AIMC Topic: Machine Learning

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Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms.

PloS one
In this study, the ability of machine learning algorithms to predict tensile properties of both heat-treated and non-heat treated LPBFed AlSi10Mg alloy is investigated. The data was analyzed using various Machine Learning Regression (MLR) models such...

Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models.

PloS one
Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indicate that work-stress contributes to severe mental strain, increased accident rates, and in extreme ca...

Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.

PloS one
Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. ...

Multi-view clustering via global-view graph learning.

PloS one
Multiview clustering aims to improve clustering performance by exploring multiple representations of data and has become an important research direction. Meanwhile, graph-based methods have been extensively studied and have shown promising performanc...

Utility of artificial intelligence-based conversation voice analysis for detecting cognitive decline.

PloS one
Recent developments in artificial intelligence (AI) have introduced new technologies that can aid in detecting cognitive decline. This study developed a voice-based AI model that screens for cognitive decline using only a short conversational voice s...

Impact of microbial profile integration on machine learning predictions of methane production: synergies and trade-offs with physicochemical parameters.

Bioresource technology
Microbial sequencing data were rarely integrated into the prediction of methane production using machine learning (ML) models because of high dimensionality and the lack of a systematic way to evaluate the change of insight gained from modelling with...

Ground-State Descriptor Enables Machine Learning-Assisted Virtual Screening of AIE-Active Mechanofluorochromic Molecules with High Contrast.

Journal of chemical information and modeling
Aggregation-induced emission mechanofluorochromic (AIE-MFC) molecules with high-contrast are in high demand for pressure-sensing devices and optoelectronic devices. However, developing AIE-MFC molecules with high-contrast beyond 100 nm still highly r...

Airbag-like Comb Flexible Pressure Sensor and Its Wearable Applications.

ACS applied materials & interfaces
Flexible wearable devices demonstrate immense potential in healthcare and human-computer interaction, yet the development of high-performance flexible pressure sensors for these applications remains a pressing technical challenge. Inspired by the str...

Targeting INF2 with DiosMetin 7-O-β-D-Glucuronide: a new stratagem for colorectal cancer therapy.

BMC cancer
BACKGROUND AND PURPOSE: Colorectal cancer (CRC) is the third most prevalent malignancy in the gastrointestinal tract and the second leading cause of cancer-related deaths. Despite the identification of numerous biomarkers, their non-specific distribu...

Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection.

International journal of cardiology
BACKGROUND: Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-in...