Embodiments of the present disclosure provide robotic systems, apparatuses, and methods. One such robotic system comprises a robotic surgical tool; and a steering system configured to steer the robotic surgical tool based on motion angle commands along X and Y axes as the robotic surgical tool moves in an Z axis direction within a tubular passageway. The system further comprises a computing device that executes an artificial intelligence program configured to control the steering system by computing the motion angle commands based on a current position of the robotic surgical tool along planar axes of the tubular passageway and center positions of the passageway along the planar axes. Other systems and methods are disclosed.

Technologies for a neural belief reasoner model generative models that specifies belief functions are described. Aspects include receiving, by a device operatively coupled to a processor, a request for a belief function, and processing, by the device, the request for the belief function in the generative model based on trained probability parameters and a minimization function to determine a generalized belief function defined by fuzzy sets. Data corresponding to the generalized belief function is output, e.g., as a belief value and plausibility value.

A non-transitory computer-readable storage medium storing a model reduction program that causes at least one computer to execute a process, the process includes identifying as deletion targets a first neuron that does not connect to an input layer in a neural network; identifying as deletion targets a second neuron that does not connect to an output layer in a neural network; combining a bias of the first neuron with a bias of a third neuron connected to the first neuron on an output side; and deleting the first neuron and the second neuron from the neural network.

A monitoring system and a monitoring method for a planter includes a computer having an unit speed fusion module, a seeding flow rate monitoring module, and a decision module for theoretical rotation speed of a drive motor, a rotation speed deviation inference module, a controlling parameter tuning module, an adjusting module for rotation speed of a seeding shaft, a controlling module for rotation speed of seeding shaft and a positioning signal receiver fixed on a top of a cab for receiving a geographic position signal. The system monitors operating state parameters of the planter and accurately controls seed amount.

A computer-implemented method is disclosed for monitoring the logical consistency of an artificial neural network. Activation data of the artificial neural network, which are produced from input data, are initially read in. The activation data are transferred to at least one trained concept model which is trained to recognize and, if applicable, localize a partial feature of the features contained in the input data and to output a calibrated partial feature mask. The final output data are linked to the partial feature truth values by means of a fuzzy logic unit in such a way that a continual logical consistency truth value is produced therefrom. The logical consistency truth value is evaluated by means of an evaluation unit, wherein a logical inconsistency of the final output data is ascertained in an inconsistency region if the consistency truth value falls short of a predefined threshold.

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

Embodiments relate to systems and methods for probabilistically filtering candidate intervention representations. Systems and methods are described that receive a candidate intervention representation; specify a plurality of parameters as a function of the candidate intervention representation; identify a plurality of analytical constraints, where each analytical constraint corresponds to an analytical parameter of the plurality of parameters; generate a probabilistic output as a function of the candidate intervention representation, the plurality of analytic constraints, and training data correlating past intervention representations to a deterministic outcome; and, filter the at least a candidate intervention representation using the probabilistic output.

A neural network model replaces the supervised labeling component of a supervised learning system with an incremental cluster validity index-based unsupervised labeling component. An implementation is presented combining fuzzy adaptive resonance theory predictive mapping (ARTMAP) and incremental cluster validity indices (iCVI) for unsupervised machine learning purposes, namely the iCVI-ARTMAP. An iCVI module replaces the adaptive resonance theory (ART) module B of a fuzzy ARTMAP neural network model and provides assignments of input samples to clusters (i.e., labels) at each learning iteration in accordance to any of several possible iCVI methods described. A map field incrementally builds a many-to-one mapping of the categories of ART module A to the cluster labels. At the end of each learning epoch, clusters may be merged and/or split using the iCVI, which is recomputed incrementally except for the newly cluster during a split. The iCVI-ARTMAP performs offline incremental multi-prototype-based clustering driven by the iCVI.

Aspects of this disclosure include fuzzy inclusion based impersonation detection technology. In one embodiment, a reverse n-gram map is created for the list of protected entities. A suspicious string may be broken into n-grams, and each n-gram of the suspicious string is to be searched in the reverse n-gram map for corresponding protected entities. A fuzzy inclusion of a protected entity may be detected in the suspicious string depending on the protected entities found during the search. Subsequently, impersonation can be identified based on the characteristics of the fuzzy inclusion. In this way, the communication system can detect impersonation techniques using visually similar text, and accordingly take various actions to help user mitigate risks caused by impersonation.

The disclosure is directed to a trading platform and, more particularly, to systems and processes for simplifying market based investments using market sentiment and dynamic risk assessment profiles. A method implemented in a computer infrastructure has computer executable code tangibly embodied on a computer readable storage medium having programming instructions operable to: determine dynamic risk assessment profiles of different users; obtain trading information from disparate electronic sources; generate at least one investment opportunity with a risk profile using the trading information and matching the investment opportunity with the dynamic risk assessment profiles of a selected user or of the different users; and provide at least one trading recipe which is configured to convert the at least one investment opportunity into a simplified executable trade for the selected user.