Abstract: According to an embodiment, a method for generating a forecast of a timeseries is disclosed. The method comprises receiving a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast. Further, the method comprises generating using the set of features, a plurality of forecast results based on an ensemble of the plurality of prediction models. Furthermore, the method comprises optimizing the plurality of forecast results associated with a respective forecast module. Additionally, the method comprises probabilistically combining the outputs of the plurality of optimization modules. Moreover, the method comprises outputting a final forecast based on the combination of the at least two forecast results.

Abstract: A method and system for sampling and augmenting a dataset associated with a first class and a second class, respectively, to balance the dataset of images is described. The method includes receiving a required number of reduced set of dataset images associated with the first class, creating a plurality of clusters from a set of images associated with the first class, and selecting a representative image from each cluster to provide a reduced set of images. Further, a median image and a non-defect artifact mask is generated corresponding to the set of images associated with the first class. Additionally, a defect foreground is extracted based on the median image and each defect image of another set of images associated with the second class. Finally, the at least one non-defect artifact is removed from the defect foreground to provide a new synthetic defect image for each defect image for augmentation.

Abstract: The present subject matter describes a method for labeling data in a computing system based on artificial intelligent techniques. The method comprises receiving input data and ordering the received input-data in a plurality of classes inferred based on at-least one of clustering and anomaly detection. The method further comprises receiving one more manual annotated labels for the ordered data. A first machine-learning (ML) model is trained with respect to the ordered data and thereby generating new labels. The performance of the first ML model is computed based on a comparison between the manual labels and the new labels. The labels are automatically propagated to unlabelled-portion of the ordered data based on execution of the first ML model based on accuracy of first ML model being above a predefined threshold.

Abstract: A system for generating a forecast including a classifier module for receiving from a user, at least one feature and classifying the at least one feature into a plurality of priority groups based on a user preference. The system further includes an artificial intelligence (AI) forecast module in communication with the classifier module for processing the plurality of priority groups with at least one feature. The AI forecast module derive a learning from classification of the at least one feature into the plurality of priority groups; and generate the forecast based on the learning.

Abstract: The present subject matter discloses a system(s) and a method(s) for determining a health state of an individual. According to an embodiment, a method comprises measuring, by a heart rate sensor, a heart rate of the individual during operation within the environment. The method further comprises outputting, by a pressure sensing platform, pressure data of the individual. Further, the method comprises outputting, by an image capturing device, image data of the individual. The method further comprises inferring, by a processing unit, an amount of fat of the individual in the image data. The method further comprises updating, by the processing unit, the amount of fat of the individual using the pressure data. The method further comprises controlling, by the processing unit, a threshold for determining the health state of the individual, using the amount of fat and the heart rate of the individual.

Abstract: A system (101) for monitoring a physiological condition of a user (104) is disclosed herein. The system (101) includes a receiving module (110) configured to receive a plurality of short-term segments of Heart Rate Variability (HMI) (302) or short-term electrocardiogram (ECG) segments (402) or short voice recordings (602) from the user (104) recorded at different time points. The system includes a stitching module (114) for stitching the plurality of short-term segments and creating a stitched segment. The system further includes an extracting module (116) extracting feature from the stitched segment and a predicting module (118) for predict the physiological condition, based on the feature.

Abstract: A method for predicting a condition of living-being in an environment, the method including capturing image-data associated with the at-least one person and based thereupon determining a current-condition of the person; receiving content from a plurality of content-sources with respect to said at least one person being imaged, said content defined by at least one of text and statistics; defining one or more weighted parameters based on allocating a plurality of weights to at least one of the captured image data and the received content based on the current-condition; and predicting, by a predictive-analysis module, a condition of the at-least one person based on analysis of the one or more weighted parameters.

Abstract: A graphical user interface (GUI) for forming hierarchically arranged clusters of items and operating thereupon through an electronic device equipped with an input-device and a display-screen is provided. The GUI comprises a first area configured to display a graphical-tree representation having a plurality of hierarchical levels, each of said level corresponds to at least one cluster of content-items formed by execution of a machine-learning classifier over a plurality of input content items. A second area is configured to display a dataset corresponding to the content-items classified within the clusters. A third area is configured to display a plurality of types of content representations with respect to each selected cluster, said representations corresponding to content-items classified within the cluster.

Abstract: The present subject matter describes a method to detect anomaly in an environment based on AI techniques. The method comprises receiving one or more data representations of one or more objects present in an environment. A first-type of information is captured from a first-area within the one or more data representations. A second-type of information from a second-area different than the first area in the data representations is also captured. A third information is generated from the first information, said third information corresponding to predicted information for the second area using one or more artificial-intelligence models for evaluating the second information. The third information is compared with the second information to determine abnormality with respect to state or operation of one or more objects within the environment.

Abstract: Method and system is disclosed for forecasting demand with respect to an entity. The method comprises receiving a plurality of input data-sets associated with time-series data, wherein each of said data-sets refers a time-based variation of one or more variables in accordance with a designated time-interval. At least one transformation-result is generated by transforming time-intervals of at least one input dataset based on a plurality of time interval transformation models. A plurality of first intermediate forecast results are predicted based on a plurality of demand forecasting models from the at-least one transformation result. An aggregated result is generated from the plurality of the first intermediate forecast results through an ensemble-model to thereby render said aggregated result as a final prediction result.

