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Characteristics of Multi-Objective Linear programming Problem and Multi-Objective Linear Fractional programming Problem Taking Maximum Value of Multi-Objective Functions

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Applied mathematics 2024年第1期15卷 22-32页

作者： Samsun Nahar Md. Asadujjaman Khadiza Begum Mahede-Ul-Hassan Md. Abdul Alim Department of Basic Sciences and Humanities University of Asia Pacific Dhaka Bangladesh Department of Mathematics University of Dhaka Dhaka Bangladesh Computer Science and Engineering Department Prime University Dhaka Bangladesh Department of Mathematics and Statistics Bangladesh University of Business and Technology Dhaka Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka Bangladesh

In this paper, a new statistical averaging technique is proposed for finding an optimal solution to a multi-objective linear fractional programming problem (MOLFPP) and multi-objective linear programming problem (MOLPP) by using new arithmetic averaging method and new geometric averaging method. It is significantly noticeable same characteristics among all the technique while taking maximum or minimum among all optimized values for multi-objective functions using simplex algorithm. The characteristics provided from the problems are verified by the numerical examples.

关键词： MOLPP MOLFPP New Arithmetic Averaging Method New Geometric Averaging Method

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Deep Learning Based Parametric Estimation in Double-Diffusive Convective Flow 6th

Deep Learning Based Parametric Estimation in Double-Diffusiv...

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6th International Conference on Frontiers in Industrial and Applied mathematics, FIAM 2023

作者： Kumar, Sumant Krishna Murthy, S.V.S.S.N.V.G. Kumar, B. V. Rathish School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Department of Mathematics and Statistics Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9789819625789

The present research work employs the artificial neural network (ANN) based deep learning (DL) approach to analyze the parametric estimation in double-diffusive convective flow in an inverted T-shaped porous enclosure. The study develops and numerically simulates a Darcy-extended Brinkmann-Forchheimer mathematical model using the penalty finite element method. Minimal training data are gathered across various flow parameters for DL model training. The DL-based technique efficiently approximates convective heat and mass transport across unknown flow parameter sets, significantly reducing computational costs. Analysis reveals that higher Rayleigh numbers prominently trigger convective phenomena, offering insights into realistic impacts of other flow parameters within porous media. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

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Computers, Materials & Continua 2023年第3期74卷 5173-5189页

作者： Hengfu Yang Mingfang Jiang Zhichen Gao School of Computer Science Hunan First Normal UniversityChangsha410205China Hunan Provincial Key Laboratory of Informationization Technology for Basic Education Changsha410205China Department of Applied Mathematics and Statistics College of Engineering and Applied SciencesStony Brook UniversityNY 11794USA

Few previous Reversible Visible Watermarking(RVW)schemes have both good transparency and watermark *** adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding(BTC)compressed domain,called TVB-RVW is proposed in this paper.A new mean image estimation method for BTC-compressed images is first developed with the help of Total ***,a visual perception factor computation model is devised by fusing texture and luminance *** adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC ***,a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark *** visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark *** experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility,and it can achieve a good balance between transparency and watermark visibility.

关键词： Reversible visible watermark block truncation coding visual perception total variation

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Comparing embedded graphs using average branching distance

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Involve 2023年第3期16卷 365-388页

作者： Batakci, Levent Branson, Abigail Castillo, Bryan Todd, Candace Chambers, Erin Wolf Munch, Elizabeth Case Western Reserve University Hummelstown PA United States Union University Jackson TN United States Department of Mathematics The University of Arizona Tucson AZ United States Department of Statistics The Pennsylvania State University University Park PA United States Department of Computer Science Saint Louis University St. Louis MO United States Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East Lansing MI United States

Graphs drawn in the plane are ubiquitous, arising from data sets through a variety of methods ranging from GIS analysis to image classification to shape analysis. A fundamental problem in this type of data is comparison: given a set of such graphs, can we rank how similar they are in such a way that we capture their geometric “shape” in the plane? We explore a method to compare two such embedded graphs, via a simplified combinatorial representation called a tail-less merge tree which encodes the structure based on a fixed direction. First, we examine the properties of a distance designed to compare merge trees called the branching distance, and show that the distance as defined in previous work fails to satisfy some of the requirements of a metric. We incorporate this into a new distance function called average branching distance to compare graphs by looking at the branching distance for merge trees defined over many directions. Despite the theoretical issues, we show that the definition is still quite useful in practice by using our open-source code to cluster data sets of embedded graphs. © 2023 MSP.

