共查询到20条相似文献,搜索用时 15 毫秒
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Whale optimization algorithm (WOA) is a new population-based metaheuristic
algorithm. WOA uses shrinking encircling mechanism, spiral rise, and random
learning strategies to update whale’s positions. WOA has merit in terms of simple
calculation and high computational accuracy, but its convergence speed is slow and it is
easy to fall into the local optimal solution. In order to overcome the shortcomings, this
paper integrates adaptive neighborhood and hybrid mutation strategies into whale
optimization algorithms, designs the average distance from itself to other whales as an
adaptive neighborhood radius, and chooses to learn from the optimal solution in the
neighborhood instead of random learning strategies. The hybrid mutation strategy is used
to enhance the ability of algorithm to jump out of the local optimal solution. A new whale
optimization algorithm (HMNWOA) is proposed. The proposed algorithm inherits the
global search capability of the original algorithm, enhances the exploitation ability,
improves the quality of the population, and thus improves the convergence speed of the
algorithm. A feature selection algorithm based on binary HMNWOA is proposed. Twelve
standard datasets from UCI repository test the validity of the proposed algorithm for
feature selection. The experimental results show that HMNWOA is very competitive
compared to the other six popular feature selection methods in improving the
classification accuracy and reducing the number of features, and ensures that HMNWOA
has strong search ability in the search feature space. 相似文献
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R. Manjula Devi M. Premkumar Pradeep Jangir B. Santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar 《计算机、材料和连续体(英文)》2022,70(1):557-579
In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the continuous HGSO into a binary variant using V- and S-shaped transfer functions (BHGSO-V and BHGSO-S). To validate the accuracy, 16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms. The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features, classification accuracy, run time, and fitness values than other state-of-the-art algorithms. The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems. The proposed BHGSO-V achieves 95% average classification accuracy for most of the datasets, and run time is less than 5 sec. for low and medium dimensional datasets and less than 10 sec for high dimensional datasets. 相似文献
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求解病态问题的一种新的正则化子与正则化算法 总被引:2,自引:0,他引:2
根据紧算子的奇异系统理论,提出了一种新的正则化子,进而建立了一类新的求解病态问题的正则化方法。证明了正则解的收敛性并得到了其最优的渐近收敛阶,数值算例说明文中建立的正则化算法是可行而有效的。 相似文献
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可调激活函数神经元参数选取方法研究 总被引:1,自引:0,他引:1
隐层神经元采用相同的Sigmoid激活函数会限制神经网络的非线性能力,对Sigmoid函数引入两个参数可改善其响应特性,增强其非线性逼近能力.本文给出了一种可调Sigmoid激活函数,分析了可调激活函数中参数所表示的几何意义;给出提升网络维数的可调激活函数中参数的快速选取方法和理论基础.这为人们在采用可调Sigmoid激活函数解决实际问题时,如何快速选取激活函数中的参数提供了一种可行方法. 相似文献
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混沌神经元耦合置乱神经元的图像加密算法研究 总被引:2,自引:2,他引:2
目的为了使当前加密系统具有更强的密钥敏感性以及更大的密钥空间,以提高抗各种攻击性能。方法提出一种新型的基于置乱神经元耦合混沌神经元的图像加密算法。加密系统的置乱和扩散由2个不同的3层神经构成,分别是置乱神经元层和混沌神经元层,混沌密钥生成模块则通过相应的权值和偏置来对这2层结构进行控制。在混沌神经元层扩散过程中,3个混沌系统用来生成权值矩阵和偏置矩阵,通过非线性标准化、按位异或操作来进行非线性组合,并通过Tent映射来进行激活,以获得扩散信息。在置乱神经元层置乱过程中,利用混沌密钥生成模块获取置乱矩阵,对扩散信息进行线性置乱处理,再通过二维Cat混沌映射对信息进行非线性置乱处理,并与当前加密算法进行对比。结果与当前加密算法相比,文中算法安全性更高,平均熵值为7.9991,且该加密算法的密钥空间大,为2160×1060,密钥敏感性强,错误与正确密钥之间的密文差异率为99.765%。结论设计的加密算法高度安全,可有效抗击各种攻击。 相似文献
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Wen Liu Kecheng Wei Juanjuan Xu Xu Ji 《Quality and Reliability Engineering International》2017,33(7):1337-1349
Chemical processes are complex dynamic systems. With the chemical industry under pressure to introduce improvements through the greater use of automation and intelligence, the need for comprehensive reliability evaluation has become more urgent both theoretically and practically. The employment of intelligent algorithms based on factory data has been the recent research hotspots. But for complex systems with available data on a smaller scale, reliability evaluation models have suffered on such problems as a result of instability and over‐fitting, which have to be resolved. The GRA–GA–BP–MCRC hybrid algorithm was proposed. It combined the two‐step genetic algorithm (GA)–back propagation (BP) and grey relational analysis (GRA) with Markov chain residual correction (MCRC). Based on the technical characteristics and the management demands, 46 influential factors of process reliability were introduced, which covered man, machine, material, method, and environment. For model convergence to be assured, GRA and attribute reduction rule were introduced. Meanwhile, based on the correlation of the factors, the two‐step GA–BP was proposed to resolve the over‐fitting problem of artificial neural network with complex input parameters. As well, MCRC was applied to modify the GA–BP error. The resulting average relative error of the hybrid algorithm was 2.36%, while the conventional algorithm was 10.28%. