A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers

Jayadev, Puneeth Guddhur; Bellary, Sreepathi

doi:10.1016/j.jksuci.2021.01.004

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…The viability of detecting the driver's condition is tested by this approach Alessandro Amodio et al [17]. Identification of eye damage levels using MDLNN architecture R Akila et al [23] is another hybrid strategy for more accurate categorization of alcohol consumers P G Jayadev et al [22]. The process of feature selection begins with the extraction of the required features from the picture, which are then chosen using BFO.…”

Section: Literature Sourcesmentioning

confidence: 99%

Review on Neural Network based Detection System for Intoxicated Driving

Hari¹,

Thomas²

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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Driving situations include a lot of safety considerations. One of the major factors in road accidents is drunk driving by drivers of vehicles. Due to advancements, notably in the area of biometrics, it is now possible to determine a person's state of drunkenness on alcohol. Therefore, creating an intelligent system to address this issue is important. These systems generally employ artificial vision or sensor networks. For the purpose of evaluating the collected data, we additionally utilize feature selection and supervised classification methods. Every technique and algorithm has been created to use the least amount of analytical resources feasible because the whole acquisition and analysis process will be completed within an embedded device.This paper provides an overview of current developments in the field of neural network-based driver intoxication detection.Different methods for detecting drunkenness have been examined, and potential areas for improvement have been identified.

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Section: Literature Sourcesmentioning

confidence: 99%

Review on Neural Network based Detection System for Intoxicated Driving

Hari¹,

Thomas²

2023

2023 International Conference on Control, Communication and Computing (ICCC)

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“…While the development of apps and services in healthcare is tailored to user needs, it is clear that services are designed based on what the developer has to offer. Recently, various ML techniques have been employed such as convolutional neural networks [ 9 , 10 ] in multiple applications across various fields, such as efficiently grading alcohol dependence, estimating accident severity in severe injuries, and identifying emotions in functional technologies [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Stress monitoring using wearable sensors: IoT techniques in medical field

Talaat

El-Balka

2023

Neural Comput & Applic

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The concept “Internet of Things” (IoT), which facilitates communication between linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports healthcare and is essential to numerous applications for tracking medical services. By examining the pattern of observed parameters, the type of the disease can be anticipated. For people with a range of diseases, health professionals and technicians have developed an excellent system that employs commonly utilized techniques like wearable technology, wireless channels, and other remote equipment to give low-cost healthcare monitoring. Whether put in living areas or worn on the body, network-related sensors gather detailed data to evaluate the patient's physical and mental health. The main objective of this study is to examine the current e-health monitoring system using integrated systems. Automatically providing patients with a prescription based on their status is the main goal of the e-health monitoring system. The doctor can keep an eye on the patient's health without having to communicate with them. The purpose of the study is to examine how IoT technologies are applied in the medical industry and how they help to raise the bar of healthcare delivered by healthcare institutions. The study will also include the uses of IoT in the medical area, the degree to which it is used to enhance conventional practices in various health fields, and the degree to which IoT may raise the standard of healthcare services. The main contributions in this paper are as follows: (1) importing signals from wearable devices, extracting signals from non-signals, performing peak enhancement; (2) processing and analyzing the incoming signals; (3) proposing a new stress monitoring algorithm (SMA) using wearable sensors; (4) comparing between various ML algorithms; (5) the proposed stress monitoring algorithm (SMA) is composed of four main phases: (a) data acquisition phase, (b) data and signal processing phase, (c) prediction phase, and (d) model performance evaluation phase; and (6) grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). From the findings, it is shown that random forest is best suited for this classification, with decision tree and XGBoost following closely behind.

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“…Recent explorations have delved into a diverse array of applications employing convolutional neural networks and other machine-learning methodologies. Notably, these techniques have been employed for accurate grading of alcohol dependence, estimation of accident severity, and recognition of emotions through technology [8,9]. The integration of the IoT and artificial intelligence has ushered in significant enhancements in daily life and healthcare.…”

Section: Introductionmentioning

confidence: 99%

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

Al-Atawi,

Alyahyan,

Alatawi

et al. 2023

Sensors

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The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients’ health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person’s physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed “Stress-Track”. The device is intended to track a person’s stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

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A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers

Cited by 6 publications

References 21 publications

Review on Neural Network based Detection System for Intoxicated Driving

Review on Neural Network based Detection System for Intoxicated Driving

Stress monitoring using wearable sensors: IoT techniques in medical field

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

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