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Coronavirus Mask Protection Algorithm:A New Bio-inspired Optimization Algorithm and Its Applications

Journal of Bionic engineering 2023年第4期20卷 1747-1765页

作者： Yongliang Yuan Qianlong Shen Shuo Wang Jianji Ren Donghao Yang Qingkang Yang Junkai Fan Xiaokai Mu School of Mechanical and Power Engineering Henan Polytechnic UniversityJiaozuo454003China School of Mechanical Engineering Dalian University of TechnologyDalian116024China School of Software Henan Polytechnic UniversityJiaozuo454003China

Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization *** this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of *** main inspiration for the CMPA originated from human self-protection behavior against *** CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune ***,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization *** study simulates the self-protection behavior mathematically and offers an optimization *** performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design *** statistical results demonstrate that the CMPA is more competitive among these state-of-the-art ***,the CMPA is performed to identify the parameters of the main girder of a gantry *** show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.

关键词： Coronavirus Mask Protection Algorithm Bionic algorithm Metaheuristic algorithm Optimization algorithm Truss optimization Parameter identification

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X-ray Security Inspection Prohibited Items Detection Model based on Improved YOLOv7-tiny

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1279-1287页

作者： Teng, Wenzhao Zhang, Haigang Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Polytechnic University Shenzhen China Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational complexity, most current object detection models are challenging to deploy on portable mobile security inspection devices. Therefore, this paper proposes an improved YOLOv7-tiny model for application in prohibited item detection tasks. Firstly, the feature extraction backbone network is replaced with the lightweight GhostNet network to reduce computational complexity and improve detection speed. Secondly, the FPN in the Neck is replaced with BiFPN, further reducing computational complexity and memory access through skip connections. Finally, a lightweight CA attention mechanism is embedded between the Backbone and Neck layers, and the Focal-EIoU Loss function is employed to enhance the detection capability for small-sized items. Experimental results on the SIXray public dataset show a 14.8% reduction in model parameters, a 19.7% reduction in computational complexity, and a 15.9% reduction in volume after the improvements. The detection speed increases from 82.4 to 90.2, and the detection accuracy for prohibited items reaches 90.3%. The experimental results demonstrate that the improved model achieves overall lightweighting while maintaining a high detection rate and improving detection speed. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Computational complexity

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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

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Computer Modeling in engineering & Sciences 2024年第4期139卷 187-224页

作者： Weisi Chen Walayat Hussain Francesco Cauteruccio Xu Zhang School of Software Engineering Xiamen University of TechnologyXiamen361024China Peter Faber Business School Australian Catholic UniversityNorth Sydney2060Australia Department of Information Engineering Polytechnic University of MarcheAncona60121Italy

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last *** traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction ***,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and *** review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other *** illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related *** the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only *** remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency *** principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and t

关键词： Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism finance

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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New analytical solutions for free vibration of embedded magneto-electro-elastic cylindrical shells with step-wise thickness variations

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 447-466页

作者： Jufang JIA Huilin YIN Qinyu YU Jiabin SUN Xinsheng XU Zhenhuan ZHOU School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian 116034Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Mechanics and Aerospace EngineeringDalian University of TechnologyDalian 116024Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Chemical EngineeringOcean and Life SciencesDalian University of TechnologyPanjin 124221Liaoning ProvinceChina

In recent years,magneto-electro-elastic(MEE)cylindrical shells with step-wise thicknesses have shown significant potential in the field of vibration energy *** aid the design of such energy harvesting devices,an accurate free vibration analysis of embedded MEE cylindrical shells with step-wise thicknesses is performed within the framework of symplectic *** using the Legendre transformation,a new known vector is defined to transform the higher-order partial differential governing equations into a set of lower-order ordinary differential ***,the original vibration analysis is regarded as an eigen problem in the symplectic space,and analytical solutions can be represented by the symplectic *** numerical examples,the new analytical solutions are compared with the existing results,and good agreement is ***,the effects of critical design parameters on free vibration characteristics are thoroughly *** numerical results can serve as benchmarks for the development of other approximate or numerical methods.

