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Diverse acoustic wave propagation to confirmable time–space fractional KP equation arising in dusty plasma

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Communications in Theoretical Physics 2021年第11期73卷 47-64页

作者： Aly R Seadawy Muhammad Younis Muhammad Z Baber Syed T R Rizvi Muhammad S Iqbal Mathematics department Faculty of ScienceTaibah UniversityAl-Madinah Al-MunawarahSaudi Arabia department of computer science University of the PunjabLahorePakistan department of Mathematics and Statistics The University of LahoreLahorePakistan department of Mathematics COMSATS University IslamabadLahore CampusPakistan

In this study,the(3+1)-dimensional fractional time–space Kadomtsev–Petviashivili(FTSKP)equation is considered and analyzed analytically,which propagates the acoustic waves in an unmagnetized dusty *** fractional derivatives are studied in a confirmable *** new modified extended direct algebraic(MEDA)approach is adopted to investigate the diverse nonlinear wave structures.A variety of new families of hyperbolic and trigonometric solutions are obtained in single and different *** obtained results are also constructed graphically with the different parametric choices.

关键词： exact solutions fractional calculus new MEDA technique unmagnetized dusty plasma

TWO-STAGE SENTENCE SELECTION APPROACH FOR MULTI-DOCUMENT SUMMARIZATION

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Journal of Electronics(China) 2008年第4期25卷 562-567页

作者： Zhang Shu Zhao Tiejun Zheng Dequan Zhao Hua department of computer science and Technology Harbin Institute of Technology Harbin 150001 China

Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summary is proposed,which has two stages,the acquisition of acandidate sentence set and the optimum selection of *** the first stage,the candidate sentenceset is obtained by redundancy-based sentence selection *** the second stage,optimum se-lection of sentences is proposed to delete sentences in the candidate sentence set according to itscontribution to the whole set until getting the appointed summary *** a test corpus,theROUGE value of summaries gotten by the proposed approach proves its validity,compared with thetraditional method of sentence *** influence of the token chosen in the two-stage sentenceselection approach on the quality of the generated summaries is analyzed.

关键词： Two-stage Sentence selection approach Multi-document summarization

Computational model for perception of objects and motions

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science China(Life sciences) 2008年第6期51卷 526-536页

作者： YANG WenLu1,2, ZHANG LiQing1 & MA LiBo1 1 department of computer science and engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 department of Electronic engineering, Shanghai Maritime University, Shanghai 200135, China department of computer science and engineering Shanghai Jiao Tong University Shanghai China department of Electronic engineering Shanghai Maritime University Shanghai China

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.

关键词： visual perception visual cortex computational model receptive fields simple cells complex cells

Radiating metric, retarded time coordinates of Kerr-Newman-de Sitter black holes and related energy-momentum tensor

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science China Mathematics 1998年第6期41卷 663-672页

作者：许殿彦 department of computer Sciience and Technology Peking University Beijing China Chinese Centre of Advanced science and Technology World Laboratory Beijing China

The charged rotating metric in de Sitter space, derived by Mallett and used by Koberlein, is shown incorrect. Mallett’s metric and his energy\|momentum tensor do not satisfy the Einstein\|Maxwell field equations with a cosmological term in the nonradiating and radiating Kerr\|Newman\|de Sitter case. The corresponding correct metric and the radiating energy\|momentum tensor are given.

关键词： radiation metric retarded lime eoordinates black holes

CVTD: A Robust Car-Mounted Video Text Detector

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computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li department of computer science and engineering Chongqing University of TechnologyChongqingChina College of Information and engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail department of computer science&engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of computer science and engineering VIT-AP UniversityAmaravati522241India department of computer science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of computer science engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

Movement-assisted service composition optimization in dynamic networks

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High Technology Letters 2013年第2期19卷 176-181页

作者：赵耀培 Yang Yang Mi Zhenqiang department of computer and Communication engineering University of Science and Technology Beijing department of Scientific Research Shan Dong Institute of Trade Union Administration Cadres

The mobility of service providers brings new features into the research of dynamic network based service *** an optimistic perspective,the mobility of services could benefit the optimization of service composition,if properly ***,the impacts of node mobility on the dynamic network based service composition are ***,a movement-assisted optimization method,namely MASCO,is proposed to improve the performance of the composited services by minimizing the length of data stream and the hop-counts of the service routes in the underlying *** correctness and efficiency of the proposed method are then verified through theoretical analysis and computer simulations.

关键词： service composition dynamic networks motion control service optimization re-versed current

Smooth interpolation on homogeneous matrix groups for computer animation

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Journal of Zhejiang University-science A(Applied Physics & engineering) 2006年第7期7卷 1168-1177页

作者： LI Jun HAO Peng-wei Center for Information science Peking University Beijing 100081 China department of computer science Queen Mary University of London London E1 4NS UK

Homogeneous matrices are widely used to represent geometric transformations in computer graphics, with interpo- lation between those matrices being of high interest for computer animation. Many approaches have been proposed to address this problem, including computing matrix curves from curves in Euclidean space by registration, representing one-parameter curves on manifold by rational representations, changing subdivisional methods generating curves in Euclidean space to corresponding methods working for matrix curve generation, and variational methods. In this paper, we propose a scheme to generate rational one-parameter matrix curves based on exponential map for interpolation, and demonstrate how to obtain higher smoothness from existing curves. We also give an iterative technique for rapid computing of these curves. We take the computation as solving an ordinary differential equation on manifold numerically by a generalized Euler method. Furthermore, we give this algorithm’s bound of the error and prove that the bound is proportional to the shift length when the shift length is sufficiently small. Compared to direct computation of the matrix functions, our Euler solution is faster.

关键词： computer animation Spline and piecewise polynomial approximation

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm Shaha Al-Otaibi Amel AAlhussan Mesfer Al Duhayyim Fuad Ali Mohammed Al-Yarimi Mohammed Rizwanullah Ishfaq Yaseen department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia department of computer science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia department of computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia department of Natural and Applied sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and *** proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm