AIMC Topic: Machine Learning

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A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML) ...

Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model.

Water research
Determination of coagulant dosage in water treatment is a time-consuming process involving nonlinear data relationships and numerous factors. This study provides a deep learning approach to determine coagulant dosage and/or the settled water turbidit...

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.

RNA (New York, N.Y.)
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowled...

Machine learning for better prediction of seepage flow through embankment dams: Gaussian process regression versus SVR and RVM.

Environmental science and pollution research international
In the present study, three machine learning methods were applied for predicting seepage flow through embankment dams, namely (i) support vector regression (SVR), relevance vector machine (RVM), and Gaussian process regression (GPR). The three models...

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method.

Frontiers in public health
Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful f...

A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters.

The Journal of laryngology and otology
OBJECTIVE: This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms.

CARES: A Corpus for classification of Spanish Radiological reports.

Computers in biology and medicine
This paper presents a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evidences in Spanish) is a high-quality corpus manually labeled and reviewed by radiologists that is fr...

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BMC medical informatics and decision making
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.

Scientific reports
Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which inte...