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Found 14 papers, 0 papers with code
Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model
Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups.
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used.
Dynamic Early Exiting Predictive Coding Neural Networks
Internet of Things (IoT) sensors are nowadays heavily utilized in various real-world applications ranging from wearables to smart buildings passing by agrotechnology and health monitoring.
Multi-Objective Decision Transformers for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) is structured to derive policies from static trajectory data without requiring real-time environment interactions.
Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting
Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging.
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations
This assumption serves to simplify the system and guarantees that the velocity adheres to the divergence-free equation.
Optical communication-based identification for multi-UAV systems: theory and practice
In such system, the UAVs are equipped with LEDs that act as beacons and with cameras allowing them to locate the LEDs of other UAVs.
When Infodemic Meets Epidemic: a Systematic Literature Review
Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts.
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models
Recently, Supervised Contrastive Learning (SCL) has been shown to achieve excellent performance in most classification tasks.
KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach
Knowledge Graph (KG) completion is an important task that greatly benefits knowledge discovery in many fields (e. g. biomedical research).
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting
In this paper, we investigate the use of Variational Mode Decomposition and deep learning techniques to improve the accuracy of the load forecasting problem.
GraphCL: Contrastive Self-Supervised Learning of Graph Representations
We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner.
Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery
If the sky is classified as cloudy, we propose to use a deep neural network having as input features the PCNP to predict intra-hour variability of the solar irradiance.
Exploratory Data Analysis of a Network Telescope Traffic and Prediction of Port Probing Rates
Understanding the properties exhibited by large scale network probing traffic would improve cyber threat intelligence.
