Abstract: An system and a method for the detection and visualization of reported ethics cases is disclosed. The system receives a set of digital records corresponding to a reported ethics violations. The system converts each of the digital records from the set of digital records into a common digital format. The system deconstructs the uniform text structure of each digital recorded by a natural language processing module to lemmatize words, remove punctuation, and remove stop words. The system inputs each deconstructed uniform text structure into a binary machine learning data model. The system inputs each deconstructed uniform text structure into a multiclass machine learning data model. The system inputs the determined value and the label to an ensemble machine learning data model. The system prioritizes reported ethics violations into one or more lists based on the determination of the possible class and transmits the list to a user interface.

Abstract: Systems, methods, and machine readable media are provided for classifying customer feedback. In exemplary embodiments, text is captured from at least one source relating to at least one product. The text is scanned and a score is produced for sentiment for the at least one product. The text is filtered into parts of speech and key words to produce filtered text. The filtered text is transformed into a term-document matrix. A risk score is calculated and prioritized based on the term-document matrix and the sentiment score. The product and the associated risk score are reported to a subject matter expert (SME), where a determination is made whether the product is reportable or non-reportable.

Abstract: An system and a method for the detection and reporting of occupational safety incidents are disclosed. The system receives a set of digital records corresponding to reported occupational safety incidents. The system converts each of the digital records from the set of digital records into a common digital format. The system deconstructs the uniform text structure of each digital recorded by a natural language processing module to lemmatize words, remove punctuation, and remove stop words. The system creates a feature vector based on the received deconstructed uniform text structure. The system inputs each feature vector to an ensemble machine learning data model, returning a determination of a possible class or characteristic of occupational safety incident. The system applies a threshold based on a probability to the determination of a possible class. The system submits a subset of the reported occupational safety incidents to a third party system.

Abstract: Exemplary embodiments relate systems, methods and computer readable medium for automatically processing and classifying regulatory reports. An example system includes an image processing module, an image segmentation module, a segment filtering module, a classification module and a validation module.

Abstract: An system and a method for the detection and reporting of occupational safety incidents are disclosed. The system receives a set of digital records corresponding to reported occupational safety incidents. The system converts each of the digital records from the set of digital records into a common digital format. The system deconstructs the uniform text structure of each digital recorded by a natural language processing module to lemmatize words, remove punctuation, and remove stop words. The system creates a feature vector based on the received deconstructed uniform text structure. The system inputs each feature vector to an ensemble machine learning data model, returning a determination of a possible class or characteristic of occupational safety incident. The system applies a threshold based on a probability to the determination of a possible class. The system submits a subset of the reported occupational safety incidents to a third party system.

Abstract: An system and a method for the detection and visualization of reported ethics cases is disclosed. The system receives a set of digital records corresponding to a reported ethics violations. The system converts each of the digital records from the set of digital records into a common digital format. The system deconstructs the uniform text structure of each digital recorded by a natural language processing module to lemmatize words, remove punctuation, and remove stop words. The system inputs each deconstructed uniform text structure into a binary machine learning data model. The system inputs each deconstructed uniform text structure into a multiclass machine learning data model. The system inputs the determined value and the label to an ensemble machine learning data model. The system prioritizes reported ethics violations into one or more lists based on the determination of the possible class and transmits the list to a user interface.

Abstract: Systems, methods, and machine readable media are provided for classifying customer feedback. In exemplary embodiments, text is captured from at least one source relating to at least one product. The text is scanned and a score is produced for sentiment for the at least one product. The text is filtered into parts of speech and key words to produce filtered text. The filtered text is transformed into a term-document matrix. A risk score is calculated and prioritized based on the term-document matrix and the sentiment score. The product and the associated risk score are reported to a subject matter expert (SME), where a determination is made whether the product is reportable or non-reportable.