Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

A device may receive, from a source system, data in a first format, data in a second format, or a combination of data in the first format and data in the second format. The device may identify a data format type, a hierarchy depth, levels, and objects at each of the levels for the data and may generate a template based on the data format type, the hierarchy depth, the levels, and the objects at each of the levels. The device may generate a query, for the data in the first format, based on the template and may execute the query, on the data in the first format, to generate query results associated with a target system. The device may process the query results, with the template, to generate output data in a third format associated with the target system and may perform actions based on the output data.

A device may receive, from a source system, data in a first format, data in a second format, or a combination of data in the first format and data in the second format. The device may identify a data format type, a hierarchy depth, levels, and objects at each of the levels for the data and may generate a template based on the data format type, the hierarchy depth, the levels, and the objects at each of the levels. The device may generate a query, for the data in the first format, based on the template and may execute the query, on the data in the first format, to generate query results associated with a target system. The device may process the query results, with the template, to generate output data in a third format associated with the target system and may perform actions based on the output data.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. The SLKRS ingests data in multiple formats from multiple sources, merges the data into a knowledge base based on computed strengths of terms in the sources, and assimilates the merged data to generate experiences. The SLKRS receives feedback from the knowledge seeker and computes a score based on the feedback and the query to artificially learn unique characteristics of the knowledge seeker. The SLKRS generates a learning path for the knowledge seeker on a graphical output, wherein the learning path's state transition points lead to a projected learning path determined by the knowledge seekers performance over one or more of subtopics, topics, and lessons.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. The SLKRS ingests data in multiple formats from multiple sources, merges the data into a knowledge base based on computed strengths of terms in the sources, and assimilates the merged data to generate experiences. The SLKRS receives feedback from the knowledge seeker and computes a score based on the feedback and the query to artificially learn unique characteristics of the knowledge seeker. The SLKRS generates a learning path for the knowledge seeker on a graphical output, wherein the learning path's state transition points lead to a projected learning path determined by the knowledge seekers performance over one or more of subtopics, topics, and lessons.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A chatbot platform with a chatbot interface provides for interaction between the knowledge seeker, a parent, a teacher, or another stakeholder. The chatbot platform allows multiple channels of engagement. The chatbot platform provides translation services comprising text to speech and speech to text service. The chatbot platform integrates third-party services into its responses to the user and queries from the user through the integration module. The chatbot platform performs pattern recognition and checks simplified and rephrased questions against a knowledge base. The chatbot platform uses conversation audits to train artificial intelligence and machine learning algorithms, to generate an appropriate response to the query of the knowledge seeker.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. A chatbot platform with a chatbot interface provides for interaction between the knowledge seeker, a parent, a teacher, or another stakeholder. The chatbot platform allows multiple channels of engagement. The chatbot platform provides translation services comprising text to speech and speech to text service. The chatbot platform integrates third-party services into its responses to the user and queries from the user through the integration module. The chatbot platform performs pattern recognition and checks simplified and rephrased questions against a knowledge base. The chatbot platform uses conversation audits to train artificial intelligence and machine learning algorithms, to generate an appropriate response to the query of the knowledge seeker.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. In response to a query received from the knowledge seeker, the SLKRS retrieves and sends in an immersive format one of the generated experiences or an experience created based on an artificially intelligent understanding of the received query. The SLKRS computes a coefficient of retention for the knowledge seeker based on a test of the ability of the knowledge seeker to recall a concept after the passage of a predetermined length of time, and after being exposed to a predetermined number of applications of the concept. The SLKRS generates interventions and improved experiences to provide adaptive and personalized e-learning to the knowledge seeker based on the computed coefficient of retention.

A computer-implemented method and a smart-learning and knowledge retrieval system (SLKRS) are provided for imparting adaptive and personalized e-learning based on continually artificially learned unique characteristics of a knowledge seeker. In response to a query received from the knowledge seeker, the SLKRS retrieves and sends in an immersive format one of the generated experiences or an experience created based on an artificially intelligent understanding of the received query. The SLKRS computes a coefficient of retention for the knowledge seeker based on a test of the ability of the knowledge seeker to recall a concept after the passage of a predetermined length of time, and after being exposed to a predetermined number of applications of the concept. The SLKRS generates interventions and improved experiences to provide adaptive and personalized e-learning to the knowledge seeker based on the computed coefficient of retention.