scholarly journals Driver Safety Alert System using Machine Learning

2021 ◽  
Vol 183 (8) ◽  
pp. 27-30
Author(s):  
Manjusha Sanke ◽  
Pranjali Savaikar ◽  
Chaitravi Parab ◽  
Mangesh Gawas ◽  
Viraj Mhalshekar
Author(s):  
Parshal Chitrakar ◽  
Yoganand Biradavolu ◽  
Siva Sankar Yellampalli
Keyword(s):  

2020 ◽  
Vol 3 (2) ◽  
pp. 114-119
Author(s):  
Samsudin Samsudin ◽  
Muhammad Ikhsan ◽  
Maya Juliana Ritonga

The research purpose to design a motorcycle safety alert system using ultrasonic sensors to detect objects within reach and using the microcontroller as the brain of the process control system so that it can be used to build electrical systems. The bike's safety-distance warning system uses fuzzy logic in the soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. With this system, it can help the community to reduce the number of a road accident to generate output in some conditions such as safe, carefully, and dangerous by using alarm warnings that can cause sound and LED as virtual and LCD notifications that can display distances in efficient and effective. Based on the results of the tests being done, the sensor system is accurate at 95.242% at 10 times the test.


Elderly and people with disabilities often rely on others for their locomotion. With this emerging world of automation and technologically advanced society we live in a smart wheelchair with appropriate automation can be a life changing innovation for them. One might wonder what is the need for a wheelchair to be smart or for that matter why anything needs to be smart. The answer is simple, to overcome the limitations of the existing technology. We aim on integrating a simple Manually operated wheelchair with features like Obstacle Detection with appropriate technologies such as voice control or gesture control for people who are not able to locomote like a normal person can. With this project, not only do we help a person using a normal wheelchair more easily but also make life easier for those who have other disabilities which are standing as an obstacle making it difficult for them to walk. For instance, a blind person can use a smart wheelchair that allows voicecontrolled movement or even gesture-controlled movement. Another might be of a person who can’t speak, will now be able to control everything with his bare hands. Wheelchair coupled with the appropriate sensors that automatically detects the obstacles/objects in the proximity and takes appropriate action consequently and In addition to that it may also be controlled by another person taking care of the disabled by giving commands such as forward, backward, upright etc. This not only reduces the user's efforts but also helps people to take care of their elderly. Voice/gesture control system makes everything simple. Just visualize the application of it in a hospital where the nurse has to manually handle the patient's wheelchair for even the slightest of movement. This system on the other hand needs only text or voice input command, and based upon the predefined command received from the user, the system will execute the task. This project even enforces a GSM module which uses the sim card, which can help in tracking the wheelchair when required, like in case where a user is in difficulty and needs emergency help, a message asking for help can be sent to the intended person.


Author(s):  
Banala Krishna Gopal

In today’s modern world where everything is being automated and security is a growing concern, we made an automated module to live-monitor the anomalies in any provided space at all times to ensure security in our personal space. By implementing our project, we can monitor anything important which would be out of our reach at the moment with a live alert system through which we can identify any anomalies. In our proposed system we integrated Machine Learning to work with an IoT system by using Bolt Wi-Fi module which also uses an LDR sensor to detect the light intensity, here LDR is used specifically to better understand the Z-Score analysis. We are using ML to do an analysis known as Z-Score, which processes a math equation to detect anomalies. This analysis is done to predict a frame of upper and lower boundaries for the light intensity. Eventually, when the LDR sensor value i.e., light intensity goes out of range in a room, it generates Real-Time alerts in the form of an SMS alert which will be directed to the user's mobile phone through Twilio. This alert system is an advanced way to increase the work efficiency of any live monitoring system as the ML is always working to increase accuracy. In our project, this system specifically uses Light Dependent Resistor to detect changes in light intensities, but this can be implemented for any sensor to detect.


Author(s):  
Lexman Raj J ◽  
Muthukumaran V ◽  
Abirami J ◽  
Maheswaran A ◽  
Mohamed Shaki M
Keyword(s):  

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