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Xin J, Khishe M, Zeebaree DQ, Abualigah L, Ghazal TM. Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification. Heliyon 2024; 10:e28147. [PMID: 38689992 PMCID: PMC11059399 DOI: 10.1016/j.heliyon.2024.e28147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024] Open
Abstract
Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.
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Affiliation(s)
- Jiayun Xin
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China
| | - Mohammad Khishe
- Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
- Center for Artificial Intelligence Applications, Yuan Ze University, Taiwan
| | - Diyar Qader Zeebaree
- Information Technology Department, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia
| | - Taher M. Ghazal
- Centre for Cyber Physical Systems, Computer Science Department, Khalifa University, United Arab Emirates
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia
- Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan
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Asiri MM, Aldehim G, Alruwais N, Allafi R, Alzahrani I, Nouri AM, Assiri M, Ahmed NA. Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches. ENVIRONMENTAL RESEARCH 2024; 245:118042. [PMID: 38160971 DOI: 10.1016/j.envres.2023.118042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/16/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.
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Affiliation(s)
- Mashael M Asiri
- Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
| | - Ghadah Aldehim
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Nuha Alruwais
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O.Box 22459, Riyadh, 11495, Saudi Arabia
| | - Randa Allafi
- Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia
| | - Ibrahim Alzahrani
- Department of Computer Science, College of Computer Science and Engineering, Hafr Al Batin University, Saudi Arabia
| | - Amal M Nouri
- Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, 34212, Saudi Arabia
| | - Mohammed Assiri
- Department of Computer Science, College of Sciences and Humanities- Aflaj, Prince Sattam Bin Abdulaziz University, Aflaj, 16273, Saudi Arabia.
| | - Noura Abdelaziz Ahmed
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
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Yolmeh M, Jafari SM. Cracking spoilage in jar cream cheese: Introducing, modeling and preventing. Heliyon 2024; 10:e25259. [PMID: 38352739 PMCID: PMC10862521 DOI: 10.1016/j.heliyon.2024.e25259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
This study aimed to investigate the modeling of antimicrobial activity (AA) of nisin and sorbate on Clostridium sporogenes in jar cream cheese (JCC) using the linear regression (LR), multilayer perceptron (MLP) neural network, and reduced error pruning tree (REPTree) methods, in order to prevent the late blowing defect (LBD) in the cheese. Both preservatives used in JCC samples showed AA against C. sporogenes; so that sorbate at all the concentrations used in JCC samples inhibited cracking spoilage during storage period at 35 °C. However, nisin could not inhibit cracking spoilage at concentration of 30 ppm in the samples, and a higher concentration of it was needed. The three models used in this study, followed the similar pattern in both training and validation datasets for nisin and sorbat in JCC. The R2 and root mean square error (RMSE) values of training and validation datasets showed the superiority of the REPTree model compared to the MLP and LR models (conventional methods) in the modeling of AA of nisin and sorbate against C. sporogenes in JCC.
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Affiliation(s)
- Mahmoud Yolmeh
- Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
| | - Seid Mahdi Jafari
- Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
- Halal Research Center of IRI, Iran Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran
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Das M, Proshad R, Chandra K, Islam M, Abdullah Al M, Baroi A, Idris AM. Heavy metals contamination, receptor model-based sources identification, sources-specific ecological and health risks in road dust of a highly developed city. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023; 45:8633-8662. [PMID: 37682507 DOI: 10.1007/s10653-023-01736-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/16/2023] [Indexed: 09/09/2023]
Abstract
The present study quantified Ni, Cu, Cr, Pb, Cd, As, Zn, and Fe levels in road dust collected from a variety of sites in Tangail, Bangladesh. The goal of this study was to use a matrix factorization model to identify the specific origin of these components and to evaluate the ecological and health hazards associated with each potential origin. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Cu, Ni, Cr, Pb, As, Zn, Cd, and Fe. The average concentrations of these elements were found to be 30.77 ± 8.80, 25.17 ± 6.78, 39.49 ± 12.53, 28.74 ± 7.84, 1.90 ± 0.79, 158.30 ± 28.25, 2.42 ± 0.69, and 18,185.53 ± 4215.61 mg/kg, respectively. Compared to the top continental crust, the mean values of Cu, Pb, Zn, and Cd were 1.09, 1.69, 2.36, and 26.88 times higher, respectively. According to the Nemerow integrated pollution index (NIPI), pollution load index (PLI), Nemerow integrated risk index (NIRI), and potential ecological risk (PER), 84%, 42%, 30%, and 16% of sampling areas, respectively, which possessed severe contamination. PMF model revealed that Cu (43%), Fe (69.3%), and Cd (69.2%) were mainly released from mixed sources, natural sources, and traffic emission, respectively. Traffic emission posed high and moderate risks for modified NIRI and potential ecological risks. The calculated PMF model-based health hazards indicated that the cancer risk value for traffic emission, natural, and mixed sources had been greater than (1.0E-04), indicating probable cancer risks and that traffic emission posed 38% risk to adult males where 37% for both adult females and children.
