Computer vision based fatigue detection using facial parameters
A. Balasundaram,S. Ashokkumar,D. Kothandaraman,SeenaNaik kora,E. Sudarshan,A. Harshaverdhan +5 more
- Vol. 981, Iss: 2, pp 022005
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The article was published on 2020-12-01 and is currently open access. It has received 12 citations till now.read more
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Decentralized Link Failure Prevention Routing (DLFPR) Algorithm for Efficient Internet of Things
D. Kothandaraman,Meenalochani Manickam,A. Balasundaram,Deepthi Pradeep,A. ArulMurugan,Arun Kumar Sivaraman,Sita Rani,Barnali Dey,R. Balakrishna +8 more
TL;DR: In this paper , a decentralized link failure prevention (DLFP) routing algorithm is proposed to promote enhanced and efficient Internet of Things (IoT) by increasing the mobility and reducing the opportunity for loss of IoT node meeting links due to both mobility and blockers/interferers.
Journal ArticleDOI
Chest X-ray image based COVID prediction using machine learning
TL;DR: The aim is to develop a machine-learning based model and design exploration to learn the architecture design starting from initial design prototype and machine learning technique to detect COVID-19 in a simpler manner.
Journal ArticleDOI
Drowsiness detection techniques comparative analysis and currently used driver fatigue detection system
Journal ArticleDOI
An adaptive system for predicting student attentiveness in online classrooms
TL;DR: In this paper , a student attentiveness model was proposed to detect and monitor a student's eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher.
Journal ArticleDOI
Liver disease prediction using ML techniques
TL;DR: In this paper , a comparison of few machine learning algorithms like random forest, logistic regression and SVM was made in predicting liver disease and compared their accuracy levels in predicting the liver disease.
References
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Journal ArticleDOI
A Survey on State-of-the-Art Drowsiness Detection Techniques
Muhammad Ramzan,Hikmat Ullah Khan,Shahid Mahmood Awan,Amina Ismail,Mahwish Ilyas,Ahsan Mahmood +5 more
TL;DR: Overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.
Journal ArticleDOI
Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection
TL;DR: A support vector machine-based posterior probabilistic model (SVMPPM) aimed at transforming the drowsiness level to any value of 0~1 instead of discrete labels is proposed, indicating that the combination of the proposed SVMPPM, the EEG headband, and the wrist-worn smart device constitutes an effective, simple, and inexpensive wearable solution for DDD.
Journal ArticleDOI
Real-time classification for autonomous drowsiness detection using eye aspect ratio
Caio Bezerra Souto Maior,Márcio das Chagas Moura,João Mateus Marques De Santana,Isis Didier Lins +3 more
TL;DR: A methodology for drowsiness detection based on eye patterns of people monitored by video streams using a low-cost real-time system to detect whether a user (operator) is drowsy using a simple web camera is developed.
Journal ArticleDOI
A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers
TL;DR: A smartphone-based system for the detection of drowsiness in automotive drivers that uses the percentage of eyelid closure obtained through images captured by the front camera with a modified eye state classification method, and its implementation on an Android smart-phone, which is readily available to most drivers or cab owners.
Journal ArticleDOI
A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems
TL;DR: A Wearable Drowsiness Detector based on sensor fusion is presented, fusing behavioral information coming from user motion through an IMU sensor and physiological informationComing from brain activity through a single EEG electrode, resulting in a wearable device capable to detect 5 different levels of drowsiness.
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