检索结果-内蒙古大学图书馆

FFBP Neural Network Optimized with woodpecker mating algorithm for Dynamic Cluster-based Secure Routing in WSN

IETE JOURNAL OF RESEARCH 2024年第7期70卷 6515-6524页

作者： Saravanaselvan, A. Paramasivan, B. Natl Engn Coll Dept Elect & Commun Engn Kovilpatti Tamil nadu India Natl Engn Coll Dept Informat Technol Kovilpatti Tamil Nadu India

Feed Forward Back Propagation Neural Network (FFBPNN) Optimized with woodpecker mating algorithm is proposed in this manuscript for Dynamic Cluster-Based Secure Routing in Wireless Sensor Networks (FFBPNN-WMA-ECHC-WSN). The proposed method contains three different stages: first stage is dynamically clustered sensor network formation based on FFBPNN, second stage is Key generation for data encryption and decryption using Elliptic Curve-based Hill Cipher (ECHC), third stage is Homomorphic encryption scheme, which is used to deliver the aggregated data in-time. woodpecker mating algorithm (WMA) is proposed to optimize the parameter values of FFBPNN. The proposed FFBPNN-WMA ECHC-WSN method was implemented in NS2 tool and its efficiency is evaluated under some metrics, like alive nodes, packet drop, network lifetime, delay, throughput, and energy consumption, Packet Delivery Ratio (PDR). The performance of FFBPNN-WMA-ECHC-WSN method provides lower delay 99.01%, 98.34%, 95.23% and 97.45%, and higher throughput 97.25%, 90.12%, 89.39% and 95.47% compared with existing models, such as EHCERA-SDT-WSN, DSA-ECC-PSO-SDT-WSN, IPECC-PDF-ABC-SDT-WSN and IBFA-LDCSN-BSHHO-SDT-WSN, respectively.

关键词： Cluster-based secure routing Data encryption and decryption Feed forward back propagation neural network Wireless sensor networks woodpecker mating algorithm

来源：评论

学校读者我要写书评

暂无评论

Health Monitoring in IoT with Context-Aware Deep Convolutional Spiking Neural Network and woodpecker mating algorithm

引用

IETE JOURNAL OF RESEARCH 2024年第7期70卷 6494-6504页

作者： Arivalahan, R. Vinoth, T. SRM Valliammai Engn Coll Dept Elect & Elect Engn Kattankulathur 603203 Tamil Nadu India Chennai Inst Technol Dept Mechatron Engn Kundrathur 600069 Tamil Nadu India

In recent years, many context-sensitive approaches have been established to offer physiological information about the wellness of each person. While the patient's health is being monitored, there are delays in the cloud data transfer. To overcome these delays, a Deep Convolutional Spike Neural Network (DCSNN) with a woodpecker mating algorithm (WMA) is proposed in this manuscript for monitoring the healthcare data in the Internet of Things (IoT)-based context-aware architecture (DCSNN-WMA-HCM-IoT-CAA). Here, the input data are amassed from real-time datasets. Then the data are supplied to the pre-processing. The pre-processing data involve the assortment phase, data storage phase, and data redundancy phase. In the redundancy phase, the Kernel co-relation method is employed to remove the repeated data. The pre-processing output is given to the feature extraction. The feature is extracted under the fast discrete Curvelet transform method. After that, the extracted features are given to DCSNN optimized with WMA to classify regular, irregular, and critical conditions of the patient. The proposed DCSNN-WMA-HCM-IoT-CAA method is activated in OMNeT++. The proposed DCSNN-WMA-HCM-IoT-CAA approach attains 4.08%, 8.17%, 9.32%, and 5.17% high accuracy, 14.3%, 15.4%, 19.51%, and 27.81% lower computation Time and 12.29%, 15.36%, 11.55%, and 13.91% higher AUC compared with existing methods.

关键词： Accuracy Deep convolutional spiking neural network Emergency response time IOT-based context-aware architecture Real-time health care monitoring system woodpecker mating algorithm

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of microaneurysms using DeTraC deep convolutional neural network classifier with woodpecker mating algorithm

引用

IMAGING SCIENCE JOURNAL 2024年第8期72卷 1081-1092页

作者： Sherine, A. P. Wilfred Franklin, S. CSI Inst Technol Dept Elect & Commun Engn Thovalai 629302 Tamil Nadu India

Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with woodpecker mating algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties.

关键词： DeTrac deep convolutional neural network diabetic retinopathy diabetic-retinopathy-detection (DRD) dataset Kill Herd histogram equalization micro aneurysms optimizing tophat and homomorphic filtering retinal images woodpecker mating algorithm

来源：评论

学校读者我要写书评

暂无评论

GWMA: the parallel implementation of woodpecker mating algorithm on the GPU

引用

JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2022年第6期45卷 556-568页

作者： Gonga, Jianhu Parizi, Morteza Karimzadeh Guangdong Peizheng Coll Sch Data & Comp Sci Guangzhou Peoples R China Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran

The woodpecker mating algorithm (WMA) is a new metaheuristic algorithm (MA) with high efficiency in solving optimization problems. Nevertheless, WMA needs many search agents to explore the problem space when handling complex problems, resulting in a long computational time. This paper focuses on the acceleration of WMA for solving optimization problems with parallelization in the graphics processing unit (GPU) to improve the speedup and decrease the execution time of the WMA, thereby retaining the quality of the optimal outcomes. The proposed algorithm provides various mechanisms for generating random factors on the host system, bulk transfer of these factors to the GPU, parallel initialization and evaluation of the initial population on GPU, updating and evaluating the search agents' position. It also uses a decreasing mechanism to determine the best agent. The comparative evaluations of the parallel and serial WMA are based on a classical benchmark and CEC2017 test functions. In addition, the execution time of the GWMA is measured by other parallel MAs to represent the speed of the GWMA. The ultimate outcomes prove that the GWMA is run about 10-82 times quicker than the sequential WMA. Also, the GWMA is faster than other parallel algorithms in solving optimization problems.

关键词： Metaheuristic woodpecker mating algorithm parallel calculations GPU

来源：评论

学校读者我要写书评

暂无评论

Auto metric graph neural network optimized with woodpecker mating algorithm for detecting network layer attacks in mobile ad hoc networks

引用

SMART SCIENCE 2024年第2期12卷 305-318页

作者： Narayanan, Sivanesan Archana, K. S. Vels Inst Sci Sch Engn Technol & Adv studies VISTAS Chennai Tamil Nadu India Vels Inst Sci Sch Engn Technol & Adv Studies VISTAS Chennai Tamil Nadu India

Nowadays, Mobile Ad hoc Network (MANET) gains more attraction due to its seamless and dynamic use. The existing methods in MANET are plagued by some weaknesses, like higher computation time, undermining their real-time responsiveness. Whereas, lower accuracy in intrusion detection compromises the reliability of these systems, potentially leading to missed threats posing a challenge for effective security measures in dynamic MANET environments. To mitigate these weaknesses, an Auto Metric Graph Neural Network optimized with woodpecker mating algorithm is proposed in this paper for detecting Network Layer Attack in MANET (AGNN-WMA-DNLA-MANET). Initially, the data are gathered from CIC-IDS 2019 dataset. The gathered data undergoes pre-processing to eliminate redundancy and missing values replacement using the Local Least Squares method. Afterward, the preprocessing output is fed to AGNN for classification, which classifies the attack as active, passive, or normal. Then, woodpecker mating algorithm (WMA) is proposed to optimize the AGNN weight parameters to ensure the accurate classification. The proposed technique is executed in NS2 tool. The metrics, like accuracy, false positive rate (FPR) and attack detection rate (ADR), computation time, and RoC is analyzed. The proposed AGNN-WMA-DNLA-MANET approach provides 14.68%, 7.142%, and 4.65% higher accuracy for active attack, 15.19%, 2.77%, and 9.85% higher accuracy for passive attack and 38.18%, 12.02%, and 7.59% better accuracy for normal compared with existing methods, such as Dependable intrusion detection scheme under deep convolutional neural network (DCNN-MANET), deep learning-based intrusion detection scheme for MANET (AE-DNN-MANET), and Benchmarking of machine learning for anomaly based intrusion detection scheme (ML-MANET), respectively.

关键词： Mobile ad hoc networks security analysis woodpecker mating algorithm auto metric graph neural network CIC-IDS 2019 dataset

来源：评论

学校读者我要写书评

暂无评论

OWMA: An improved self-regulatory woodpecker mating algorithm using opposition-based learning and allocation of local memory for solving optimization problems

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期40卷 919-946页

作者： Parizi, Morteza Karimzadeh Keynia, Farshid bardsiri, Amid Khatibi Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the woodpecker mating algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the H alpha parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems.

