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Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

computers, Materials & Continua 2021年第3期66卷 2691-2708页

作者： Zhengbo Luo Hamïd Parvïn Harish Garg Sultan Noman Qasem Kim-Hung Pho Zulkefli Mansor Graduate School of Information Production and SystemsWaseda UniversityTokyoJapan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjab147004India Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan MalaysiaSelangorMalaysia

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and ***,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this *** main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be *** data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over *** the next step,the base classifier is trained on the newly created ***,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art *** show that our method has betterresults than the other state-of-the-art methods on our experimental study.

关键词： Imbalanced learning classification borderline examples

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Topological Authentication Technique In Topologically Asymmetric Cryptosystem

arXiv

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arXiv 2022年

作者： Yao, Bing Su, Jing Ma, Fei Wang, Hongyu Yang, Chao College of Mathematics and Statistics Northwest Normal University Lanzhou730070 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai 201620 China Center of Intelligent Computing and Applied Statistics Shanghai University of Engineering Science Shanghai 201620 China

Making topological authentication from theory to practical application is an important and challenging task. More and more researchers pay attention on coming quantum computation, privacy data protection, lattices and cryptography. Research show the advantages of topological authentications through graph operations, various matrices, graph colorings and graph labelings are: related with two or more different mathematical areas, be not pictures, there are huge number of colorings and labelings, rooted on modern mathematics, diversity of asymmetric ciphers, simplicity and convenience, easily created, irreversibility, computational security, provable security, and so on. Topological authentications based on various graph homomorphisms, degree-sequence homomorphisms, graph-set homomorphisms. Randomly topological coding and topological authentications are based on Hanzi authentication, randomly adding-edge-removing operation, randomly leaf-adding algorithms, graph random increasing techniques, operation graphic lattice and dynamic networked models and their spanning trees and maximum leaf spanning trees. Realization of topological authentication is an important topic, we study: number-based strings generated from colored graphs, particular graphs (complete graphs, trees, planar graphs), some methods of generating public-keys. some techniques of topologically asymmetric cryptosystem are: W-type matching labelings, dual-type labelings, reciprocal-type labelings, topological homomorphisms, indexed colorings, graphic lattices, degree-sequence lattices, every-zero Cds-matrix groups of degree-sequences, every-zero graphic groups, graphic lattices having coloring closure property, self-similar networked lattices. We propose two problems: Parameterized Number-based String Partition Problem (PNBSPP) and Number-Based String No-order Decomposition Problem (NBSNODP). It is preliminarily concluded that the graph lattice based on PNBSPP and NBSNODP can resist quantum computing. We, al

关键词： Authentication

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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Detection of Cassava Leaf Diseases Using Self-supervised Learning

Detection of Cassava Leaf Diseases Using Self-supervised Lea...

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computer Science and Management Technology (ICCSMT), International Conference on

作者： Hengyi Zhang Yushen Xu Jialiang Sun School of Automation and Software Engineering Shanxi University Taiyuan China Mathematics and Applied Mathematics (Innovation Class) Foshan University Foshan China Computer Science and Technology College CUMT Luoyang China

ISBN: (纸本)9781665420648

Identifying infected plants such as cassava leaves in advance and removing them early can effectively increase production. The traditional approach is to use many image sets to improve the classification accuracy of neural networks. However, this must be established on the premise that the data sets have enough labels that botanists can classify. We applied self-supervised learning to image classification and trained a reliable cassava disease detection model to reduce the difficulty and cost of collecting these tags. We propose a new hybrid loss that combines the classification and contrastive losses for the whole classification process. Experiments show that our method performs well in the lacking of labeled data. Specifically, the model uses 2/3 of the total data set and reaches an accuracy (90%) close to supervised learning (91.59%) by adding the contrastive term. In addition, we also prove that the change of the percentage of data without labels is linearly independent of the model detection accuracy.

关键词： Training computer science Costs Supervised learning Neural networks Production Data models

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Modeling CO2 solubility in polyethylene glycol polymer using data driven methods

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Separation Science and Technology (Philadelphia) 2025年第11期60卷 1451-1470页

作者： Lei, YunLi Sead, Fadhel F. Sur, Dharmesh Yadav, Anupam Ballal, Suhas Singh, Abhayveer Krithiga, T. Kumar, Akshay Yuldashev, Farrukh Ahmad, Irfan Sherzod, Samim Guangxi Technological College of Machinery and Electricity Nanning China Department of Dentistry College of Dentistry The Islamic University Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Babylon Babylon Iraq Department of Chemical Engineering Faculty of Engineering & Technology Marwadi University Research Center Rajkot India Department of Computer engineering and Application GLA University Mathura India Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of Chemistry Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Department of Informatics and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Clinical Laboratory Sciences College of Applied Medical Sciences King Khalid University Abha Saudi Arabia Faculty of Engineering Nangarhar University Nangarhar Afghanistan

