like wind energy. However, despite its advantages, the nonlinear …
Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations
Among the various physical properties of polypropylene composites (PPCs), heat deflection
temperature (HDT) during PPC production is significant because it is directly related to the …
temperature (HDT) during PPC production is significant because it is directly related to the …
[HTML][HTML] TumorDetNet: A unified deep learning model for brain tumor detection and classification
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment
process and helps to save the lives of a large number of people worldwide. Because they …
process and helps to save the lives of a large number of people worldwide. Because they …
[PDF][PDF] Novel power transformer fault diagnosis using optimized machine learning methods
IBM Taha, DA Mansour - Intelligent Automation & Soft …, 2021 - cdn.techscience.cn
Power transformer is one of the more important components of electrical power systems. The
early detection of transformer faults increases the power system reliability. Dissolved gas …
early detection of transformer faults increases the power system reliability. Dissolved gas …
A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization
X Liu, D Zhang, T Zhang, J Zhang… - Engineering …, 2022 - emerald.com
Purpose To solve the path planning problem of the intelligent driving vehicular, this paper
designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) …
designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) …