检索结果-内蒙古大学图书馆

Artificial immune systems based multi-agent architecture to perform distributed diagnosis

JOURNAL OF INTELLIGENT MANUFACTURING 2019年第4期30卷 2025-2037页

作者： Rocha, Andre Dionisio Lima-Monteiro, Pedro Parreira-Rocha, Mafalda Barata, Jose Univ Nova Lisboa FCT Dept Engn Electrotecn Uninova CTS P-2825149 Caparica Portugal

Cyber-physical systems (CPS) emerge as a new idea to implement new manufacturing paradigms. There paradigms aim at answering the socio-economic factors that characterise modern enterprises, such as mass customisation and new markets. The authors propose an architecture that performs distributed diagnosis. The proposed solution uses artificial immune systems (AIS) to perform evolutionary diagnose. Industrial approaches to machine diagnosis are centralised. The authors pretend to make a CPS capable of distributed diagnosis with learning capabilities. An architecture capable of machine diagnosis and learning is also presented. This is done by bio-inspired algorithms. These were rated by a fuzzy inference system. The algorithms were tested for situations a system may endure and for their learning capability. The results of the obtained research, study and development are hereby presented. These results constitute proof of the sustainability of the AIS paradigm as a solution to distributed diagnosis.

关键词： Artificial immune systems Cyber-physical systems Multi-agent systems bio-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

CryptoHHO: a bio-inspired cryptosystem for data security in Fog-Cloud architecture

引用

JOURNAL OF SUPERCOMPUTING 2024年第11期80卷 15834-15867页

作者： Jawed, Md Saquib Sajid, Mohammad Aligarh Muslim Univ Dept Comp Sci Aligarh India

The exponential growth of Internet-of-Things (IoT) has raised several data security risks to the Fog-Cloud architecture. The performance and the computation cost of security algorithms hinder providing a secure real-time environment for IoT. This study proposes a novel two-layer cryptosystem, Cryptographic Harris Hawks Optimization (CryptoHHO), for Fog-Cloud architecture that reduces security overheads while maintaining confidentiality, integrity, and availability. The first layer of the proposed CryptoHHO is responsible for generating a highly randomized key using HHO to optimize Shannon entropy incorporation with transfer functions and a binarization mechanism. The second layer of CryptoHHO introduces a novel encipher model for encryption and decryption based on the Shift cipher, XOR operator, and an instance of crossover and mutation. The job execution avenue, i.e., Fog or cloud computing, is selected depending on the size of IoT requests, security sensitivity, and time sensitivity. The performance of CryptoHHO is compared against other emerging bio-inspired cryptographic algorithms. It was found that the CryptoHHO performs better than CryptoSSA, CryptoGWO, CryptoPSO, and CryptoWOA algorithms based on entropy, key generation time, transfer function comparison, execution time, and throughput. Further, the robustness of CryptoHHO is examined by various security analyses like brute-force attack resistivity, confusion-diffusion, CIA achievement, and statistical evaluations suggested by NIST and FIPS.

关键词： Internet-of-Things Fog computing Cloud computing bio-inspired algorithms Entropy Symmetric key cryptography Data security

来源：评论

学校读者我要写书评

暂无评论

Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing Microarray data Cancer classification bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

A Novel Model to Predict the Effects of Enhanced Students' Computer Interaction on Their Health in COVID-19 Pandemics

引用

NEW GENERATION COMPUTING 2023年第3期41卷 635-668页

作者： Agarwal, Nidhi Mohanty, Sachi Nandan Sankhwar, Shweta Dash, Jatindra Kumar Indira Gandhi Delhi Tech Univ Women Dept Comp Sci & Engn Delhi India Galgotias Univ Dept Comp Sci Engn Greater Noida India Vardhaman Coll Engn Autonomous Dept Comp Sci Engn Hyderabad Telengana India Univ Delhi Maitreyi Coll Dept Comp Sci Bapu Dham New Delhi India SRM Univ Dept Comp Sci & Engn Amaravati Andhra Pradesh India

