Interval reliability sensitivity analysis using Monte Carlo simulation and mouth brooding fish algorithm (MBF)

Babazadeh, M.; Rezayfar, O.; Jahani, Ehsan

doi:10.1016/j.asoc.2023.110316

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…Thus, the MBF algorithm selects one pair of parents from each cichlid using a probability distribution or Roulette Wheel selection (where higher point values have a higher likelihood). Cichlids that hatch in a new position replace their parents in the population without moving [32]. Before assessing the fitness of the newly hatched fish using a fitness function, we need to ensure that the new positions for the offspring are within the boundaries of the search space.…”

Section: ) Mouth Brooding Fish (Mbf)mentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

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Data classification, a crucial practice in information management, involves categorizing data based on its sensitivity to determine appropriate access levels and protection measures. This paper explores the utilization of novel algorithms, including mouth-brooding fish (MBF), alongside machine learning techniques, for the analysis of medical health data. The SVM exhibits suboptimal performance in the task of data categorization. Therefore, Adaboost may be considered a viable substitute for MBF due to its superior performance in terms of Fscore, accuracy, specificity, and sensitivity. The accuracy of MBF, which stands at about 95%, surpasses that of Adaboost by a significant margin of 77%. The F-score, accuracy, and specificity values obtained for MBF are exceptional when compared to the other chosen models, with values of 97.17%, 93.6%, and 96.5%, respectively. The proposed algorithm exhibits promising advancements in health data categorization, offering a potential breakthrough in data classification methodologies. Leveraging this innovative approach could facilitate more accurate and efficient management of sensitive medical data, thereby enhancing healthcare systems' capabilities for data protection and analysis. The main novelty of this study lies in the introduction and evaluation of the MBF algorithm for data classification within the medical domain. Unlike traditional algorithms, MBF draws inspiration from the collective behavior of mouth-brooding fish, offering a unique optimization strategy that enhances both exploration and exploitation of the solution space. This novel approach presents a promising avenue for advancing healthcare analytics and decision-making processes.

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Section: ) Mouth Brooding Fish (Mbf)mentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…Thus, the MBF algorithm selects one pair of parents from each cichlid using a probability distribution or Roulette Wheel selection (where higher point values have a higher likelihood). Cichlids that hatch in a new position replace their parents in the population without moving [24]. Before assessing the fitness of the newly hatched fish using a fitness function, we need to ensure that the new positions for the offspring are within the boundaries of the search space.…”

Section: ) Mouth Brooding Fish (Mbf)mentioning

confidence: 99%

Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection

Zhou,

Wang,

2024

ijacsa

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Android Malware Detection has become increasingly prevalent, with the highest market share among all other mobile operating systems due to its open-source nature and user-friendliness. This has resulted in an uncontrolled proliferation of malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis evasion techniques, rendering traditional signature-based detection methods less effective in identifying modern and unknown malware.Alternative approaches, such as Machine Learning methods, have emerged as leading solutions for timely zero-day anomaly detection. Ensemble learning, a common meta-approach in machine learning, seeks to improve predictive performance by amalgamating predictions from multiple models. This paper introduces an enhanced strategy, Mouth Brooding Fish (MBF), based on ensemble learning for Android Malware Detection (AMD). The findings are further compared with the outputs of various algorithms including Support Vector Machine (SVM), AdaBoost, Multilayer Perceptron (MLP), Gaussian Kernel (GK), and Random Forest (RF). Compared to the other selected models, MBF exhibits remarkable performance with an F-score of 98.57%, precision of 99.65%, sensitivity of 97.51%, and specificity of 97.51%. Thus, the significant novelty of this work lies in the accuracy and authenticity of the selected algorithms, demonstrating their superior performance overall.

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“…In recent studies the sensitivity analysis of structural reliability has been explored. M.Babazadeh et al [5] transformed the nonlinear limit state function into a linear limit state function by Monte Carlo simulation. On this basis, random variables were introduced, and the reliability sensitivity parameters of the mouth brooder algorithm were adopted.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model

Yafeng,

Yan,

Yongheng

et al. 2024

Preprint

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To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

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Interval reliability sensitivity analysis using Monte Carlo simulation and mouth brooding fish algorithm (MBF)

Cited by 6 publications

References 39 publications

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model

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