Resource Type

Journal Article 464

Conference Videos 14

Year

2024 1

2023 74

2022 85

2021 52

2020 53

2019 39

2018 24

2017 32

2016 15

2015 17

2014 2

2013 9

2012 4

2011 6

2010 6

2009 4

2008 7

2007 7

2006 5

2005 5

open ︾

Keywords

Machine learning 50

Deep learning 36

machine learning 24

Artificial intelligence 15

Reinforcement learning 15

deep learning 15

artificial neural network 7

artificial intelligence 6

Active learning 4

Big data 4

Support vector machine 4

fault diagnosis 4

Attention 3

Autonomous driving 3

Bayesian optimization 3

Adaptive dynamic programming 2

Additive manufacturing 2

Adversarial attack 2

Autonomous learning 2

open ︾

Search scope:

排序： Display mode:

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1735-8

Abstract:

● Data-driven approach was used to simulate VFA production from WAS fermentation.

Keywords： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

HTML PDF Collect

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 928-945 doi: 10.1007/s11709-022-0837-x

Abstract: compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machinelearning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid andNine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine

Keywords： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization     extreme gradient boosting    

HTML PDF Collect

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Article

Ruixin Yang, Rui Xiong, Weixiang Shen, Xinfan Lin

Engineering 2021, Volume 7, Issue 3,   Pages 395-405 doi: 10.1016/j.eng.2020.08.015

Abstract: Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT)

Keywords： Electric vehicles     Battery safety     External short circuit     Temperature prediction     Extreme learning machine    

HTML PDF Collect

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extremelearning machine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 444-460 doi: 10.1007/s11709-021-0697-9

Abstract: The implementation of novel machine learning models can contribute remarkably to simulating the degradationThe models include three different types of extreme learning machines, including the standard, onlinesequential, and kernel extreme learning machines, in addition to the artificial neural network, classificationFor the first assessment, the machine learning models were developed using all the available cement andThe online sequential extreme learning machine model demonstrated superior performance over the other

Keywords： sewer systems     environmental engineering     data-driven methods     sensitivity analysis    

HTML PDF Collect

Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

HTML PDF Collect

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

HTML PDF Collect

State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords： Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

HTML PDF Collect

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machinelearning models has recently been proposed.

Keywords： machine learning     flowsheet simulations     constraints     exploration    

HTML PDF Collect

Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

HTML PDF Collect

Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

HTML PDF Collect

Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: advanced data processing, storage and analysis, advanced process control, artificial intelligence and machinelearning, cloud computing, and virtual and augmented reality.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

HTML PDF Collect

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

HTML PDF Collect

Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

HTML PDF Collect

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1

Abstract:

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Keywords： Elemental composition     Infrared spectroscopy     Machine learning     Moisture interference     Solid waste     Spectral    

HTML PDF Collect

Shot classification and replay detection for sports video summarization Research Article

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 790-800 doi: 10.1631/FITEE.2000414

Abstract: Automated analysis of sports is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective framework based on and for field sports videos. Accurate is mandatory to better structure the input video for further processing, i.e., key events or . Therefore, we present a based method for . Then we analyze each shot for and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for and to summarize field sports videos.

Keywords： Extreme learning machine     Lightweight convolutional neural network     Local octa-patterns     Shot classification    

HTML PDF Collect

Title Author Date Type Operation

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Journal Article

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Journal Article

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External

Ruixin Yang, Rui Xiong, Weixiang Shen, Xinfan Lin

Journal Article

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extremelearning machine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Journal Article

Shot classification and replay detection for sports video summarization

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Journal Article