In recent years, vehicle-to-vehicle communication has become a significant aspect of detecting anomalous environmental activities through many wireless devices. Transferring data from one place to another is one of the important daily activities. Though using many wireless devices like Wi-Fi-Bluetooth readers helps us connect, there are also many limitations like providing signal only in the shortest range with fewer security features, speed, and range between the communication. These challenges will give rise to the solution using recent technology developed by Light Fidelity (Li-Fi), which will concise the vehicle-to-vehicle communication to optical networking technology. The proposed system with Li-Fi technology includes an ultrasonic sensor, gas sensor, vibration sensor, temperature sensor, LCD display, normal robot setup, Li-Fi transmitter, and receiver. If any abnormal circumstances are in front of the vehicle, the vehicle will be stopped, and a notification will be sent to the beside vehicle. Li-Fi transmitter and receiver are connected to the microcontroller's UART (Universal Asynchronous Receiver/Transmitter) function. In the proposed system, the object is detected using a Machine Learning (ML) algorithm called the Haar Cascade classification algorithm, where a cascade classification process is trained from the collection of positive and negative images. The proposed system increases the performance metrics like data transfer speed and decreases the time for transferring the communication data. Finally, the system saves many lives of persons from road accidents.
The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.
According to the law of the Indian government as per section 129 of the motor vehicle act of 1988 briefly explains the motorcycle or two-wheeler rider is mandatory to wear the helmet while driving and the recent survey conducted on road accidents by the world health organization (WHO). This WHO organization has briefly mentioned the cause and the prevention of road accidents that are happened around the world. They also mentioned the highest death rate that took place in India and the survey also reported as per the rate 1.5 lakh of road death has been accounted for by each year approximately. The above article motivates us to develop a system that is capable of providing safety and precaution to the bike rider. We designed a system that is capable of detecting the rider is whether wearing the helmet or not. Then detecting if the rider has consumed alcohol or not, whether if these two conditions are yet satisfied then only the motor will ignite or else it will not ignite. In case an accident occurred, our system is capable of detecting the accident and its location approximately. We implanted the led strip indication in the helmet unit to reduce the percentage of an accident during night times.
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