Using RRT (Rapidly-exploring Random Tree) in automated assembly lines involves planning the paths for robotic arms or other automation equipment to optimize their movement through the workspace. Process: Define the...
Category - Artificial Intelligence
Artificial Intelligence related posts will be share here.
Generative AI refers to a type of artificial intelligence that is capable of generating new content, such as images, text, music, or even videos, that is similar to what it has been trained on. It often uses deep learning...
Anomaly detection is a crucial task in various domains such as finance, cybersecurity, healthcare, and industrial operations. There are several tools and libraries available that can assist in anomaly detection, ranging from...
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Easiest example of Artificial Intelligence Edge Computing with Code
Edge computing refers to the practice of processing data near the edge of the network where the data is generated instead of relying on centralized data centers. This approach is particularly useful in scenarios where real-time...
Naïve Bayes algorithm is a probabilistic classifier based on Bayes’ theorem, which assumes independence between features. It’s widely used in text classification, spam filtering, and recommendation systems due to its...
Supervised machine learning algorithms are models trained on labeled data, meaning the data is already tagged with the correct answer. These algorithms learn from the labeled examples provided during training to generalize...
Cognitive modeling is an interdisciplinary approach that aims to understand and simulate human cognitive processes, such as perception, memory, learning, problem-solving, and decision-making. It involves creating computational...
Bayesian Networks: Definition: Bayesian networks, also known as belief networks or directed graphical models, are graphical representations of probabilistic relationships among a set of variables. They consist of nodes...
K-Means Algorithm: Explanation:K-Means is a popular clustering algorithm used for partitioning a dataset into a set of K distinct, non-overlapping clusters. The algorithm aims to minimize the within-cluster variance, meaning...
Data Preprocessing:Before training a model, it’s crucial to clean and prepare the data. This involves handling missing values, outliers, and noise. For example, suppose you have a dataset containing information about...