AIMC Topic: Machine Learning

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Real-time streamflow forecasting in a reservoir-regulated river basin using explainable machine learning and conceptual reservoir module.

The Science of the total environment
Real-time streamflow forecasting is essential to manage water resources effectively in a reservoir-regulated basin. However, forecasting becomes challenging without weather and upstream reservoir outflows forecasts in real-time. In this context, a no...

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database.

Computational biology and chemistry
Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack ...

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams.

Bioresource technology
Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This r...

Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods.

Diabetes & metabolic syndrome
BACKGROUND & AIM: Diabetes mellitus has become one of the out brakes causing major health issues in developing countries like India. The need for leveraging technology is felt in diabetes management. The main objective of this work is to deploy machi...

Machine-learning-based risk stratification for probability of dying in patients with basal ganglia hemorrhage.

Scientific reports
To confirm whether machine learning algorithms (MLA) can achieve an effective risk stratification of dying within 7 days after basal ganglia hemorrhage (BGH). We collected patients with BGH admitted to Sichuan Provincial People's Hospital between Aug...

Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks.

Scientific reports
In engineering practices, it is critical and necessary to either measure or estimate the uniaxial compressive strength (UCS) of the rock. Measuring the UCS of rocks requires comprehensive studies in the field and in the laboratory for the rock block ...

Artificial intelligence in veterinary diagnostic imaging: A literature review.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imag...

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors.

Sensors (Basel, Switzerland)
The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets' behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior...

Machine-learning-based ground sink susceptibility evaluation using underground pipeline data in Korean urban area.

Scientific reports
Ground subsidence caused by natural factors, including groundwater, has been extensively researched. However, there have been few studies on ground sink caused mainly by artifacts, including underground pipelines in urban areas. This paper proposes a...

Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data.

Animal genetics
Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high-throughput genomic dat...