During the COVID-19 pandemic time, educational institutions have really played a good role in imparting online education to students. Their career and academic tenure were not affected as contrary to the past pandemics throughout world history. All this has been possible through long sessions of classes, quizzes, assignments, discussions, chat interactions, and examinations through online video-based learning using computer interactive measures. The students were privileged to utilize digital technologies for longer durations for learning purposes. However, these long stretches have adversely affected their body postures, and physical and mental health as they majorly remain confined to chairs with restricted levels of physical activities. Thus, there is a need to have a model which can act as an insight for parents, doctors (pediatricians), and academic policymakers to decide on maximum hours for online teaching and related activities during future pandemics. The novel model proposed in this work helps to predict the impact of enhanced students' computer interactions on their physical and mental health. The method proposed uses a novel model which is advanced and computationally strong. The model follows a two-step methodology, where at the first level, a variant of already existing machine learning algorithm is proposed and at the next level, it is optimized further using a hybrid bio-inspired optimization algorithm. The model consists of proposing a variant of XGBoost model (step1 optimization) followed by a hybrid bio-inspired algorithm (step2 optimization). The work considers a humongous dataset with varied age groups of students with more than 10 attributes. The proposed model is highly efficient in making predictions with 98.07% accuracy level and 98.43% F1-score. The time complexity of the model obtained is also of order of "n" where "n" depicts the number of input variables. Strong empirical results for other parameters also like specificity (95.63%) and se

关键词： COVID-19 Effect on health Machine learning Academia Students bio-inspired algorithms XGBoost model Heuristic approach

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cancer Classification through a Hybrid bio-inspired Evolutionary Algorithm for biomarker Gene Selection

引用

Computers, Materials & Continua 2024年第4期79卷 675-694页

作者： Hala AlShamlan Halah AlMazrua Department of Information Technology College of Computer and Information SciencesKing Saud UniversityP.O.Box 145111Riyadh4545Saudi Arabia

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature *** for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization *** aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer *** selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray *** proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is *** hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of *** outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment *** proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer *** improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification *** harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

关键词： bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression grey wolf optimizer harris hawks optimization k-nearest neighbor support vector machine

来源：评论

学校读者我要写书评

暂无评论

A review on the coral reefs optimization algorithm: new development lines and current applications

引用

PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第1期6卷 1-15页

作者： Salcedo-Sanz, S. Univ Alcala Escuela Politecn Super Dept Signal Proc & Commun Madrid 28871 Spain

The simulation of biological processes has produced some of themost important meta-heuristics algorithms for optimization. Evolutionary algorithms were the first, and probably the most applied, algorithms coming from biological inspiration, but there have been many more, specially in the last few years. This paper describes a special class of evolutionary algorithms recently proposed, the coral reefs optimization algorithm (CRO), which simulates some specific biological processes that occur in real coral reefs. The simulation of these processes leads to an evolutionary algorithm in which similarities with Simulated Annealing have been introduced. Moreover, the inclusion of alternative processes occurring in coral reefs produces very effective co-evolution versions of the CRO algorithm, specially well suited for optimization problems with inherent variable length encodings, or able to co-evolve several exploration patterns within the same population. All these issues related to the CRO approach are thoroughly described in the paper, and also a fully description of the main applications of the algorithm in engineering optimization problems is given to close this first review on the CRO.

关键词： Coral reefs optimization algorithm Meta-heuristics bio-inspired algorithms Applications

来源：评论

学校读者我要写书评

暂无评论

The Anglerfish algorithm: a derivation of randomized incremental construction technique for solving the traveling salesman problem

引用

EVOLUTIONARY INTELLIGENCE 2019年第1期12卷 11-20页

作者： Pook, Mei F. Ramlan, Effirul I. Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Nat Comp Lab Kuala Lumpur 50603 Malaysia Minist Sci Technol & Innovat MOSTI Natl Inst Biotechnol Malaysia Malaysia Genome Inst Jalan Bangi Kajang 43000 Selangor Malaysia Univ Malaya Ctr Res Computat Sci & Informat Biol Bioind Envir Kuala Lumpur 50603 Malaysia

