Article
Materials Science, Composites
Gokce Ozden, Mustafa Ozgur Oteyaka, Francisco Mata Cabrera
Summary: Polyetheretherketone (PEEK) and its composites are widely used in various industries. This study employed Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) to predict cutting forces during the machining of PEEK with different reinforcements. The experimental results showed that both ANN and ANFIS models provided accurate predictions of cutting forces.
JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Mohammed Al-Yaari, Theyazn H. H. Aldhyani, Sayeed Rushd
Summary: In this study, a new artificial neural network (ANN) model was developed using different architectures of an adaptive network-based fuzzy inference system (ANFIS) to predict the adsorption efficiency of arsenate (As(III)) from polluted water. The results showed that the ANFIS model had high prediction accuracy and identified the dominant factors affecting the adsorption process efficiency.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Aamer Bilal Asghar, Saad Farooq, Muhammad Shahzad Khurram, Mujtaba Hussain Jaffery, Krzysztof Ejsmont
Summary: Circulating Fluidized Bed gasifiers are commonly used to convert solid fuel into liquid fuel. This study employs Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System to estimate the solid circulation rate in the gasifier, and experimental results demonstrate the superiority of the Adaptive Neuro-Fuzzy Inference System.
Article
Engineering, Chemical
Musamba Banza, Hilary Rutto
Summary: The current study examines the effectiveness of removing nickel (II) from aqueous solutions using an adsorption method. ANN and ANFIS models were used to predict the adsorption potential of blend hydrogels for nickel (II) removal. The results show that the ANN and ANFIS models are promising approaches for predicting metal ions adsorption. The adsorption process is spontaneous and well explained by the Langmuir model.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Agricultural Engineering
Pu-Yun Kow, Mei-Kuang Lu, Meng-Hsin Lee, Wei-Bin Lu, Fi-John Chang
Summary: A hybrid machine learning approach (ANFIS-NM) was proposed to optimize the cultivation conditions of Antrodia cinnamomea (A. cinnamomea) based on a 32 fractional factorial design. The approach successfully identified three key factors and significantly boosted mycelia yield. It reduces time consumption and increases mycelia yield, showing great potential for biomass production.
BIORESOURCE TECHNOLOGY
(2023)
Article
Engineering, Civil
Aida H. Baghanam, Amirreza Tabataba Vakili, Vahid Nourani, Dominika Dabrowska, Marek Soltysiak
Summary: This study focuses on the prediction of leachate pollutants using Electrical Conductivity (EC) as an indicator. Lysimeter experiments were conducted to simulate landfill conditions, and Artificial Neural Network (ANN), Neuro-Fuzzy Inference System (ANFIS), and Emotional ANN (EANN) models were developed to predict the EC value. The results showed that moisture had a significant impact on EC prediction, and the EANN model performed the best in estimating EC.
JOURNAL OF HYDROLOGY
(2022)
Article
Meteorology & Atmospheric Sciences
Tarate Suryakant Bajirao, Pravendra Kumar, Manish Kumar, Ahmed Elbeltagi, Alban Kuriqi
Summary: The study analyzed the performance of various models in predicting daily runoff of the Koyna River basin in India, and found that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). The Coiflet wavelet-coupled ANFIS model was identified as successfully applicable for the highly dynamic and complex river basin, with sensitivity analysis indicating the previous 1-day runoff as the most crucial variable for prediction.
THEORETICAL AND APPLIED CLIMATOLOGY
(2021)
Article
Environmental Sciences
Seyyed Ahmad Naghibi, Ehsan Salehi, Mohammad Khajavian, Vahid Vatanpour, Mika Sillanpaa
Summary: In this study, machine learning methods were applied to assess batch adsorption of MG dye on CPZ membrane adsorbents. ANFIS was found to be more effective than the ANN approach for predicting adsorption performance, with a RMSE of 0.01822 and R-square of 0.9958. Sensitivity analysis revealed that residence time plays a crucial role in removal efficiency. This study demonstrates the effectiveness of ANFIS in optimizing membrane adsorption processes.
