Results in Control and Optimization最新文献

英文中文

A novel approach in controlling the spread of a rumor within a crowd

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-02-02 DOI: 10.1016/j.rico.2025.100534

Imane Dehaj , Abdessamad Dehaj , Abdessamad Tridane , M.A. Aziz-Alaoui , Mostafa Rachik

The spread of rumors within a crowd can lead to harmful consequences, ranging from misinformation and social disturbances to public panic and injuries or fatalities. In this work, we propose a novel approach to an effective strategy for reducing the number of individuals affected by a rumor within a crowd. This strategy relies on developing control functions operating between zero and one, ensuring that the number of individuals affected by the rumor remains below a predetermined threshold at any given time. We analyze this strategy within the frameworks of continuous-time and discrete-time SIR models, which divide the population into Susceptible (S), Infectious (I), and Recovered (R) individuals, considering both practical constraints and theoretical limitations. Our results demonstrate that the proposed control functions ensure a gradual decrease in the number of affected and susceptible individuals over time, effectively limiting the spread of rumors and preventing uncontrollable situations. Numerical simulations illustrate the efficiency of this approach, highlighting its ability to achieve specific objectives in real-world scenarios.

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引用次数: 0

Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-02-01 DOI: 10.1016/j.rico.2025.100533

R.M. Haggag , Eman M. Ali , M.E. Khalifa , Mohamed Taha

Multiple Sclerosis (MS) is an auto-immune disorder affecting the central nervous system, affecting 2.8 million people worldwide. Early diagnosis is crucial due to its profound social and economic impacts. MRI is commonly used for monitoring abnormalities. This study proposes a novel Content-Based Medical Image Retrieval (CBMIR) framework using Convolutional Neural Networks (CNN) and Transfer Learning (TL) for MS diagnosis using MRI data. Our framework utilizes The Inception V3 model that is pre-trained on ImageNet and RadImageNet datasets, and we modified the model by adding a new block of six layers to reduce the features’ dimensionality in the feature extraction phase. Fine-tuning the hyper-parameters for the whole system was done using the Bayesian optimizer. We experiment with Nine different distance metrics to measure query and database image similarity. Experiments on four public MS-MRI datasets demonstrated the end-to-end deep learning framework’s generalizability without extensive pre-processing, with mAP scores of 86.20%, 93.77%, 94.18%, and 90.46%, respectively demonstrating its effectiveness in retrieval. Moreover, a comparison with related CBMIR systems confirmed the effectiveness of our model.

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引用次数: 0

A comparison study on optical character recognition models in mathematical equations and in any language

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-02-01 DOI: 10.1016/j.rico.2025.100532

Sofi.A. Francis, M. Sangeetha

Optical Character Recognition[OCR] is a technology that makes use of artificial intelligence and machine learning to extract readable text from documents, images, tags or any other type of sources. It allows one to convert characters and text objects into digital data that can be easily processed, analyzed, and modified. OCR can be applied to various types of languages in both written and spoken format. It can process everything from hand-written documents to typed-out text, making it a highly versatile technology. OCR makes use of a variety of algorithms and methods to process images, and then produces readable output, whatever language it is used for. This technology has the potential to be used for industries, banking, the medical field, security, and document storage among others. OCR faces significant challenges in accurately predicting language and mathematical expressions due to variations in handwriting styles, complex layouts, and the ambiguity of symbols. In this research, we propose assessing the results of different models that have been trained to identify an improved OCR system. The best OCR model is With the help of a decision tree model chosen.

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引用次数: 0

Integral invariant manifold method applied to a mathematical model of osteosarcoma

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-30 DOI: 10.1016/j.rico.2025.100529

Ophir Nave

In this study, an asymptotic method called the method of integral invariant manifold (MIM) was applied to a mathematical model of Osteosarcoma cancer. The mathematical model describes the interactions of the immune system cells and the osteosarcoma tumor. These interactions are described by nonlinear ordinary differential equations. By applying the MIM method, two critical dynamic variables (Cancer cells and Necrotic cells) were identified, providing insights into the dynamics of osteosarcoma over time during treatment.

引用次数: 0

Evaluating consumers benefits in electronic-commerce using fuzzy TOPSIS

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-29 DOI: 10.1016/j.rico.2025.100535

Rakesh Kumar , Janjhyam Venkata Naga Ramesh , Sachi Nandan Mohanty , Muhammad Rafiq , Iskandar Shernazarov , M. Ijaz Khan

Online platforms are preferred by customers when choosing and buying products. The contemporary digital era is witness to e-commerce platforms. Customers rely on various aspects of an e-commerce website for their needs. Sometimes they get more benefit from other digital platforms. This research focusses on how consumers can benefit from digital platforms. The study explores how consumers can embrace a website by leveraging various factors on an online business. The study identified five advantage factors, each offering six benefits, based on previous literature reviews. The study conducted interviews with E-marketing experts based on a questionnaire. This Research used Fuzzy TOPSIS method to determined ranking of most advantageous factor. The factors that provide advantages include usability, service quality, information quality, and online trust. The research analyses the ranking of these factors based on results. The research finds that usability of a website, i.e., functionality, efficiency, and search mechanism, is the most important advantage factor. This research is useful for businesspeople who engage in e-commerce platforms. The study explores future scope of research like cost, time, and regional issues on different products and regions.

