Abstract: Deep learning (DL) requires massive data collection. DL is rarely used in applications without a large dataset. Getting enough data takes time, money, and effort. The scientific community uses synthetic images and real data…
Abstract: Heart failure is difficult to predict. Deep learning models on rich electronic health records may improve prediction but are unexplainable, limiting their use in medicine. A deep-learning framework for accurate and explainable 6-month incident…
Abstract: The Iteratively Optimized Patch Label Inference Network (IOPLIN) is a novel deep learning framework for automatically detecting pavement distresses beyond cracks and potholes. IOPLIN can infer patch labels from pavement images using the Expectation-Maximization…
Abstract: This paper presents an end-to-end neural network model for autonomous driving road detection. We use road boundary and deep convolutional network multi-task learning. To improve performance, we reassign the label and rebalance road pixel…
Abstract: Visual change detection is a fundamental task in computer vision and video analytics. Anomaly detection, object tracking, traffic monitoring, human-machine interaction, behavior analysis, action recognition, and visual surveillance are change detection applications. Change detection…
Abstract: Before entering cities and highways, autonomous vehicles must be thoroughly tested. Most autonomous vehicle evaluation methods are static and inefficient at generating challenging scenarios for tested vehicles. This paper proposes an adaptive evaluation framework…
Abstract: This paper presents an adaptive model-free deep reinforcement approach that can recognize and adapt to ride-sharing diurnal patterns with car-pooling. Deep Reinforcement Learning (RL) suffers from catastrophic forgetting because it is agnostic to experience…
Abstract: Chronic and common obstructive sleep apnea (OSA) has many comorbidities. Polysomnography (PSG), the gold standard for OSA diagnosis, is unavailable in many severe cases. Home-based OSA screening methods that identify high-risk patients who need…
Abstract: This paper introduces a Lasso Logistic Regression model based on feature-based time series data to assess COVID-19 disease severity and when to administer drugs or escalate intervention procedures. The dynamic feature-based classification model used…
Abstract: Re-ID via gait features in 3D skeleton sequences is a new topic with many benefits. Solutions use hand-crafted descriptors or supervised gait representation learning. This paper proposes a self-supervised gait encoding method to learn…
Abstract: ITS needs accurate and timely traffic flow prediction. Graph-based neural networks have improved prediction results. Graph construction and model time complexity remain challenges. This paper proposes a multi-stream feature fusion approach to extract and…
Abstract: Since some progressive MCI patients will develop Alzheimer’s disease, early diagnosis is crucial. A patch-based imaging-fed multi-stream deep convolutional neural network classifies stable and progressive MCI. First, we use a multivariate statistical test to…
WhatsApp us