A method of training a machine learning system, the method including: training the machine learning system in category identification against a test case wherein the machine learning system outputs an answer vector and the answer vector is compared against a control vector where the control vector comprising three different values, the values comprising: a first value for a matching category for the test case; a second value for a non-matching category for the test case; and a third value for a first category related to the matching category for the test case wherein the third value differs from the first value differs from the second value.

Fusion of trained artificial intelligence (AI) neural networks to produce more accurate classifications is disclosed. Concatenation from each network being merged may be performed. The new set of features, which includes the concatenated layers, is then fed through a new classifier to form a single final classifier that uses the best parts of each input classifier.

An artificial intelligence (AI) processor includes at least one memory; a plurality of neural network operators comprising circuitry configured to process an image; and a controller configured to control the at least one memory and the plurality of neural network operators. The controller controls input image data of an image to be stored in the at least one memory and controls at least one of the plurality of neural network operators to perform a neural network operation on image data split based on a size of the image and data processing capabilities of the plurality of neural network operators, and output upscaled image data.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

A plant locating system may include a vehicle supporting a Global Positioning System (GPS) antenna and a monocular camera. The system may further include a plant locating unit comprising a processing unit and a non-transitory computer-readable medium containing instructions to direct the processing unit to: acquire a sample image of a plant of interest captured at a time with the monocular camera at an unknown distance from the plant of interest (POI); determine a geographic location estimate of the GPS antenna at the time; identify a selected portion of the sample image comprising the POI; determine a distance between the POI and the monocular camera based upon the selected portion; and determine a geographic location estimate of the POI based on the geographic location estimate of the GPS antenna at the time and the determined distance between the monocular camera and the POI.

A plant locating system may include a vehicle supporting a Global Positioning System (GPS) antenna and a monocular camera. The system may further include a plant locating unit comprising a processing unit and a non-transitory computer-readable medium containing instructions to direct the processing unit to: acquire a sample image of a plant of interest captured at a time with the monocular camera at an unknown distance from the plant of interest (POI); determine a geographic location estimate of the GPS antenna at the time; identify a selected portion of the sample image comprising the POI; determine a distance between the POI and the monocular camera based upon the selected portion; and determine a geographic location estimate of the POI based on the geographic location estimate of the GPS antenna at the time and the determined distance between the monocular camera and the POI.

A method for evaluating material properties includes an image processing for evaluation step, a material properties prediction step, and an evaluation step. The image processing for evaluation step includes scanning one or more images for evaluation of a material to be evaluated, creating a low-gradation image for evaluation by lowering gradation of the image for evaluation, and creating a virtual image by processing the low-gradation image for evaluation. The material properties prediction step includes extracting features for evaluation from the low-gradation image for evaluation, predicting a first material property of the material to be evaluated from the features for evaluation through a regression model, extracting a virtual-image feature from the virtual image, and predicting a second material property of the material to be evaluated from the virtual-image features through the regression model. The evaluation step is for comparing the first material property with the second material property.