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Deep reinforcement learning-based pairwise DNA sequence alignment method compatible with embedded edge devices.

Scientific reports
Sequence alignment is an essential component of bioinformatics, for identifying regions of similarity that may indicate functional, structural, or evolutionary relationships between the sequences. Genome-based diagnostics relying on DNA sequencing ha...

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning.

Journal of dairy science
Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observatio...

A Co-Designed Neuromorphic Chip With Compact (17.9K F) and Weak Neuron Number-Dependent Neuron/Synapse Modules.

IEEE transactions on biomedical circuits and systems
Many efforts have been made to improve the neuron integration efficiency on neuromorphic chips, such as using emerging memory devices and shrinking CMOS technology nodes. However, in the fully connected (FC) neuromorphic core, increasing the number o...

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike.

Biophysical journal
Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel softw...

Identification of Active Pulmonary Tuberculosis Among Patients With Positive Interferon-Gamma Release Assay Results: Value of a Deep Learning-based Computer-aided Detection System in Different Scenarios of Implementation.

Journal of thoracic imaging
PURPOSE: To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest radiographs (CRs) of patients with positive interferon-gamma release assay (IGRA) results in differ...

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Molecules (Basel, Switzerland)
Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural net...

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.

Sensors (Basel, Switzerland)
The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are require...

Human-Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention.

Sensors (Basel, Switzerland)
Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person's behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker's emotional state from their speech signal. SER is ...

[A survey study towards the opinions of clinicians, patients and care partners regarding computer tools in the memory clinic: sense or nonsense?].

Tijdschrift voor gerontologie en geriatrie
INTRODUCTION: Computer tools based on artificial intelligence could aid clinicians in memory clinics by supporting diagnostic decision-making and communicating diagnosis and prognosis. We aimed to identify preferences of end-users, and barriers and f...

SPIDE: A purely spike-based method for training feedback spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural netwo...