Study on Fuzzy Logic Decision Support System for Hypovigilance Detection Based on CNN Feature Extractor and WN Classifier | Chapter 13 | Recent Advances in Mathematical Research and Computer Science Vol. 1

Excessive speed and drinking are the leading causes of road accidents, followed by fatigue and drowsiness. It is critical to regularly check the driver's alertness level in order to improve their capacity to drive safely and efficiently. This paper is about the issue of road safety. It makes an attempt to present a video-based driver vigilance monitoring system. The goal of this project is to develop an assistance driving application that uses eye closure length and head position estimate as effective indicators of alertness control. The suggested system may be broken down into three steps: video eye recognition and tracking, categorization of eye states, and integration of both sub-systems based on blinking and head position. Because of its efficiency in real-time applications, we employed the Viola and Jones algorithm for interest area recognition to complete the prior tasks. We used two innovative architectures of transfer learning classifiers based on fast wavelet transform and separator wavelet networks for the classification stage, which is the paper's key contribution. This unique design outperforms the standard version of transfer learning based on SVM classifiers as well as our previous classifier based solely on fast wavelet networks without a deep learning component. The goal of our research is to compare the performance of CNNs with wavelet networks in the classification phase. We also want to highlight the value of fuzzy logic as a tool for combining multiple inputs, allowing us to create a more accurate vigilance control system.

Author (S) Details

Ines Teyeb
RTIM: Research Team in Intelligent Machines, University of Gabes, National Engineering School of Gabes (ENIG), Tunisia.

Ahmed Snoun
RTIM: Research Team in Intelligent Machines, University of Gabes, National Engineering School of Gabes (ENIG), Tunisia.

Olfa Jemai
RTIM: Research Team in Intelligent Machines, University of Gabes, National Engineering School of Gabes (ENIG), Tunisia.

Mourad Zaied
RTIM: Research Team in Intelligent Machines, University of Gabes, National Engineering School of Gabes (ENIG), Tunisia.

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Study of Prevalence & Vigilance of Adverse Cutaneous Reactions in Hairdye Users: Cosmetovigilance | Chapter 7 | Recent Developments in Medicine and Medical Research Vol. 4

 Cosmetics are dispersed substances that are applied to the skin to clean, smell, or improve one's appearance. Despite the fact that the majority of cosmetics adverse responses are small and mainly include skin changes, the number of documented adverse reactions is extremely low, owing to underreporting. Cosmetic product regulations are primarily concerned with the safety of products that may be used by large groups of healthy people.

Methods: We interviewed 500 participants and gathered questionnaires about hair dye use, the prevalence and features of adverse cutaneous reactions, and reporting of following steps taken by the volunteers in order to fulfil the study's purpose.

The response rate was approximately 78 percent. A total of 360 persons were found to have a history of hair dye dermatitis. Edematous skin reactions are seen in 65% of cases, allergic dermatitis is shown in 49% of cases, and both types of reactions are present in 30% of cases.

Despite the fact that our experience with reporting unfavourable aesthetic impacts is restricted to a small geographic area, it emphasises that a reliable and effective monitoring system should be in place.

Author(S) Details

Archana G. Dhavalshankh
D. Y. Patil Medical College, CHINMAY Bungalow, Near Maisaheb Bavdekar School, Tarabai Park, Kolhapur, 416003, India.

Ganesh P. Dhavalshankh
R. C. S. M. Government Medical College, Kolhapur, India.

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