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Razavi-Termeh SV, Sadeghi-Niaraki A, Farhangi F, Khiadani M, Pirasteh S, Choi SM. Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping. CHEMOSPHERE 2024; 363:142859. [PMID: 39025307 DOI: 10.1016/j.chemosphere.2024.142859] [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: 11/30/2023] [Revised: 06/17/2024] [Accepted: 07/14/2024] [Indexed: 07/20/2024]
Abstract
Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
| | - Abolghasem Sadeghi-Niaraki
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
| | - Farbod Farhangi
- Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran.
| | - Mehdi Khiadani
- School of Engineering, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia.
| | - Saied Pirasteh
- Institute of Artificial Intelligence, School of Mechanical and Electrical Engineering, Shaoxing University, China; Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India.
| | - Soo-Mi Choi
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
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Choudhary V, Tanwar S, Choudhury T, Kotecha K. Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset. MethodsX 2024; 12:102747. [PMID: 38774685 PMCID: PMC11107349 DOI: 10.1016/j.mex.2024.102747] [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: 02/26/2024] [Accepted: 05/03/2024] [Indexed: 05/24/2024] Open
Abstract
The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks-blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.
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Affiliation(s)
- Vandana Choudhary
- Amity Institute of Information Technology, Amity University, Noida 201313, India
| | - Sarvesh Tanwar
- Amity Institute of Information Technology, Amity University, Noida 201313, India
| | - Tanupriya Choudhury
- Research Professor, CSE Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand 248002, India
- Adjunct Professor, CSE Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale Campus, Pune, Maharashtra 412115, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University) (SIU), Symbiosis Institute of Technology, Lavale Campus, Pune 412115, India
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Jakšić Z, Devi S, Jakšić O, Guha K. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics. Biomimetics (Basel) 2023; 8:278. [PMID: 37504166 PMCID: PMC10807478 DOI: 10.3390/biomimetics8030278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/29/2023] Open
Abstract
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
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Affiliation(s)
- Zoran Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Swagata Devi
- Department of Electronics and Communication Engineering, B V Raju Institute of Technology Narasapur, Narasapur 502313, India;
| | - Olga Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Koushik Guha
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar 788010, India;
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Wu Q, Huang D, Wei J, Chen W. Adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:11482-11501. [PMID: 37322991 DOI: 10.3934/mbe.2023509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
How to improve the robustness to resist attacks and how to adaptively match the key parameters of the watermarking algorithm with the performance requirements to achieve the best performance in different applications are two hot issues in the research of audio watermarking algorithms. An adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm (BOA) is proposed. Based on the convolution operation, a stable feature is designed to carry the watermark, which will improve the robustness by means of the stability of this feature to prevent the watermark loss. Blind extraction will be achieved only by comparing the feature value and the quantized value without the original audio. The BOA is used to optimize the key parameters of the algorithm which can be matched with the performance requirements by coding the population and constructing the fitness function. Experimental results confirm that this proposed algorithm can adaptively search for the optimal key parameters that match the performance requirements. Compared with other related algorithms in recent years, it exhibits strong robustness against various signal processing attacks and synchronization attacks.
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Affiliation(s)
- Qiuling Wu
- School of Cyber Security, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China
| | - Dandan Huang
- School of Cyber Security, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China
| | - Jiangchun Wei
- Jiangsu Liyuan Power Engineering Co., Ltd., Nanjing, Jiangsu 211100, China
| | - Wenhui Chen
- School of Cyber Security, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China
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Aala Kalananda VKR, Komanapalli VLN. A competitive learning-based Grey wolf Optimizer for engineering problems and its application to multi-layer perceptron training. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-59. [PMID: 37362670 PMCID: PMC10031199 DOI: 10.1007/s11042-023-15146-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 09/02/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
This article presents a competitive learning-based Grey Wolf Optimizer (Clb-GWO) formulated through the introduction of competitive learning strategies to achieve a better trade-off between exploration and exploitation while promoting population diversity through the design of difference vectors. The proposed method integrates population sub-division into majority groups and minority groups with a dual search system arranged in a selective complementary manner. The proposed Clb-GWO is tested and validated through the recent CEC2020 and CEC2019 benchmarking suites followed by the optimal training of multi-layer perceptron's (MLPs) with five classification datasets and three function approximation datasets. Clb-GWO is compared against the standard version of GWO, five of its latest variants and two modern meta-heuristics. The benchmarking results and the MLP training results demonstrate the robustness of Clb-GWO. The proposed method performed competitively compared to all its competitors with statistically significant performance for the benchmarking tests. The performance of Clb-GWO the classification datasets and the function approximation datasets was excellent with lower error rates and least standard deviation rates.
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Sharma V, Tripathi AK, Mittal H. DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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A Smart and Mechanized Agricultural Application: From Cultivation to Harvest. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.
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