关键词： analytical solution free vibration magneto-electro-elastic(MEE)material cylindrical shell with variable thickness symplectic method

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Prediction of Burn-Through Point in Sintering Process Based on Improved Hybrid Optimization Algorithm and TCN-BiGRU-AM 5

Prediction of Burn-Through Point in Sintering Process Based ...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Chen, Jiayin Liao, Liefa Wu, Jiale College of Software Engineering Jiangxi University of Science and Technology Nanchang China College of Software Engineering Jiangxi University of Science and Technology Jiangxi Modern Polytechnic College Nanchang China

ISBN: (纸本)9798331528911

Sintering is a crucial upstream process in the steelmaking process, and accurately predicting the burning through point (BTP) is vital for the yield and quality of sintered ore. The rise of artificial intelligence and deep learning has introduced innovative data-driven methods for predicting the BTP. In this paper, we propose a model that combines Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism to predict the sintering end point. Before training the model, we employ an innovative Improved Grey Wolf Optimizer-Whale Optimization Algorithm (IGWO-WOA) to find the optimal hyperparameters for the model. To validate the proposed method, we conduct experiments using actual production data from a steel company, comparing the proposed model with pre-improvement algorithms and single models. The results indicate that the proposed method has good prediction accuracy, with a Mean Absolute Error (MAE) of 0.0306 and a Root Mean Square Error (RMSE) of 0.0619, confirming the effectiveness of the proposed method. © 2024 IEEE.

关键词： Steelmaking

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Applications of Machine Learning in software Defect Prediction: A Literature Review 18

Applications of Machine Learning in Software Defect Predicti...

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18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Liubchenko, Vira Odesa Polytechnic National University Odesa Software Engineering Department Ukraine

ISBN: (纸本)9798350360462

software Defect Prediction (SDP) employs resources to identify defects and improve quality. This study investigates Machine Learning (ML) techniques in SDP and associated performance measures. Three research questions guide the exploration, covering popular techniques, common performance metrics, and dataset sources. The review highlights a shift towards leveraging rich source code information, particularly Abstract Syntax Trees. Notably, Artificial Neural Networks dominate ML techniques. AUC and G-mean gain prominence for performance evaluation, addressing class imbalance. Dataset scarcity remains a challenge, leading to reliance on external repositories. This study enriches SDP insights and informs future research directions in the evolving landscape of software defect prediction. © 2023 IEEE.

关键词： Machine learning

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Maintaining a stable point-to-point remote control link based on transceivers nRF24L01+ PA/LNA in presence of interferences 22

Maintaining a stable point-to-point remote control link base...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Saletović, Enes Babović, Elmir Hadžić, Dulaga University of Zenica Polytechnic Faculty Department of Software Engineering Zenica Bosnia and Herzegovina

ISBN: (纸本)9781665475464

This paper presents the hardware implementation of a stable wireless adaptive remote control point-to-point link, based on the nRF24L01+ PA/LNA transceiver module. On the transmitter and receiver side, a microcontroller integrated circuit STM32F103C8T6 has been used to implement appropriate adaptive algorithms that enable a synchronized change of the communication channel in case of interferences. The link stability has been verified experimentally in real conditions, in the presence of random interferences. The presented experimental results show how interferences affect the stability and range of the presented communication link. © 2023 IEEE.

关键词： Microcontrollers

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Fuzzy-Based Client Selection for Enhanced Model Convergence in Federated Learning 6

Fuzzy-Based Client Selection for Enhanced Model Convergence ...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Zhou, Yulong Bao, Ruiming School of Software Yunnan University Kunming China School of Resources and Environmental Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350353174

Federated learning enables the collaborative training of a global model while allowing the data of multiple clients to remain local. This characteristic renders federated learning highly sensitive to the quality of client data, computational performance, and network conditions. In real-world scenarios, there can be significant disparities in data quality, computational capabilities, and network conditions among clients. Such heterogeneity can severely impact the convergence speed and quality of the global model. To address these challenges, we propose a client selection algorithm that comprehensively considers various metrics of clients. Utilizing fuzzy reasoning, this algorithm analyzes and evaluates clients, enabling the server to select those most conducive to federated training. Extensive experiments conducted across multiple datasets demonstrate that, compared to other client selection algorithms, our algorithm not only marginally improves the accuracy of the global model but also significantly enhances its convergence speed. © 2024 IEEE.

关键词： Federated learning

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