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Shear capacity of slender FRP-RC beams utilizing a hybrid ANN with the firefly optimizer

JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2024年

作者： Sharifi, Yasser Zafarani, Nematullah Vali E Asr Univ Rafsanjan Dept Civil Engn 22 Bahman Sq Rafsanjan 518 Iran

The popularity of fiber-reinforced polymer (FRP) bars as a structural element has soared due to their advantageous mechanical and physical properties. Despite an abundance of code requirements and heuristic equations, engineers specializing in structural retrofitting and analysis often struggle to utilize a suitable yet precise equation. This study introduces a novel approach by presenting a firefly optimization algorithm (FOA) combined with an artificial neural network (ANN)-termed as FOA-ANN-as an advanced hybrid machine learning model. The primary objective is to predict the shear capacity of slender FRP reinforced concrete (FRP-RC) beams without stirrup. An extensive experimental database of slender FRP-RC beams without stirrup was compiled. Leveraging this database and the proposed hybrid method, a simple yet accurate closed-form equation for determining the shear capacity of slender FRP-RC beams without stirrup was formulated. Additionally, a selection of pre-existing equations was provided for comparison of accuracy. Results indicate that the suggested FOA-ANN equation offers a more accurate alternative, outperforming equations derived from CSA S806-12 and AASHTO LRFD. The FOA-ANN hybrid technique proves to be highly effective in predicting the shear capacity of slender FRP-RC beams without stirrup.

关键词： Artificial neural network fiber-reinforced polymer fiber-reinforced polymer-reinforced concrete beams firefly optimization algorithm shear capacity prediction

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Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images

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IMAGING SCIENCE JOURNAL 2024年第8期72卷 1243-1262页

作者： Abirami, S. Lanitha, B. Karpagam Acad Higher Educ Dept Comp Sci & Engn Coimbatore 641021 Tamilnadu India

Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie firefly optimization algorithm (BBCFO). The BBCFO is the incorporation of Border Collie optimization (BCO), the firefly optimization algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.

关键词： Shepard convolutional neural network bald eagle search algorithm border collie optimization magnetic resonance imaging firefly optimization algorithm U-Net plus plus Shepard Convolutional Neural Network feature extraction

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TOWARD EFFECTIVE BREAST CANCER DETECTION IN THERMAL IMAGES USING EFFICIENT FEATURE SELECTION algorithm AND FEATURE EXTRACTION METHODS

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2024年第2期36卷

作者： Moayedi, Seyedeh Maryam Zareh Rezai, Abdalhossein Hamidpour, Seyedeh Shahrbanoo Fallaheye ACECR Inst Higher Educ Isfahan Branch Esfahan Iran Univ Sci & Culture Dept Elect Engn Tehran Iran

In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.

关键词： Computer aided detection system breast cancer feature selection firefly optimization algorithm thermography

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Intelligent Path Planning System for Shallow Submerged Unmanned Ship Based on MS-FA Method

Intelligent Path Planning System for Shallow Submerged Unman...

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IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)

作者： Zhao, Lei Zhi, Pengfei Zhu, Wanlu Qiu, Haiyang Wang, Hui Wang, Weiran Jiangsu Univ Sci & Technol Sch Elect & Informat Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781665434416

Multi-Sensorfusion (MS) has the characteristics of acquiring accurate and real-time information. firefly algorithm (FA) has strong local search capabilities, and it has certain advantages to find the optimal path, but its solution speed is slow. Therefore, on the basis of optimizing the firefly algorithm, this paper proposes a shallow submersible unmanned ship intelligent path planning system based on the combination of multi-sensor information fusion (MS) and firefly optimization algorithm (FA). This system solves the problem of traditional unmanned ship path planning, and can achieve the expected effect of shallow submersible unmanned ship avoiding underwater obstacles and shortening the path planning time and distance. Use the machine vision and sonar sensors on the unmanned ship to obtain real-time data for information fusion, and establish a real-time model of the environment. In addition, the FA algorithm is optimized to increase the calculation speed. After that, the real-time model based on MS is combined with the FA optimization algorithm to form a shallow submersible unmanned ship intelligent path planning system. Finally, using Matlab as a simulation tool, based on the establishment of a real-time water environment model, the method proposed is verified through simulation analysis.

关键词： shallow submersible unmanned ship data fusion firefly optimization algorithm intelligent path planning

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Research on the Combination of firefly Intelligent algorithm and Asphalt Material Modulus Back Calculation

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MATERIALS 2022年第9期15卷 3361-3361页

作者： Zhao, Runmin Gong, Jinzhi Zheng, Yangzezhi Huang, Xiaoming Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Transportat Bur Jinhu Huaian 211600 Peoples R China Southeast Univ Natl Demonstrat Ctr Expt Educ Rd & Traff Engn Nanjing 211189 Peoples R China

The modulus of asphalt pavement material is a necessary parameter for the design, strength measuring and stability evaluation of asphalt pavement. To get more precise test data for asphalt pavement material modulus, a new modulus back calculation method is proposed in this article, named as the firefly Asphalt Back Calculation Method (FABCM). This novel method uses the firefly optimization algorithm, which is a kind of particle swarm intelligence algorithm imitating the information transfer process among fireflies. To demonstrate the reliability and stability of FABCM, and to study the feasibility of multi-parameter modulus back calculation methods, this article used theoretical deflection curves calculated by BISAR3.0 and the actual measurement data of deflection curves and vertical pressures on the subgrade top surfaces on the full-scale test circular track in the Research Institute of Highway, Ministry of Transport (RIOHTrack) to conduct a modulus back calculation. The results show that FABCM only takes 0.5-1 s for each calculation, and the back calculation errors in the verification of FABCM are mostly smaller than 1%, which means that the firefly optimization algorithm was modified effectively in this article. Moreover, this article also indicates some key factors influencing the accuracy of modulus back calculation, and several reasonable suggestions to the application of modulus back calculation.

