AIMC Topic: Machine Learning

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Advanced susceptibility analysis of ground deformation disasters using large language models and machine learning: A Hangzhou City case study.

PloS one
To address the prevailing scenario where comprehensive susceptibility assessments of ground deformation disasters primarily rely on knowledge-driven models, with weight judgments largely founded on expert subjective assessments, this study initially ...

Comprehensive financial health assessment using Advanced machine learning techniques: Evidence based on private companies listed on ChiNext.

PloS one
This study develops a specific and measurable framework for assessing the financial health (FH) of privately-owned companies listed on ChiNext, aimed at identifying financially sound enterprises and helping investors avoid losses caused by financial ...

PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning.

PloS one
With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge comput...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine ...

Evaluation of plant-based coagulants for turbidity removal and coagulant dosage prediction using machine learning.

Environmental technology
This study investigates the use of six plant-based coagulants - , , , , , and for the removal of turbidity from wastewater effluent. The coagulants were characterized using Scanning Electron Microscopy (SEM) to determine morphological structure, X-r...

Exploring Shared and Unique Predictors of Positive and Negative Risk-Taking Behaviors Among Chinese Adolescents Through Machine-Learning Approaches: Discovering Gender and Age Variations.

Journal of youth and adolescence
Despite extensive research on the impact of individual and environmental factors on negative risk-taking behaviors, the understanding of these factors' influence on positive risk-taking, and how it compares to negative risk taking, remains limited. T...

Antibiotic SERS spectral analysis based on data augmentation and attention mechanism strategy.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
The analysis of Raman spectrum data has gradually transitioned into the era of machine learning. However, it is still constrained by the challenge of acquiring large volumes of raw data and the issue of losing characteristic information from spectral...

Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...

Novel models based on machine learning to predict the prognosis of metaplastic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice.

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.

Computers in biology and medicine
BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic r...