Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning

Rostami, Omid; Kaveh, Mehrdad

doi:10.1007/s10596-020-10030-1

Cited by 44 publications

(29 citation statements)

References 75 publications

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“…Recommendation algorithms work by discovering relevant information about the user from a large amount of information and using this information (such as what is frequently watched). That year GroupLens Research Group conducted a long period of research to develop a movie recommendation system to bring a personalized experience to the user [ 6 ]. Also, they went further and came up with a new idea of using collaborative algorithms as a key technology for recommendation systems [ 7 ].…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Fang

2021

Computational Intelligence and Neuroscience

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This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

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Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Fang

2021

Computational Intelligence and Neuroscience

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“…Deep learning-based approaches could have improved the accuracy of the NIDS, though there were still some important features that needed to be improved, including achieving a higher detection rate and decreasing the computational cost. One important thing to do on these scores, which has been rarely considered in the literature, is to optimally train the fully connected neural network in the deep architecture [58][59][60][61][62][63]. Due to the fact that better training the fully connected neural network leads to better classification accuracy, the used classifier can be designed in a more lightweight manner (in an equal detection rate), and thus less data will be required to train the network.…”

Section: Rekated Workmentioning

confidence: 99%

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Baniasadi

Rostami

Martín

et al. 2022

Sensors

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The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.

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“…Metaheuristic algorithms are a subset of stochastic algorithms that have been employed for solving complex problems such as feature selection [8][9][10][11][12], engineering [13][14][15][16][17][18][19][20][21][22][23][24][25][26], community detection [27][28][29][30], and continuous optimization [31][32][33][34][35][36][37] problems. Metaheuristic algorithms employ stochastic techniques to discover the promising areas by exploring the search space in early iterations and improve solutions quality by exploiting the promising areas in the final iterations.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

et al. 2022

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The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

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Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning

Cited by 44 publications

References 75 publications

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

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