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Securing smart agriculture networks using bio-inspired feature selection and transfer learning for effective image-based intrusion detection

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INTERNET OF THINGS 2025年 29卷

作者： Saadouni, Rafika Gherbi, Chirihane Aliouat, Zibouda Harbi, Yasmine Khacha, Amina Mabed, Hakim Ferhat Abbas Univ Set 1 Sci Fac Comp Sci Dept LRSD Lab Setif 19000 Algeria Univ Bourgogne Franche Comte DISC FEMTO ST Inst F-25200 Montbeliard France

The smart agricultural system integrates advanced technologies to optimize farming practices and increase productivity. It gathers images from sensors, drones, and other sources to create detailed maps of crop yield variability, soil composition, and vegetation indices. In the realm of network security, the rise of smart agricultural systems presents both challenges and opportunities. This study addresses the crucial need for a tailored Intrusion Detection System (IDS) for agricultural networks, focusing on image-based traffic. Employing advanced techniques such as transfer learning and bio-inspired algorithms, we propose a novel IDS architecture adept at handling the unique characteristics of image traffic. Our methodology includes imbalanced data handling, transformation, feature extraction utilizing the pre-trained VGG16 model, feature selection via the bio-inspired Binary Greylag Goose (BGGO) algorithm, and classification employing a Random Forest classifier. Evaluation on the new CICIoT2023 dataset showcases high accuracy, with 99.41% for multiclass and 99.83% for binary classifications with a reduced number of features from 25088 to 6327. These results underscore the significance of efficient IDS solutions for the evolving landscape of agricultural technologies, promising enhanced network security in smart agriculture environments.

关键词： IDS IoT Image-based traffic VGG16 BGGO bio-inspired algorithm RF CICIoT2023

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A metaheuristic optimization framework inspired by virus mutations and its ability to optimize the structural design of 2D and 3D steel frames compared to other methods

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RESULTS IN ENGINEERING 2025年 26卷

作者： Ghasri, Mehdi Karimi, Hamid Reza Salarnia, Abdolhamid Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Traditional metaheuristic approaches for structural optimization often encounter challenges such as premature convergence, computational inefficiency, and limited adaptability to complex constraints in high-dimensional search spaces. To address these limitations, this study proposes the mutation-based virus pandemic optimization (MVPO) algorithm, a novel bio-inspired metaheuristic that emulates virus transmission dynamics and mutation mechanisms. MVPO integrates population-based exploration, simulating viral spread through intracommunity interactions (guided by wavelet functions to model transmission variability) and inter-community propagation, with a two-phase mutation strategy: the first phase enhances global exploration via elongated search steps during accelerated spread, while the second phase introduces structural perturbations to escape local optima. The algorithm's efficacy is evaluated through three benchmark problems, two planar (2D) frames and a three-dimensional (3D) space frame, with comparisons against established methods (e.g., particle swarm optimization, cuckoo search) and contemporary algorithms (e.g., marine predators algorithm, pelican optimization algorithm). Results demonstrate MVPO's superior performance, achieving weight reductions of 0.18 %-24.2 % across all structures, including the complex 3D frame, while requiring 11.2 %-61.62 % fewer structural analyses than competitors. Reliability is evidenced by minimal standard deviations (7.88-69.58) across multiple runs, indicating consistent convergence to near-optimal solutions. Stress-ratio analyses confirm adherence to design constraints, ensuring feasible lightweight configurations. These findings establish MVPO as a computationally efficient and robust tool for structural optimization, capable of navigating intricate 2D and 3D design spaces with enhanced adaptability. The algorithm's bio-inspired mechanics bridge biological principles with engineering precision, offering practical advancemen

关键词： Metaheuristic optimization bio-inspired algorithm Virus pandemic dynamics Structural optimization Mutation strategy Computational efficiency 3D frameworks

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High-Precision Real-Time Flow Prediction in a Multi-tributary River System: A bio-inspired Dynamic Neural Network Model

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EARTH SYSTEMS AND ENVIRONMENT 2025年 1-17页

作者： Yang, Jinying Liu, Bao Xu, Mei Marcos-Martinez, Raymundo Gao, Lei China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China Commonwealth Sci & Ind Res Org CSIRO Canberra ACT 2601 Australia Commonwealth Sci & Ind Res Org CSIRO Waite Campus Glen Osmond SA 5064 Australia