Combinatorial optimization focuses on arriving at a globally optimal solution given constraints, incomplete information and limited computational resources. The combination of possible solutions are rather vast and often overwhelms the limited computational power. Smart algorithms have been developed to address this issue. Each offers a more efficient way of traversing the search landscapes. Critics have called for a realignment in the bio-inspired metaheuristics field. We propose an algorithm that simplifies the search operation to only randomized population initialization following the Randomized Incremental Construction Technique, which essentially compartmentalizes optimization into smaller sub-units. This relieves the need of complex operators normally imposed on the current metaheuristics pool. The algorithm is more generic and adaptable to any optimization problems. Benchmarking is conducted using the traveling salesman problem. The results are comparable with the results of advanced metaheuristic algorithms. Hence, suggesting that arbitrary exploration is practicable as an operator to solve optimization problems.

关键词： Combinatorial optimization bio-inspired algorithms Randomized incremental construction Traveling salesman problem

来源：评论

学校读者我要写书评

暂无评论

bio-inspired machine learning: programmed death and replication

引用

NEURAL COMPUTING & APPLICATIONS 2023年第27期35卷 20273-20298页

作者： Grabovsky, Andrey Vanchurin, Vitaly Budker Inst Nucl Phys Novosibirsk 630090 Russia Novosibirsk State Univ Novosibirsk 630090 Russia Natl Ctr Biotechnol Informat NIH Bethesda MD 20894 USA Duluth Inst Adv Study Duluth MN 55804 USA

We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop machine learning algorithms for adding neurons to the system (i.e., replication algorithm) and removing neurons from the system (i.e., programmed death algorithm). We argue that the programmed death algorithm can be used for compression of neural networks and the replication algorithm can be used for improving performance of the already trained neural networks. We also show that a combined algorithm of programmed death and replication can improve the learning efficiency of arbitrary machine learning systems. The computational advantages of the bio-inspired algorithms are demonstrated by training feedforward neural networks on the MNIST dataset of handwritten images.

关键词： Machine learning Neural networks bio-inspired algorithms Neuron correlations Pruning algorithms Constructive algorithms Classification

来源：评论

学校读者我要写书评

暂无评论

The Receptor Density Algorithm

引用

THEORETICAL COMPUTER SCIENCE 2013年 481卷 51-73页

作者： Owens, Nick D. L. Greensted, Andrew Timmis, Jon Tyrrell, Andy Univ York Dept Elect York YO10 5DD N Yorkshire England

This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem. (C) 2012 Elsevier B.V. All rights reserved.

关键词： bio-inspired algorithms Artificial immune systems T cell signalling Kernel density estimation Anomaly detection Exponential smoothing Weighted moving average Recurrent equations

来源：评论

学校读者我要写书评

暂无评论

Policy-constrained bio-inspired processes for autonomic route management

引用

COMPUTER NETWORKS 2009年第10期53卷 1666-1682页

作者： Balasubramaniam, Sasitharan Botvich, Dmitri Jennings, Brendan Davy, Steven Donnelly, William Strassner, John Waterford Inst Technol TSSG Waterford Ireland

Autonomic networking systems must be designed to achieve an appropriate balance between the operation of decentralized algorithms and processes that seek to maintain optimal or near-optimal behavior in terms of global stability, improved performance and adaptability, robustness and security, with the requirement for top-down control of the system by humans to ensure business goals are met. Taking a communications networking survivability case study, we show how the operation of decentralized algorithms, inspired by the operation of biological systems, can be controlled and constrained through the deployment of management policies authored by network administrators. We present survivability-related routing algorithms (inspired by chemotaxis, reaction-diffusion and quorum sensing biological processes) which work together to effectively reconfigure network resources when transient link failures occur and demonstrate how these algorithms can be re-parameterized via policies to improve performance given prevailing network conditions. Simulation results show how the combined operation of these algorithms, as controlled by policies, allows the network to react well to survive link failure events. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Autonomic networking Autonomic communications bio-inspired algorithms Policy-based network management Survivability Routing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：