Abstract: The technical solutions of the present disclosure are directed to providing validated responses to form specific user queries using machine learning. A system can include a processor to receive, via a user interface, a query corresponding to an entry of a form and identify a data structure. The processor can generate, using the data structure, a prompt to validate the entries and identify, based on the prompt and the data structure input a ML model, an error in an entry of the plurality of entries of the form. The processor can generate, based on the error and the data structure input into the ML model, a response to the query comprising a proposed correction to the error in the form, and provide, for display via the user interface, the response to the query indicating the error and the proposed correction.

Abstract: A method for detecting anomalous data is provided. The method comprises collecting a training dataset comprising a number of transactional time series, wherein the time series comprise non-anomalous data entries for a specified transaction type. The training dataset is fed into a gated recurrent unit (GRU) network, which learns the data distribution for the transactional time series. The GRU predicts expected future values of the specified transaction type according to the learned data distribution. An upper bound and a lower bound for future values are calculated based a standard deviation of the predicted values. When new data entries of the specified transaction type are received their values are compared to the upper bound and the lower bound, and an error notification is provided if the values of the new data entries fall outside the upper bound or lower bound.

Abstract: A method for detecting an AFB1 content using fluorescence analysis based on optical sectioning is provided, which relates to the field of biological detection technologies. The method includes steps for detecting the AFB1 content by using a fluorescence probe combination, which includes fluorescence probes aptamer and PC-strand. The nucleotide sequence of the aptamer is shown as SEQ ID NO: 1, and a FAM group is labeled at a 3? end of the nucleotide sequence shown as SEQ ID NO: 1. The nucleotide sequence of the PC-strand is shown as SEQ ID NO: 2, a TAMRA group is labeled at a 5? end of the nucleotide sequence shown as SEQ ID NO: 2, and a PC group is labeled between 10th bp and 11th bp of the nucleotide sequence shown as SEQ ID NO: 2. The method can achieve photosensitive detection of AFB1, with high detection sensitivity and strong specificity.

Abstract: A method for detecting an AFB1 content using fluorescence analysis based on optical sectioning is provided, which relates to the field of biological detection technologies. The method includes steps for detecting the AFB1 content by using a fluorescence probe combination, which includes fluorescence probes aptamer and PC-strand. The nucleotide sequence of the aptamer is shown as SEQ ID NO: 1, and a FAM group is labeled at a 3? end of the nucleotide sequence shown as SEQ ID NO: 1. The nucleotide sequence of the PC-strand is shown as SEQ ID NO: 2, a TAMRA group is labeled at a 5? end of the nucleotide sequence shown as SEQ ID NO: 2, and a PC group is labeled between 10th bp and 11th bp of the nucleotide sequence shown as SEQ ID NO: 2. The method can achieve photosensitive detection of AFB1, with high detection sensitivity and strong specificity.

Abstract: A method for detecting anomalous data is provided. The method comprises collecting a training dataset comprising a number of transactional time series, wherein the time series comprise non-anomalous data entries for a specified transaction type. The training dataset is fed into a gated recurrent unit (GRU) network, which learns the data distribution for the transactional time series. The GRU predicts expected future values of the specified transaction type according to the learned data distribution. An upper bound and a lower bound for future values are calculated based a standard deviation of the predicted values. When new data entries of the specified transaction type are received their values are compared to the upper bound and the lower bound, and an error notification is provided if the values of the new data entries fall outside the upper bound or lower bound.

Abstract: A method includes receiving a search query from a user interface. The method includes performing a keyword search at a computer device based on the search query to generate keyword search results. The method includes performing a natural language search of a frequently-asked question database based on the search query to generate frequently-asked question search results. The method also includes outputting a display page for display at a display device. The display page categorizes the keyword search results and the frequently-asked question search results into a plurality of categories.

Abstract: A method includes receiving a search query from a user interface. The method includes performing a keyword search at a computer device based on the search query to generate keyword search results. The method includes performing a natural language search of a frequently-asked question database based on the search query to generate frequently-asked question search results. The method also includes outputting a display page for display at a display device. The display page categorizes the keyword search results and the frequently-asked question search results into a plurality of categories.

Abstract: A search query is received from a single input field of a user interface. A keyword search is performed based on the search query to generate keyword search results. A natural language search is performed of a frequently-asked question (FAQ) database based on the search query to generate FAQ search results. The keyword search results and the FAQ search results are combined in a display page.

Abstract: A predefined interface is presented to a remote user. A natural language inquiry from the remote user is received via the predefined interface. The natural language inquiry is configured for use in an analysis. A customized interface is presented to the remote user based on the analysis of the natural language inquiry. The customized interface includes a customized set of communications options for the remote user to route communications.

Abstract: A search query is received from a single input field of a user interface. A keyword search is performed based on the search query to generate keyword search results. A natural language search is performed of a frequently-asked question (FAQ) database based on the search query to generate FAQ search results. The keyword search results and the FAQ search results are combined in a display page.

Abstract: A search query is received from a single input field of a user interface. A keyword search is performed based on the search query to generate keyword search results. A natural language search is performed of a frequently-asked question (FAQ) database based on the search query to generate FAQ search results. The keyword search results and the FAQ search results are combined in a display page.

Abstract: A search query is received from a single input field of a user interface. A keyword search is performed based on the search query to generate keyword search results. A natural language search is performed of a frequently-asked question (FAQ) database based on the search query to generate FAQ search results. The keyword search results and the FAQ search results are combined in a display page.

Abstract: A predefined interface is presented to a remote user. A natural language inquiry from the remote user is received via the predefined interface. The natural language inquiry is configured for use in an analysis. A customized interface is presented to the remote user based on the analysis of the natural language inquiry. The customized interface includes a customized set of communications options for the remote user to route communications.

Abstract: A search query is received from a single input field of a user interface. A keyword search is performed based on the search query to generate keyword search results. A natural language search is performed of a frequently-asked question (FAQ) database based on the search query to generate FAQ search results. The keyword search results and the FAQ search results are combined in a display page.