scholarly journals Neural Network Based Detection of Driver’s Drowsiness

2021 ◽  
Author(s):  
Shraddha Bhandarkar ◽  
Tanvi Naxane ◽  
Sayli Shrungare ◽  
Shivani Rajhance
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Traditional Methods ◽  
Drowsiness Detection ◽  
Proposed Model ◽  
Falling Asleep ◽  
Image Dataset ◽  
Behavioral Aspects ◽  
The Face ◽  
Image Templates

<div>The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.</div>

scholarly journals Neural Network Based Detection of Driver’s Drowsiness

2021 ◽  
Author(s):  
Shraddha Bhandarkar ◽  
Tanvi Naxane ◽  
Sayli Shrungare ◽  
Shivani Rajhance
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Traditional Methods ◽  
Drowsiness Detection ◽  
Proposed Model ◽  
Falling Asleep ◽  
Image Dataset ◽  
Behavioral Aspects ◽  
The Face ◽  
Image Templates

<div>The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.</div>

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

scholarly journals Robot movement controller based on dynamic facial pattern recognition

2021 ◽  
Vol 22 (2) ◽  
pp. 733
Author(s):  
Siti Nurmaini ◽  
Ahmad Zarkasi ◽  
Deris Stiawan ◽  
Bhakti Yudho Suprapto ◽  
Sri Desy Siswanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Face Detection ◽  
Detection System ◽  
Accuracy Rate ◽  
True Value ◽  
The Face ◽  
Facial Pattern ◽  
Lower Frame ◽  
The Right

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

scholarly journals Fall Detection Using Neural Network Based on Internet of Things Streaming Data

2020 ◽  
Vol 4 (2) ◽  
pp. 91
Author(s):  
Zana Azeez Kakarash ◽  
Sarkhel H.Taher Karim ◽  
Mokhtar Mohammadi
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Detection System ◽  
Fall Detection ◽  
Streaming Data ◽  
Accelerometer Data ◽  
Physical Activity Monitoring ◽  
Proposed Model ◽  
The Internet Of Things ◽  
Processing Framework

Fall event has become a critical health problem among elderly people. We propose a fall detection system that analyzes real-time streaming data from the Internet of Things (IoT) to detect irregular patterns related to fall. We train a deep neural network model using accelerometer data from an online physical activity monitoring dataset named, MobiAct. An IBM Cloud-based IoT data processing framework is used to manage streaming data. About 96.71% of accuracy is achieved in assessing the performance of the proposed model.

scholarly journals Age-based facial recognition using convoluted neural network deep learning algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 424
Author(s):  
Julius Yong Wu Jien ◽  
Aslina Baharum ◽  
Shaliza Hayati A. Wahab ◽  
Nordin Saad ◽  
Muhammad Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Well Being ◽  
Deep Learning Algorithm ◽  
Business Applications ◽  
Image Dataset ◽  
The Face ◽  
The Individual

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

scholarly journals Disease Identification in Chilli Leaves using Machine Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 325-329
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Detection System ◽  
Network Models ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Neural Network Models ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model

Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.

scholarly journals Anomaly Detection Approach Based on Deep Neural Network and Dropout

2020 ◽  
Vol 17 (2(SI)) ◽  
pp. 0701
Author(s):  
Zaid Hussien et al.
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Early Stage ◽  
High Detection Rate ◽  
Network Intrusion ◽  
Proposed Model

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss function to enforce the proposed model in multiple classification, including five labels, one is normal and four others are attacks (Dos, R2L, U2L and Probe). Accuracy metric was used to evaluate the model performance. The proposed model accuracy achieved to 99.45%. Commonly the recognition time is reduced in the NIDS by using feature selection technique. The proposed DNN classifier implemented with feature selection algorithm, and obtained on accuracy reached to 99.27%.

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