AIMC Topic: Algorithms

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Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO.

IET systems biology
Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre-processing process...

Place recognition with deep superpixel features for brain-inspired navigation.

The Review of scientific instruments
Navigation in primates is generally supported by cognitive maps. Such a map endows an animal with navigational planning capabilities. Numerous methods have been proposed to mimic these natural navigation capabilities in artificial systems. Based on s...

Semi-supervised audio-driven TV-news speaker diarization using deep neural embeddings.

The Journal of the Acoustical Society of America
In this paper, an audio-driven, multimodal approach for speaker diarization in multimedia content is introduced and evaluated. The proposed algorithm is based on semi-supervised clustering of audio-visual embeddings, generated using deep learning tec...

[Artificial intelligence systems and precision medicine: hopes and realities.].

Recenti progressi in medicina
Precision medicine (MP), using machine learning (ML) techniques of artificial intelligence (AI), analyzes the so-called "big data" to improve diagnostic skills and predictive response to therapy, in order to tailor the treatment on individual charact...

Performance improvement of wastewater treatment processes by application of machine learning.

Water science and technology : a journal of the International Association on Water Pollution Research
Improving wastewater treatment processes is becoming increasingly important, due to more stringent effluent quality requirements, the need to reduce energy consumption and chemical dosing. This can be achieved by applying artificial intelligence. Mac...

Development of a machine learning algorithm for early detection of opioid use disorder.

Pharmacology research & perspectives
BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for e...

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.

Critical care medicine
OBJECTIVES: Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and machine learning techniques for early prediction of sepsis.

Image-based laparoscopic tool detection and tracking using convolutional neural networks: a review of the literature.

Computer assisted surgery (Abingdon, England)
Intraoperative detection and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware, such as tracking systems or the robot encoders, are cumbersome and lack accuracy, surgica...

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.

Journal of digital imaging
Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an...