AIMC Topic: Neural Networks, Computer

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Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization.

Sensors (Basel, Switzerland)
The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increa...

Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping.

Scientific reports
Rapid detection and mapping of landforms are crucially important to improve our understanding of past and presently active processes across the earth, especially, in complex and dynamic volcanoes. Traditional landform modeling approaches are labor-in...

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms.

Scientific reports
Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in diff...

Resolving the neural mechanism of core object recognition in space and time: A computational approach.

Neuroscience research
The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recog...

Variable three-term conjugate gradient method for training artificial neural networks.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs) have been widely adopted as general computational tools both in computer science as well as many other engineering fields. Stochastic gradient descent (SGD) and adaptive methods such as Adam are popular as robust opt...

Factorizing time-heterogeneous Markov transition for temporal recommendation.

Neural networks : the official journal of the International Neural Network Society
Temporal recommendation which recommends items to users with consideration of time information has been of wide interest in recent years. But huge event space, highly sparse user activities and time-heterogeneous dependency of temporal behaviors make...

Non-fragile output-feedback synchronization for delayed discrete-time complex-valued neural networks with randomly occurring uncertainties.

Neural networks : the official journal of the International Neural Network Society
This paper is step forward to establish an exponential synchronization criterion for discrete-time complex-valued neural networks (CVNNs) having time-varying delays subject to randomly occurring uncertain weighting parameters, in order to overcome th...

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.

Bioresource technology
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimenta...

Data augmentation for medical imaging: A systematic literature review.

Computers in biology and medicine
Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it pos...

Fully automatic tumor segmentation of breast ultrasound images with deep learning.

Journal of applied clinical medical physics
BACKGROUND: Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis...