AIMC Topic: Neural Networks, Computer

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TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition.

PloS one
Convolutional Neural Networks (CNNs) are frequently used algorithms because of their propensity to learn relevant and hierarchical features through their feature extraction technique. However, the availability of enormous volumes of data in various v...

Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks.

Network (Bristol, England)
An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority U...

ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax.

Journal of imaging informatics in medicine
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image a...

A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images.

Journal of imaging informatics in medicine
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and mul...

Prediction of DNA origami shape using graph neural network.

Nature materials
Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami...

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

Internal medicine journal
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data...

High-dose-rate Brachytherapy Monotherapy in Patients With Localised Prostate Cancer: Dose Modelling and Optimisation Using Computer Algorithms.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: Interstitial high-dose-rate brachytherapy (HDR-BT) is an effective therapy modality for patients with localized prostate carcinoma. The objectives of the study were to optimise the therapy regime variables using two models: response surface met...

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

Environmental pollution (Barking, Essex : 1987)
The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine l...

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants.

Journal of environmental management
Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, in...

A neural network-based model framework for cell-fate decisions and development.

Communications biology
Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the r...