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CSA-DE-LR: enhancing cardiovascular disease diagnosis with a novel hybrid machine learning approach

PEERJ COMPUTER SCIENCE 2024年 10卷 e2197-e2197页

作者： Dedeturk, Beyhan Adanur Dedeturk, Bilge Kagan Bakir-Gungor, Burcu Abdullah Gul Univ Dept Comp Engn Kayseri Turkiye Erciyes Univ Dept Software Engn Kayseri Turkiye

Cardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DELR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study's findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis.

关键词： Cardiovascular diseases Machine learning clonal selection algorithm Differential evolution Logistic regression Medical diagnostics

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Local feature selection based on artificial immune system for classification

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APPLIED SOFT COMPUTING 2020年 87卷 105989-000页

作者： Wang, Yi Li, Tao Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Conventional feature selection algorithms select a global feature subset for the entire sample space. In contrast, in this paper we propose an efficient filter local feature selection algorithm based on artificial immune system, which assigns a locally relevant feature subset for each neighboring region of the sample space. This algorithm introduces a clonal selection algorithm to explore the search space for the optimal feature subsets, and adopts local clustering idea as an evaluation criterion that maximizes the inter-class distance and minimizes the intra-class distance in the small region of each sample. Experimental results on a wide variety of synthetic and UCI datasets demonstrates that our proposed method achieves better performance than both state-of-the-art global feature selection algorithms and local feature selection algorithms. In addition, a main parameter analysis of the proposed method is carried out. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Local feature selection Artificial immune system clonal selection algorithm Dimensionality reduction Classification

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A Nonlinear Optimal Iterative Learning Control algorithm Based on RBF Neural Network and clonal selection algorithm

A Nonlinear Optimal Iterative Learning Control Algorithm Bas...

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3rd International Conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Li, Hengjie Hao, Xiaohong Pei, Xiping Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9783037857649

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration;another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

关键词： clonal selection algorithm RBF neural networks Iterative Learning Control

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Probabilistic assessment of transport network vulnerability with equilibrium flows

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INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 2021年第7期15卷 512-523页

作者： Jiang, Yu Wang, Yi Szeto, W. Y. Chow, Andy H. F. Nagurney, Anna Tech Univ Denmark Transport DTU Lyngby Denmark Singapore ETH Ctr ETH Zurich Future Resilient Syst Singapore Singapore Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Univ Massachusetts Isenberg Sch Management Dept Operat & Informat Management Amherst MA 01003 USA

This article develops a probabilistic approach for assessing transport network vulnerability. A novel performance measure is proposed to evaluate the expected impact when multiple transport network components fail simultaneously at various degrees. The proposed measure captures both the likelihood and consequence of a combination of transport network component failures. The most critical combination of transport network component failures is obtained by solving a bi-level optimization problem. The upper-level problem is to solve for the combination of transport network components together with their corresponding disruption levels, which induces the maximum reduction in the performance measure. The lower-level problem is to capture the response of travelers to network changes due to network component failures and is formulated as a traffic assignment problem. The clonal selection algorithm (CSA), a biologically inspired approach, is adopted to tackle the proposed bi-level optimization problem. Numerical results indicate that neglecting partial capacity degradation and its probability of occurrence could misestimate the worst scenario, and different vulnerability assessment approaches could identify similar critical components but our approach can discover some components that are not found by other existing approaches. Moreover, it is shown that the CSA outperforms the well-known genetic algorithm in terms of solution quality in a large network.

关键词： Bi-level optimization clonal selection algorithm genetic algorithm transport network vulnerability

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Intelligent colour matching method for 3D character animation based on texture features

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INTERNATIONAL JOURNAL OF ARTS AND TECHNOLOGY 2021年第2期13卷 123-136页

作者： Tang, Fei Univ Sci & Technol Liaoning Sch Architecture & Art Design Anshan 114051 Peoples R China

In order to overcome the problems of low matching degree and long matching time in traditional intelligent colour matching methods of 3D character animation, this paper proposes a colour intelligent matching method based on texture features. Three parameters H, S and V are taken as the main parameters of 3D character animation colour recognition, and the animation colour is compared with the colour quantisation template. According to the comparison results and colour difference formula, the animation colour recognition is realised. On this basis, SIFT algorithm is used to obtain colour features of 3D character animation, and clonal selection algorithm is used to realise colour intelligent matching of 3D character animation. The experimental results show that the accuracy rate of colour feature extraction is always above 96%, the colour matching degree is always above 97%, and the average colour matching time is 0.4 s. The practical application effect is good.

关键词： texture features 3D character animation animation colour intelligent matching clonal selection algorithm

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Selecting effective features on prediction of delay in servicing ships arriving to ports using a combination of clonal selection and Grey Wolf Optimization algorithms-Case study: Shahid Rajaee port in Bandar Abbas

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COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 1140-1160页

作者： Golzari, Shahram Haji, Mojtaba Khalili, Abdullah Univ Hormozgan Dept Elect & Comp Engn Bandar Abbas Iran

Predicting the delay in servicing incoming ships to ports is crucial for maritime transportation. In this study, we use support vector regression (SVR) in order to accurately predict this delay for ships arriving to the terminal No. 1 of Shahid Rajaee's port in Bandar Abbas. To achieve this goal, a combination of clonal selection and Grey Wolf Optimization algorithms (named as CLOGWO) is used for two purposes: (i) selecting the most important features among the features that affect prediction of this delay and (ii) optimizing SVR parameters for a more accurate prediction. Performance of the proposed method was compared with Genetic algorithm (GA), clonal selection (CS), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) algorithms on the following metrics: correlation, rate of feature reduction, root mean square error (RMSE), and normalized RMSE (NRMSE). Evaluations on Shahid Rajaee dataset showed that the mean value of these metrics in 10 independent runs of the proposed method were 0.867, 74.45%, 0.080, and 9.02, respectively. These results and evaluations on standard datasets indicate that the proposed method provides competitive results with other evolutionary algorithms.

