AIMC Topic: Biometry
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Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.
PloS one
Feb 1, 2024
Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and impr...
Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split.
The Science of the total environment
Jan 28, 2024
Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY dat...
Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Jan 8, 2024
OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substand...
Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning.
Nature communications
Nov 3, 2023
Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yi...
KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences.
Sensors (Basel, Switzerland)
Oct 10, 2023
Keystroke dynamics is a soft biometric based on the assumption that humans always type in uniquely characteristic manners. Previous works mainly focused on analyzing the key press or release events. Unlike these methods, we explored a novel visual mo...
Synthetic EMG Based on Adversarial Style Transfer Can Effectively Attack Biometric-Based Personal Identification Models.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 18, 2023
Biometric-based personal identification models are generally considered to be accurate and secure because biological signals are too complex and person-specific to be fabricated, and EMG signals, in particular, have been used as biological identifica...
A Scalable Open-Set ECG Identification System Based on Compressed CNNs.
IEEE transactions on neural networks and learning systems
Aug 4, 2023
Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In thi...
Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks.
European radiology
Jul 1, 2023
OBJECTIVES: To develop and validate a fully automated AI system to extract standard planes, assess early gestational weeks, and compare the performance of the developed system to sonographers.
Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.
Sensors (Basel, Switzerland)
Jun 20, 2023
The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing de...
Estimation of separable direct and indirect effects in a continuous-time illness-death model.
Lifetime data analysis
Jun 4, 2023
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corres...
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