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引用次数: 0

A novel investigation of quaternion Julia and Mandelbrot sets using the viscosity iterative approach

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-27 DOI: 10.1016/j.rico.2025.100525

Nabaraj Adhikari, Wutiphol Sintunavarat

This paper presents a novel technique for visualizing quaternion Julia and Mandelbrot sets of a quaternion-valued polynomial mapping

T (q) = q^{n} + m q + c

, where

q

is a quaternion variable,

n \in N ∖ {1}

, and

m, c

are quaternion parameters, by employing the viscosity approximation method. The investigation begins with a study of a new escape criterion, specifically designed for generating quaternion Julia and Mandelbrot sets using the viscosity approximation technique. Based on this result, two dimensions and three dimensions cross-sections of quaternion Julia and Mandelbrot sets are created. The paper also examines how variations in the parameters of the iterative methods impact the resulting sets’ characteristics, such as shape, size, symmetry, and color.

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引用次数: 0

Mathematical analysis of COVID-19 dynamics in Iraq utilising empirical data

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-27 DOI: 10.1016/j.rico.2025.100528

Wasan I. Khalil, Ayad R. Khudair

This study examines the impact and transmission of COVID-19 in Iraq via the development of a mathematical model grounded in the

S I I_{h} R

framework. We assess and quantify the biological parameters of the model utilising empirical data. To ascertain the model’s reliability, we examine the existence and uniqueness of a positive solution. Utilising the validated model, we establish an optimal control problem aimed at minimising daily infections via vaccination, employing a cost function. We also present algorithms utilising Pontryagin’s minimum principle, with time-delay modifications, to identify the optimal vaccination strategy.

引用次数: 0

Intentionally-underestimated value function at terminal state for temporal-difference learning with mis-designed reward

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-26 DOI: 10.1016/j.rico.2025.100530

Taisuke Kobayashi

Robot control using reinforcement learning has become popular, but its learning process often terminates midway through an episode for safety and time-saving reasons. This study addresses the problem of the most popular exception handling that temporal-difference (TD) learning performs at such termination. That is, by forcibly assuming zero value after termination, unintentional implicit underestimation or overestimation occurs, depending on the reward design in the normal states. If the termination by failure is highly valued with the unintentional overestimation, the wrong policy may be acquired. Although this problem can be avoided by paying attention to the reward design, it is essential in the practical use of TD learning to review the exception handling at termination. Therefore, this paper proposes a method to intentionally underestimate the value after termination to avoid learning failures due to the unintentional overestimation. This intentional underestimation is heuristically derived with the assumption of two-step transition to absorbing state. In addition, the degree of underestimation is adjusted according to the degree of steadiness at termination, thereby preventing excessive exploration due to the intentional underestimation. Simulation results showed that the proposed method improves the success rate for 24 tasks with different reward designs from 10/24 in the conventional method to 20/24. Real-robot experiments also demonstrated that the proposed method enables to learn the optimal policy even in the case that the conventional method fails.

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引用次数: 0

Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-25 DOI: 10.1016/j.rico.2025.100515

Abdullah Al Siam , Md Maruf Hassan , Md Atikur Rahaman , Masuk Abdullah

Medical imaging plays a critical role in contemporary healthcare, although it confronts issues relating to storage, security, and confidentiality in machine learning-based diagnostic systems. The proposed framework, Diegif, presents an efficient and safe mechanism for converting DICOM (Digital Imaging and Communications in Medicine) data into EGIF (Encrypted Graphics Interchange Format) files to overcome these challenges. The framework comprises four key components: (1) converting DICOM files to GIF format with encryption, (2) decrypting EGIF files for processing, (3) enabling confidentiality-preserving machine learning training using EGIF data, and (4) facilitating physician diagnosis and report generation based on trained machine learning models. The Diegif framework aims to enhance storage efficiency by decreasing file sizes by 66.32%, thereby improving data transport efficacy and cloud storage affordability while preserving strong encryption for data confidentiality. Pseudocode algorithms are provided for each phase, ensuring reproducibility and transparency. This paper illustrates the framework’s potential to medical image processing, secure storage, and AI-driven diagnostic functions in healthcare.

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引用次数: 0

Mixed-type duality approach for interval-valued programming problems with vanishing constraints

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-01-24 DOI: 10.1016/j.rico.2025.100527

Vivek Singh, Neelima Shekhawat

In this article, we present a new mixed-type dual problem for the challenging class of the interval-valued optimization problem with vanishing constraints. The introduced dual problem does not directly include the index set, but it still requires calculations related to index sets, which makes it challenging to address these models from an algorithm perspective. The relationship between the original interval-valued programming problem with vanishing constraints and its mixed-type dual are discussed by weak, strong and strict converse duality theorems using the assumption of generalized convexity. We also present a non-trivial example to illustrate the theoretical aspects. Our proposed interval-valued mixed-type dual technique unifies the dual techniques discussed in Hu et al. (2020).

引用次数: 0

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