In the 21st century, we can shop anytime and anywhere without leaving our comfort zones but there is still a noteworthy difference between offline and online shopping resulting high return rates especially in the appa...
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For a mathematical model to describe vague (uncertain) problems effectively, it must have the ability to explain the links between the objects and parameters in the problem in the most precise way. There is no suitabl...
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The presented work aims to implement speech recognition systems for Hindi and English using advanced deep learning. Enhancing ASR technologies is crucial for improving digital communication's accessibility and eff...
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A common requirement for the robot motion planning problem is to find the free space for a given map with polygonal obstacles. If the map changes dynamically where obstacles are inserted or deleted consequently, the f...
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Diabetes mellitus poses a significant health challenge globally, emphasizing the importance of early prediction and intervention. In this information, we offer a novel approach for diabetes forecast in healthcare util...
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Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to ...
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Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to achieve this by using meta-heuristic algorithms, specifically population-based algorithms that are able to provide a subset of features that is optimal and not exact. Meta-heuristic algorithms face challenges such as staying in local minima, easily falling into local optimum, weakly global searchability, premature convergence, and slow convergence speeds. However, recent research has limitations such as high complexity and weak initialization. In order to overcome these limitations, a three-stage model is proposed. In the first stage, the correlation of features and the correlation of features with class are considered during feature selection and used to create the initial population in the pathfinder optimization algorithm (PFA). PFA is a population-based algorithm and has some drawbacks, in the last iterations, the fluctuation rate (A) and vibration vector (Ε) parameters converge to 0, and finding a new solution is impossible. As a second stage, a fuzzy inference system is designed to adjust these parameters adaptively and is called fuzzy-pathfinder optimization (FPO). In the third stage, FPO is used to select relevant features based on classification error, proportion of selected features, and redundancy. Finally, different algorithms such as simulated annealing (SA), differential evolutionary (DE), genetic algorithm (GA), particle swarm optimization (PSO), PFA, estimation of distribution algorithm (EDA), and symmetrical uncertainty criterion (SUC-PSO) are used for comparison. Based on the results, the proposed model is able to reach an average accuracy of 96% on average. Based on a comparison of the proposed algorithm with SA, DE, GA, PSO, PFA, EDA, and SUC-PSO, the objective function is improved by 17.3%, 5.6%, 3.0%, 4.5%, 5.0%, 0.5%, and 1.2%, re
The large language model has demonstrated its ability to reason and interpret in text-to-text applications. Current Chain of Thought (CoT) research focuses on either explaining reasoning steps or improving prediction ...
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Text style transfer is the task of modifying the stylistic attributes of a given text while preserving its original meaning. This task has also gained interest with the advent of large language models. Although knowle...
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The process of bringing criminals to justice can be complicated when reporter information and sensitive data related to the case are revealed and may involve international law enforcement cooperation, especially when ...
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Accurate cattle identification is an essential but complicated issue in the field of livestock management. Traditional identifying methods can involve invasive procedures, posing ethical difficulties and compromising ...
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