AIMC Topic: Machine Learning

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Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

Methods (San Diego, Calif.)
DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is im...

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.

Journal of chemical information and modeling
To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computat...

A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint.

Sensors (Basel, Switzerland)
For decades, co-relating different data domains to attain the maximum potential of machines has driven research, especially in neural networks. Similarly, text and visual data (images and videos) are two distinct data domains with extensive research ...

A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring.

Sensors (Basel, Switzerland)
Sensor fusion is becoming increasingly popular in condition monitoring. Many studies rely on a fusion-level strategy to enable the most effective decision-making and improve classification accuracy. Most studies rely on feature-level fusion with a cu...

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.

Sensors (Basel, Switzerland)
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding pro...

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier.

Scientific reports
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification mod...

Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

European radiology experimental
OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep l...

A Deep Machine Learning-Based Assistive Decision System for Intelligent Load Allocation under Unknown Credit Status.

Computational intelligence and neuroscience
Nowadays, the banks are facing increasing business pressure in loan allocations, because more and more enterprises are applying for it and financial risk is becoming vaguer. To this end, it is expected to investigate effective autonomous loan allocat...

Machine learning applications in gynecological cancer: A critical review.

Critical reviews in oncology/hematology
Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with cons...

Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

Aging clinical and experimental research
BACKGROUND: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.