关键词： embedded graph merge tree topological data analysis

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A Neutrosophic Logic Ruled Based Machine Learning Approaches for Chronic Kidney Disease Risk Prediction

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Neutrosophic Sets and Systems 2025年 79卷 76-95页

作者： Mohanty, Bhabani S. Krishna Priya, R. Alias, Liya Vijaya Kumar Reddy, R. Raut, Prasanta Kumar Isaac, Samson Manibharathi, D. Saravanakumar, C. Panda, Nihar Ranjan Broumi, Said Department of Statistics and Applied Mathematics Central University of Tamil Nadu India Research and Consultancy Department University of Technology and Applied Sciences P.O. Box: 12 Khasab Governorate of Musandam Musandam811 Oman College of Engineering and Technology University of Technology and Applied Science Shinas Oman Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India Department of Mathematics Trident Academy of Technology Odisha Bhubaneswar India Division of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electronics and Communication Engineering Government College of Engineering Tamilnadu Salem India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Kattankulathur Tamilnadu Chengalpattu India Department of Medical Research IMS & SUM Hospital SOA Deemed to be university India Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

Chronic kidney disease (CKD) represents a significant global health challenge in society, and early detection of risk is essential for on-time treatment and intervention. This research suggests a novel machine-learning technique to create a reliable and accurate CKD risk prediction model by combining neutrosophic logic with various classification algorithms. We use neutrosophic logic to address the inherent imprecision and uncertainty in medical data, resulting in a more realistic portrayal of real-world scenarios. We measure the effectiveness of the proposed neutrosophic logic-based models using various metrics, including precision, specificity, and sensitivity. The results show that the neutrosophic logic method is better than traditional machine learning methods at finding people who are likely to develop CKD because it is more accurate and stable. This study illustrates the potential for incorporating neutrosophic logic into machine learning frameworks to improve risk prediction in medical fields. © 2025, University of New Mexico. All rights reserved.

关键词： Prediction models

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Evaluated Material Attractiveness of Plutonium Composition from Economic Simplified Boiling Water Reactor (ESBWR) 10

Evaluated Material Attractiveness of Plutonium Composition f...

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10th Asian Physics Symposium 2023, APS 2023

作者： Nasyidiah, Fungky Iqlima Permana, Sidik Pramuditya, Syeilendra Master Program in Nuclear Science and Engineering Faculty of Mathematics and Natural Sciences Institute Technology of Bandung Ganesa 10th Street Bandung40132 Indonesia Department of Nuclear Science and Engineering Faculty of Mathematics and Natural Sciences Institute Technology of Bandung Ganesa 10th Street Bandung40132 Indonesia

The potential for misuse of nuclear fuel has prompted many to make efforts to prevent a country from acquiring nuclear explosive devices (weapons). Weapon utility equated with nuclear material attractiveness when applied to protection and security. Material attractiveness evaluated using an equation first developed by Masaki Saito, namely ATTR. ESBWR is one of the reactors of the BWR reactor type that is still operating. The purpose of this study is to evaluate Material Attractiveness based on the composition of plutonium in the ESBWR reactor, during reactor operation and after reactor operation. The ATTR concept utilizes the plutonium isotope composition to evaluate the material attractiveness aspect. Parameters such as BCM, Rossi-alpha, and neutron prompt life become additional aspects in evaluating material attractiveness. In the research results, the ATTR value decreased from the beginning of reactor operation until the reactor stopped operating. At the beginning of the reactor operation, it was categorizing as a weapon-grade with an ATTR value of 0.19 and when the reactor stopped operating it was categorizing as an unusable grade with an ATTR value of 0.012. At the time of cooling time up to 1x106 (one million) cooling time, the ATTR value increases by 0.019 which was categorizing as an unusable grade. After a cooling time of 1x106 (one million) years up to 1x107 (ten million) years, the ATTR value was 0.001 which categorized as an exempt level. This states that the increasing burnup value will further reduce the ATTR value. Likewise with the Rossi-Alpha and Bare Critical Mass values. Conversely, it will decrease the value of neutron lifetime. The ATTR value after the reactor operates up to one hundred years of cooling time increases and then decreases significantly after 1x106 (one million) years due to the half-life of plutonium isotopes. Likewise, the Rossi-Alpha and Bare critical mass values. However, it is different with the neutron fast lifetime. © Publis

关键词： Boiling water reactors

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Neural Bayes estimators for censored inference with peaks-over-threshold models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 19070-19118页

作者： Jordan Richards Matthew Sainsbury-Dale Andrew Zammit-Mangion Raphaël Huser School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK and Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia and School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.

