scholarly journals Post-Acne Hyperpigmentation: Evaluation of risk factors and the use of Artificial Neural Network as a predictive classifier

2021 ◽  
Author(s):  
Firas Al-Qarqaz ◽  
Khaldon Bodoor ◽  
Ala Baba ◽  
Ali Al-Yousef ◽  
Jihan Muhaidat ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Patient Education ◽  
Skin Color ◽  
Sunlight Exposure ◽  
Dark Skin ◽  
Severe Acne ◽  
Factors Associated ◽  
Artificial Neural

Background: Acne is common among young individuals. People with dark skin have a higher risk for developing pigmentary complications.  Inflammation is an important factor in post-acne hyperpigmentation however other factors are also involved in developing this complication however these factors are not well studied. Objective: The aim of this study is to identify risk factors involved in post-acne hyperpigmentation. Materials and Methods: Clinical data related to acne, acne- related hyperpigmentation were collected. Data was analyzed for risk factors associated with acne pigmentation. Artificial neural network was used as predictive disease classifier for the outcome of pigmentation. Results: Majority of patients in this study (339 patients) had dark skin phototypes (3 and 4). Post- acne hyperpigmentation was seen in more than 80% of patients. Females, darker skin color, severe acne, facial sites, and excessive sunlight exposure, squeezing or scratching lesions are important risk factors for post-acne hyperpigmentation. Conclusion: Post-acne hyperpigmentation is multifactorial. Several factors implicated in PAH are modifiable by adequate patient education (lesion trauma, excessive sunlight exposure). The use of ANN was helpful in predicting appearance of post-acne hyperpigmentation based on identified risk factors.

scholarly journals Improved prediction of postoperative paediatric cerebellar mutism syndrome using an artificial neural network

2022 ◽  
Author(s):  
Jai Sidpra ◽  
Adam P Marcus ◽  
Ulrike Löbel ◽  
Sebastian M Toescu ◽  
Derek Yecies ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Posterior Fossa ◽  
Area Under The Curve ◽  
Rank Test ◽  
Cerebellar Mutism ◽  
Posterior Fossa Tumours ◽  
Artificial Neural

Abstract Background Postoperative paediatric cerebellar mutism syndrome (pCMS) is a common but severe complication which may arise following the resection of posterior fossa tumours in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalisation of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). Methods An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumours. The ANN was trained on this dataset and its performance evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. Area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed rank test, with p<0.05 considered statistically significant. Results 204 children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (p<0.05) and both external models (p<0.001). Conclusion Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.

scholarly journals Artificial Neural Network Models for Material Classification by Photon Scattering Analysis

2020 ◽  
Vol 9 (03) ◽  
pp. 01-04
Author(s):  
Dr. Naveen Jain
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Network Models ◽  
Photon Scattering ◽  
Neural Network Models ◽  
Material Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
The Impact

This article explains the risk factors involved in a business. In each type of business, there are certain risk factors for the implementation of anything in the business. The type of risks involved can depend upon many factors. It also depends on the type of business an organisation is doing. But it is very important that the risk analyst does all the analysis of the risks that might arise in future and must take necessary actions in order to avoid those risks. The risk analyst can also try to reduce the impact of the risks on the business. Therefore, it is very important that the risk analyst should have the knowledge of how to analyse risk and then can act upon them.

scholarly journals Learning-Based Detection of Harmful Data in Mobile Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Color ◽  
Multimedia Content ◽  
Body Parts ◽  
Human Face ◽  
Neural Network Learning ◽  
Network Learning ◽  
Tablet Pcs ◽  
Artificial Neural

The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

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