AIMC Topic: Machine Learning

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Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Computers in biology and medicine
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and...

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

Environmental science and pollution research international
The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this resea...

Informatics and machine learning to define the phenotype.

Expert review of molecular diagnostics
For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained...

Classifying vortex wakes using neural networks.

Bioinspiration & biomimetics
Unsteady flows contain information about the objects creating them. Aquatic organisms offer intriguing paradigms for extracting flow information using local sensory measurements. In contrast, classical methods for flow analysis require global knowled...

Robust Latent Regression with discriminative regularization by leveraging auxiliary knowledge.

Neural networks : the official journal of the International Neural Network Society
For a domain adaptation learning problem, how to minimize the distribution mismatch between different domains is one of key challenges. In real applications, it is reasonable to obtain an optimal latent space for both domains so as to reduce the doma...

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels.

Journal of chemical information and modeling
To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of a...

Manifold regularized matrix completion for multi-label learning with ADMM.

Neural networks : the official journal of the International Neural Network Society
Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, ...

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.

Sensors (Basel, Switzerland)
In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical...

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Informatics for health & social care
Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, ...