Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: In some examples, target object color analysis and tagging may include ascertaining an attribute of an image, and determining, based on the ascertained attribute, a target object that is to be identified and color tagged in the image. Based on a learning model, a plurality of objects may be extracted from the image. Based on a comparison of the target object and the plurality of extracted objects, the target object may be identified in the image. Color information may be extracted from the identified target object, and a plurality of color tags associated with the identified target object may be ascertained. A plurality of color distances may be determined between the color information and the plurality of color tags. Based on a determination of a minimum color distance from the plurality of color distances, a color tag that is to be assigned to the identified target object may be determined.

Abstract: A device may receive information that identifies a first medical term and may determine whether the first medical term corresponds to a first medical code, which may be a medical code previously input by a first user in association with a second medical term. Or, the first medical code may be included in a dictionary. The device may determine a valid code corresponding to the first medical term based on determining whether the first medical term corresponds to the first medical code. The valid code may be determined based on the first medical code when the first medical term corresponds to the first medical code. The valid code may be determined based on a second medical code, input by a second user, when the first medical term does not correspond to the first medical code. The device may provide information that identifies the valid code.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: In some examples, target object color analysis and tagging may include ascertaining an attribute of an image, and determining, based on the ascertained attribute, a target object that is to be identified and color tagged in the image. Based on a learning model, a plurality of objects may be extracted from the image. Based on a comparison of the target object and the plurality of extracted objects, the target object may be identified in the image. Color information may be extracted from the identified target object, and a plurality of color tags associated with the identified target object may be ascertained. A plurality of color distances may be determined between the color information and the plurality of color tags. Based on a determination of a minimum color distance from the plurality of color distances, a color tag that is to be assigned to the identified target object may be determined.

Abstract: A device may receive information that identifies a first medical term and may determine whether the first medical term corresponds to a first medical code, which may be a medical code previously input by a first user in association with a second medical term. Or, the first medical code may be included in a dictionary. The device may determine a valid code corresponding to the first medical term based on determining whether the first medical term corresponds to the first medical code. The valid code may be determined based on the first medical code when the first medical term corresponds to the first medical code. The valid code may be determined based on a second medical code, input by a second user, when the first medical term does not correspond to the first medical code. The device may provide information that identifies the valid code.