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Application of Metaheuristic Algorithms for Optimizing Longitudinal Square Porous Fins

Computers, Materials & Continua 2021年第4期67卷 73-87页

作者： Samer H.Atawneh Waqar A.Khan Nawaf N.Hamadneh Adeeb M.Alhomoud College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Mechanical Engineering College of EngineeringPrince Mohammad bin Fahd UniversityAl Khobar31952Saudi Arabia Department of Basic Sciences College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms.A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 *** temperature of the base surface is higher than the fin surface,and the fin tip is kept adiabatic or cooled by convection heat *** other pertinent parameters include Rayleigh number(100≤Ra≤10^(4)),Darcy number,(10^(−4)≤Da≤10^(−2)),relative thermal conductivity ratio of solid phase to fluid(1000≤kr≤8000),Nusselt number(10≤Nu≤100),porosity(0.1≤φ≤0.9).The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic *** computer science,metaheuristic algorithms are one of the most widely used techniques for optimization *** this research,three metaheuristic algorithms,including the firefly algorithm(FFA),particle swarm algorithm(PSO),and hybrid algorithm(FFAPSO)are employed to examine the performance of square *** is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms.

关键词： Optimization firefly algorithm particle swarm algorithm hybrid algorithms porous media entropy generation rate

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Research on Real-Time Linux Task Scheduling Algorithm for Multiprocessors Based on Particle Swarm Optimisation 9

Research on Real-Time Linux Task Scheduling Algorithm for Mu...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Qiu, Tong Yin, Zhenyu Wang, Jun Wang, Zisong School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic HardwareSoftware Shenyang China Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350317251

Schedulability analysis in real-time operating systems is one of the hot research topics in the field. While Linux has become the preferred choice for industrial systems due to its excellent portability, it cannot meet the hard real-time requirements of specific industrial environments as a time-sharing operating system. To address this issue, we introduce the Real-Time Application Interface (RTAI) 5.2 real-time operating system kernel patch and combine it with Linux 4.4.176 to form the RTAI-Linux dual-core real-time system. Building upon this foundation, we proposes a Particle Swarm Optimization algorithm based on Earliest Deadline First (PSO-EDF) to improve task scheduling in real-time Linux multiprocessor environments. This method introduces a metric for evaluating processor utilization, when a new task arrives, it considers the allocation of free processor resources. Compared with the original method EDF, PSO-EDF provides a broader search space with better global characteristics. The algorithm finds the globally optimal scheduling scheme through iteration, which effectively improves the processor resource utilisation and alleviates the task deadline missing problem encountered in the original method to a certain extent, thus improving the real-time task completion rate. Through extensive simulation experiments, the PSO-EDF scheduling algorithm proves that the scheduling model can further improve the real-time performance of the RTAI-Linux system compared with the classical EDF algorithm. © 2023 IEEE.

关键词： Linux

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Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification 39

Boosting ViT-based MRI Reconstruction from the Perspectives ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Meng, Yucong Yang, Zhiwei Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai 200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032 China Academy for Engineering and Technology Fudan University Shanghai 200433 China

ISBN: (纸本)157735897X

The accelerated MRI reconstruction process presents a challenging ill-posed inverse problem due to the extensive undersampling in k-space. Recently, Vision Transformers (ViTs) have become the mainstream for this task, demonstrating substantial performance improvements. However, there are still three significant issues remain unaddressed: (1) ViTs struggle to capture high-frequency components of images, limiting their ability to detect local textures and edge information, thereby impeding MRI restoration;(2) Previous methods calculate multi-head self-attention (MSA) among both related and unrelated tokens in content, introducing noise and significantly increasing computational burden;(3) The naive feed-forward network in ViTs cannot model the multi-scale information that is important for image restoration. In this paper, we propose FPS-Former, a powerful ViT-based framework, to address these issues from the perspectives of frequency modulation, spatial purification, and scale diversification. Specifically, for issue (1), we introduce a frequency modulation attention module to enhance the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. For issue (2), we customize a spatial purification attention module to capture interactions among closely related tokens, thereby reducing redundant or irrelevant feature representations. For issue (3), we propose an efficient feed-forward network based on a hybrid-scale fusion strategy. Comprehensive experiments conducted on three public datasets show that our FPS-Former outperforms state-of-the-art methods while requiring lower computational costs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Frequency modulation

