AIMC Topic: Humans

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OPTUNA optimization for predicting chemical respiratory toxicity using ML models.

Journal of computer-aided molecular design
Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combinatio...

Digital Innovations in Orthognathic Surgery: A Systematic Review of Virtual Surgical Planning, Digital Transfer, and Conventional Model Surgery.

Orthodontics & craniofacial research
OBJECTIVES: Orthognathic surgery has evolved due to the use of virtual surgical planning (VSP) and digital model surgery, which are technological advancements replacing conventional approaches with accurate personalised digital models made from compu...

ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction.

Interdisciplinary sciences, computational life sciences
With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the la...

AI image analysis tools quantify schisis cystic volume in XLRS retinal dysmorphology.

Acta ophthalmologica
PURPOSE: To provide a perspective on the feasibility and utility of automating image segmentation with artificial intelligence (AI)-based deep-learning algorithms to quantify retinoschisis cystic cavity volume in patients with X-linked retinoschisis ...

Navigating the frontier of AI-assisted student assignments: challenges, skills, and solutions.

Advances in physiology education
The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content v...

Sign potential-driven multiplicative optimization for robust deep reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Deep Reinforcement Learning (DRL) has attracted the interest of researchers due to its ability to provide valuable solutions to a variety of problems in different fields, such as robotics, autonomous driving, financial trading, and more. However, DRL...

Multi-modal sentiment recognition with residual gating network and emotion intensity attention.

Neural networks : the official journal of the International Neural Network Society
Multimodal emotion recognition focuses on the prediction of emotions using text, visual and acoustic modalities, and some results have been generated in this field. Previous approaches fall short in two aspects, one is the processing of complementary...

Communication-efficient distributed learning with Local Immediate Error Compensation.

Neural networks : the official journal of the International Neural Network Society
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compressio...

Layer Frozen Multi-Net & Latent Space Feature-Concealed Backdoor Samples Detection.

Neural networks : the official journal of the International Neural Network Society
Identifying feature-concealed backdoor samples that entangle with benign semantics of target-class or possess dynamic triggers challenges backdoor attack detection. Existing methods focus on sample distribution differences in latent space of victim m...