Article
Chemistry, Multidisciplinary
Serge Balonji, Lagouge K. K. Tartibu, Imhade P. P. Okokpujie
Summary: In this study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) approaches were used to predict and monitor the surface roughness of aluminum Al6061 machined blocks. The results showed that factors such as population size, acceleration values, choice of membership functions, and number of neurons and layers significantly influenced the prediction performance of the models.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Vali Rasooli Sharabiani, Mohammad Kaveh, Ebrahim Taghinezhad, Rouzbeh Abbaszadeh, Esmail Khalife, Mariusz Szymanek, Agata Dziwulska-Hunek
Summary: In this study, the IR-HA drying kinetics of parboiled hull was modeled and predicted using three different models: ANFIS, ANN, and SVR. The results showed that higher inlet air temperature and IR power led to shorter drying time. Among the three models, SVR performed the best in terms of prediction performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
M. G. K. Machesa, L. K. Tartibu, M. O. Okwu
Summary: The Stirling engine is considered as a promising alternative to traditional combustion devices, and this study analyzes the prediction performance of power and torque using soft computing techniques and hybrid algorithms. The results show that the Fuzzy Mamdani Model (FMM) performs best in predicting power, while the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO) models perform best in predicting torque.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Abdullah H. Alenezy, Mohd Tahir Ismail, S. Al Wadi, Muhammad Tahir, Nawaf N. Hamadneh, Jamil J. Jaber, Waqar A. Khan
Summary: This study models and enhances the forecasting accuracy of Saudi Arabia's stock exchange data patterns using the MODWT-LA8-ANFIS model with input variables of oil prices and repo rates. The performance of the model surpasses traditional models, making it suitable for stock market analysis and prediction.
JOURNAL OF MATHEMATICS
(2021)
Article
Thermodynamics
Daniel Jia Sheng Chong, Yi Jing Chan, Senthil Kumar Arumugasamy, Sara Kazemi Yazdi, Jun Wei Lim
Summary: This study utilizes machine learning algorithms such as RSM, ANFIS, and ANN to model biogas production and methane yield in a local anaerobic covered lagoon. The models show high accuracy with R² up to 0.98. ANFIS has the highest prediction accuracy with the lowest MAE and RMSE values. Optimal conditions obtained through multi-objective optimization show increased biogas production and methane yield. pH is identified as the most influential factor on methane yield through sensitivity analysis.
Article
Engineering, Multidisciplinary
Senlin Zheng, Haodong Xu, Azfarizal Mukhtar, Ahmad Shah Hizam Md Yasir, Nima Khalilpoor
Summary: This study aims to develop a new conceptual system to predict cooling load in the residential building sector. The authors employed artificial intelligence methods (ANN and ANFIS) in conjunction with teaching-learning-based optimization (TLBO) to predict the cooling load. The experimental results showed that TLBO-MLP technique can accurately predict the cooling load in residential buildings.
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
(2023)
Article
Computer Science, Artificial Intelligence
Selcuk Cankurt, Abdulhamit Subasi
Summary: This study develops an ensemble model for tourism demand forecasting by integrating neural networks with ANFIS. The results demonstrate that the stacking ensemble of ANFIS and ANN models outperforms its stand-alone counterparts. This novel application of ensemble systems shows better results compared to single expert systems based on artificial neural networks.
Article
Toxicology
Sima Zamand, Hossein Alidadi, Maryam Sarkhosh, Aliakbar Dehghan, Hamid Heidarian, Maryam Paydar, Vahid Taghavimanesh
Article
Engineering, Environmental
Maryam Dolatabadi, Saeid Ahmadzadeh
WATER SCIENCE AND TECHNOLOGY
(2019)
Article
Green & Sustainable Science & Technology
Maryam Dolatabadi, Saeid Ahmadzadeh, Mohammad T. Ghaneian
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2020)
Review
Clinical Neurology
Fateme Barjasteh-Askari, Mojtaba Davoudi, Homayoun Amini, Mohammad Ghorbani, Mehdi Yaseri, Masoud Yunesian, Amir Hossein Mahvi, David Lester
JOURNAL OF AFFECTIVE DISORDERS
(2020)
Article
Chemistry, Physical
Aliasghar Najafpoor, Raziyeh Norouzian-Ostad, Hossein Alidadi, Tahereh Rohani-Bastami, Mojtaba Davoudi, Fateme Barjasteh-Askari, Jafar Zanganeh
JOURNAL OF MOLECULAR LIQUIDS
(2020)
Review
Engineering, Environmental
Mohammad Ghorbani, Hossein Najafi Saleh, Fateme Barjasteh-Askari, Simin Nasseri, Mojtaba Davoudi
JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE AND ENGINEERING
(2020)
Review
Public, Environmental & Occupational Health
Mojtaba Davoudi, Fateme Barjasteh-Askari, Mohammad Sarmadi, Mohammad Ghorbani, Mehdi Yaseri, Edris Bazrafshan, Amir Hossein Mahvi, Mohsen Moohebati
Summary: The systematic review and meta-analysis indicate that populations exposed to high-fluoride drinking water have significantly higher systolic and diastolic blood pressure, as well as an increased prevalence of essential hypertension. Further research is needed to draw firm conclusions on the adverse effects of excess fluoride intake on the cardiovascular system at an individual level.
