Ethics

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum.

Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic comp...

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.

Non-coding genomic variants constitute the majority of trait-associated genome variations; however, ...

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS.

Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds usi...

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine.

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to am...

Safe and Ethical Artificial Intelligence in Radiotherapy - Lessons Learned From the Aviation Industry.

Ethical artificial intelligence (AI) frameworks can be the catalyst in improving the safety and well...

Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.

Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response predictio...

Artificial Neural Network Analysis-Based Immune-Related Signatures of Primary Non-Response to Infliximab in Patients With Ulcerative Colitis.

Infliximab (IFX) is an effective medication for ulcerative colitis (UC) patients. However, one-third...

Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field.

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful ...

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. H...

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network.

Several infectious diseases have affected the lives of many people and have caused great dilemmas al...

Byzantine-robust federated learning via credibility assessment on non-IID data.

Federated learning is a novel framework that enables resource-constrained edge devices to jointly le...

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properl...

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many nc...

AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness.

Methods used in artificial intelligence (AI) overlap with methods used in computational psychiatry (...

Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.

The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened...

A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.

PURPOSE: This study investigated the feasibility of a new image analysis technique (radiomics) on co...

Non-human primate epidural ECoG analysis using explainable deep learning technology.

With the development in the field of neural networks,(XAI), is being studied to ensure that artifici...

Incidence and risk factors of in-hospital venous thromboembolism in non-oncological urological inpatients: A single center experience.

OBJECTIVE: To determine incidence and risk factors for venous thromboembolism (VTE) development of i...

The ethics of using biased artificial intelligence programs in the clinic.

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c.

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements...

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach.

BACKGROUND AND OBJECTIVE: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy chara...