关键词： back calculation of modulus Falling Weight Deflectometer (FWD) firefly optimization algorithm RIOHTrack

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A novel discrete firefly algorithm for Bayesian network structure learning

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KNOWLEDGE-BASED SYSTEMS 2022年 242卷 108426-108426页

作者： Wang, Xianchang Ren, Hongjia Guo, Xiaoxin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Chengdu Kestrel Artificial Intelligence Inst Chengdu 610000 Peoples R China

As an effective tool for the representation and reasoning of uncertain theories, Bayesian networks are widely used in various fields of artificial intelligence. However, learning the optimal Bayesian network structure is an NP-hard problem. The score-and-search approach is a common method for solving the problems associated with Bayesian network structure learning. This paper presents a novel method for learning the structure of a Bayesian network using a discrete firefly optimization algorithm, which has been successfully applied to solve various optimization problems. In the proposed algorithm, each firefly moves in a discrete space based on a redefined movement strategy. Then, the mutation operator is employed to prevent the algorithm from stopping prematurely and falling into a local optimum. Finally, a local optimizer is used to enhance the exploitation ability of the firefly to obtain the best feasible solution. We compared the proposed algorithm with state-of-the-art algorithms on well-known benchmark networks. The experimental results show that the proposed algorithm has better convergence accuracy and higher scores in most cases, compared to other algorithms, indicating that the proposed algorithm can be used as an effective and feasible method for learning Bayesian network structures. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

关键词： Bayesian networks Structure learning firefly optimization algorithm

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Quality improvement and performance analysis of High Efficiency Video Coding under high quantization parameters and rain streaks

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SIGNAL IMAGE AND VIDEO PROCESSING 2020年第2期14卷 387-395页

作者： Thiyagarajan, J. Gowri Shankar, C. KSR Coll Engn Fac Elect & Elect Engn Namakkal India

High Efficiency Video Coding standard is an advanced and widely used video coding standard in recent high-definition (HD) video application. This video codec faces many challenges during the video transmission over lower bandwidth channel. One of the main challenges is the noise removal from HD video sequences. For noise removal, this video codec has in-loop filters specifically. Although these loop filters are efficient, they are not effective in removing rain pattern from the video sequences. To overcome this problem, a denoising filter has to be added to the loop filters. For denoising, an optimized multi-delay block frequency domain (OMBFD) adaptive filter is presented in this paper. Based on the intensity of raindrop, it is identified from the successive video frames. Then, these predicted raindrops are removed using the existing loop filters. Nevertheless, still, noises present in the frame are removed using the proposed OMBFD filter. To control the process of filtering, the exponential forgetting factor which is used in the proposed denoising filter is optimized using firefly optimization algorithm. Simulation results show the proposed denoising algorithm affords promising performance improvement compared to other denoising filters.

关键词： High Efficiency Video Coding (HEVC) standard Quantization parameters (QPs) Optimized multidelay block frequency domain (OMBFD) adaptive filter firefly optimization algorithm

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firefly algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第7期13卷 461-468页

作者： Mahfuz, Nurshazwani Muhamad Yusoff, Marina Nordin, Muhammad Shaiful Ahmad, Zakiah Univ Teknol MARA Fac Comp & Math Sci Shah Alam Malaysia Math Sci Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence Fac Comp & Math Sci Shah Alam Malaysia Malaysian Timber Ind Board Menara PGRM Jalan Pudu Ulu Kuala Lumpur 56100 Malaysia Univ Teknol MARA Coll Engn Shah Alam Malaysia

clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to high-quality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch k-means (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.

关键词： Clustering mini batch k-means entropy heterogeneous categorical firefly optimization algorithm

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Optimal Cluster-Based Topology and Deep LSTM-Based Prediction Method for Data Reduction in IoT

Journal of Uncertain Systems

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Journal of Uncertain Systems 2023年第3期16卷

作者： Jagdale, B.N. Sugave, Shounak Rushikesh Kulkarni, Yogesh R. Faculty at School of CET Dr. Vishwanath Karad MIT World Peace University Pune India

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the firefly optimization algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001s, 0.161J and 99.054%, respectively. © 2023 World Scientific Publishing Company.

关键词： cluster Data aggregation deep long short-term memory firefly optimization algorithm water cycle algorithm

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Path Planning of Shallow Submersible Unmanned Ship Based on MSFA Method

Path Planning of Shallow Submersible Unmanned Ship Based on ...

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第32届中国过程控制会议(CPCC2021)

作者： Lei Zhao Pengfei Zhi Wanlu Zhu Haiyang Qiu Hui Wang Weiran Wang School of Electronic and Information Engineering Jiangsu University of Science and Technology

In order to enable the shallow submersible unmanned ship to quickly and accurately collect water area information when working in unknown waters,carry out real-time path planning,shorten the planned path,and solve the limitations of traditional unmanned ship path *** paper proposes a shallow submersible unmanned ship path planning method based on multi-sensor information fusion and firefly optimization algorithm(MSFA).First of all,real-time information of the water area is acquired through the sensors mounted on the shallow-submersible unmanned ship,information fusion is carried out,and real-time modeling of the water area ***,the firefly algorithm is optimized to increase the computing speed to meet the needs of real-time path ***,the real-time model of the water environment based on multi-sensor information fusion is combined with the firefly optimization algorithm to form a shallow submersible unmanned ship path planning based on MSFA ***,the method proposed by me was verified by simulation analysis.

关键词： shallow submersible unmanned ship data fusion firefly optimization algorithm route plan

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