Environmental monitoring and assessment
Mar 3, 2021
Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in m...
The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile...
Computer methods and programs in biomedicine
Mar 2, 2021
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However...
International journal of computer assisted radiology and surgery
Mar 2, 2021
OBJECTIVES: It is with a great prospect to develop an auxiliary diagnosis system for dental periapical radiographs based on deep convolutional neural networks (CNNs), and the indications and performances should be investigated. The aim of this study ...
BACKGROUND: Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, p...
The structure of relationships in the past, the present and the future is shaped by the idea of humanism. Based on this construct, the article illuminates various aspects and configurations of humanism on a timeline from "here and now" to "there and ...
The article summarizes various publications on the application of "learning algorithms" and "artificial neural networks" in psychiatry to describe a dystopian future scenario. The draft of a nosology based on molecular biology is opposed to the ecolo...
We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D ...
Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-base...
High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more c...
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