Introduction to Principal Component Analysis

This is the Part 35 of the Lecture. For the complete lecture on PCA click here.

Principal Component Analysis (PCA) is a statistical technique commonly used in data analysis to simplify the complexity of large datasets by transforming the original variables into a smaller set of uncorrelated variables known as principal components. These components capture the maximum amount of variance in the data, thereby allowing for easier interpretation and visualization of the underlying patterns and relationships. PCA is widely used in fields such as machine learning, image processing, and genetics, providing researchers with valuable insights and actionable information for decision-making. By identifying the key dimensions driving variation in the data, PCA enables researchers to better understand and extract meaningful information from complex datasets.

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Seven Most Tenable Application of Artificial Intelligence on Water Resource Management Problems

AI or Artificial Intelligence is a pioneering technique that has enabled the creation of intelligent machines. or smart machines which have the power to self adapt based on the situation presented to them. It requires situations whose response is known and based on this training data set it learns the problems which it has to solve when it is ready. Due to the alarming success with AI in robotics, electronics, etc fields the same technique is now used to solve the problems of water resource management. This ppt shows the seven most notable use of AI in water resources-based problems where satisfactory improvement has encouraged the further application of the technique. View the Presentation Dr.Mrinmoy Majumder, My ResearchGate Id : Mrinmoy_Majumder Home Page: http://www.mrinmoymajumder.com Author of: Lecture Notes on MCDM Indian Link ; Global Link :

Hydroinformatics to make farms SMART

Smart farming focuses on increasing farm productivity through the use of technology - both hardware and software. Smart farming is concerned with the management of farms, plantations, and all associated farming activities through the use of IoT, drones, robotics, machinery, and artificial intelligence to determine a path to predictable farm output. Smart farming is concerned with the management of farm activities through the use of data obtained from various sources (historical, geographical, and instrumental). Technological advancement does not necessarily imply that a system is intelligent. Smart agriculture technologies distinguish themselves by their ability to collect and analyze data. Smart farming uses hardware (Internet of Things) and software (Software as a Service or SaaS) to collect data and provide actionable insights to manage all farm operations, both pre and post-harvest. Hydroinformatics, on the other hand, is a subject that deals with the application of data science an...