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Identification and Categorization of Fruit's Leaf Disease Using Deep Learning

Identification and Categorization of Fruit's Leaf Disease Us...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Amit Juyal Banipreet Kaur Chetan Pandey Aditya Harbola Aditya Joshi Rashmi Verma School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Biotechnology School of Basic and Applied Sciences Shri Guru Ram Rai University Dehradun India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Fruit infections are the most frequent cause of severe reductions in fruit output. Fruit diseases lead to significant harm to productivity and result in financial deprivation in the global cultivation sector. Hence, it is crucial to spread an automated approach for detecting fruit plant illnesses. Deep learning approaches have lately yielded encouraging outcomes in a variety of artificial intelligence problems, prompting us to employ them for the task of identifying diseases in fruits and leaves. Therefore, this research proposes a convolutional neural network (CNN) model using an image processing method that can automatically diagnose infections affecting passion fruit. The vital stages of this proposed approach entail: obtaining images, preprocessing images, dividing images into segments, extracting characteristics, conducting training, and evaluating. The goal of the recommended CNN architecture is to differentiate between healthy fruits and those affected by frequent fruit leaf diseases. By integrating numerous layers, the envisioned CNN model captures distinctive attributes that are complementary to one another. The proposed CNN model utilizes the Fruit Disease dataset and the New Plant Disease dataset to compare numerous deep learning algorithms that determine the illness in fruit leaves, and once the cause of illness has been determined, appropriate action can be taken for improvement in the agricultural domain.

关键词： Deep learning Training Productivity Plant diseases Image segmentation Plants (biology) Neural networks Information processing Convolutional neural networks Medical diagnosis

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Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation

Separate and Conquer: Decoupling Co-occurrence via Decomposi...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiwei Yang Kexue Fu Minghong Duan Linhao Qu Shuo Wang Zhijian Song Academy for Engineering and Technology Fudan University Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shandong Computer Science Center (National Supercomputer Center in Jinan)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks with-out dense annotations. However, attributed to the frequent coupling of co-occurring objects and the limited supervision from image-level labels, the challenging co-occurrence problem is widely present and leads to false activation of objects in WSSS. In this work, we devise a ‘Separate and Conquer’ scheme SeCo to tackle this issue from di-mensions of image space and feature space. In the im-age space, we propose to ‘separate’ the co-occurring ob-jects with image decomposition by subdividing images into patches. Importantly, we assign each patch a category tag from Class Activation Maps (CAMs), which spatially helps remove the co-context bias and guide the subsequent rep-resentation. In the feature space, we propose to ‘conquer’ the false activation by enhancing semantic representation with multi-granularity knowledge contrast. To this end, a dual-teacher-single-student architecture is designed and tag-guided contrast is conducted, which guarantee the cor-rectness of knowledge and further facilitate the discrepancy among co-contexts. We streamline the multi-staged WSSS pipeline end-to-end and tackle this issue without external supervision. Extensive experiments are conducted, validating the efficiency of our method and the superiority over previous single-staged and even multi-staged competitors on PASCAL VOC and MS COCO. Code is available here.

关键词： Couplings Computer vision Codes Semantic segmentation Semantics Pipelines Noise reduction

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Production of exotic hadrons in pp and nuclear collisions

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Nuclear science and Techniques 2025年第4期36卷 1-22页

作者： Jin-Hui Chen Feng-Kun Guo Yu-Gang Ma Cheng-Ping Shen Qi-Ye Shou Qian Wang Jia-Jun Wu Bing-Song Zou Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan UniversityShanghai 200438China CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China Southern Center for Nuclear-Science Theory(SCNT) Institute of Modern PhysicsChinese Academy of SciencesHuizhou 516000China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing CenterSouth China Normal UniversityGuangzhou 510006China Key Laboratory of Atomic and Subatomic Structure and Quantum Control(MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of MatterInstitute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Department of Physics Tsinghua UniversityBeijing 100084China

Exotic hadrons,beyond the conventional quark model,have been discovered over the past two *** these states can lead to a deeper understanding of the nonperturbative dynamics of the strong *** this review,we focus on the production of exotic hadrons in pp,PP^(-),and nuclear *** observations of light and hypernuclei as prototypes of hadronic molecules in heavy-ion collisions are also briefly discussed.

