Article
Multidisciplinary Sciences
Zhengcai Li, Xinmin Hu, Chun Chen, Chenyang Liu, Yalu Han, Yuanfeng Yu, Lizhi Du
Summary: This paper investigates the optimization algorithms based on machine learning for settlement prediction. By comparing the performance of different algorithms, the study finds that Sparrow Search Algorithm (SSA) significantly improves the optimization effect of the gradient descent model and enhances its stability to a certain degree.
SCIENTIFIC REPORTS
(2022)
Article
Thermodynamics
A. G. Olabi, Aasim Ahmed Abdelghafar, Hussein M. Maghrabie, Enas Taha Sayed, Hegazy Rezk, Muaz Al Radi, Khaled Obaideen, Mohammad Ali Abdelkareem
Summary: Energy storage is crucial in maximizing energy sources, reducing costs, and enhancing the reliability of power grids. Artificial intelligence, such as particle swarm optimization and artificial neural networks, has been widely studied and applied in this field. This study explores the progress of using AI in optimizing and controlling thermal energy storage systems, analyzing their performance and providing recommendations for future research.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2023)
Article
Chemistry, Physical
Gulbahar Bilgic, Basak Ozturk, Sema Atasever, Mukerrem Sahin, Hakan Kaplan
Summary: In this study, a machine learning approach based on an artificial neural network model was developed to improve the hydrogen production system with a magnetic field effect. The model accurately predicted the effect of input parameters on hydrogen output and demonstrated strong predictive performance.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Engineering, Environmental
Eloiza Laisla Lino Tochio, Bruno Cezar do Nascimento, Sandro Rogerio Lautenschlager
Summary: This research developed artificial neural network models to predict the dosage of coagulant in water treatment plants, based on full-scale WTP data. The best model showed high accuracy and can contribute to the automation of coagulant dosage in WTPs.
Article
Environmental Sciences
B. S. Reddy, A. K. Maurya, V Sathishkumar, P. L. Narayana, M. H. Reddy, Alaa Baazeem, Kwon-Koo Cho, N. S. Reddy
Summary: The formation of celestite and barite leads to contamination of barium and strontium ions in oilfield water, hindering purification processes. Utilizing biochar sorbent from rice straw for removal of these ions in saline water from petroleum industries has proven to be effective. The artificial neural network (ANN) model shows higher accuracy in assessing the correlation between process variables and sorption responses compared to existing kinetic and isotherm equations.
ENVIRONMENTAL RESEARCH
(2021)
Article
Engineering, Environmental
Aliasghar Azma, Yakun Liu, Masoumeh Azma, Mohsen Saadat, Di Zhang, Jinwoo Cho, Shahabaldin Rezania
Summary: Measuring water quality parameters is important for hydrological assessments, and this study proposes two intelligent models, BBO-ANN and ASO-ANN, to predict daily dissolved oxygen (DO) using biogeography-based optimization (BBO), atom search optimization (ASO), and artificial neural network (ANN). The models are compared with benchmark techniques and validated using five-year water quality data from Rock Creek station. The results show that the models can accurately predict DO, with MAPEs of around 4% and correlations of 97.5%. The BBO-ANN and ASO-ANN models outperform similar hybrids in the literature.
JOURNAL OF WATER PROCESS ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Rana Muhammad Adnan Ikram, Ahmed A. Ewees, Kulwinder Singh Parmar, Zaher Mundher Yaseen, Shamsuddin Shahid, Ozgur Kisi
Summary: A novel hybridized machine learning method is developed for streamflow prediction in high altitude mountainous glacier melting affected basins, and its prediction accuracy is compared with several benchmark algorithms, showing superior performance.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Multidisciplinary
Mona Rafat Elkharbotly, Mohamed Seddik, Abdelkawi Khalifa
Summary: This research paper studied the parameters affecting non-revenue water in Egypt and developed a neural model to forecast the NRW ratio. The results showed that the machine-learning algorithm accurately identifies complex relationships between different parameters, providing an immediate, cost-effective, and long-term solution for water companies seeking improvement in their water distribution systems.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Energy & Fuels
Changsu Kim, Jiyong Kim
Summary: In this study, a machine learning-based approach was developed to predict the performance of Pt/CexZr1-xO2 catalysts in water-gas shift reaction (WGSR). The study identified the key properties of the support material, such as reducibility and thermal stability, that determine the catalyst performance.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Sara Kaviani, Ki Jin Han, Insoo Sohn
Summary: In recent years, the improvement of medical images and the performance of deep learning networks have led to the application of deep learning approaches in medical image classification and segmentation. However, there are concerns about the security and accuracy of healthcare systems, as well as the vulnerability of medical deep learning networks to adversarial attacks. This paper reviews the proposed adversarial attack methods and defense techniques for medical imaging DNNs and discusses future directions for improving neural network's robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Artificial Intelligence
Sara Kaviani, Insoo Sohn
Summary: Recent research on artificial neural networks has focused on changing network architecture to optimize performance, with a particular interest in designing more efficient and simpler structures. The application of complex systems theory, as well as small-world and scale-free topologies, in ANNs has attracted much attention and shown promising results in improving performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Environmental
Shakeri Narges, Asgari Ghorban, Khotanlou Hassan, Khazaei Mohammad
Summary: The study utilized artificial fuzzy neural network (ANFIS) and subtractive clustering (SUB) method to determine the optimal dosage of coagulant in water treatment plants, improving model accuracy and intelligent recognition.
JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Saban Gulcu
Summary: The training algorithm is a crucial component of artificial neural networks (ANN) that affects their performance. This article presents a new hybrid algorithm called DA-MLP, which uses the dragonfly algorithm to train feed-forward multilayer neural networks (MLP). The experimental study shows that the DA-MLP algorithm is more efficient than other algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Multidisciplinary
Manal R. Abd-Elhamied, Wael A. Hashima, Sherien ElKateb, Ibrahim Elhawary, Adel El-Geiheini
Summary: Machine learning and computer vision technologies were used to evaluate textile quality, analyzing different parameters through image processing and artificial neural networks for ring-spun and compact cotton yarns.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Geosciences, Multidisciplinary
Kubra Yazici, Alev Taskin
Summary: This study presents a prediction methodology using artificial neural networks and traditional machine learning algorithms to determine the size of the area to be burned in a forest fire. The methodology achieved successful results in experiments conducted in Turkey and Portugal, showing high accuracy and speed in predicting the burned area. The findings are important for resource planning during wildfire response and predicting burned areas in other countries.
