AIMC Topic: Machine Learning

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Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures.

Sensors (Basel, Switzerland)
Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficien...

Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques.

Sensors (Basel, Switzerland)
This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main proc...

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review.

The American surgeon
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field...

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS.

Analytica chimica acta
Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibil...

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Academic radiology
RATIONALE AND OBJECTIVES: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

Computational signatures for post-cardiac arrest trajectory prediction: Importance of early physiological time series.

Anaesthesia, critical care & pain medicine
BACKGROUND: There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significan...

Online machine learning algorithms to optimize performances of complex wireless communication systems.

Mathematical biosciences and engineering : MBE
Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive f...

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data.

Sensors (Basel, Switzerland)
The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for gene...

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography.