Take a look at these five mind-blowing innovations that have been made feasible by AI
Information technology, computer science, and computer science engineering are all terms used to describe the study and creation of software for computers (IT). Inspiration for senior projects in computer science and information technology can’t be found anywhere else. This repository stores all CSE and IT student work, both completed and in progress.
As a result, it compiles the most innovative and forward-thinking concepts for AI projects in computer science, information technology, and another software engineering. You need to be impressed by robotics before you can understand artificial intelligence. Before getting deep into the topic you must also know about Machines and Technologies. If you are a senior in an engineering or IT school and you want papers on the five most interesting theses you can achieve using AI, you have found the ideal site.
To begin, we have developed a computer-vision based system for counting and identifying vehicles.
Migrants go to cities because they have greater access to things like jobs, healthcare, and education. Congestion on the roadways is a problem in many of the world’s major cities. Multiple factors contribute to gridlock in the transportation system.
Increased traffic has led to gridlocked roadways and hindered development. Congestion is a constant problem in major cities since there are often more roadways than cars. It is well known that growing metropolitan populations cause more traffic congestion.
Taking public transit is the same as utilising a system to recognise and count cars for the purposes of intelligent transportation, which may be used for traffic control and other reasons. A lack of real-time traffic data is another contributor to inefficient traffic management.
Methods for Identifying Impaired Motorists
According to the World Health Organization, there were an estimated 1.3 million traffic-related deaths worldwide in 2018. (WHO).
According to the latest data from the National Highway Traffic Safety Administration’s (NHTSA) annual road fatality study, 91,000 individuals were killed in car accidents in 2017 due to drowsy driving, while another 795 people were killed due to fatigue.
Drivers who are too fatigued to pay attention to the road are a common cause of car accidents. After two or three hours of driving, a person’s attention span and ability to drive the car decline dramatically, according to scientific studies.
Noontime is just as defenceless as four in the morning or midnight. Thus, it’s possible to confuse exhaustion when awake and occupied with sleep.
Because of this, the Driver Drowsiness Detection System can distinguish between being wide awake, REM sleep, and a non-REM sleep state (NREM).
A Synopsis of the Story, Including Potential Keywords
Complex social tagging might be used to identify film’s many genres, narrative, soundtracks, statistics, and sensory experiences. Data like this might be used to create automated processes for setting up film labelling infrastructure.
With the use of automatic grading systems and similar content locators, users may obtain a sense of the film’s mood and subject matter. The eventual goal of the project is to create a database of information describing and summarising films.
Our work resulted in the creation of 70 tags that show various characteristics of film plots, as well as multi-label correlations between these tags and the over 14,000 plot summaries.
These labels are analysed for their plausibility in light of the film’s genre and the protagonist’s growth. Using this dataset, researchers may at least test the hypothesis that tag values can be inferred from plot summaries.
Our research suggests that this corpus will be useful for future applications requiring narrative analysis.
Incorrect labelling might have a significant negative impact on the user experience. It is desirable to predict as many tags as possible with high recall and accuracy and minimal latency.
Digitally Generated Forensic Illustrations
To improve or rectify the picture, image processing was used, and it did a fantastic job. As machine learning methods have advanced, image processing has become more easier. Forensic drawings made using a GAN may now be accessed with the help of data generated by image generators.
Scientists in the fields of computer vision, image processing, and machine learning have been very interested in creating automatic face sketching and recognition systems for some time.
To faithfully reproduce a hand-drawn image digitally, we use machine learning methods and technologies. Automation means that little human effort is needed to complete the task at hand. More convincing forensic visuals may be produced using this technology because to the increased speed and accuracy of their generation.
construction of a prototype
Before the network can be utilised, both its generator and discriminator must be trained.
It is possible to conduct training for discriminators and generators independently.
A Guide to Identifying Credit Card Fraud’s 5 Most Useful Methods
Credit card fraud has serious repercussions in the court system. Two of the primary goals of this study are (1) identifying and describing distinct types of counterfeit credit cards, and (2) analysing the effectiveness of different methods for detecting fraud. As an added bonus, we’ll look at and analyse the results of current research on spotting credit card fraud.
To better understand credit card fraud, this website defines crucial topics and provides helpful information. In light of the many forms of fraud that are increasingly being experienced, the credit card industry or financial institutions may need a wide range of measures.
In comparison to alternative possibilities, the recommendations made in this research will save you money and have a less impact on your budget. Such measures are emphasised as necessary for reducing credit card theft.
When good individuals are falsely accused of credit card fraud, ethical questions emerge.
Classifier, Random Forest, Autoencoder, and SMOTE are all terms for logistic regression.
The key objectives of this study are to examine the inappropriate behaviours made using counterfeit credit cards and to explore the present status of deep learning and machine learning algorithms.
Conclusion
Therefore, there is a wide variety of AI-related initiatives you might pursue.
Solving these issues might provide valuable practise for creating more capable AI systems. These projects will not only put you on the fast track to AI expertise, but will also set you up for success in an AI-related field of work.
Anyone, regardless of experience or expertise, is encouraged to participate in these exciting AI projects.