关键词： Optimization metaheuristic woodpecker mating algorithm distance opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

HWMWOA: A Hybrid WMA-WOA algorithm with Adaptive Cauchy Mutation for Global Optimization and Data Classification

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第4期22卷 1195-1252页

作者： Zhang, Jiali Li, Haichan Parizi, Morteza Karimzadeh Guangzhou Coll Technol & Business Coll Engn Guangzhou 510800 Guangdong Peoples R China Software Engn Inst Guangzhou Dept Network Technol Guangzhou 510990 Guangdong Peoples R China Tech & Vocat Univ TVU Fac Shahid Chamran Dept Comp Engn Kerman Branch Kerman Iran

Combinatorial metaheuristic optimization algorithms have newly become a remarkable domain for handling real-world and engineering design optimization problems. In this paper, the Whale Optimization algorithm (WOA) and the woodpecker mating algorithm (WMA) are combined as HWMWOA. WOA is an effective algorithm with the advantage of global searching ability, where the control parameters are very less. But WOA is more probable to get trapped in the local optimum points and miss diversity of population, therefore suffering from premature convergence. The fundamental goal of the HWMWOA algorithm is to overcome the drawbacks of WOA. This betterment includes three basic mechanisms. First, a modified position update equation of WMA by efficient exploration ability is embedded into HWMWOA. Second, a new self-regulation Cauchy mutation operator is allocated to the proposed hybrid method. Finally, an arithmetic spiral movement with a novel search guide pattern is used in the suggested HWMWOA algorithm. The efficiency of the suggested algorithm is appraised over 48 test functions, and the optimal outcomes are compared with 15 most popular and newest metaheuristic optimization algorithms. Moreover, the HWMWOA algorithm is applied for simultaneously optimizing the parameters of SVM (Support Vector Machine) and feature weighting to handle the data classification problem on several real-world datasets from the UCI database. The outcomes prove the superiority of the suggested hybrid algorithm compared to both WOA and WMA. In addition, the results represent that the HWMWOA algorithm outperforms other efficient techniques impressively.

关键词： Whale Optimization algorithm woodpecker mating algorithm Cauchy mutation Support Vector Machine data classification

来源：评论

学校读者我要写书评

暂无评论

Recalling-enhanced recurrent neural network optimized with wood pecker mating algorithm for brain tumor classification

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第22期35卷 e7729-e7729页

作者： Suganthy, M. Ashok, S. Maheswari, A. Uma Subha, T. D. Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala En Dept Elect & Commun Engn Chennai Tamil Nadu India Anna Univ Chennai Anna Univ Tiruchirappalli Reg Off Dept Elect & Commun Engn Trichy Tamil Nadu India RMK Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India

Brain tumors are caused by the uncontrollable division and proliferation of abnormal cell groupings inside or around the brain. This cell grouping affects the function of brain activities and destroys healthy cells. Several methods have been used to detect the brain tumor, but none of the methods present adequate accuracy and increasing computational time. To overcome these issues, this article proposes recalling-enhanced recurrent neural network (RERNN) optimized with woodpecker mating algorithm for brain tumor classification (BTC) to accurately classify the four types of brain tumors, namely, glioma, meningioma, pituitary gland, and normal. The brain MRI images are collected from Brats MRI image data set. The simulation is activated in MATLAB. From the simulation, the proposed BTC-RE-RNN-WMA achieves better accuracy 29.98%, 26.74%, 33.27%, higher precision 19.24%, 34.82%, 26.92%, when comparing to the existing models, such as efficient identification with categorization of brain tumor utilizing kernel based SVM for MRI (BTC-KSVM-HHO), combined training of two-channel deep neural network for brain tumor categorization (BTC-JT-TCDNN), improved structure for brain tumor analysis utilizing MRI depending on YOLOv2 with convolutional neural network (BTC-YOLOv2-CNN) methods.

关键词： brain tumor classification glioma meningioma pituitary tumor recalling-enhanced recurrent neural network classifier visual geometry group network (VGG16) woodpecker mating algorithm

来源：评论

学校读者我要写书评

暂无评论

HSCWMA: A New Hybrid SCA-WMA algorithm for Solving Optimization Problems

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2021年第2期20卷 775-808页

作者： Parizi, Morteza Karimzadeh Keynia, Farshid Bardsiri, Amid Khatibi Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

关键词： Hybrid metaheuristic woodpecker mating algorithm sine cosine algorithm multi-layer perceptron software development effort estimation

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：