Evaluation of weighted fusion for scalar images in multi-sensor network

Jittawiriyanukoon, Chanintorn; Srisarkun, Vilasinee

doi:10.11591/eei.v10i2.1792

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…From the chi-square analysis [27] it was identified critical value is greater. As a result, the alternate hypothesis was found to be true as there is a direct association between symptoms and stages of endometriosis.…”

Section: Figure 1 Heat Map For Correlating Endometriosismentioning

confidence: 99%

Symptoms based endometriosis prediction using machine learning

Visalaxi

Thalavaipillai

2021

Bulletin EEI

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Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.

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Section: Figure 1 Heat Map For Correlating Endometriosismentioning

confidence: 99%

Symptoms based endometriosis prediction using machine learning

Visalaxi

Thalavaipillai

2021

Bulletin EEI

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“…The logarithmic cost function is increasing and convex. Also, the results obtained after solving the optimization problem using the Jaya algorithm [34]- [68] contributed to improving the optimization of the system reliability with appropriate costs [33], [69]- [80].…”

Section: Introductionmentioning

confidence: 99%

Application of the Jaya algorithm to solve the optimal reliability allocation for reduction oxygen supply system of a spacecraft

Abed

Aljanabi

Ameer

et al. 2021

IJEECS

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In this paper the reliability of reduction oxygen supply system (ROSS) of a spacecraft which was calculated as a complex system using minimal cut method. The reliability of each component of system was calculated as well as the reliability importance of the system. The cost of each component of the system was possible approaches of the allocation values of reliability based the minimization of the overall cost in this system. The advantage of this algorithm can be used to allocate the optimization of reliability for simple or complex system. This optimization is achieved using the Jaya algorithm. The proposed technique is based on the notion that a conclusion reached on a particular problem should pass near the best results and avoid the worst outcomes. The original findings of this paper are: i) the system used in this paper is a spacecraft’s reduced oxygen supply system with the logarithmic cost function; and ii) the results obtained were by using the Jaya algorithm to solve specific system reliability optimization problems.

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“…The research aims to develop a method that can effectively remove haze and restore precise details in hazy images. The authors propose that haze introduces spatially varying light attenuation in the image, leading to more information loss and reduced contrast [16], [17]. To address this, the algorithm leverages non-local image patch analysis to estimate the haze-free scene radiance.…”

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confidence: 99%

SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques

Moorthy,

Tripathi,

Joshi

et al. 2024

IJEECS

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<p>We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.</p>

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Evaluation of weighted fusion for scalar images in multi-sensor network

Cited by 3 publications

References 21 publications

Symptoms based endometriosis prediction using machine learning

Symptoms based endometriosis prediction using machine learning

Application of the Jaya algorithm to solve the optimal reliability allocation for reduction oxygen supply system of a spacecraft

SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques

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