AIMC Topic: Machine Learning

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Multimodal Learning of Social Image Representation by Exploiting Social Relations.

IEEE transactions on cybernetics
Learning the representation for social images has recently made remarkable achievements for many tasks, such as cross-modal retrieval and multilabel classification. However, since social images contain both multimodal contents (e.g., visual images an...

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

International journal of environmental research and public health
A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arteria...

Artificial intelligence (AI) in medicine as a strategic valuable tool.

The Pan African medical journal
Humans' creativity led to machines that outperform human capabilities in terms of workload, effectiveness, precision, endurance, strength, and repetitiveness. It has always been a vision and a way to transcend the existence and to give more sense to ...

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

Surgical endoscopy
OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate mult...

Discrimination of white wine ageing based on untarget peak picking approach with multi-class target coupled with machine learning algorithms.

Food chemistry
The complexity of the chemical reactions occurring during white wine storage, such as oxidation turns the capacity of prediction and consequently the capacity to avoid it extremely difficult. This study proposes an untarget methodology based on machi...

Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.

Toxicologic pathology
The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxic...

The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.

Metabolic syndrome and related disorders
Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can a...

An Experimental Review on Deep Learning Architectures for Time Series Forecasting.

International journal of neural systems
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mi...

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase.

Sensors (Basel, Switzerland)
Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a rec...