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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Kotov, Mykola Volchuk, Volodymyr Konoplianyk, Oleksandr Plakhtii, Yevhen Rabich, Olena Meshcheriakova, Irina Department of Reinforced Concrete and Masonry Structures Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Materials Science and Materials Processing Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Computer Sciences Information Technologies and Applied Mathematics Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine Department of Life Safety Prydniprovska State Academy of Civil Engineering and Architecture Dnipro Ukraine

ISBN: (纸本)9798350395532

The study of the relationship between the structure and properties of cement mortars that make up concrete is complicated by the complex geometric structure of structural elements. The fractal approach was used to assess adequately the fracture surface of cement mortar with a plasticizer. The fractal dimension of the identified elements of mortar macrostructure is calculated: dark areas with cement stone predominance, light areas with sand predominance and pores. Linear models of strength prediction based on the influence of dark areas of the structure (the pair correlation coefficient is 0.77), light areas (R2=0.76) and pores (R2=0.72) are obtained, which indicates the sensitivity of physical and mechanical properties to changes in the fractal dimension of structural elements. An increase in the strength indicators of the mortar was recorded from 8 to 27 MPa with an increase in the fractal dimension of cement stone from 1.787 to 1.959 (by 8.9 %). Strength indicators decrease with an increase in the fractal dimension of light sections of the structure from 1.534 to 1.891 (by 19.9 %) and the fractal dimension of pores from 1.632 to 1.807 (by 9.7 %). A multiparametric fractal model of the influence of fractal dimensions of the identified structural components is constructed, which has a relatively high correlation coefficient of 0.89. The proposed approach can be used in industrial conditions as an additional method for operational forecasting of flexural strength indicators of cement mortar based on the fractal dimension of its macrostructure elements. © 2023 IEEE.

关键词： Fractal dimension

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Modeling of Memory Dependent Derivative Under Three-Phase Lag in Generalized Thermo-Viscoelasticity

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International Journal of applied and Computational mathematics 2021年第6期7卷 1-20页

作者： Singh, Biswajit Sarkar, Smita Pal Barman, Krishnendu Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur Howrah 711103 West Bengal India Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Paschim Medinipur 721102 West Bengal India

In this study, a new generalized model of thermo-viscoelasticity with three phase-lag (TPL) theory concerning memory-dependent derivative (MDD) theory is emphasized. The governing combined equations of the novel model associated with kernel function and time delay are considered in a two-dimensional semi-infinite space. The bounding surface of the medium is assumed to be free of traction and subjected to time-dependent thermal loading. Using Laplace and Fourier Transform techniques, the problem is transformed from the space–time domain and then solved numerically. Suitable numerical technique is used to find the inversion of Fourier and Laplace transforms. In the simulation, the effects of the time-delay parameter and kernel function on the distributions of the displacement components, stresses and temperature field are studied and represented graphically. The results shows that the presence of TPL, the time-delay and kernel function extensively affect all the distributions. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Laplace and Fourier transform Memory dependent derivative Thermo-viscoelasticity Three phase-lag Vector matrix differential equation

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Fitted And Full Mimetic Finite Difference Methods For Two Dimensional American and European Options Pricing

SSRN

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SSRN 2024年

作者： Attipoe, David Sena Tambue, Antoine 6-8 Melrose Road Muizenberg 7945 South Africa Department of Mathematics and Applied Mathematics University of Cape Town Rondebosch7701 South Africa Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen5063 Norway

In this paper, we present novel numerical methods based on mimetic finite difference method for American and European options pricing. The mimetic finite difference method is a special spatial discretization technique that mimics and preserves important fundamental properties of the underlying continuum problem in the discrete case. Our novel method called fitted full mimetic finite difference method is able to handle the degeneracy of the Black-Scholes differential operator which underlies the option pricing problems. The unique solvability and the consistency of the resulting semi-discrete solutions are rigorously proved for the full mimetic finite difference method and fitted full mimetic finite difference method. To obtain a full discrete solutions, the standard implicit time stepping scheme is considered for temporal discretization. Furthermore, numerical simulations are conducted to demonstrate the accuracy of the fitted full mimetic method compared to the full mimetic method and fitted finite volume method presented in the literature. © 2024, The Authors. All rights reserved.

