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The new soliton solutions for long and short-wave interaction system

Journal of Ocean engineering and Science 2022年第5期7卷 485-491页

作者： Sayyed Masood Zekavatmand Hadi Rezazadeh Mustafa Inc Javad Vahidi Mohammad Bagher Ghaemi Department of Applied Mathematics Iran University of Science and TechnologyTehranIran Faculty of Engineering Technology Amol University of Special Modern TechnologiesAmolIran Department of Computer Engineering Biruni UniversityIstanbulTurkey Firat University Science FacultyDepartment of MathematicsElazig 23119 Turkey Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan Department of Mathematical Sciences University of South AfricaPretoria 0002South Africa

The goal of this paper is to discover modern soliton solutions to long and short-wave interaction system by procedures called extended rational sine-cosine and rational sinh-cosh *** assume that the equation has a hypothetical soliton *** reorganizing the resulting equations,we obtain a system of *** Maple software,we get unknown coefficients in the system and writing them in the original equation,we obtain new solition solutions of the *** results show that the soliton solutions generated by the method for the long and short-wave interaction system are bright,kink type,bright periodic and dark *** provided 3-D figures to illustrate the *** results indicate that the method employed in this paper is superior than some other methods used in the literature to solve the same system equations.

关键词： Extended rational sine-cosine method Extended rational sinh-cosh method

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On Some Methods of Digital Assessment for Mathematical Courses 7

On Some Methods of Digital Assessment for Mathematical Cours...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Takács, Anna Mária Takács, Anna Nagy, Bálint Augusztinyi, Dániel Gyula Institute of Computer Engineering University of Dunaújváros Department of Mathematics Dunaújváros Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary Institute of Electrophysics Óbuda University Department of Natural Sciences KandÓ Kálmán Faculty of Electrical Engineering Budapest Hungary

ISBN: (纸本)9798331531119

While the practice in higher education has begun to reflect a much greater emphasis on active and, more generally, experiential approaches to learning, mathematics instruction, when set against these broader trends, still has some distance to travel. Information and communication technologies (ICTs), and computer algebra systems (CAS) in particular, open up new possibilities for the teaching of mathematics, providing dynamic and interactive approaches to the representation of ideas that enable students to explore concepts that, when presented in other guises, are well beyond their capability. The implementation of hybrid learning models, mixing traditional and digital pedagogies, raises major challenges: from choosing the right software to requiring complete teacher training. The COVID-19 pandemic strongly accelerated online education by providing for large-scale usage in learning management systems (LMS) such as MOODLE and CooSpace. These platforms, however, have been instrumental in keeping educational continuity;effective assessment strategies within the online environment still lag. The potential of integrating ICTs and CAS into mathematics education in enhancing student learning outcomes exists but requires surmounting technical, pedagogical, and logistical challenges. The paper explores some of the methods relevant to such questions as the roles of ICTs in experiential learning, the efficacy of hybrid models of teaching, and the establishment of frameworks for assessment online. Grounded in the critical review of current practice and exploration of innovative strategies, this research intends to have added to the discourse on how best mathematics education can be optimized within the digital age so that all students benefit from a comprehensive, engaging, and effective learning experience. © 2024 IEEE.

关键词： Contrastive Learning

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Air Quality Image Classification using Ensemble Based Transfer Learning Techniques

Air Quality Image Classification using Ensemble Based Transf...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Saradha, S. Kumar, M. Praveen Asha, J. Saranya, R. Santhoshkumar, S.P. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India PSG Institute of Technology and Applied Research Neelambur Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9781839539848

In recent years, research has focused on the efficacy of neural networks, Learning by Transfer (TL), and Ensemble Learning (EL) techniques in image processing. Ensemble approaches, when employed on image classification tasks improve the overall predictive performance and robustness. This work examines the studies related to ensemble approach and applies the same for the classification of images based on different air pollution levels. The proposed research work uses three Convolution Neural Networks (CNN) models namely VGG, MobileNet, and EfficientNet and ensembles them using stacking technique for air quality image classification. The results exhibited in the suggested model generated an overall classification accuracy of 95%. © The Institution of engineering & Technology 2023.

关键词： Image classification

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A CNN-BiLSTM and KNN based missing data imputation for wind power generation forecasting 6

A CNN-BiLSTM and KNN based missing data imputation for wind ...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Wang, Chaohong Ren, Bowen Li, Xiaozhu Chen, Laijun Qinghai University Department of Computer Technology and Applications Xining China Industry Research Center Xining China Tsinghua University Department of Electrical Engineering and Applied Electronics Technology Beijing China

ISBN: (纸本)9798350346671

A missing data imputation method combined with the KNN (K-nearest Neighbors) and BiLSTM (Bi-directional Long Short-Term Memory) is proposed to catering the solve challenges of noise interference and large variance. In this method, Firstly, the influence of dimensions on wind power is analyzed by the correlation coefficient of Pearson. And the information is extracted by the CNN (Convolutional Neural Networks) model. At the same time, the BiLSTM model exploits the ability to learn the internal representation of time series data. Then the missing data are imputed one by one by the CNN-BiLSTM model and the KNN model respectively. Finally, the final dataset is composed of these imputation values. The numerical simulation is carried out based on the actual data, the result shows that the combining model has a better imputation effect and its R2 increases by 0.02. © 2023 IEEE.

关键词： Wind power

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems Science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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A Systematic Review to Know How Interventions Realized with Immersive Virtual Reality-Based Serious Games for Individuals with Autism are Evaluated 23

A Systematic Review to Know How Interventions Realized with ...

