Abstract: The present invention relates to an ultrasound image enhancement processing system and method thereof. The ultrasound image enhancement processing system includes at least one first ultrasound device and at least one server apparatus. The server apparatus receives a first ultrasound original image file, processes the first ultrasound original image file through a speckle reduction algorithm to generate a first processed image file, and performs a deep learning training on the first ultrasound original image file and the first processed image file to generate a first neural network model. The first neural network learning module is used to output a first speckle reduction enhancement image file. The image content of the first speckle reduction enhancement image file is approximating to the image content of the first processed image file.

Abstract: The present invention relates to a license plate identification system and method thereof. The license plate identification system comprises at least a server apparatus storing at least one license plate picture file to be identified and a plurality of license plate sample picture files. Each of the license plate sample picture files has a double-row of character. Through deep learning training, the system generates a neural network model, inputs the license plate picture file to be identified into the neural network model for outputting analysis result information, and decodes the analysis result information through a decoding algorithm to obtain an identification license plate character content.

Abstract: A method according to the present disclose may include presenting, on a graphical user interface (GUI), an interactive element; receiving, via the interactive element on the GUI, a research target and a type of research; autonomously retrieving, from a search engine, search results related to the research target; identifying, using a predictive machine learning model, at least one relevant portion of the search results, the at least one relevant portion comprising information related to the research target and responsive to the type of research; generating a prompt based on the type of research and the at least one relevant portion of the search results; and receiving, from a generative machine learning model in response to receipt of the generated prompt, a report indicative of the research target.

Abstract: There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.

Abstract: A system may include a processor and a non-transitory computer readable medium having stored thereon instructions that are executable by the processor to cause the system to process a document to derive a plurality of document chunks; generate, for a generative machine learning (ML) model, a first prompt configured to cause the generative ML model to provide a first report based on a first of the plurality of document chunks; extract a feature from the first report and comparing the extracted feature to a table of known features; and in response to and based on the comparison, generate, for the generative ML model, a second prompt configured to cause the generative ML model to provide a second report based on a second of the plurality of document chunks.

Abstract: Disclosed are a system for neural stimulation, a device for neural stimulation, and a computer-readable storage medium. The system includes a neural stimulator, the neural stimulator includes a stimulation module, configured to: determine, based on a received local field potential signal, a first energy value of a frequency band of interest in the local field potential signal; determine a threshold range in which the first energy value is; and output, according to a stimulation protocol corresponding to the threshold range, a corresponding stimulation signal. According to technical solution of the embodiments of this disclosure, a closed-loop neural stimulation mode can be achieved.

Abstract: A method according to the present disclose may include presenting, on a graphical user interface (GUI), an interactive element; receiving, via the interactive element on the GUI, a research target and a type of research; autonomously retrieving, from a search engine, search results related to the research target; identifying, using a predictive machine learning model, at least one relevant portion of the search results, the at least one relevant portion comprising information related to the research target and responsive to the type of research; generating a prompt based on the type of research and the at least one relevant portion of the search results; and receiving, from a generative machine learning model in response to receipt of the generated prompt, a report indicative of the research target.

Abstract: There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.

Abstract: There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.

Abstract: Provided is a graphene electrostatic filtering device. The device includes a filtering mechanism, and an auxiliary device is arranged on the filtering mechanism. The filtering mechanism includes a workbench, and a perforated plate. A water pump is arranged in a groove at the top of the workbench, a pressure relief valve is installed at an output end of a first water pipe, and two anode plates and two cathode plates are placed in four mounting grooves, respectively. A controller is installed at a position, adjacent to an edge, of a front surface of the workbench.

Abstract: A method of categorizing text entries on a document can include determining, for each of a plurality of text bounding boxes in the document, respective text, respective coordinates, and respective input embeddings. The method may further include defining a graph of the plurality of bounding boxes, the graph comprising a plurality of connections among the plurality of bounding boxes, each connection comprising a first and second bounding box and zero or more respective intermediate bounding boxes. The method may further include determining a respective attention value for each connection according to a quantity of intermediate bounding boxes in the connection and, based on a the respective attention values and a transformer-based machine learning model applied to the respective input embeddings and respective coordinates, determining output embeddings for each bounding box and, based on the respective output embeddings, generating a bounding box label for each bounding box.

Abstract: The present invention relates to an ultrasound image enhancement processing system and method thereof. The ultrasound image enhancement processing system includes at least one first ultrasound device and at least one server apparatus. The server apparatus receives a first ultrasound original image file, processes the first ultrasound original image file through a speckle reduction algorithm to generate a first processed image file, and performs a deep learning training on the first ultrasound original image file and the first processed image file to generate a first neural network model. The first neural network learning module is used to output a first speckle reduction enhancement image file. The image content of the first speckle reduction enhancement image file is approximating to the image content of the first processed image file.

Abstract: The present invention relates to a license plate identification system and method thereof. The license plate identification system comprises at least a server apparatus storing at least one license plate picture file to be identified and a plurality of license plate sample picture files. Each of the license plate sample picture files has a double-row of character. Through deep learning training, the system generates a neural network model, inputs the license plate picture file to be identified into the neural network model for outputting analysis result information, and decodes the analysis result information through a decoding algorithm to obtain an identification license plate character content.

Abstract: A method includes defining a first data vector of a first entity based on a set of data records associated with representative activities of the first entity, wherein the set of data records includes product data associated with the first entity, and an activities relationship between the first entity and a plurality of second entities. The method further includes defining a second data vector of the first entity based on supply chain topological connections between the second entities and the first entity, utilizing, a clustering space machine learning model to generate an entity vector representing the first entity based on the first data vector and the second data vector, and utilizing a classification machine learning model to generate an entity-specific classification of the first entity based on the entity vector.

Abstract: A heart rhythm detection method and system by using radar sensor is capable of collecting an original signal using a radar sensor toward at least one subject, and converting the original signal to a two dimensional image information (i.e., spectrogram) using the concept of image vision. Then, the neural network automatically learns which heartbeat frequency should be focused on and which heartbeat frequency should be filtered out in the two dimensional image information through deep learning, so that the heartbeat frequencies can be extracted effectively.

Abstract: A method of categorizing text entries on a document can include determining, for each of a plurality of text bounding boxes in the document, respective text, respective coordinates, and respective input embeddings. The method may further include defining a graph of the plurality of bounding boxes, the graph comprising a plurality of connections among the plurality of bounding boxes, each connection comprising a first and second bounding box and zero or more respective intermediate bounding boxes. The method may further include determining a respective attention value for each connection according to a quantity of intermediate bounding boxes in the connection and, based on a the respective attention values and a transformer-based machine learning model applied to the respective input embeddings and respective coordinates, determining output embeddings for each bounding box and, based on the respective output embeddings, generating a bounding box label for each bounding box.

Abstract: A radar detection and identification device is disclosed, comprising at least one display host, at least one camera and at least one radar detector, wherein the camera and the radar detector, after photographing and detecting, are capable of performing masked face recognition and radar physiological detection recognition processes in order to identify the identity information and human physiological signals and display them on the display host.

Abstract: A system can access, from an interaction database, customer interaction information comprising a customer interaction, wherein the customer interaction information represents an interaction between a customer and an entity, and determine, based on a complaint identification model, whether a segment of the interaction comprises a complaint, wherein the complaint identification model has been generated based on machine learning applied to past customer interaction information representative of past customer interactions other than the customer interaction.

Abstract: A heart rhythm detection method and system by using radar sensor is capable of collecting an original signal using a radar sensor toward at least one subject, and converting the original signal to a two dimensional image information (i.e., spectrogram) using the concept of image vision. Then, the neural network automatically learns which heartbeat frequency should be focused on and which heartbeat frequency should be filtered out in the two dimensional image information through deep learning, so that the heartbeat frequencies can be extracted effectively.