AIMC Topic: Models, Theoretical

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Inversion of lake transparency using remote sensing and deep hybrid recurrent models.

Ecotoxicology and environmental safety
Utilizing computer technology and remote sensing data, the extraction of water-related features of lakes has become a hot topic in lake ecological research. Addressing challenges like the high optical complexity of lake water bodies, the inadequacy o...

Session interest model for CTR prediction based on feature co-action network.

Scientific reports
The main purpose of click-prediction models is to predict the probability of customers clicking on products and provide support for advertising decisions of businesses. However, most previous models often use deep neural networks to capture implicit ...

Some novel concepts of interval-valued q-rung orthopair fuzzy graphs and computational framework of fuzzy air conditioning system.

PloS one
The interval-valued q-rung orthopair fuzzy sets being an extension of interval-valued intuitionistic and interval-valued Pythagorean fuzzy sets is more flexible model to address vague information that has only two attributes yes or no. The combinatio...

An interpretable approach to estimate the self-motion in fish-like robots using mode decomposition analysis.

Nature communications
The artificial lateral line system, composed of velocity and pressure sensors, is the sensing system for fish-like robots by mimicking the lateral line system of aquatic organisms. However, accurately estimating the self-motion of the fish-like robot...

Adaptive lift chiller units fault diagnosis model based on machine learning.

PloS one
The early minor faults generated by the chiller in operation are not easy to perceive, and the severity will gradually increase with time. The traditional fault diagnosis method has low accuracy and poor stability for early fault diagnosis. In this p...

Network traffic prediction based on transformer and temporal convolutional network.

PloS one
This paper proposes a hybrid model combining Transformer and Temporal Convolutional Network (TCN). This model addresses the shortcomings of current approaches in capturing long-term and short-term dependencies in network traffic prediction tasks. The...

Research on memory failure prediction based on ensemble learning.

PloS one
Timely prediction of memory failures is crucial for the stable operation of data centers. However, existing methods often rely on a single classifier, which can lead to inaccurate or unstable predictions. To address this, we propose a new ensemble mo...

Source apportionment of PM particles in the urban atmosphere using PMF and LPO-XGBoost.

Environmental research
Atmospheric particulate matter (PM), as a leading part of air pollution, affects health in many ways. Thus, identifying and quantifying the contribution of atmospheric particulate matter sources of PM is vital for developing effective air quality man...

A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation.

International journal of biometeorology
This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal comfort (OTC) across three key indices: Universal Thermal Climate Index (UTCI), Physiological Equiv...

Enhancing the resilience of urban drainage system using deep reinforcement learning.

Water research
Real-time control (RTC) is an effective method used in urban drainage systems (UDS) for reducing flooding and combined sewer overflows. Recently, RTC based on Deep Reinforcement Learning (DRL) has been proven to have various advantages compared to tr...