关键词： Finite volume method

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FMC-CEE: A Collaborative Execution Environment with Flash Memory Controllers in IoT

FMC-CEE: A Collaborative Execution Environment with Flash Me...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Zheng Zhang Jingfeng Xue Thar Baker Tian Chen Yuhang Zhao Weizhi Meng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Architecture Technology and Engineering University of Brighton Brighton UK School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark

Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with security sensitive code. Trusted execution environments (TEEs) and TEE-based applications have been widely adopted to run security sensitive workloads and protect the security of applications. However, existing approaches require specialized CPU support or hardware peripherals equipped with co-processors, precluding widely deployment on low-cost IoT devices. In this paper, we propose a flash memory controller-based collaborative execution environment (FMC-CEE), a lightweight security solution constructed on the target flash device to provide code confidentiality and basic security primitives for low-cost IoT devices and embedded devices. FMC-CEE leverages the microprocessor of the target flash device as a co-processor that executes security-sensitive workloads collaboratively with the target system. We implemented a prototype of FMC-CEE on a Trans-Flash (TF) card and executed security-sensitive tasks of the target host. The experimental results show that FMC-CEE takes $590.748 \mu \mathrm{s}$ to execute the remote code (512 bytes), thus incurring very little overhead on the host system.

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Autonomous Electron Tomography Reconstruction Using Bayesian Optimization

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1915-1916页

作者： Millsaps, William Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Hovden, Robert Department of Nuclear Engineering Radiological Sciences University of Michigan Ann Arbor MI United States Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

Enhancing Transfer Learning for Medical Image Classification...

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International Conference on computer and Information Technology (ICCIT)

作者： Md Zehan Alam Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with re-sampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.

关键词： Diabetic retinopathy Accuracy Sensitivity Transfer learning Brain tumors Brain modeling Reliability Medical diagnostic imaging Image classification Residual neural networks

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： John Anand V. Thamilarasi Ashish Rayal Himanshu Kumar Gupta Kamboji Jyothi Pramatma Vishwakarma Department of Computer Science & Engineering (AI) IIMT College of Engineering Greater Noida India Department of Computer Science & Engineering Sri Sarada College for Women(Autonomous) Salem-16 Tamilnadu India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India Department of Mathematics Chaitanya Bharathi Institute of Technology Proddatur AP India Department of Computer Aplication Lovely Professional University Punjab India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments.

关键词： Deep learning Human computer interaction Fuzzy logic Accuracy Gesture recognition Transformers Noise measurement Long short term memory Noise level Resilience

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Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

arXiv

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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Cross-Cultural Insights in Health: Integrating Machine Learning and Deep Learning for Early Predictive Diabetes Detection

Cross-Cultural Insights in Health: Integrating Machine Learn...

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International Conference on computer and Information Technology (ICCIT)

作者： Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Md. Zehan Alam Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Mathematics University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Diabetes, a potentially life-threatening condition if undetected, necessitates an early and accurate diagnosis. This study evaluates traditional machine learning (ML) models and deep neural networks (DNNs) for diabetes prediction, utilizing the Pima Indians Diabetes Dataset (PIDD) and the Sylhet Diabetes Hospital dataset (SDHD). We optimized six ML models—Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines, and K-Nearest Neighbors—using GridSearchCV and ensemble learning, and deployed DNNs with varied train-test splits (90-10%, 80-20%, and 70-30%). Evaluations on accuracy, precision, recall, F1-score, and AUC-ROC highlighted RF’s outstanding performance after GridSearchCV on the SDHD with Accuracy: 98%, Precision: 100%, Recall: 96%, F1 Score: 98%, AUC: 100% and on PIDD with Accuracy: 76%, Precision: 63%, Recall: 80%, F1 Score:70%, AUC:81%. This underscores advanced ML’s effectiveness in handling complex datasets and enhancing early diabetes detection strategies.

关键词： Support vector machines Accuracy Hospitals Artificial neural networks Predictive models Prediction algorithms Diabetes Ensemble learning Regression tree analysis Random forests

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Decentralized and Equitable Optimal Transport

arXiv

引用

arXiv 2024年

作者： Lau, Ivan Ma, Shiqian Uribe, César A. The Department of Computer Science National University of Singapore Singapore The Department of Electrical and Computer Engineering Rice University HoustonTX United States The Department of Computational Applied Mathematics and Operations Research Rice University HoustonTX United States

This paper considers the decentralized (discrete) optimal transport (D-OT) problem. In this setting, a network of agents seeks to design a transportation plan jointly, where the cost function is the sum of privately held costs for each agent. We reformulate the D-OT problem as a constraint-coupled optimization problem and propose a single-loop decentralized algorithm with an iteration complexity of O(1/ϵ) that matches existing centralized first-order approaches. Moreover, we propose the decentralized equitable optimal transport (DE-OT) problem. In DE-OT, in addition to cooperatively designing a transportation plan that minimizes transportation costs, agents seek to ensure equity in their individual costs. The iteration complexity of the proposed method to solve DE-OT is also O(1/ϵ). This rate improves existing centralized algorithms, where the best iteration complexity obtained is O(1/ϵ2). Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

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