关键词： censored data convolutional neural network likelihood-free inference neural estimation spatial extremes threshold exceedance

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Direct Machine Translation Indonesian-Batak Toba 7

Direct Machine Translation Indonesian-Batak Toba

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Togatorop, Frengky Marulitua Ahmad, Imam Puspaningrum, Ajeng Savitri Universitas Lampung Doctoral Program of Mathematics and Natural Sciences Bandarlampung Indonesia Universitas Lampung Department of Computer Science Bandarlampung Indonesia Universitas Lampung Department of Mathematics Bandarlampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350307504

The translation application was developed using Python and Visual Studio Code as the primary programming environment. Technological progress has led to the younger generation forsaking the Batak language in their daily interactions, perceiving it as inconsequential to their social circle and contributing to the decline of regional languages. There is concern that regional languages, namely the Batak language, may lose significance or face extinction if this trend persists. This translation program is being implemented to aid in preserving regional languages, specifically the Batak language, enabling it to conform to contemporary circumstances. The development of this translation engine utilized the Batak language dictionary. The process of designing, coding, and testing is responsible for creating this direct machine translation. The BLEU score is employed to assess the caliber of machine translation. The study's findings indicate that the % BLEU value for ten sentences was 32.3%. This score falls within the category of sentences whose meaning can be understood, but the sentence structure still needs development. The direct machine translation developed in this Web platform is highly proficient and practical. © 2023 IEEE.

关键词： Python

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Automatic Detection of COVID-19 Using Ensemble Transfer Learning Based on Lung CT Scans 1

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2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

作者： Pillay, Ricardo Viriri, Serestina Heymann, Reolyn School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Durban South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa

ISBN: (数字)9783031252716

ISBN: (纸本)9783031252709

In order to curb the rapid spread of COVID-19, early and accurate detection is required. Computer Tomography (CT) scans of the lungs can be utilized for accurate COVID-19 detection because these medical images highlight COVID-19 infection with high sensitivity. Transfer learning was implemented on six state-of-the-art Convolutional Neural Networks (CNNs). From these six CNNs, the three with the highest accuracies (based on empirical experiments) were selected and used as base learners to produce hard voting and soft voting ensemble classifiers. These three CNNs were identified as Vgg16, EfficientNetB0 and EfficientNetB5. This study concludes that the soft voting ensemble classifier, with base learners Vgg16 and EfficientNetB5, outperformed all other ensemble classifiers with different base learners and individual models that were investigated. The proposed classifier achieved a new state-of-the-art accuracy on the SARS-CoV-2 dataset. The accuracy obtained from this framework was 98.13%, the recall was 98.94%, the precision was 97.40%, the specificity was 97.30% and the F1 score was 98.16%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： COVID-19

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Statistical Modeling: A New Regression Curve Approximation using Mixed Estimators Truncated Spline and Epanechnikov Kernel

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engineering Letters 2023年第4期31卷 1649-1655页

作者： Sifriyani Dani, Andrea Tri Rian Fauziyah, Meirinda Budiantara, I. Nyoman Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In the era of computation, researchers have paid significant attention to the nonparametric regression method. Nonparametric regression has the benefit of a high degree of modeling flexibility. Developing a mixed estimator truncated spline-Epanechnikov kernel is the most recent innovation in this study. The truncated spline estimator excels at handling data whose behavior varies at predetermined intervals. In contrast, the Epanechnikov kernel estimator has a more flexible structure and excels at modeling data that does not adhere to a particular pattern. Maximum Likelihood Estimation is utilized to estimate parameters. The concluding section of this study will discuss the estimator properties of the mixed estimators truncated spline and Epanechnikov kernel models. The proposed can be utilized for additional analysis in the field of nonparametric regression. © 2023, International Association of Engineers. All rights reserved.

关键词： Maximum likelihood estimation

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