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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Farrukh Zia Isma Irum Nadia Nawaz Qadri Yunyoung Nam Kiran Khurshid Muhammad Ali Imran Ashraf Muhammad Attique Khan 《计算机、材料和连续体(英文)》2022,70(2):2261-2276
Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can train models with high accuracy. The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction (DR). The proposed deep learning framework utilizes the strength of selected models (VGG and Inception V3) by fusing the extracated features. To select the most discriminant features from a pool of features, an entropy concept is employed before the classification step. The deep learning models are fit for measuring the highlights as veins, liquid dribble, exudates, haemorrhages and miniaturized scale aneurysms into various classes. The model will ascertain the loads, which give the seriousness level of the patient’s eye. The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures. 相似文献
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Epigenetics is the study of phenotypic variations that do not alter DNA sequences. Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers. One of these alterations is DNA methylation; an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer. Therefore, studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences. Currently, microarray technologies; such as Illumina Infinium BeadChip assays; are used to study DNA methylation at an extremely large number of varying loci. At each DNA methylation site, a beta value (β) is used to reflect the methylation intensity. Therefore, clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers as well as identify the relevant loci without user bias. This study proposed a Nested Big Data Clustering Genetic Algorithm (NBDC-GA); a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites. The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas (TCGA); a cancer genomics program created by the National Cancer Institute (NCI) and the National Human Genome Research Institute. The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm (IGA) that was tested using the same data sets. The NBDC-GA outperformed the IGA in terms of convergence performance. Furthermore, the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximum of 67% and of 94.5% for individual cancer type and collective cancer, respectively. The proposed NBDC-GA was also able to identify two chromosomes with highly contrasting DNA methylations activities that were previously linked to cancer. 相似文献
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A new algorithm for obtaining extreme vertices designs for linear mixture models is proposed. The algorithm generally produces designs that are as efficient as those produced by the XVERT algorithm of Snee and Marquardt (1974) but with less computational effort. Use of the algorithm in obtaining designs is also described. 相似文献
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José Escorcia-Gutierrez Jordina Torrents-Barrena Margarita Gamarra Natasha Madera Pedro Romero-Aroca Aida Valls Domenec Puig 《计算机、材料和连续体(英文)》2022,70(2):2971-2989
Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm. 相似文献
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Adel Hamdan Mohammad Tariq Alwada’n Omar Almomani Sami Smadi Nidhal ElOmari 《计算机、材料和连续体(英文)》2022,73(1):133-150
Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system. 相似文献
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《IEEE transactions on instrumentation and measurement》1972,21(2):168-171
The feature selection problem associated with a reading machine for the blind is considered. A multistrategy concept is introduced to permit generalization of the feature set. An example is presented to illustrate the concept. 相似文献