AIMC Topic: Machine Learning

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Non-invasive identification of mesenchymal glioblastoma using quantitative radiomic features from advanced diffusion MRI: a preclinical-to-clinical transfer learning strategy.

European radiology experimental
BACKGROUND: Glioblastoma (GBM) is no longer regarded as a single disease, as distinct molecular subgroups exist, with the mesenchymal (MES) having the worst prognosis. As such, there is a critical need for noninvasive methods to determine GBM molecul...

The need for speed: using nystagmus velocity profiles and machine learning models to separate canalithiasis BPV from its mimics.

Journal of neurology
PURPOSE: Separating BPV from other positional nystagmus types can be challenging. We examined the utility of nystagmus slow-phase velocity (SPV) profiles when seeking to separate canalithiasis benign positional vertigo (BPV) from its mimics.

FedMedSecure: federated few-shot learning with cross-attention mechanisms and explainable AI for collaborative healthcare cybersecurity.

Scientific reports
The proliferation of Internet of Medical Things (IoMT) devices has created cybersecurity challenges that requiring advanced threat detection techniques along with preserving patient privacy. This paper introduces FedMedSecure, a federated few-shot le...

Improving bank customer churn prediction with feature reduction using GA.

Scientific reports
Customer churn is one complex problem that all banking institutes struggle to deal with. The increased competition in the industry is forcing the banks to maintain customer loyalty by providing apt services to the demanding customers. Analysing the c...

A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery.

Scientific reports
Surgical site infection (SSI) after colorectal cancer (CRC) surgery is still a significant healthcare issue. This study aimed to analyze risk factor associated with SSI. A total of 528 consecutive CRC patients who underwent curative resections betwee...

Integrating machine learning and experimental validation identifies a post-translational modification gene signature for prognosis and treatment response in breast cancer.

Scientific reports
Breast cancer (BC) is the most prevalent malignancy among women, and the steadily increasing disease burden has garnered considerable global attention. Post-translational modifications (PTMs) are critical in the initiation and progression of BC. This...

Machine learning based prediction of the performance and emission characteristics of CRDI diesel engine using diethyl ether and carbon nanotube additives with Spirulina platensis as a third-generation biofuel.

Scientific reports
Alternative fuels are required to provide the world's energy demands due to excessive fossil fuel use, harmful petrol emissions, environmental pollution, growing demand, rising costs, and fossil fuel degradation. The additives are utilized in biodies...

Graph-theoretical analysis of EEG-based functional connectivity during emotional experience in virtual reality for emotion recognition.

Scientific reports
Virtual reality (VR) technologies can induce realistic emotions in controlled experimental settings, offering unprecedented opportunities to study how the human brain processes emotions under real-world conditions. The integration of VR experiences w...

HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis.

Scientific reports
Hematological biomarkers have emerged as powerful tools in diagnosing Acute Heart Failure (AHF). This study introduces a novel diagnostic framework that integrates Explainable Artificial Intelligence (XAI) with Morris Sensitivity Analysis (MSA) to en...

CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests.

Nature communications
Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carb...