Floods are among the most severe natural disasters globally, particularly in densely populated areas with extensive agriculture, concentrated rivers, and abundant rainfall. In recent years, human activities have altered river confluence conditions, exacerbating the frequency and severity of floods. To address the limitations of existing multi-tributary stream flow prediction models, which suffer from poor real-time performance and low prediction accuracy, we developed a bio-inspired neural network (bio-NN) model motivated by a cooperative regulation mechanism in biological systems. Considering the problem that there is less feedback information in existing neural networks, the proposed model combines a biohormone multi-level nonlinear feedback regulation mechanism with a neural network. This enhances traditional neural networks by improving network structure and dynamically incorporating feedback information, allowing real-time optimization and improving optimization speed and precision over time. We tested the bio-NN model by applying it to predict river flow along the lower Murray River in Australia. To obtain deeper insights into the performance of bio-NN, indicators such as NSE, RSR, PCC, and KGE, were determined in the basin. The simulation demonstrated its superior performance, achieving a Nash-Sutcliffe efficiency coefficient (NSE) of 0.991, root mean squared to standard deviation ratio (RSR) of 0.096, a Pearson's correlation coefficient (PCC) of 0.996, and a Kling-Gupta efficiency coefficient (KGE) of 0.995. Compared to a back propagation neural network (BP-NN), a dynamic learning BP-NN, and a self-feedback BP-NN, the bio-NN showed significant improvements in prediction performance: improved by 8-65% (NSE), 4-28% (PCC), 67-85% (RSR), 9-27% (KGE). The results underscore bio-NN's capability to significantly enhance the accuracy and stability of flood prediction models.

关键词： bio-inspired algorithm Flow Prediction Neural Network River System Real-time Prediction

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bio-inspired algorithms for industrial robot control using deep learning methods

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷 101473-101473页

作者： Guan, Jiwen Su, Yanzhao Su, Ling Sivaparthipan, C. B. Muthu, BalaAnand Guangdong Univ Sci & Technol Dongguan 523083 Guangdong Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Changan Automobile Co Ltd Chongqing 401120 Peoples R China Adhiyamaan Coll Engn Dept Comp Sci & Engn Hosur Tamil Nadu India

The biologically inspired algorithm is a significant embranchment of sequence on computational intelligence and plays a critical role in industrial robot control. Industrial robots are well suited for conducting manipulation or material handling functions in extremely constrained and predictable environments. By comparison, living organisms are highly resilient and able to perform tasks in evolving environments, such as stable locomotion over uneven terrain. The robotics challenge is to use inspiration from biology to build devices capable of functioning in unrestrained or moderately constrained environments. Hence, in this study, a bio-inspired Intelligent Industrial Robot Control System (BIIRCS) has been suggested using Deep Learning methods. A bio-inspired neural network is considered to model the complex environment and to guide a team of robots for the coverage task. The collected data is fed into a Deep Learning Neural Network to comprehend the localization and recognition of present objects from various classes. With the derived data, appropriate robot actions can be planned and executed. Usable objects are identified and seized in the robot's workspace, or that feed is sufficient for unattainable objects. This study confirms the ability to create intelligent systems using existing Deep Learning algorithms and industrial robotics. The simulations' findings reveal that the new approach achieves a high-performance ratio of 83.5%, accuracy ratio of 95.4%, less operational time of 7.8%, low RMSE rate of 11.2%, and increased coverage task rate of 96.7% when compared to other existing approaches.

关键词： Deep learning bio-inspired algorithm Industrial robot control

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A bio-inspired fall webworm optimization algorithm for feature selection and support vector machine optimization for retinal abnormalities detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第21期82卷 32443-32462页

作者： Durai, B. Sakthi Karthi Raja, J. Benadict Velammal Coll Engn & Technol Dept Comp Sci & Engn Madurai India PSNA Coll Engn & Technol Dept Comp Sci & Engn Dindigul India

The early detection of retinal abnormalities such as diabetic retinopathy (DR) can be performed using the computerized analysis of retinal fundus images. The most significant complications associated with the DR detection are noise artifacts occurred as a result of unsuitable illumination, the overlapping of blood vascular structure and lesions as they have same intensities, and missing of data happened due to the analysis of large amount of data. Hence the improved technique capable of overcoming all these limitations must be presented in early detection of DR and other retinal abnormalities. Some of previous blood vessel segmentation methods provide better accuracy with normal retinal images and requires less computation time. In this work, an automated process using an optimized SVM classifier and a new feature extraction is presented. The proposed feature extraction process is sum of minimum (SOM) local difference pattern (LDP) (SOMLDP) which is developed from the computation of difference between pixels. This feature extraction produces precise feature information with reduced size. In addition, feature selection process is employed to select more important features, using a new optimization algorithm developed from the behavior of fall webworm (FWW) species. FWW optimization algorithm is also applied for the optimal tuning of support vector machine (SVM) classifier. The main objective of the paper is to present an automated detection of DR with more accuracy, less memory and reduced computation time. The performance of proposed technique is validated with publicly available standard dataset Messidor-2 by evaluating the metrics such as sensitivity, specificity and accuracy. The simulation results depict that sensitivity, specificity and accuracy of 0.8235, 0.9892 and 0.9879 is attained respectively. The FWW optimization algorithm is also validated by analyzing the computation time performance and comparing the performance of FWW algorithm with other optimizatio