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15th Biannual Conference of the Italian SIGCHI Chapter: Crossing HCI and AI, CHItaly 2023

作者： Peretti, Sara Caruso, Federica Pino, Maria Chiara Di Mascio, Tania Center of Excellence DEWS University of l'Aquila Italy Department of Information Engineering Computer Science and Mathematics University of l'Aquila Italy Italy Department of Biotechnological and Applied Clinical Sciences University of l'Aquila Italy

ISBN: (纸本)9798400708060

Today, the growing diffusion of Information Communication Technology and the concomitant system cost reductions encouraged, even more, the exploitation of innovative solutions, especially in the field of interventions for autism. The need for efficient and effective interventions for autism is becoming more pressing for several reasons ranging from the dramatic increase in its prevalence to the high direct and indirect costs of its care over a lifetime. Among all Information Communication Technology-based interventions for autism, those realized with Immersive Virtual Reality based Serious Games appear to be the most promising. However, even if an intervention is considered promising for a clinical population does not mean that it is also efficient and effective. In fact, to be efficient and effective, an intervention must be evidence-based. Unfortunately, nowadays, there is a lack of evaluation protocols that allow for avoiding methodological biases thus providing interventions evidence-based. Consequently, clinicians are reluctant to use these interventions in clinical practice. This awareness led to the emergence of the research question of this systematic review: how interventions realized with Immersive Virtual Reality based Serious Games for individuals with autism are evaluated? The present systematic review answered this research question showing consistent methodological issues related to how these kinds of interventions are evaluated and laying the foundation for a reflection on the need to create specific evaluation protocols to overcome these issues and promoting thus the blossoming of rigorous studies on this topic. © 2023 ACM.

关键词： Serious games

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A Secure and Reliable Supply chain management approach integrated with IoT and Blockchain 2

A Secure and Reliable Supply chain management approach integ...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Hanif, Maria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Nowadays, secure and reliable management of logistics is highly needed. Logistics is the delivery of goods from producers to legitimate consumers in accurate amounts and good conditions. The use of low-capable sensor nodes in smart logistics makes it vulnerable to many security threats. Smart logistics necessitated the delivery of suitable information to the authorized person at the appropriate time and place, which is only feasible with a stable infrastructure. This paper presents an authentication scheme together with blockchain technology to provide a secure supply chain management system. The presented authentication mechanism is based on standard KERBOROS scheme which is a ticket-based scheme. The scheme is evaluated by BAN logic which shows that it is an effective scheme in terms of improved response time and encryption/decryption time along with key generation time. © 2023 IEEE.

关键词： Supply chain management

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Optimal Experimental Design to Estimate Insulin Response in Type 2 Diabetes

Optimal Experimental Design to Estimate Insulin Response in ...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Engell, Sarah Ellinor Bengtsson, Henrik Benam, Karim Davari Fougner, Anders Lyngvi Jorgensen, John Bagterp Novo Nordisk A/S BagsværdDK-2880 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science LyngbyDK-2800 Kgs. Denmark Department of Engineering Cybernetics Trondheim Norway

ISBN: (纸本)9798350335446

In late-stage type 2 diabetes, automated titration algorithms provide a promising alternative to the current standard-of-care. Many published methods rely on personalized dose-response models to predict a safe and effective insulin dose. In this case study, we address the challenge of how to collect an informative data set to ensure practical identifiability of such models. We apply optimal experimental design to enhance the performance of a published titration algorithm. For a 24-hour experiment, we solve an optimization problem to select the size of three meals and the hourly fast-acting insulin infusion rate. In simulation, we demonstrate how the optimized protocol improves the safety of the algorithm's dose-predictions. The results indicate that optimal experimental design has the potential to improve model-based algorithms and may be used as a qualitative tool when planning clinical experiments. © 2023 IEEE.

关键词： Insulin

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Mushroom Classification Model to Check Edibility using Machine Learning 10

Mushroom Classification Model to Check Edibility using Machi...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pal, Surya Kant Pant, Ritvik Roy, Rita Singh, Shakshi Choudhary, Lareb Naaz, Shafaque Sharda School of Basic Sciences and Research Sharda University Department of Mathematics U.P. Greater Noida India Department of Computer Science and Engineering A.P. Visakhapatnam India

ISBN: (纸本)9789380544472

The human body gains a lot from mushrooms. At the same time, not every mushroom that we see is edible. Some mushrooms have medical properties;at the same time, some might have deadly viruses that are not good for humans. We as human beings love to eat these mushrooms, but it is essential to check and then consume them as it can be a matter of life and death. Here we are set to study this paper to examine behavioral attributes of mushrooms such as the structure, size of the surface, cap tone, lamella, which is also known as the gill, and stalk as well as the smell place where the mushroom was grown, and population. The machine learning algorithm we will use would be K Means clustering. The accuracy of the classification problem, the coefficient matrix, and the duration are taken to build a classification model on the datafile be taken into consideration. The highest-ranked column for classification is the odour contributing to the highest classification accuracy. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： K-means clustering

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ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM TRANSFER FUNCTIONS 10

ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM T...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Xia, Yuanxin Engsig-Karup, Allan P. Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark

ISBN: (纸本)9788888942674

The estimation of the absorption coefficients of the boundary surfaces in a room is important in room acoustic engineering. This research presents a machine learning method learns from simulated data to estimate the room dimensions and frequency-dependent absorption coefficients. We employ multi-task convolutional neural networks for inferring the frequency-dependent absorption coefficients and the dimensions of the room from transfer functions calculated by wave-based room acoustic methods. The proposed method provides reasonably accurate estimation of the boundary conditions and dimensions. © 2023 Xia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Boundary conditions

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