AIMC Topic: Algorithms

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HyGloadAttack: Hard-label black-box textual adversarial attacks via hybrid optimization.

Neural networks : the official journal of the International Neural Network Society
Hard-label black-box textual adversarial attacks present a highly challenging task due to the discrete and non-differentiable nature of text data and the lack of direct access to the model's predictions. Research in this issue is still in its early s...

Analysis of medical images super-resolution via a wavelet pyramid recursive neural network constrained by wavelet energy entropy.

Neural networks : the official journal of the International Neural Network Society
Recently, multi-resolution pyramid-based techniques have emerged as the prevailing research approach for image super-resolution. However, these methods typically rely on a single mode of information transmission between levels. In our approach, a wav...

Adversarial Infrared Curves: An attack on infrared pedestrian detectors in the physical world.

Neural networks : the official journal of the International Neural Network Society
Deep neural network security is a persistent concern, with considerable research on visible light physical attacks but limited exploration in the infrared domain. Existing approaches, like white-box infrared attacks using bulb boards and QR suits, la...

Inverse-free zeroing neural network for time-variant nonlinear optimization with manipulator applications.

Neural networks : the official journal of the International Neural Network Society
In this paper, the problem of time-variant optimization subject to nonlinear equation constraint is studied. To solve the challenging problem, methods based on the neural networks, such as zeroing neural network and gradient neural network, are commo...

IU-Net: A dual-path U-Net with rich information interaction for medical image segmentation.

Medical image analysis
Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively ...

Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.

Molecular informatics
The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost...

A parameter estimation method for chromatographic separation process based on physics-informed neural network.

Journal of chromatography. A
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...

Location-enhanced syntactic knowledge for biomedical relation extraction.

Journal of biomedical informatics
Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guida...

DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction.

Computers in biology and medicine
Non-sugar sweeteners (NSSs) or artificial sweeteners have long been used as food chemicals since World War II. NSSs, however, also raise a concern about their mutagenicity. Evaluating the mutagenic ability of NSSs is crucial for food safety; this ste...