关键词： bio-inspired algorithm Parameter optimization Support vector machine Feature selection Segmentation

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Optimizing Security Protocol: A Synergy of bio-inspired Planet Optimization algorithm With Ensemble Learning-Based Attack Detection for Connected and Autonomous Vehicles

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IEEE ACCESS 2024年 12卷 173209-173222页

作者： Alrayes, Fatma S. Aljebreen, Mohammed Maray, Mohammed Alshuhail, Asma Alrslani, Faheed A. F. Salama, Ahmed S. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh Saudi Arabia King Saud Univ Community Coll Dept Comp Sci Riyadh 11437 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha 62521 Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hasa 31982 Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Informat Technol Rafha 91911 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo Egypt

Connected and Autonomous Vehicles (CAVs) enable different functionalities and capabilities such as navigation and path planning, automated driving assistance, cruise control, low-carbon transportation, and independent decision-making in real-world scenarios. However, the increased usage of CAVs reduces the possible vulnerabilities in the Internet of Vehicles (IoVs) framework, exposing it to cyber-attacks. An Intrusion Detection System (IDS) is a method to report network attacks by Autonomous Vehicles (AVs) without authorization and encryption techniques for internal and external vehicular transmission. To alleviate this risk, the lightweight IDS system must identify attacks on vehicular systems. Deep learning (DL) approaches promising algorithms for intrusion detection in CAVs, which leverage their capability to automatically extract and learn complex patterns from intricate and vast datasets. DL-based IDS can efficiently identify anomalous activities indicative of system vulnerabilities or cyberattacks by analyzing vehicle behaviour, network traffic, and sensor data in real-time, thus ensuring the integrity and security of CAV operations. bio-inspired optimization algorithms have recently been used for feature selection (FS) and hyperparameter tuning processes, commonly stimulated by physical ideologies, evolution theories, and specific characteristics of living beings, to solve optimization issues competently in different applications. Therefore, this study introduces a novel Planet Optimization algorithm with a Deep Ensemble Learning-based Intrusion Detection (POADEL-ID) technique for CAV networks. The presented POADEL-ID model concentrates on identifying intrusions in the CAV networks. In the POADEL-ID technique, linear scaling normalization (LSN) is utilized for the data scaling process. Besides, the high-dimensionality problem is resolved by the POA-based FS approach. Moreover, intrusion detection is performed by using an ensemble of three techniques, namely l

关键词： Intrusion detection Optimization methods Long short term memory Data models Tuning Security Computational modeling Autonomous vehicles Accuracy bio-inspired computing Ensemble learning bio-inspired algorithm autonomous vehicles intrusion detection system planet optimization algorithm ensemble learning hyperparameter tuning

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bio-inspired algorithms Applied in Multi-objective Vehicle Routing Problem: Frameworks and Applications 10th

Bio-inspired Algorithms Applied in Multi-objective Vehicle R...

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10th International Conference on bio-inspired Computing - Theories and Applications (BIC-TA)

作者： Wang, Yuan He, Yongming He, Lei Xing, Lining Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783662490143;9783662490136

Multi-objective vehicle routing problem (MOVRP) is developed from vehicle routing problem (VRP). MOVRP is a classic multi-objective optimization problem. During the recent years, the MOVRPs had a progress in problem scales and complex level. As a result, to get better solutions of MOVRPs, bio-inspired algorithms were introduced into this area. This article first analyses the MOVRP framework, and then reviews the bio-inspired algorithm frameworks that designed to solve MOVRPs. This analysis leads to the identification of bio-inspired algorithms which can get better solutions for MOVPRs and can be applied to real-life cases successfully.

