AIMC Topic: Algorithms

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TypeSeg: A type-aware encoder-decoder network for multi-type ultrasound images co-segmentation.

Computer methods and programs in biomedicine
PURPOSE: As a portable and radiation-free imaging modality, ultrasound can be easily used to image various types of tissue structures. It is important to develop a method which supports the multi-type ultrasound images co-segmentation. However, state...

Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism.

Sensors (Basel, Switzerland)
Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle's position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, rou...

Hyperparameter Optimization Techniques for Designing Software Sensors Based on Artificial Neural Networks.

Sensors (Basel, Switzerland)
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data wh...

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data.

Sensors (Basel, Switzerland)
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-p...

Gene prediction of aging-related diseases based on DNN and Mashup.

BMC bioinformatics
BACKGROUND: At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predi...

Identification of Barrett's esophagus in endoscopic images using deep learning.

BMC gastroenterology
BACKGROUND: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems.

Computational intelligence and neuroscience
Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and fe...

Cluster-Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors.

Computational intelligence and neuroscience
The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the fin...

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm.

Computers in biology and medicine
Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model ca...

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.

Abdominal radiology (New York)
PURPOSE: In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiati...