AIMC Topic: Probability

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A novel MCGDM technique based on correlation coefficients under probabilistic hesitant fuzzy environment and its application in clinical comprehensive evaluation of orphan drugs.

PloS one
Probabilistic hesitant fuzzy sets (PHFSs) are superior to hesitant fuzzy sets (HFSs) in avoiding the problem of preference information loss among decision makers (DMs). Owing to this benefit, PHFSs have been extensively investigated. In probabilistic...

A probabilistic knowledge graph for target identification.

PLoS computational biology
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone....

Data reduction for SVM training using density-based border identification.

PloS one
Numerous classification and regression problems have extensively used Support Vector Machines (SVMs). However, the SVM approach is less practical for large datasets because of its processing cost. This is primarily due to the requirement of optimizin...

[ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials].

Revista espanola de patologia : publicacion oficial de la Sociedad Espanola de Anatomia Patologica y de la Sociedad Espanola de Citologia
INTRODUCTION AND OBJECTIVE: Artificial intelligence is fully present in our lives. In education, the possibilities of its use are endless, both for students and teachers.

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

International journal of radiation oncology, biology, physics
PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of d...

An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering.

Biomechanics and modeling in mechanobiology
Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in critical-size fractures. The differentiation of the cells on a scaffold is impacted among other factors by the surface deformation of the scaffold due to mecha...

Enhancing adversarial attacks with resize-invariant and logical ensemble.

Neural networks : the official journal of the International Neural Network Society
In black-box scenarios, most transfer-based attacks usually improve the transferability of adversarial examples by optimizing the gradient calculation of the input image. Unfortunately, since the gradient information is only calculated and optimized ...

Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis.

Neural networks : the official journal of the International Neural Network Society
Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limi...

Cancer detection and classification using a simplified binary state vector machine.

Medical & biological engineering & computing
Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient mac...

A QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...