Local search-based dynamically adapted bat algorithm in image enhancement domain

Dhal, Krishna Gopal; Das, Sanjoy

doi:10.1504/ijcsm.2020.105447

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…Medical institutions, rehabilitation centers, elderly care centers, sports centers, and fitness centers in four provinces were observed, and preliminary unified data were obtained by means of video recording, heartbeat detection, and vital capacity test. Combined with relevant domestic literature [ 13 ], 23 physical health indexes and 12 physical health evaluation indexes are obtained. After experts' determination and 120 sampling questionnaires, there is no significant correlation between the above indexes, which can be used as evaluation indexes and samples.…”

Section: The Algorithm Description Based Online Sequential Extreme Le...mentioning

confidence: 99%

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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The rapid development of social economy not only increases people’s living pressure but also reduces people’s health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals’ health evaluation.

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Section: The Algorithm Description Based Online Sequential Extreme Le...mentioning

confidence: 99%

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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“…Another domain where using BA shows its superiority is image and signal processing, in which the algorithm solves image segmentation [ 28 , 35 , 45 , 77 , 78 , 196 , 204 , 221 , 229 , 255 , 322 , 323 , 327 , 328 ], face recognition and fingerprint identification [ 23 , 44 , 80 , 230 ], magnetic resonance (MR) brain tumour classification [ 151 – 153 , 271 ], multilevel image thresholding [ 79 , 207 , 254 ], and signal processing application [ 5 , 161 , 302 ]. Figure 19 plots the applications of BA in image and signal processing domain.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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Malware detection based on multi‐objective convolution restricted Boltzmann machine model and constraint‐dividing crossover strategy algorithm

Cui

Zhao

Líu

et al. 2022

Concurrency and Computation

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Malicious code, which is used to threaten network security, has been improved by many methods and strategies. However, the use of unreasonable deep learning models and single-objective algorithms often affects the accuracy of data classification. Moreover, as an optimization problem, the existence of low-quality datasets (imbalanced datasets) constrains the training effect of neural network models. Therefore, it is a big challenge to effectively design data processing methods and suitable training models. To enhance the robustness of the model and obtain better data classification results, it is essential to develop an efficient training model and strategy. To resolve this problem, a novel multi-objective convolution restricted Boltzmann machine (CRBM) model based on the spatial pyramid pooling strategy is proposed to identify images and classify data in this study. Moreover, a fast nondominated sorting genetic algorithm based on the constraint-dividing crossover strategy (CDCS-NSGA-II) is designed to optimize the imbalance datasets of the malware families. Extensive experimental results show that the multi-objective CRBM model designed in this study combined with the CDCS-NSGA-II algorithm can effectively enhance the learning ability of the dataset, increase the effect of data classification and improve the robustness of the model.

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Local search-based dynamically adapted bat algorithm in image enhancement domain

Cited by 9 publications

References 35 publications

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

Recent advances of bat-inspired algorithm, its versions and applications

Malware detection based on multi‐objective convolution restricted Boltzmann machine model and constraint‐dividing crossover strategy algorithm

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