The solubility of CO2 in polyethylene glycol (PEG) polymer is a critical parameter for optimizing its use in various industrial processes, underscoring the need for precise predictive models. In this research, a Random Forest (RF) machine learning model is meticulously tuned through four sophisticated optimization algorithms: Batch Bayesian Optimization (BBO), Self-Adaptive Differential Evolution (SADE), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). The model leverages a dataset of 164 experimental samples, incorporating essential input parameters such as pressure, PEG molar mass, and temperature to predict CO2 solubility. To prevent overfitting, K-fold cross-validation is applied throughout model training. The efficacy of each optimization method is evaluated using computational runtime and performance metrics, including R-squared (R2), mean squared error (MSE), and average absolute relative error (AARE%). Correlation analysis indicates that pressure has a moderate positive relationship with CO2 solubility (correlation coefficient: 0.58), while PEG molar mass and temperature exhibit weaker associations (0.2 and 0.05, respectively). Among the optimization techniques, RF-BPI proves to be the most effective, delivering superior predictive performance. The results show RF-BPI achieves an R2 of 0.9625 for the training set and 0.9307 for the test set, surpassing other methods. For benchmarking, a conventional multiple linear regression (LR) model was tested, demonstrating significantly lower accuracy (R2 notably inferior to RF-BPI’s 0.9307 on the test set). In terms of computational efficiency, BPI records the shortest runtime (63.9 seconds), while SADE is the least efficient, requiring 2370.2 seconds. Sensitivity analysis further clarifies the relative impact of each input variable on CO2 solubility, affirming the power of data-driven approaches in modeling complex systems. The created models can be seen as promising predictive tools

关键词： Mean square error

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zolman, Nicholas Fasel, Urban Nathan Kutz, J. Brunton, Steven L. Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Aeronautics Imperial College LondonSW7 2AZ United Kingdom Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Data Science and Artificial Intelligence Department The Aerospace Corporation El SegundoCA90245 United States

Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems. SINDy-RL achieves comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a deep neural network policy. © 2024, CC BY.

关键词： Reinforcement learning

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A Fractal-Fractional Model for the MHD Flow of Casson Fluid in a Channel

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computers, Materials & Continua 2021年第5期67卷 1385-1398页

作者： Nadeem Ahmad Sheikh Dennis Ling Chuan Ching Thabet Abdeljawad Ilyas Khan Muhammad Jamil Kottakkaran Sooppy Nisar Fundamental and Applied Science Department Universiti Teknologi PETRONASPerak32610Malaysia Department of Mathematics City University of Science and Information TechnologyPeshawar25000Pakistan Department of Mathematics and General Sciences Prince Sultan University11586RiyadhSaudi Arabia Department of Medical Research China Medical University40402TaichungTaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwanChina Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam Department of Geosciences Universiti Teknologi PETRONASPerak32610Malaysia Department of Earth Sciences COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

An emerging definition of the fractal-fractional operator has been used in this study for the modeling of Casson fluid *** magnetohydrodynamics flow of Casson fluid has cogent in a channel where the motion of the upper plate generates the flow while the lower plate is at a static *** proposed model is non-dimensionalized using the Pi-Buckingham theorem to reduce the complexity in solving the model and computation *** non-dimensional fractal-fractional model with the power-law kernel has been solved through the Laplace transform *** Mathcad software has been used for illustration of the influence of various parameters,i.e.,Hartman number,fractal,fractional,and Casson fluid parameters on the velocity of fluid *** graphs and tables,the results have been implemented and it is shown that the boundary conditions are fully *** results reveal that the flow velocity is decreasing with the increasing values of the Hartman number and is increasing with the increasing values of the Casson fluid *** findings of the fractal-fractional model have elucidated that the memory effect of the flow model has higher quality than the simple fractional and classical ***,to show the validity of the obtained closed-form solutions,special cases have been obtained which are in agreement with the already published solutions.

关键词： Fractal-fractional derivative Casson fluid MHD flow exact solutions

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Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

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Expert Systems with Applications 2024年 238卷

作者： Das, Soumen Kumar Yu, Vincent F. Roy, Sankar Kumar Weber, Gerhard Wilhelm Department of Pure and Applied Sciences Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan Faculty of Engineering Management Poznan University of Technology Poznan Poland IAM METU Ankara Turkey

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. © 2023

关键词： Sustainable development

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A Robust Subtree-Based Authentication Scheme for TMIS Using Chaotic Hash Function

A Robust Subtree-Based Authentication Scheme for TMIS Using ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Chandrashekhar Meshram Mohammad S. Obaidat Balqies Sadoun Agbotiname Lucky Imoize Vishesh P. Gaikwad Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post Graduate College College of Chhindwara University Betul M.P. India King Abdullah II School of Information Technology University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University SRM Nagar Kattankulathur TN India Amity University Noida UP India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

The widespread availability of broadband telecommunication and the relatively affordable cost of vital signs monitoring devices have enabled telemedicine to bring its benefits directly to patients' homes. In contemporary healthcare, Telecare Medicine Information System (TMIS) facilitates remote disease diagnosis, treatment, and healthcare delivery through the capabilities of wireless communication and information technology. To protect patient confidentiality, TMIS must develop robust authentication systems since it operates on the public Internet, which exposes its operations to security vulnerabilities. To ensure patient privacy, we present a novel Subtree-based Authentication technique for TMIS. An evaluation of the proposed scheme reveals that it is resistant to known potential attacks, and offers more security effectiveness and resource efficiency than existing schemes, making it an excellent solution for enhancing the security of TMIS implementations.

关键词： Privacy Chaotic communication Telemedicine Authentication Passwords Internet Security

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