AIMC Topic: Machine Learning

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Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data.

Sensors (Basel, Switzerland)
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, ...

RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System.

Sensors (Basel, Switzerland)
In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent appro...

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia.

BMC public health
BACKGROUND: The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and...

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks.

BMC biology
BACKGROUND: Deep-learning-based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of tr...

Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms.

Physical chemistry chemical physics : PCCP
We present explainable machine learning approaches for the accurate prediction and understanding of solvation free energies, enthalpies, and entropies for different salts in various protic and aprotic solvents. As key input features, we use fundament...

Develop a hybrid machine learning model for promoting microbe biomass production.

Bioresource technology
Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid mac...

An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation.

Journal of environmental management
Evaporation is an important hydrological process in the water cycle, especially for water bodies. Machine Learning (ML) models have become accurate and powerful tools in predicting pan evaporation. Meanwhile, the "black-box" character and the consist...

Antibiotic discovery in the artificial intelligence era.

Annals of the New York Academy of Sciences
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an...

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.

Sensors (Basel, Switzerland)
Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier ...

Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.

BMC public health
BACKGROUND: Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of...