Abstract: A method performed by a node for predicting a behavior of users of a communications network is described. The node manages an artificial neural network. The node merges a first pre-existing predictive model of the behavior in a first group of users with a second model of the behavior in a second group of users. The merging comprises establishing connections between the first model and the second model. Each of the connections has a respective weight. The respective weights of the connections are learned by respective connections of neurons in the artificial neural network based on data from a third group of users. The node also obtains a third model for predicting the behavior in the third group of users, based on the merged models and the data from the third group of users.

Abstract: It is provided a method for adjusting alignment for microwave transmissions from a microwave transmitter to a microwave receiver based on a reinforcement learning, RL, model. The method comprises the steps of: obtaining state space comprising external state space and internal state space, the external state space comprising at least one value of a parameter related to environmental conditions, and the internal state space relates to alignment of the microwave transmitter; determining an action in an action space, the action space comprising actions to adjust alignment of the microwave transmitter; obtaining a measurement of path loss for a transmission from the microwave transmitter to the microwave receiver; determining a reward value based on the path loss, wherein an increase in path loss results in a reduced reward value; and adjusting the RL model based on the obtained state space, the determined action and the determined reward value.

Abstract: (57) Abstract: A method, performed by a first node (111), for handling parameters to configure a second node (112). The first node (111) determines (207) parameters to configure the second node (112). The determining (207) is based on an analysis by a Generative Adversarial Network comprising performing iteratively: i) generating a set of parameters estimated to be linked to a performance of a first group of nodes (121) being above a threshold, and ii) discriminating between the generated set of parameters and a first set of parameters observed to be linked to the performance of the first group of nodes (121) being above the threshold to obtain a score for every parameter. The score indicates how different the generated parameters and the first set of parameters are. The determined parameters have the score resulting from the discriminating being lower than anotherthreshold. The first node (111) also outputs an indication comprising the determined parameters.

Abstract: A method (100) for managing a group of Unmanned Aerial Vehicles (UAVs) operable to fly in a formation is disclosed, each UAV being programmed with a task to be performed by the UAV. The method is performed in a controller UAV of the group and comprises receiving UAV status information from UAVs in the group (110), obtaining information on a current formation of the group (120) and combining the received UAV status information with the information on current group formation to form a representation of a first state of the group (130). The method further comprises using a machine learning model to predict, on the basis of the first state of the group, an optimal formation transition to a new formation (140) and to instruct the UAVs in the group to perform the predicted optimal formation transition (150). An optimal formation transition is a transition to a formation that will minimise predicted total energy consumption for all UAVs in the group to complete their tasks.

Abstract: Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.

Abstract: Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.