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Three decades of activations: A comprehensive survey of 400 activation functions for neural networks
Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …
many areas of life. Recently, their importance and practical usability have further been …
Adaptive collision avoidance decisions in autonomous ship encounter scenarios through rule-guided vision supervised learning
Limitations are identified in the expressive capabilities of the deep feature extraction network
employed in deep reinforcement learning (DRL), particularly in complex scenarios …
employed in deep reinforcement learning (DRL), particularly in complex scenarios …
Exploration of image and 3D data segmentation methods: an exhaustive survey
The field of image and 3-dimensional (3D) data segmentation is growing fast and has many
uses, like in medicine, and robotics. In this article, we explain how computers understand …
uses, like in medicine, and robotics. In this article, we explain how computers understand …
[HTML][HTML] Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution …
The objectives of this paper are to investigate the trade-offs between a physically
constrained neural network and a deep, convolutional neural network and to design a …
constrained neural network and a deep, convolutional neural network and to design a …
Evaluation of ResNet Architecture's Performance for Early Brain Infarction Detection
S Bajaj, M Bala - 2024 11th International Conference on …, 2024 - ieeexplore.ieee.org
The main objective of this research, is to compare the performance of ResNet models when
deployed on the images of Brain CT scans. In this research three ResNet models are …
deployed on the images of Brain CT scans. In this research three ResNet models are …
Investigating the Efficacy of a Newly Proposed Activation Function on Deep Neural Networks
The use of an AF is crucial in DNNs (DNNs) since it serves to incorporate non-linearity into
the model. Although numerous AFs have been put forth in the literature, a more potent AF is …
the model. Although numerous AFs have been put forth in the literature, a more potent AF is …
LUN-Net: Deep Learning Approach Based Medical Image Processing for Lung Cancer Detection
DSS Suggu, N Vedula, P Pandiyarajan… - … on Intelligent Cyber …, 2024 - ieeexplore.ieee.org
This research study proposes LUN-Net, a deep learning-based method for lung cancer
detection, which represents a substantial breakthrough in medical image processing. Using …
detection, which represents a substantial breakthrough in medical image processing. Using …
Synergistic application of advanced machine learning and computer vision techniques for the detection of exoplanet and star: leveraging contrastive learning in …
AK Alif, SJ Hossain, MI Hossain, SS Antor, AP Roy - 2024 - dspace.bracu.ac.bd
The detection of exoplanets through direct imaging has become increasingly feasible with
the advent of modern telescopes like the James Webb Space Telescope (JWST) and its …
the advent of modern telescopes like the James Webb Space Telescope (JWST) and its …
[PDF][PDF] Sorting algorithm for product classification using deep learning
ER Carmona - laccei.org
This article presents the development of a simulated product sorting environment through
identification and localization using regional convolutional neural networks. This type of …
identification and localization using regional convolutional neural networks. This type of …