关键词： Exotic hadrons Hadron-hadron collision Heavy-ion collision

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Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

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Intelligent Automation & Soft computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

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Computers, Materials & Continua 2023年第2期74卷 2889-2903页

作者： Mahmoud Ragab Mohammed W.Al-Rabia Sami Saeed Binyamin Ahmed A.Aldarmahi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Medical Microbiology and Parasitology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Health Promotion Center King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah 21589Saudi Arabia Basic Science Department College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia King Abdullah International Medical Research Center Ministry of National Guard-Health AffairsJeddah 21423Saudi Arabia

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of *** important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional *** accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition *** study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest *** attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature *** addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 ***,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of *** ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive *** experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

关键词： COVID-19 artificial intelligence intelligent systems deep learning decision making

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Proposing an Effective Deep Learning Model for Vitamin D Deficiency Diagnosis

Proposing an Effective Deep Learning Model for Vitamin D Def...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Asad Khattak Junaid Asghar Naila Tabassum Hayat Ullah Aurangzeb Khan Zubair Asghar College of Technological Innovation Zayad University Abu Dhabi Campus Abu Dhabi UAE Dept. of Basic Medical Sciences Faculty of Pharmacy Gomal University D.I.Khan (KP) Pakistan Dera Ismail Khan Pakistan Gomal Research Institute of Computing (GRIC) Faculty of Computing Gomal University D.I.Khan KP Pakistan Department of Computer Science University of Science and Technology Bannu KP Pakistan

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Following the declaration of the COVID-19 pandemic by the World Health Organization, the importance of Vitamin D and the incidence of Vitamin D Deficiency (VDD) have emerged as serious worldwide healthconcerns. There is an increasing demand for noninvasive prediction approaches for determining the severity of VDD. This work collected primary data on serum Vitamin D levels from a benchmark dataset and used machine learning classifiers and categorical data encoding to predict VDD. It introduces a deep learning strategy that uses a Bidirectional Long Short-Term Memory (Bi-LSTM) model, which is improved by balancing the dataset, to better predict VDD severity. Originally intended to predict VDD level, the Bi-LSTM model beat previous models in predicting the severity of VDD, with 95% accuracy, 95% precision, 96% recall, and 95% F1 score. Our methodology appreciably raises the capacity to correctly recognize vitamin D deficiency when compared to earlier methods.

关键词： Deep learning COVID-19 Pandemics Computational modeling Organizations Predictive models Benchmark testing

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A Versatile Low-Light Enhancement Module for Improved Workpiece Detection

A Versatile Low-Light Enhancement Module for Improved Workpi...

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Information and Communication Technology (ICTech), International Conference of

作者： ZhenYu Yin DaHeng Lin XiaoHui Wang Qi Xin XueMei Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This study presents a versatile Low-Light Enhancement Module (LLEM) to enhance object detection models, particularly for workpieces in challenging lighting conditions. The LLEM seamlessly integrates into existing models, addressing low-light challenges through a denoising branch, a global branch, and a Structural Perception loss for image quality improvement. The denoising branch utilizes Residual Denoising Modules to effectively reduce noise and preserve low-level features. Meanwhile, the global branch focuses on color recovery and gamma correction. Experimental results show YOLOv3+LLEM significantly improves detection performance, achieving a mAP increase from 88.0% to 91.9%. This enhancement, especially with the introduction of Structural Perception loss, maintains computational efficiency, validating its practicality for industrial workpiece detection in low-light conditions.

关键词： YOLO Measurement Image quality Image color analysis Computational modeling Noise reduction Lighting Computational efficiency Information and communication technology Colored noise

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