AIMC Topic: Machine Learning

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Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis.

Computational intelligence and neuroscience
Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis fram...

Predicting Substance Use Treatment Failure with Transfer Learning.

Substance use & misuse
Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical da...

Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.

Journal of digital imaging
Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in t...

Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT.

Radiology
Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or...

TaskDrop: A competitive baseline for continual learning of sentiment classification.

Neural networks : the official journal of the International Neural Network Society
In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i.e., a model is sequentially trained to classify the sentiment of reviews of products in a particular category. The use of common sentiment wo...

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies.

Forensic science international
BACKGROUND /PURPOSE: Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, bo...

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

Contrast media & molecular imaging
Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can ...

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model.

Computational intelligence and neuroscience
In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer's c...

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer.

Frontiers in public health
BACKGROUND: This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.