AIMC Topic: Algorithms

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Comparing automated valuation models for real estate assessment in the Santiago Metropolitan Region: A study on machine learning algorithms and hedonic pricing with spatial adjustments.

PloS one
This study compares the precision and interpretability of two automated valuation models for evaluating the real estate market in the Santiago Metropolitan Region of Chile: machine learning algorithms, specifically LightGBM, and hedonic prices with s...

Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

PloS one
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...

NNSFMDA: Lightweight Transformer Model with Bounded Nuclear Norm Minimization for Microbe-Drug Association Prediction.

Journal of molecular biology
Identifying potential connections between microbe-drug pairs play an important role in drug discovery and clinical treatment. Techniques like graph neural networks effectively derive accurate node representations from sparse topologies,however, they ...

Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055.

International journal of biological macromolecules
The application of machine learning in pullulan biofermentation has demonstrated significant potential. Explainable machine learning enhances model transparency and interpretability by revealing the relationships between variables. In this study, we ...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets.

Computers in biology and medicine
Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a n...

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.

Journal of chemical information and modeling
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

Biosensors
The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO is a powerf...

Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI.

Sensors (Basel, Switzerland)
In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language models within the field of human-robot interaction and human-computer interaction. While research in...