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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Zhou, Chengxu Wang, Dongxia Cai, Haoran School of Electronics and Information Engineering Liaoning University of Technology China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Jinzhou Dalian China School of Electronics and Information Engineering Tianjin University of Technology and Education Tianjin China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China

ISBN: (纸本)9798350332421

Smoke is the key feature of fire detection in its early age. Thus, an efficient smoke detection approach (i. e. an accurate and rapid method) is essential important to prevent fires. However, it is difficult to obtain an efficient method due to non-obvious details and monotonous color of smoke images. Moreover, traditional methods of smoke detection based on CNN contains lots of parameters and operations, which severely influents the computing efficiency in practical applications. Thus, we propose an efficient dual-channel neural network (EDCNN) on the basis of the state-of-The-Art DCNN. Concretely, we use the linear inverted bottleneck (LIB) to replace the traditional convolution layers on DCNN to build a light weight deep neural network. The introduction of the LIB block can efficiently trade off between accuracy and latency. Moreover, ReLU6 is used as the activation function, because it is more suitable for low-precision hardware devices. We then use some experimental results to demonstrate the effectiveness of EDCNN compared with the competitors for smoke detection in terms of model and computational complexity. © 2022 IEEE.

关键词： Smoke

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Adamr-Grus: Adaptive Momentum-Based Regularized Gru for Hmer Problems

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SSRN 2022年

作者： Pal, Aniket Singh, Krishna Pratap Machine Learning & optimization lab Department of Information technology Indian Institute of Information Technology Allahabad India

Handwritten Mathematical Expression Recognition(HMER) which has many applications in online education & scientific research. Recognizing the length & characters of each mathematical expression is a challenging task. In this work, we have used encoder-decoder architecture and proposed adaptive momentum-based regularized GRU(AdamR-GRU). Experiments are carried out on CROHME 2014,2016 & 2019 datasets. Results show that the proposed model performance is comparable to state of the art for exprate & it is better than others for exprate(≤ 1%,≤ 2%,≤ 3%). Also, proposed model has fewer parameters compare to other models. © 2022, The Authors. All rights reserved.

关键词： Momentum

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Detecting Community Through User Similarity Analysis on Twitter

Detecting Community Through User Similarity Analysis on Twit...

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International Conference on Ubiquitous information Management and Communication (IMCOM)

作者： Md Ahsan Ul Hasan Azuraliza Abu Bakar Mohd Ridzwan Yaakub Sentiment Analysis Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Data Mining and Optimization Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia

Community detection in social relationship networks is a significant research domain within the field of social network analysis. However, Twitter sets itself apart from conventional friends’ and followers’ networks due to its users’ distinct focus on sharing thoughts and ideas related to specific topics rather than prioritising personal connections. As a result, relying solely on friends’ and followers’ relations in Twitter networks for community detection would be insufficient and incomplete. In this paper, we proposed a Twitter community detection method that measures similarity using user profiles, tweet subjects, and tweet sentiment in this research. We constructed a similarity network using real-time Twitter API data and compared it to Zachary's Karate Network. The proposed approach enhances the quality function of modularity and conductance in the Louvain community detection algorithm, resulting in a more precise and meaningful community structure within the Twitter network.

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SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation

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Journal of Applied Remote Sensing 2021年第1期15卷

作者： Wang, Deyi Han, Min Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Ganjingzi District Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Ganjingzi District Dalian China

Marine floating raft aquaculture (FRA) monitoring is vital for environment protection and mariculture management. Synthetic aperture radar (SAR) could provide high-quality remote sensing images under all weather conditions compared with the existing optical remote-sensing-based methods. Traditional SAR monitoring methods extract the pixel feature of marine FRA in single patches, which commonly leads to poor generalization. We propose a self-Attention semantic segmentation method based on modified U-Net++ (SA-U-Net++) for FRA segmentation, which could automatically extract semantic feature information, and provide superior performance under complicated scenes. The proposed self-Attention backbone could help to extract more precise features and enhance the overall accuracy. Furthermore, we propose a FRA-ISAR data generation method based on inverse SAR (ISAR) imaging to alleviate the sample shortage problem. We introduce the semantic segmentation method into FRA-SAR segmentation for the first time. The experiments verify the effectiveness and superiority of SAR FRA segmentation based on the proposed SA-U-Net++ model compared with the existed semantic segmentation approaches. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Deep learning

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Al-Biruni Earth Radius(BER)Metaheuristic Search optimization Algorithm

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Computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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An Aerial Image Stitching Algorithm Based on Long-distance Features 11

An Aerial Image Stitching Algorithm Based on Long-distance F...

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11th International Conference on information Science and technology, ICIST 2021

作者： Liu, Qiang Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Aerial image stitching is very important in obtaining UAV information. The Speeded Up Robust Features Algorithm (SURF) is an image matching method with high robustness. However, the SURF algorithm subjects to the problems of inexact edge positioning and low matching accuracy. In order to obtain high-precision aerial stitching images with high efficiency, an aerial image stitching algorithm based on long-distance features is proposed in this paper. First, the Canny-SURF algorithm is used for feature detection. After that, the Long-Fast Retina Keypoint (L-FREAK) binary symbol is used to describe and match the feature points. Finally, the Random Sample Consensus Algorithm (RANSAC) is used to compute the projection transformation model, and the weighted average fusion algorithm is used to fuse the pixels. Experimental results show that the proposed algorithm is outperform the SIFT, SURF, ORB, and BRISK algorithms. The proposed algorithm has good stitching accuracy, and can stitch aerial images with wide rotation characteristics. © 2021 IEEE.

关键词： Antennas

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A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction 11

A Quantized Kernel Least-mean-square Algorithm Based on Echo...

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11th International Conference on information Science and technology, ICIST 2021

作者： Zhao, Chaochao Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Kernel least mean square (KLMS) algorithm is a popular method for online time-series prediction. It has the advantages of high robustness, low computational complexity, model simplicity, and online learning ability. Unfortunately, as input data grows, its dictionary size increases and the computational complexity rises significantly. In addition, how to improve the adaptability in uncertain environments with noise is also one of the main challenges. In this paper, we propose a Quantized Kernel Least-mean-square Based on Echo State Network (ES-QKLMS) algorithm in response to the above problems. In the new algorithm, the reservoirs of the echo state network (ESN) are integrated with the KLMS algorithm for online prediction. The ESN reservoir can store the associated information of historical data, which improves the algorithm's ability to track temporal characteristics. Then, the vector quantization method is adopted to suppress the size of the KLMS dictionary. Moreover, the adaptive weight adjustment is used to improve the noise immunity in an uncertain environment. At last, The Lorenz time series and the El Nino-Southern Oscillation (ENSO) time series are discussed to demonstrate the efficacy of the ES-QKLMS algorithm. © 2021 IEEE.

关键词： Time series

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Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection

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SN Computer Science 2021年第4期2卷 295页

作者： Chantar, Hamouda Tubishat, Mohammad Essgaer, Mansour Mirjalili, Seyedali Faculty of Information Technology Sebha University Sebha Libyan Arab Jamahiriya School of Information Technology Skyline University College Sharjah United Arab Emirates Center for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley Brisbane 4006 QLD Australia Yonsei Frontier Lab Yonsei University Seoul South Korea

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Dragonfly algorithm Feature selection optimization Simulated annealing algorithm

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _optimization -norm usually achieve better performance than the convex case (e.g., \ell _optimization -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： optimization

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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Computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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