AIMC Topic: Models, Theoretical

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Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model.

Environmental research
Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) ...

AI-based plastic waste sorting method utilizing object detection models for enhanced classification.

Waste management (New York, N.Y.)
The export ban on plastic waste by China has brought domestic plastic recycling to the forefront of environmental concerns, with sorting being a crucial step in the recycling process. This study assessed the performance of advanced AI models, Mask R-...

Exploring α-ψ-ϕ contractive mapping: novel fixed point theorems in complete b-metric spaces.

F1000Research
This paper explores the concept of contractive mappings, contributing to the advancement of self-map extensions and fixed-point theorems within b-metric spaces. We introduce a new class of contractive mappings and demonstrate how they extend traditi...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

Environmental science and pollution research international
This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-water...

A novel strategy for the MPPT in a photovoltaic system via sliding modes control.

PloS one
This paper proposes a robust maximum power point tracking algorithm based on a super twisting sliding modes controller. The underlying idea is solving the classical trajectory tracking control problem where the maximum power point defines the referen...

Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers.

Water research
Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, film...

Plane coexistence behaviors for Hopfield neural network with two-memristor-interconnected neurons.

Neural networks : the official journal of the International Neural Network Society
Memristors are commonly used as the connecting parts of neurons in brain-like neural networks. The memristors, unlike the existing literature, possess the capability to function as both self-connected synaptic weights and interconnected synaptic weig...

A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants.

Environmental research
Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD) is crucial for optimizing wastewater treatment processes, enhancing efficiency, and reducing costs. Traditional predict...

Advanced susceptibility analysis of ground deformation disasters using large language models and machine learning: A Hangzhou City case study.

PloS one
To address the prevailing scenario where comprehensive susceptibility assessments of ground deformation disasters primarily rely on knowledge-driven models, with weight judgments largely founded on expert subjective assessments, this study initially ...

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

International journal of biometeorology
In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34 years were collected from t...