Abstract: System and method for diagnosing a disease using image classification is provided. The method includes receiving diagnostic images associated with a subject. The diagnostic images comprise a first image and a second image captured at a first instance of time and at a second instance of time, respectively. The method further includes extracting feature maps from the first image and the second image, using a first pipeline and a second pipeline of stacked convolutional layers of a temporal convolutional neural network (CNN) model respectively. The method further includes identifying relevant feature maps from the feature maps using temporal CNN layer of the temporal CNN model, generating a concatenated feature map based on the relevant feature maps, determining an image class corresponding to the second image using fully connected layers of the temporal CNN model, and diagnosing the disease associated with the subject based on the image class.

Abstract: This disclosure relates generally to artificial intelligence system, and more particularly to method and system for tracing a learning source of an explainable artificial intelligence (AI) model. In one example, the method may include receiving a desired behavior of the explainable AI model with respect to input data, generating a learning graph based on similarities among a plurality of learning sources with respect to the input data for the desired behavior and for a current behavior, retracing a learning of the explainable AI model by iteratively comparing the learning graph for the desired behavior and for the current behavior at each of a plurality of layers of the explainable AI model starting from an outer layer, and detecting the learning source responsible for the current behavior based on the retracing.

Abstract: This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.

Abstract: A method and system for performing real-time sentiment modulation in conversation systems is disclosed. The method includes generating an impact table comprising a plurality of sentiment vectors and a plurality of emotion vectors associated with the plurality of sentences. The method further includes generating for each of the plurality of sentences, a dependency vector based on the associated sentiment vector and the associated emotion vector. The method further includes stacking the dependency vector generated to generate a waveform representing variance in sentiment and emotions across words within the plurality of sentences. The method further includes altering at least one portion of the waveform based on a desired emotional output to generate a reshaped waveform. The method further includes generating a set of rephrased sentences associated with the at least one portion, based on the reshaped waveform, the set of sentences, a user defined sentiment output.

Abstract: A method and system for generating user driven adaptive object visualizations using Generative Adversarial Network (GAN) models is disclosed. The method includes the steps of generating a first set of object vectors for an object based on at least one input received from a user. The first set of vectors corresponds to a first set of visualizations for the object The method further includes capturing at least one tacit reaction type of the user in response to user interaction with each of the first set of visualizations, computing a score for each portion of each of the first set of visualizations, identifying a plurality of portions from at least one of the first set of object visualizations, generating a second set of object vectors, and processing the second set of object vectors sequentially through a plurality of GAN models to generate a final object visualization of the object.

Abstract: The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

Abstract: The present invention relates to a method of purchasing one or more goods from a physical store through a virtual environment. The method includes obtaining one or more physical store details and one or more user details from a user device for authenticating a physical user. Further, a unique avatar indicative of a virtual user corresponding to the physical user is generated. Furthermore, the virtual environment comprising one or more virtual stores is generated from a real-time video. The virtual environment is provided to the user device. Subsequently, an optimal path for traversing the virtual user in the virtual environment is determined in real-time based on one or more shopping details and a time period associated with the one or more shopping details. Finally, the virtual user is navigated in the one or more virtual stores through the optimal path for purchasing the one or more goods.

Abstract: Disclosed herein is method and fault detection system for detecting faults in one or more products. In an embodiment, method comprises generating plurality of wavelet coefficients corresponding to transformed images of each of the one or more products and determining a set of invariant features from the plurality of wavelet coefficients. Further, a dynamic set of invariant features is generated by grouping invariant features into a set of groups based on type of the one or more products. Subsequently, the dynamic set of invariant features is quantized based on a predetermined quantization threshold and a representative coefficient signature is associated for each group in the dynamic set of invariant features. Finally, faults in the one or more products are detected by comparing coefficient signatures associated with the one or more products with the representative coefficient signature of each group in the dynamic set of invariant features.

Abstract: Disclosed herein is a method and system for providing personalized content to a user. The method comprises categorizing original content to be provided to user into a plurality of data packets. The data packets include data of similar domain. The user is categorized into one of plurality of classes and a vocabulary of words suitable for the class is identified. The class is associated with a domain. The system identifies relevant content for the class. Thereafter, the system modifies the original content by either by inserting a new data packet or deleting a data packet. A target content is generated for the class based on vocabulary of words associated with class and modified original content. Thereafter, the target content is provided to the class by incorporating one or more features of a presenter for presenting the target content. The present disclosure enhances user experience by personalizing content for the user.

Abstract: A method and a system are described for context based clustering of one or more objects. The method comprises receiving, by the object clustering system, receiving, by an object clustering system, an object clustering request for one or more objects associated with a plurality of contextual parameters, where the plurality of contextual parameters comprises one or more physical attributes and one or more non-physical attributes. It further includes tagging the one or more non-physical attributes respectively to the one or more physical attributes. It further includes identifying a common context from the one or more physical attributes associated with the one or more objects based on the tagging. It further includes mapping the one or more physical attributes to the one or more objects based on the common context. It then includes clustering the one or more objects based on the mapping.

Abstract: A technique is provided for minimizing impact of a faulty node associated with an artificial network. The technique includes detecting a faulty node associated with the artificial neural network. The faulty node causes a faulty path in the artificial neural network. Further, a plurality of alternate paths are identified to reroute the faulty path. Based on the identified plurality of alternate paths, the faulty path is rerouted by assigning one or more weights associated with the faulty node to one or more nodes associated with the plurality of alternate paths.