INTERNATIONAL ARCHIVES OF OCCUPATIONAL AND ENVIRONMENTAL HEALTH
(2021)
Review
Environmental Sciences
Mojtaba Davoudi, Fateme Barjasteh-Askari, Homayoun Amini, David Lester, Amir Hossein Mahvi, Vahid Ghavami, Mohammad Rezvani Ghalhari
Summary: The study supports a positive association between air pollution and suicide mortality. No immediate risk was elucidated but the possible effects seem to be exerted cumulatively.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Multidisciplinary Sciences
Fateme Barjasteh-Askari, Mojtaba Davoudi, Maryam Dolatabadi, Saeid Ahmadzadeh
Summary: This study utilized iron-modified activated carbon derived from pistachio shells for the removal of Acid Red 14 dye, showing high efficiency and cost-effectiveness in treating industrial wastewater.
Article
Infectious Diseases
Nasrin Rostami, Hossein Alidadi, Hossein Zarrinfar, Damon Ketabi, Hamed Tabesh
Summary: This study evaluated the effect of nanosilver paint on reducing fungal contaminants in hospital ward environments. The results showed that the use of nanosilver paint was effective in reducing air fungal contamination. The study also found that the active sampling method was more sensitive in measuring concentration changes for fungal bioaerosols.
CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY
(2021)
Review
Environmental Sciences
Fateme Barjasteh-Askari, Simin Nasseri, Ramin Nabizadeh, Aliasghar Najafpoor, Mojtaba Davoudi, Amir-Hossein Mahvi
Summary: This systematic review sheds light on the various aspects of photocatalytic diazinon removal. The most widely used processes with the highest efficacy were ZnO/UV, WO3/UV, TiO2/UV, and TiO2/Vis. The optimal conditions included solution pH in the range of 5-8, catalyst dose below 600 mg/L, diazinon initial concentration below 40 mg/L, and contact time of 20-140 min. Diazinon degradation followed a first-order kinetic model.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Public, Environmental & Occupational Health
Bob Lew, David Lester, Kairi Kolves, Paul S. F. Yip, Ying-Yeh Chen, Won Sun Chen, M. Tasdik Hasan, Harold G. Koenig, Zhi Zhong Wang, Muhamad Nur Fariduddin, Emek Yuce Zeyrek-Rios, Caryn Mei Hsien Chan, Feisul Mustapha, Mimi Fitriana, Housseini Dolo, Burak M. Gonultas, Mahboubeh Dadfar, Mojtaba Davoudi, Ahmed M. Abdel-Khalek, Lai Fong Chan, Ching Sin Siau, Norhayati Ibrahim
Summary: This study examines the trend of suicide in 46 Muslim-majority countries and compares their rates with the global average. The results show that most Muslim-majority countries have lower suicide rates than the global average, potentially due to religious beliefs and underreporting caused by Muslim laws.
Correction
Public, Environmental & Occupational Health
Bob Lew, David Lester, Kairi Kolves, Paul S. F. Yip, Ying-Yeh Chen, Won Sun Chen, M. Tasdik Hasan, Harold G. Koenig, Zhi Zhong Wang, Muhamad Nur Fariduddin, Emek Yuce Zeyrek-Rios, Caryn Mei Hsien Chan, Feisul Mustapha, Mimi Fitriana, Housseini Dolo, Burak M. Gonultas, Mahboubeh Dadfar, Mojtaba Davoudi, Ahmed M. Abdel-Khalek, Lai Fong Chan, Ching Sin Siau, Norhayati Ibrahim
Article
Multidisciplinary Sciences
Hossein Alidadi, Ali Akbar Mohammadi, Alia Asghar Najafpoor, Aliakbar Dehghan, Sima Zamand, Vahid Taghavimanesh
Article
Engineering, Chemical
Bahman Ramavandi, Ali Asghar Najafpoor, Hossein Alidadi, Ziaeddin Bonyadi
DESALINATION AND WATER TREATMENT
(2019)
Article
Automation & Control Systems
Haifei Peng, Jian Long, Cheng Huang, Shibo Wei, Zhencheng Ye
Summary: This paper proposes a novel multi-modal hybrid modeling strategy (GMVAE-STA) that can effectively extract deep multi-modal representations and complex spatial and temporal relationships, and applies it to industrial process prediction.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2024)