Tom Blair

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural… Show more

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. Show less

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural… Show more

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. Show less

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural… Show more

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. Show less

Detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point. Images are clipped to align with the seed point and a bounding box around the seed point, and a mask is built by extracting edges of the field from the images. The workflow floods an area around the seed point that has pixels of a similar color, using the mask as an initial boundary. The flooded area is compared to… Show more

Detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point. Images are clipped to align with the seed point and a bounding box around the seed point, and a mask is built by extracting edges of the field from the images. The workflow floods an area around the seed point that has pixels of a similar color, using the mask as an initial boundary. The flooded area is compared to threshold parameter values, which are tuned to refine the identified boundary. Flooded areas in multiple images are combined, and a boundary is built based on the combined flooded set. Manual, interactive tuning of floodfill areas allows for a separate boundary detection and identification workflow or for refinement of the automatic boundary detection workflow. Show less

A method and system is provided in an adaptive framework for modeling livestock growth. The adaptive framework processes input data relative to livestock growth in an ensemble of one or more models and an artificial intelligence layer configured to select the most appropriate or primary model to optimize, predict, and recommend livestock feed operations based upon environmental, physiological, location and time variables within such input data. The adaptive framework also optimizes workflow by… Show more

A method and system is provided in an adaptive framework for modeling livestock growth. The adaptive framework processes input data relative to livestock growth in an ensemble of one or more models and an artificial intelligence layer configured to select the most appropriate or primary model to optimize, predict, and recommend livestock feed operations based upon environmental, physiological, location and time variables within such input data. The adaptive framework also optimizes workflow by pen and by producer, based upon historical performance, gender and breed and the management practices of the producer. Show less

A method and system is provided in a framework and workflow for assisting in livestock feeding operations by tracking and weighing ingredients from an electronic scale associated with feed mixing equipment. The framework and workflow captures, transfers, stores and processes parameters for cattle feeder ration weight data collection. The method and system include initializing a collection of weight data related to components of a feed ration for a mixing of feed for livestock, capturing a scale… Show more

A method and system is provided in a framework and workflow for assisting in livestock feeding operations by tracking and weighing ingredients from an electronic scale associated with feed mixing equipment. The framework and workflow captures, transfers, stores and processes parameters for cattle feeder ration weight data collection. The method and system include initializing a collection of weight data related to components of a feed ration for a mixing of feed for livestock, capturing a scale identifier from a scale interface on mixing equipment, and capturing weight information from the scale interface as the components of a feed ration are loaded into the mixing equipment. Such information is broadcasted over a wireless radio communication connection, such as via a Bluetooth® device, to a mobile application, and displayed during feed mixing operation. Show less

A system for monitoring the operation of a vehicle. The system comprises a plurality of sensors; a plurality of optical capture devices; a memory in which at least a recording schema is stored, the at least recording schema contains rules for operation of at least one of the plurality of optical capture devices responsive of at least one of the plurality of sensors respective of at least one activity to be captured; and a recorder coupled to the plurality of sensors, the plurality of optical… Show more

A system for monitoring the operation of a vehicle. The system comprises a plurality of sensors; a plurality of optical capture devices; a memory in which at least a recording schema is stored, the at least recording schema contains rules for operation of at least one of the plurality of optical capture devices responsive of at least one of the plurality of sensors respective of at least one activity to be captured; and a recorder coupled to the plurality of sensors, the plurality of optical capture devices and the memory, the recorder determines based on the at least recording schema and responsive of at least an input from at least one of the plurality of sensors which of the at least one of the plurality of optical capture devices to operate. Show less