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AI Based Projects
Posted: Jun 25, 2021
Artificial Intelligence is advancing throughout the world. According to a study by Creative Strategies, 95% of mobile users are using AI-enabled voice assistance. It is hard to seek out a society that doesn’t use AI techniques. This technique brings numerous uses in a number of ways. It includes decision-making capabilities, diagnosis generation, identifying the connection between causes and consequences, forecasting events, controlling devices like smart sensors, mechanical arms, etc.
https://takeoffprojects.com/ai-based-projects
We are providing you with some of the greatest ideas for building Final Year projects with proper guidance and assistance.
Takeoff Projects supports final year projects for computing and Engineering, Computer Networks, Computer Communications, Computer Applications, and knowledge Technology streams that cause BS/ME/MTECH/MS/MSC – any Post Graduate degree courses offered by the schools across the India.
Today the interest in machine learning is so great that
it is the most active research area in artificial intelligence.
The area may be divided into two sub-areas, symbolic and non-symbolic machine learning. In symbolic learning, the result of the learning process is represented as symbols, either in form of logical statements or as graph structures. In non-symbolic learning, the result is represented as quantities, for example as weights in a neural network (a model of the human brain).
In recent years the research in neural networks has been very intensive and remarkably good results have been achieved. This is especially true in connection with speech and image recognition. But research in symbolic machine learning has also been intensive. An important reason for this is that humans can understand the result of a learning process (in contrast to neural networks), which is significant if one should trust the result. The following two projects deal with symbolic machine learning and are both using so-called induction.
In recent years the research in neural networks has been very intensive and remarkably good results have been achieved. This is especially true in connection with speech and image recognition. But research in symbolic machine learning has also been intensive. An important reason for this is that humans can understand the result of a learning process (in contrast to neural networks), which is significant if one should trust the result. The following two projects deal with symbolic machine learning and are both using so-called induction.
Takeoff Edu Group https://takeoffprojects.com/ml-projects-for-final-year