关键词： bio-inspired algorithm Multi-objective Vehicle routing Literature review

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Mining High Utility Itemsets Using bio-inspired algorithms: A Diverse Optimal Value Framework INDEX

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IEEE ACCESS 2018年 6卷 19568-19582页

作者： Song, Wei Huang, Chaomin North China Univ Technol Coll Comp Sci & Technol Beijing 100144 Peoples R China North China Univ Technol Beijing Key Lab Integrat & Anal Large Scale Strea Beijing 100144 Peoples R China

Mining high utility itemsets (HUI) is an interesting research problem in the field of data mining and knowledge discovery. Recently, bio-inspired computing has attracted considerable attention, leading to the development of new algorithms for mining HUIs. These algorithms have shown good performance in terms of efficiency, but are not guaranteed to find all HUIs in a database. That is, the quality is comparatively poor in terms of the number of discovered HUIs. To solve this problem, a new framework based on bioinspired algorithms is proposed. This approach adjusts the standard roadmap of bio-inspired algorithms by proportionally selecting discovered HUIs as the target values of the next population, rather than maintaining the current optimal values in the next population. Thus, the diversity within populations can be improved. Three new algorithms based on the bio-HUI framework are developed using the genetic algorithm, particle swarm optimization, and the bat algorithm, respectively. Extensive tests conducted on publicly available datasets show that the proposed algorithms outperform existing state-of-the-art algorithms in terms of efficiency, quality of results, and convergence speed.

关键词： Data mining high utility itemset mining bio-inspired algorithm genetic algorithm particle swarm optimization bat algorithm

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Feature selection and classification using bio-inspired algorithms for the diagnosis of pulmonary emphysema subtypes

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第4期33卷 1353-1367页

作者： Isaac, Anisha Nehemiah, H. Khanna Dunston, Snofy D. Kannan, A. Anna Univ Ramanujan Comp Ctr Chennai India Anna Univ Dept Comp Sci & Engn Chennai India Vellore Inst Technol Sch Comp Sci & Engn Vellore India

A computer-assisted diagnosis framework to examine computed tomography (CT) slices for diagnosing pulmonary emphysema is designed. Partitioning of lung tissues and regions of Interest (ROIs) from the CT slices is achieved using Spatial Intuitionistic Fuzzy C-Means (SIFCM) clustering algorithm. Shape features, texture features, and run-length features are extracted from each ROI. Feature selection is performed as a wrapper technique by employing manta ray foraging optimization (MRFO) algorithm and random forest (RF) classifier. A backpropagation neural network (BPNN) using gradient descent is used to train the selected features. Adaptive butterfly optimization algorithm (ABOA) is used to fix the optimal topology and parameters, namely weights and biases of the neural network. The BPNN classifier is initialized with the optimized topology and parameters obtained by the ABOA. The developed framework attained an accuracy of 87.52% when tested on an emphysema dataset, which outperforms the BPNN classifier in terms of accuracy, specificity, precision, and recall.

关键词： adaptive butterfly optimization bio-inspired algorithm classification feature selection manta ray foraging optimization multilayer perceptron

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Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions - A review

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年第Part1期81卷 840-873页

作者： Li, Guiqiang Jin, Yi Akram, M. W. Chen, Xiao Ji, Jie Univ Sci & Technol China Dept Thermal Sci & Energy Engn 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Precis Machinery & Instrumentat 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China State Key Lab Fire Sci 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Agr Faisalabad Dept Farm Machinery & Power Jail Rd Faisalabad 38000 Pakistan

Solar energy is one of the most promising renewable energy resource due to its variety of advantages. The photovoltaic systems have a remarkable development over the past few decades. As the maximum power point of the photovoltaic system varies with the change in environmental conditions, the maximum power point tracking technology is necessary to harvest maximum power from the photovoltaic systems. However, multiple peaks occur in the power-voltage (P-V) curve during partial shading conditions. In such condition, many traditional maximum power point tracking methods like perturbation and observation, and incremental conductance may become invalid due to involvement in the local maximum power point. Many advanced methods based on the artificial intelligence like artificial neural network, and fuzzy logic control can track the global maximum power point. However, they are not feasible in real complex environment because they need massive training and broader experience. Alternatively, bio-inspired maximum power point tracking algorithms deal properly with such situations. In recent years, researchers have widely applied bio-inspired algorithms to track the global maximum power point of photovoltaic system during partial shading situations. This paper presents a comprehensive review of the bio-inspired algorithms used for global maximum power point tracking. Various tracking methods are discussed and compared in terms of their characteristics and corresponding improved methods. It also presents the advantages and disadvantages of each method. The modified and combined forms of these methods found to have better performance than original algorithms. Overall, the performance of swarm intelligence based algorithms is found better than evolutionary algorithms. This review may help the researchers to acquire comprehensive information about the application of bio-inspired algorithms to gain maximum power from the photovoltaic systems, and furthermore, help them to choose an

关键词： Photovoltaic (PV) system Maximum power point tracking bio-inspired algorithm Partial shading situations Global maximum power point tracking

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