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Affiliation(s)
- Mukta Das
- Department of Zoology, Government Saadat College, Tangail, 1903, Bangladesh
| | - Ram Proshad
- Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, Sichuan, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Krishno Chandra
- Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, Bangladesh
| | - Maksudul Islam
- Department of Environmental Science, Patuakhali Science and Technology University, Dumki, Patuakhali, 8602, Bangladesh
| | - Mamun Abdullah Al
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Aquatic Eco-Health Group, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
| | - Artho Baroi
- Department of Crop Botany, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, 62529, Abha, Saudi Arabia
- Research Center for Advanced Materials Science (RCAMS), King Khalid University, 62529, Abha, Saudi Arabia
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Li J, Ma H, Shi W, Tan Y, Xu H, Zheng B, Liu J. Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information. MATERIALS (BASEL, SWITZERLAND) 2023; 16:6539. [PMID: 37834675 PMCID: PMC10573948 DOI: 10.3390/ma16196539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/19/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023]
Abstract
Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.
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Affiliation(s)
- Jilu Li
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Hua Ma
- Xingtai Pavement & Bridge Construction Group Co., Ltd., Xingtai 054000, China
| | - Wei Shi
- Heilongjiang Transportation Investment Group Co., Ltd., Harbin 150000, China
| | - Yiqiu Tan
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Huining Xu
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Bin Zheng
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Jie Liu
- Xingtai Pavement & Bridge Construction Group Co., Ltd., Xingtai 054000, China
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Iftikhar B, Alih SC, Vafaei M, Javed MF, Rehman MF, Abdullaev SS, Tamam N, Khan MI, Hassan AM. Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming. Sci Rep 2023; 13:12149. [PMID: 37500697 PMCID: PMC10374568 DOI: 10.1038/s41598-023-39349-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023] Open
Abstract
Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.
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Affiliation(s)
- Bawar Iftikhar
- School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
| | - Sophia C Alih
- Institute of Noise and Vibration, School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
| | - Mohammadreza Vafaei
- School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
| | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
| | - Muhammad Faisal Rehman
- Department of Architecture, University of Engineering and Technology Peshawar, Abbottabad Campus, Abbottabad, Pakistan
| | - Sherzod Shukhratovich Abdullaev
- Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan
- Department of Science and Innovation, Tashkent State Pedagogical University Named after Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan
| | - Nissren Tamam
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - M Ijaz Khan
- Department of Mathematics and Statistics, Riphah International University, I-14, Islamabad, 44000, Pakistan.
- Department of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon.
| | - Ahmed M Hassan
- Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo, 11835, Egypt
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Lin L, Zhang J, Zhang N, Shi J, Chen C. Optimized LightGBM Power Fingerprint Identification Based on Entropy Features. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1558. [PMID: 36359649 PMCID: PMC9689363 DOI: 10.3390/e24111558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
The huge amount of power fingerprint data often has the problem of unbalanced categories and is difficult to upload by the limited data transmission rate for IoT communications. An optimized LightGBM power fingerprint extraction and identification method based on entropy features is proposed. First, the voltage and current signals were extracted on the basis of the time-domain features and V-I trajectory features, and a 56-dimensional original feature set containing six entropy features was constructed. Then, the Boruta algorithm with a light gradient boosting machine (LightGBM) as the base learner was used for feature selection of the original feature set, and a 23-dimensional optimal feature subset containing five entropy features was determined. Finally, the Optuna algorithm was used to optimize the hyperparameters of the LightGBM classifier. The classification performance of the power fingerprint identification model on imbalanced datasets was further improved by improving the loss function of the LightGBM model. The experimental results prove that the method can effectively reduce the computational complexity of feature extraction and reduce the amount of power fingerprint data transmission. It meets the recognition accuracy and efficiency requirements of a massive power fingerprint identification system.
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Affiliation(s)
- Lin Lin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
| | - Jie Zhang
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
| | - Na Zhang
- State Grid Liaoning Economic Research Institute, Shenyang 110015, China
| | - Jiancheng Shi
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
| | - Cheng Chen
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
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Zhang J, Fu P, Meng F, Yang X, Xu J, Cui Y. Estimation algorithm for chlorophyll-a concentrations in water from hyperspectral images based on feature derivation and ensemble learning. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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