Abstract: This disclosure relates to method and system for building ensemble models using competitive reinforcement learning (CRL). The method may include creating a plurality of clusters, each including a set of predictive models forming the ensemble model. For each of the plurality of clusters, the method may further include initializing each of the set of predictive models with random values for a set of first parameters to obtain a first accuracy score, categorizing each of the set of predictive models into a first associated category based on a first accuracy score, calculating a first reward for each of the set of predictive models based on the first associated category, and determining a set of second parameters for each of the set of predictive models using CRL to obtain a second accuracy score.

Abstract: A method and a system of selective encryption of a test dataset is disclosed. In an embodiment, the method may include determining a relevancy grade associated with each of a plurality of datapoints within a test dataset by comparing the test dataset with a common heat map, wherein the common heat map is generated using a plurality of training datasets. The method may further include calculating, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints. The method may further include selectively encrypting at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the plurality of datapoints. The at least one data point is rendered to a user after being decrypted.

Abstract: A method and system for allocating resources to containers includes receiving a plurality of resource allocation requests from a plurality of containers, receiving a preference of a container from each of the plurality of containers, determining a priority-sequence for the plurality of containers, allocating a first associated resource for an associated first time-period to each container of the plurality of containers, determining a current consumption of the first associated resource by each container of the plurality of containers during the associated first time-period, and allocating a second associated resource to each container of the plurality of containers based on the current consumption of the first associated resource by each container of the plurality of containers.

Abstract: The disclosure relates to method and system for performing optimized image search. The method includes receiving an input image and user requirements with respect to an image search, identifying a sub-section from various sub-sections of the input image based on semantic parameters corresponding to each sub-section and the user requirements, and determining an optimal resolution of the sub-section based on a pixel density of various image formats derived for the sub-section. The method further includes identifying an optimal set of layers from a plurality of layers of an Artificial Neural Network (ANN) based image search model for performing the image search based on the semantic parameters for the sub-section, the optimal resolution of the sub-section, and historical data, and performing the image search to identify a set of output images similar to the sub-section based on a modified ANN based image search model comprising the optimal set of layers.

Abstract: The present disclosure relates to method and system for performing classification of real-time input sample using compressed classification model. Classification system receives classification model configured to classify training input sample. Relevant neurons are identified from neurons of the classification model. Classification error is identified for each class. Reward value is determined for the relevant neurons based on relevance score of each neuron and the classification error. Optimal image is generated for each class based on the reward value of the relevant neurons. The optimal image is provided to the classification model for generating classification error vector for each class. The classification error vector is used for identifying pure neurons from the relevant neurons. A compressed classification model comprising the pure neurons is generated. The generated compressed classification model is used for performing the classification of real-time input sample.

Abstract: A method, device and system for managing collaboration amongst robots is disclosed. The method may include assigning a color tag from a set of predefined color tags to each of a plurality of robots, based on associated functional capabilities. The method may further include dynamically creating a plurality of groups for a plurality of tasks based on at least one attribute associated with each of the plurality of tasks and functional capabilities associated with the plurality of robots. The method may include electing a plurality of chief robots for the plurality of groups based on a first predefined logic. The method may include selecting a prime robot from the plurality of chief robots based on a second predefined logic and the selected prime robot may be configured to monitor activity of each of the plurality of groups and each robot in each of the plurality of groups.

Abstract: System and method for diagnosing a disease using image classification is provided. The method includes receiving diagnostic images associated with a subject. The diagnostic images comprise a first image and a second image captured at a first instance of time and at a second instance of time, respectively. The method further includes extracting feature maps from the first image and the second image, using a first pipeline and a second pipeline of stacked convolutional layers of a temporal convolutional neural network (CNN) model respectively. The method further includes identifying relevant feature maps from the feature maps using temporal CNN layer of the temporal CNN model, generating a concatenated feature map based on the relevant feature maps, determining an image class corresponding to the second image using fully connected layers of the temporal CNN model, and diagnosing the disease associated with the subject based on the image class.

Abstract: Disclosed herein is method and fault detection system for detecting faults in one or more products. In an embodiment, method comprises generating plurality of wavelet coefficients corresponding to transformed images of each of the one or more products and determining a set of invariant features from the plurality of wavelet coefficients. Further, a dynamic set of invariant features is generated by grouping invariant features into a set of groups based on type of the one or more products. Subsequently, the dynamic set of invariant features is quantized based on a predetermined quantization threshold and a representative coefficient signature is associated for each group in the dynamic set of invariant features. Finally, faults in the one or more products are detected by comparing coefficient signatures associated with the one or more products with the representative coefficient signature of each group in the dynamic set of invariant features.

Abstract: This disclosure relates to method and system for providing explanation for output generated by artificial intelligence (AI) model. The method may include receiving encrypted input data and a public encryption key from a client device, wherein the encrypted input data is encrypted using the public encryption key. The method may further include generating an encrypted AI model by encrypting an AI model using the public encryption key. The method may further include generating an encrypted output and an encrypted feature data based on the encrypted input data using the encrypted AI model, and generating an encrypted explanation for the encrypted output based on the encrypted feature data. The method may further include providing the encrypted output and the encrypted explanation to the client device for rendering, wherein the encrypted output and the encrypted explanation are decrypted by the client device using a private encryption key.