AIMC Topic: Neural Networks, Computer

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Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation.

Sensors (Basel, Switzerland)
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared ...

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

Sensors (Basel, Switzerland)
This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The propos...

Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks.

Medical & biological engineering & computing
Accurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body ...

Protocol for creating representations of molecular structures using a polymer-specific decoder.

STAR protocols
To supply chemical structures of polymers for machine learning applications, decoding is necessary. Here, we present a protocol for generating polymer fingerprintsĀ (PFPs), which are representations of molecular structures, using a polymer-specific de...

ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier.

Computational biology and chemistry
Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-traine...

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.

Chemosphere
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept...

SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery.

Journal of chemical information and modeling
Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph n...

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

Scandinavian journal of urology
OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in ...

A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (...