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Portable beef-freshness detection platform based on colorimetric sensor array technology and bionic algorithms for total volatile basic nitrogen (TVB-N) determination. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B. Advances in Sparrow Search Algorithm: A Comprehensive Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023. [PMID: 36034191 DOI: 10.1007/s11831-021-09698-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.
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Affiliation(s)
| | - Mohammad Namazi
- Department of Computer Engineering, Maybod Branch. Islamic Azad University, Maybod, Iran
| | - Laya Ebrahimi
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
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Wu R, Ren G, Yin L, Xie T, Zhang X, Zhang Z. Characterization of Congou Black Tea by an Electronic Nose with Grey Wolf Optimization (GWO) and Chemometrics. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2155833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Rui Wu
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Guangxin Ren
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Lingling Yin
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Tian Xie
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Xinyu Zhang
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
- Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Zhao C, Ma J, Jia W, Wang H, Tian H, Wang J, Zhou W. An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm. BIOSENSORS 2022; 12:bios12090692. [PMID: 36140077 PMCID: PMC9496132 DOI: 10.3390/bios12090692] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022]
Abstract
To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.
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Affiliation(s)
- Changtong Zhao
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Ma
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Wenshen Jia
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Correspondence: ; Tel.: +86-13521217121
| | - Huihua Wang
- Department of Food and Bioengineering, Beijing Vocational College of Agriculture, Beijing 102206, China
| | - Hui Tian
- Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jihua Wang
- Institute of Quality Standard and Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wei Zhou
- Hebei Food Safety Key Laboratory, Hebei Food Inspection and Research Institute, Shijiazhuang 050091, China
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Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B. Advances in Sparrow Search Algorithm: A Comprehensive Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:427-455. [PMID: 36034191 PMCID: PMC9395821 DOI: 10.1007/s11831-022-09804-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/02/2022] [Indexed: 05/29/2023]
Abstract
Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.
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Affiliation(s)
| | - Mohammad Namazi
- Department of Computer Engineering, Maybod Branch. Islamic Azad University, Maybod, Iran
| | - Laya Ebrahimi
- Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
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