Abstract: A graphical user interface (GUI) for forming hierarchically arranged clusters of items and operating thereupon through an electronic device equipped with an input-device and a display-screen is provided. The GUI comprises a first area configured to display a graphical-tree representation having a plurality of hierarchical levels, each of said level corresponds to at least one cluster of content-items formed by execution of a machine-learning classifier over a plurality of input content items. A second area is configured to display a dataset corresponding to the content-items classified within the clusters. A third area is configured to display a plurality of types of content representations with respect to each selected cluster, said representations corresponding to content-items classified within the cluster.

Abstract: The present disclosure relates to a method and system for apportioning tasks to person in an environment. The method comprises capturing a first-value indicating a sympathetic-nerve based activity and a second-value indicating a parasympathetic-nerve based activity for at least one person operating in an environment. Thereafter, a quantitative-relation is determined between the first and second values. At-least one task is assigned for execution by said person within the environment based on such quantitative relation.

Abstract: A system for gaze direction detection is disclosed herein. The system comprises a face detection unit configured to detect face information from a captured image of a spectator, a head pose unit configured to detect a head pose angle based on the face information; an eye measurement unit configured to detect eye information of at-least one of a left eye or a right eye of the spectator based on the detected face information; an eye-gaze detection unit configured to estimate a gaze angle of the at-least one of the left eye or the right eye of the subject based on the eye information; a conditional gaze detection unit configured to calculate a resultant gaze angle based on the estimated gaze angles of the left eye and right eye and the head pose angle; and an effective gaze detection unit configured to estimate an effective gaze angle based the resultant gaze angle and head-pose angle.

Abstract: A method for predicting a condition of living-being in an environment, the method including capturing image-data associated with the at-least one person and based thereupon determining a current-condition of the person; receiving content from a plurality of content-sources with respect to said at least one person being imaged, said content defined by at least one of text and statistics; defining one or more weighted parameters based on allocating a plurality of weights to at least one of the captured image data and the received content based on the current-condition; and predicting, by a predictive-analysis module, a condition of the at-least one person based on analysis of the one or more weighted parameters.

Abstract: The present subject matter describes a method to detect anomaly in an environment based on AI techniques. The method comprises receiving one or more data representations of one or more objects present in an environment. A first-type of information is captured from a first-area within the one or more data representations. A second-type of information from a second-area different than the first area in the data representations is also captured. A third information is generated from the first information, said third information corresponding to predicted information for the second area using one or more artificial-intelligence models for evaluating the second information. The third information is compared with the second information to determine abnormality with respect to state or operation of one or more objects within the environment.

Abstract: The present subject matter discloses a system(s) and a method(s) for determining a health state of an individual. According to an embodiment, a method comprises measuring, by a heart rate sensor, a heart rate of the individual during operation within the environment. The method further comprises outputting, by a pressure sensing platform, pressure data of the individual. Further, the method comprises outputting, by an image capturing device, image data of the individual. The method further comprises inferring, by a processing unit, an amount of fat of the individual in the image data. The method further comprises updating, by the processing unit, the amount of fat of the individual using the pressure data. The method further comprises controlling, by the processing unit, a threshold for determining the health state of the individual, using the amount of fat and the heart rate of the individual.

Abstract: The present disclosure relates to a method and system for apportioning tasks to person in an environment. The method comprises capturing a first-value indicating a sympathetic-nerve based activity and a second-value indicating a parasympathetic-nerve based activity for at least one person operating in an environment. Thereafter, a quantitative-relation is determined between the first and second values. At-least one task is assigned for execution by said person within the environment based on such quantitative relation.

Abstract: There is provided a computer-implemented method for generating an output with respect to a group of individuals. The method includes: identifying the group of individuals amongst a plurality of individuals in an area being monitored by one or more sensors; determining, for each individual in the group of individuals, one or more individual-based features associated with the individual based on sensing data obtained from the one or more sensors; determining a group characteristic information associated with the group of individuals based on the one or more individual-based features determined for each individual in the group; and generating the output based on the group characteristic information determined for the group of individuals. There is also provide a corresponding system for generating an output with respect to a group of individuals.

Abstract: There is provided a computer-implemented method for generating an output with respect to a group of individuals. The method includes: identifying the group of individuals amongst a plurality of individuals in an area being monitored by one or more sensors; determining, for each individual in the group of individuals, one or more individual-based features associated with the individual based on sensing data obtained from the one or more sensors; determining a group characteristic information associated with the group of individuals based on the one or more individual-based features determined for each individual in the group; and generating the output based on the group characteristic information determined for the group of individuals. There is also provide a corresponding system for generating an output with respect to a group of individuals.

Abstract: The present invention relates to a system and method for monitoring and predicting the mental health of a person. Data is collected from multiple sensors including a camera and microphone. Additional sensors can be added to improve the robustness of the system such as heart rate sensors and respiration sensors. The data can be collected in phases that provide contextual awareness to the system. An algorithm can synchronize the data collected in the different phases and the data can be analyzed individually and collectively. Historical data can be included in the analysis to evaluate and predict the mental condition of a person.