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Tracking and Tracing of Fake News Detection

Tracking and Tracing of Fake News Detection

Price : 10000

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Course Duration
Approx 10

Course Price
₹ 10000

Course Level
High

Course Content

 

ABSTRACT

In the present scenario vehicular travel is increasing all over the world, especially in large urban areas. Therefore for simulating and optimizing traffic control to better accommodate this increasing demand is arises. In this paper we studied the optimization of traffic light controller in a City using wireless sensor. We have proposed a traffic light controller and simulator that allow us to study different situation of traffic density in City. Using wireless sensor we can easily senses the density of traffic because the general architecture of wireless sensor network is an infrastructure less communication network. Urban traffic system is the key problem faced by first world countries these days. It affects day to day life of people by increasing the problem faced by world because of inefficient human management of traffic. Traffic research has the goal to optimize traffic flow of people and goods. As the number of road users constantly increases, and resources provided by current infrastructures are limited, intelligent control of traffic will become a very important issue in the future. However, some limitations to the usage of intelligent traffic control exist. Avoiding traffic jams for example is thought to be beneficial to both environment and economy, but improved traffic-flow may also lead to an increase in demand. The use of Artificial Neural Networks, which has been demonstrated as one of the most frequently used alternative for traffic controlling and predicting mobility patterns. Here we suggests the measures those should be taken for the perfect implementation of Artificial Intelligence in traffic system and reduce the problem faced. In our project we focus on optimization of traffic light controller in a city using embedded system, image processing with machine learning techniques.

 

INTRODUCTION

Traffic congestion is a recurring daily problem that affects all classes of society with inhabitants of cities being the most severely affected. Moreover, traffic jams lead to numerous issues such as: tardiness of citizens to their duties due to wasted time spent on the road, increase in the consumption of fuel which leads to a higher pollution footprint and higher transportation costs, and increase in the rate of accidents and crashes. While there are many causes for such issues, such as the rising number of vehicles and the inefficient public transportation and infrastructure, this paper is focusing on the problems caused by the traffic light systems. The majority of the traffic lights implemented are pre-timed which means that their green and red timers are preconfigured to a static value causing congestion in most roads. To remedy the inefficiency of the current traffic light systems, an adaptive traffic light system is proposed. This system takes into consideration the state of the traffic at a given time before deciding which phase should be applied to the intersection. Artificial Intelligence is defined as intelligent behavior in artifacts. This kind of intelligent behavior includes perception, reasoning, earning, communicating and acting in complex surrounding .It mainly focuses on developing machines that performs and studies the task from the surrounding and performs action which requires human intelligence. AI basically means that the computer uses its own brain and algorithm to design the solution for the problems and suggests the measure required for the different region. All we need to do is to program the machine or system in a way that it can perform different tasks simultaneously and predicts the accurate solution for the larger which is not possible for human brain to deal at a particular time.

BLOCK DIAGRAM

BLOCK DIAGRAM

SYSTEM REQUIREMENTS

A. Hardware Requirements

1. Power supply (solar panel, dc battery)

2. Microcontroller

3. IR sensor

4. Buzzer module

5. Led lights

 

B. Software Requirements

1. Embedded C

2. Arduino IDE 

 

 

CONCLUSION

 

We presented a thorough study of existing works where Artificial Intelligence techniques were applied to propose new applications and services, or mitigate problems in Intelligent Traffic Systems. In particular, traffic congestion is a problem on street network that happens as automobiles upsurge. This problem is categorized by slower speeds, longer trip times, and increased vehicular queuing. It has an effects on the economic growth of a country, upsurges accidents, resource cost and environment pollution. One of the most real ways to deal with this problem is by using traffic control signals at intersections. Nowadays, most signal controls are implemented with static cycle time control or manual control. These traditional methods for traffic signal control fail to deal efficiently with situations of traffic congestion. To overcome this problem we proposed a system that used integration of machine learning, Image processing and embedded system to yield a better performance. Traffic congestion is a problem on street network that happens as automobiles upsurge. This problem is categorized by slower speeds, longer trip times, and increased vehicular queuing. It has an effects on the economic growth of a country, upsurges accidents, resource cost and environment pollution. One of the most real ways to deal with this problem is by using traffic control signals at intersections. Nowadays, most signal controls are implemented with static cycle time control or manual control. These traditional methods for traffic signal control fail to deal efficiently with situations of traffic congestion. To overcome this problem we proposed a system that used integration of machine learning, Image processing and embedded system to yield a better performance. We are going to use deep learning object detection technique along with image processing and embedded system to control the traffic lights based on traffic density.

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