关键词： clonal selection algorithm feature selection Grey Wolf Optimization maritime transportation support vector regression

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A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2021年第1期15卷 1-25页

作者： Dung Hoang Le Nguyen Thanh Vu Tuan Dinh Le Vietnam Natl Univ Univ Informat Technol Hanoi Vietnam Ho Chi Minh City Univ Food Ind Ho Chi Minh City Vietnam Long An Univ Econ & Ind Tan An City Long An Vietnam

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system, in which an artificial immune network is used as a pool to create and develop virus detectors that can detect unknown data. Besides, a deep learning model is also used as the main classifier because of its advantages in binary classification problems. This method can achieve a detection rate of 99.08% on average, with a very low false positive rate.

关键词： Artificial Immune Network Artificial Immune System clonal selection algorithm Deep Belief Network Negative selection algorithm Portable Executable

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An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

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ENERGIES 2022年第12期15卷 4398页

作者： Demertzis, Konstantinos Taketzis, Dimitrios Demertzi, Vasiliki Skianis, Charalabos Hellen Open Univ Informat Studies Sch Sci & Technol Patras 26335 Greece Hellen Natl Def Gen Staff Mesogeion 227-231 Athens 15561 Greece Int Hellen Univ Dept Comp Sci Kavala Campus Thessaloniki 57001 Greece Univ Aegean Dept Informat & Commun Syst Engn Karlovassi 83200 Greece

The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks' efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem's significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. The most significant findings of this work are that the transfer learning architecture's shared learning rate significantly adds to the speed of generalization and convergence approach. In addition, the ensemble combination improves the accuracy of the model because the overall behavior of the numerous models is less noisy than a comparable individual single model. Finally, the Izhikevich Spiking Neural Network used here, due to its dynamic configuration, can reproduce different spikes and triggering behaviors of neurons, which models precisely the problem of digital security of energy infrastructures, as proved experimentally.

关键词： smart energy grids critical infrastructure protection artificial immune system Izhikevich spiking neural networks clonal selection algorithm transfer learning ensemble learning

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One run to attack them all: finding simultaneously multiple targeted adversarial perturbations

One run to attack them all: finding simultaneously multiple ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Custode, Leonardo Lucio Iacca, Giovanni Univ Trento Dept Informat Engn & Comp Sci Trento Italy

ISBN: (纸本)9781728190488

Deep neural networks are ubiquitous in computer vision applications such as autonomous vehicles, face recognition, and medical imaging. However, deep neural networks may be subject to adversarial perturbations (also called adversarial attacks) which, when performed in high-stakes environments, may lead to severe consequences. A particular class of adversarial attacks, called universal adversarial attacks, consists of attacks that do not fool a single image. Instead, they aim to fool as many images as possible. Universal adversarial attacks may be either untargeted or targeted. While the goal of an untargeted attack is to deceive the neural network under attack without caring about its output, a targeted attack aims to "steer" the predictions of the neural network towards a specific target. In this work, we propose a method, based on the clonal selection algorithm, to generate targeted universal adversarial attacks for multiple target classes in a single run of the algorithm. Our results show that our approach is able to find multiple targeted universal adversarial attacks for the network under attack, with good success rates. Moreover, by visualizing the attacked images, we observe that our algorithm is able to produce adversarial attacks that fool the network by attacking a relatively small number of pixels. Finally, by analyzing how the attacks generalize on different architectures, we observe intriguing properties of the discovered universal adversarial attacks.

关键词： Adversarial attacks clonal selection algorithm deep learning

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A full-featured cooperative coevolutionary memory-based artificial immune system for dynamic optimization

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APPLIED SOFT COMPUTING 2022年第0期117卷 108389-108389页

作者： Etaati, Bahareh Ghorrati, Zahra Ebadzadeh, Mohammad Mehdi Amirkabir Univ Technol Dept Comp Engn & Informat Technol 424 Hafez Ave Tehran 15914 Iran Purdue Univ Comp & Informat Technol M2M Lab W Lafayette IN 47907 USA

In this paper, a novel cooperative coevolutionary memory-based artificial immune system enhanced by a new clonal selection algorithm is proposed for dynamic optimization problems. In the proposed algorithm, the whole n-dimensional population is decomposed into n one-dimensional subpopulations. Then, each subpopulation is evaluated separately using a set of context vectors called short-term memory. Also, inspired by the production of new cells in bone marrow, each subpopulation is divided into multiple regions to track and locate multiple optima cooperatively. This division helps the algorithm exploit search space effectively. Additionally, inspired by the immune memory concept, a memory-based approach called long-term memory is proposed to store and retrieve essential information when a fitness change occurs. Furthermore, a new clonal selection method, a combination of negative selection and clonal selection mechanisms, is proposed. This proposed algorithm is faster than the basic clonal selection algorithm. Finally, compared to other immune-based algorithms, which usually are implemented based on one or two qualities of the biologic immune system, the proposed approach exploit almost all immune qualities. Several experiments are conducted on different configurations of the moving peaks benchmark to examine the efficiency of the proposed method. The experimental results confirm that the proposed method is competitive with other state-of-the-art algorithms to optimize dynamic problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： clonal selection algorithm Negative selection mechanism Cooperative approach Artificial immune system Dynamic optimization

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