AIMC Topic: Neural Networks, Computer

Clear Filters Showing 13761 to 13770 of 31376 articles

Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm.

Sensors (Basel, Switzerland)
Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system ...

Musical Instrument Identification Using Deep Learning Approach.

Sensors (Basel, Switzerland)
The work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related t...

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey.

Sensors (Basel, Switzerland)
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficien...

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Scientific reports
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural n...

Deep learning model for tongue cancer diagnosis using endoscopic images.

Scientific reports
In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five universi...

Cross subkey side channel analysis based on small samples.

Scientific reports
The majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in...

Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural network.

Scientific reports
With the development of Internet of vehicles, the information exchange between vehicles and the outside world results in a higher risk of external network attacks to the vehicles. The attack modes to the most widely used vehicle-mounted CAN bus are c...

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

BMC medical informatics and decision making
BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) comp...

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

Genome biology
Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general stra...

Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution.

BMC bioinformatics
BACKGROUND: Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users...