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Papers
61,005 resultsShowing papers similar to Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
ClearCondition Monitoring in Power Transformers Using IoT: A Model for Predictive Maintenance
This paper presented an IoT-based condition monitoring model for power transformers using multi-sensor data, cloud analytics, and predictive algorithms to forecast failures and optimize maintenance. It does not contain microplastics research.
Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings
Researchers developed a machine learning method combining neural networks and stochastic optimization to predict cooling energy loads in residential buildings. This engineering modeling paper is unrelated to microplastic research.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
AI-Enabled Energy Forecasting and Fault Detection in Off-Grid Solar Networks for Rural Electrification
Despite its title referencing rural electrification and solar energy, this paper studies AI-based energy forecasting and fault detection for off-grid solar networks — not microplastic pollution. It examines machine learning approaches for managing solar power systems in remote areas and is not relevant to microplastics or human health.
Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
AI Prediction of Power Grid Faults Based on Deep Learning and Improvement of Emergency Response Efficiency in Automated Repair
Despite its title referencing grid faults and emergency response, this paper applies deep learning algorithms to predict and respond to power grid failures — not microplastic pollution or environmental health. It examines fault detection accuracy in electrical systems and is entirely unrelated to microplastics.
An Internet-of-Things (IoT) Sustainable Water Filtering and Monitoring System using Big Data Analysis and Clean Energy
Researchers developed MyRiiver, a solar-powered IoT-based water filtration and monitoring system designed to remove microplastics from freshwater ecosystems, integrating big data analytics for real-time environmental monitoring. The system addresses limitations of current microplastic filtration methods and demonstrates the potential of smart technologies for freshwater pollution management.
Application of Machine learning techniques in environmental governance: A review
This paper is not relevant to microplastics research — it reviews the application of machine learning methods in environmental governance broadly, covering air and water quality monitoring and land use management.
Leveraging Municipal Solid Waste Management with Plasma Pyrolysis and IoT: Strategies for Energy Byproducts and Resource Recovery
This review examines how plasma pyrolysis technology, combined with Internet of Things monitoring, can improve the treatment of municipal solid waste that contains hazardous materials including microplastics. The approach converts waste into valuable energy products like syngas and bio-oil while significantly reducing waste volume. The integration of real-time sensor data and machine learning could optimize operational conditions and improve treatment efficiency.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Air Quality Testing- a Design Thinking Approach
Not relevant to microplastics — this paper describes a design-thinking methodology for building IoT-based air quality monitoring systems, with no connection to plastic particle research.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
Exploring the Research on Utilizing Machine Learning in E-Learning Systems
Not relevant to microplastics — this systematic literature review surveys how machine learning techniques are applied in e-learning systems to improve educational outcomes and predict student performance.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments
This paper proposes using Internet of Things (IoT) sensors to build a real-time monitoring network for microplastics in aquatic environments. Automated, continuous monitoring systems could provide much better spatial and temporal coverage than current sampling-based approaches.
An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework
Researchers used an optimization algorithm to tune a neural network for predicting solar energy availability from environmental conditions. This renewable energy modeling paper is unrelated to microplastic research.
Survey on IoT Based Microplastic Detection
This research review summarizes new technology that uses internet-connected sensors to detect tiny plastic particles (microplastics) in water in real-time, rather than relying on slow lab tests. Microplastics are a growing health concern because they can get into our drinking water and food chain, potentially harming human health. Better detection methods could help protect our water supplies by catching pollution problems faster.
Synthesizing Multi-Layer Perceptron Network with Ant Lion, Biogeography-Based, Dragonfly Algorithm, Evolutionary Strategy, Invasive Weed, and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings
This study evaluated neural network models trained with metaheuristic algorithms for predicting building heating load, comparing several optimization approaches. While focused on energy efficiency modeling, similar machine learning techniques are used to predict environmental pollutant distributions, including microplastics.
Internet of Things and Health: A literature review based on Mixed Method
This literature review examines how Internet of Things technology is being applied in healthcare, covering areas like remote patient monitoring, diagnosis, and treatment. The review identified key trends and limitations in current implementations, noting the need for more interdisciplinary research. While not related to microplastics, the IoT sensing technologies discussed could potentially be adapted for real-time environmental monitoring of microplastic contamination in water and air.
Harnessing Artificial Intelligence to Increase the Efficiency of Education Management in the Future
This paper is not about microplastics; it examines the use of artificial intelligence to improve educational management and teaching effectiveness.
A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health
Researchers proposed a hybrid IoT-based artificial intelligence framework for predicting environmental toxicity and its effects on human health, combining sensor networks with machine learning to improve real-time assessment of chemical exposure risks in the environment.
Intelligent classification and pollution characteristics analysis of microplastics in urban surface waters using YNet
Researchers developed an AI-based system called YNet to automatically identify and classify microplastics in urban water samples from their visual appearance. The system achieved over 90% accuracy in distinguishing different microplastic shapes and was used to analyze pollution patterns in wetlands and reservoirs. The study demonstrates that artificial intelligence can make microplastic monitoring faster and more consistent compared to traditional manual identification methods.