Abstract: For performing analysis of enterprise application, code analysis tools may be used. But currently available analysis tools lack support for analyzing database processing statements, such as structured query language (SQL) statements and inter-service communication statements that may be present along with the programming language statements in the computer program. Present disclosure provides method and system for performing program transformations for precise analysis of enterprise applications. The system transform each database processing statement present in a computer program associated with the enterprise application into host programming language statement and each service call into function call statements. In particular, the system replaces syntax of embedded language with equivalent host programming language syntax. The system then uses existing code analyzers for performing analysis of the computer program to get data flow information of the computer program.

Abstract: The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

Abstract: Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA.

Abstract: Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources.

Abstract: Techniques used by the state of the art alarm prediction systems rely mostly on LSTM, which have technical limitation in predicting a sequence with long durations. Embodiments of the present disclosure provide a method and system for long duration alarm sequence predictions from past alarm sequence using a Bi-LSTM and operator sequence recommendations thereof. The BiLSTM with an encoder decoder technique uses true output sequence as input to decoder at each time step during training. This allows the BiLSTM to learn dependencies among input and output sequence effectively. The operator sequence recommended is identified based on closeness of the predicted future output alarm sequence with one among the unique alarm sequences in a mapping table. The alarm sequence closeness is computed using a Matching Sequence Score (MSS) disclosed by the method, since known sequence evaluation metrics such as Blue Score has limitations to be directly applied in alarm sequence evaluation.

Abstract: Post pandemic, retailers are adopting more contactless services for shopper's checkout such as self-checkout, hybrid checkout and mobile checkout, and these touchpoints have become the potential areas for fraudulent activity during check out process. For detecting fraud carried out by a customer at the time of self-checkout, existing approaches require respective customer identity and his purchase history. Embodiments of the present disclosure do not require customer identity and information about his historical shopping carts and provide a method and system for approving a user shopping cart for self-checkout from the items picked by the customers in real time.

Abstract: This disclosure provides methods and systems for enabling a hybrid architecture in an enterprise application. The present disclosure addresses problems of conventional approaches which falls back on a common representation or pluggable framework to handle challenges of composing multiple libraries. Conventional approaches work for the problem of maintaining performance while handling diversity of libraries which cannot be reused either in as-is form or with a slight modification. The present disclosure provide a method and system that enable hybrid architecture in an enterprise application to mitigate quantum attacks. The method of the present disclosure changes codebase of the enterprise application to enable hybrid architecture. New paths are created within the enterprise application to support execution of a new library code that enables double encryption of data being processed by the application.

Abstract: The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.

Abstract: Current approaches for detecting water saturated regions in a rockmass uses geophysical methods, such as electrical resistivity tomography (ERT), self-potential (SP), and seismic imaging to spatially detect and map the rock water content in underground mines. However, all these approaches are contact based. Present disclosure provides a non-contact method and system for inspecting and detecting water saturated regions in a rockmass. The system uses coherent radar generated range-compressed data collected from a plurality of rock specimens for generating a generalized calibration function using a range doppler algorithm and a phase tracking algorithm. The system then uses the generated generalized calibration function for estimating water saturation of a target rockmass along with the use of the RDA and the phase tracking algorithm.

Abstract: The present disclosure provides a system for securing communication between Central Controller and signaling devices including actuator devices and signaling sensors used in railway and road traffic signaling networks. Conventional signaling systems use unsecured metal cables to communicate between the Central Controller and the signaling devices, making them vulnerable to unauthorized intrusion and mischief. In the present disclosure, two uniquely addressable communication modules, one (SCM1) securely housed with the Central Controller and another (SCM2) securely housed in an assembly also containing the signaling device and the related power switches are used to establish a transparent but standard secure digital communication protocol between them to authenticate and validate mutual communication, making them secure and safe from intrusion and undesirable manipulation.

Abstract: Combined cycle gas turbine (CCGT) power plants have become common for generation of electric power due to their high efficiencies. There are various problem related with improving the efficiency of CCGT plants by optimizing the manipulated variables. The method and system for optimizing the operation of a combined cycle gas turbine has been provided. The system is configured to calculate an optimal value of manipulated variables (MV) with efficiency as one of the key performance parameters. The MVs from the existing CCGT automation system, i.e. a first set of manipulated variables and the manipulated variables from the optimization approach, i.e. a second set of manipulated variables are combined to determine an optimal set of manipulated variables. The method further checks for the anomalous behavior of the system and define the root cause of the identified anomaly and the operational state of the CCGT plant.

Abstract: In state of the art methods for object goal navigation, scene understanding is implicit in their goal oriented exploration policies. Implicit scene understanding coupled with navigation is shown to be specific to tasks for which training is done and not generalizable to new tasks. Thus, embodiments of present disclosure propose a method of goal-conditioned exploration wherein scene understanding is decoupled from the exploration policies. Here, the scene understanding required for navigation is provided by a region classification network that is trained using semantic graphs representing the scene and agent can be navigated towards the goal either by using any state of the art pure exploration policies or by traversing through potential sub-goals identified based on a Co-occurrence Likelihood score calculated by using predictions from the region classification network. Hence, the method of present disclosure can be easily generalized to new tasks and new environments.

Abstract: This disclosure relates generally to a system and method for monitoring performance of low-cost sensors plied in a field for soil moisture measurement. The low-cost sensors are calibrated to give useful derived parameters to support farming such as volumetric water content (VWC) of the soil. Further, the steps are being incorporated to de-noise their response to derive stable measurements similar to expensive rugged sensors. The calibration of the low-cost sensor and normalization of incoming values from the low-cost sensor are based on values determined through rugged sensors for soil moisture measurement. The normalization involves finding a minimum value and maximum value of soil moisture. Performance of the low-cost sensors are analyzed based on a range of values of the soil moisture. Finally, the performance analysis provides degradation stages and based on the degradation stages evaluated recommendations to modify the sensor are shared with the user.

Abstract: Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are recommended. System described herein analyses popularity bias in session-based RS obtained via deep learning (DL) models. DL models trained on historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand source of this bias amplification, potential sources of bias at data-generation stage (user-item interactions captured as session logs) and model training stage are considered by the system for recommendation wherein popularity of item has causal effect on user-item interactions via conformity bias, and item ranking from models via biased training process due to class imbalance.

Abstract: Monitoring of animals in a herd has various challenges. A method and a system for monitoring and measuring the behavior of a plurality of animals in a herd chamber is provided. The disclosure provides a method to develop a model to identify the animal interactions between a given set of herds of two or more animals. The system uses image, RFID sensors, animal phenotyping information such as species, breed, age and reproduction cycle information to generate an animal profile. This profile is used to quantify the interaction into an aggression score to the animals in the given herd. The aggression score over a given duration can help herd manager to put the animals with similar aggression score together. This will also help analyze the temporal aspects of the aggression score for a given animal and take preventive measures.

Abstract: State of the art predictive maintenance systems that generate predictions with respect to maintenance of High Performance Computing (HPC) systems have the disadvantage that they either are reactive, or the predictions are affected due to quality issues associated with the data being collected from the HPC systems. The disclosure herein generally relates to predictive maintenance, and, more particularly, to a method and system for predictive maintenance of High Performance Computing (HPC) systems. The system performs abstraction and cleansing on performance data collected from the HPC systems, and generates a cleansed performance data, on which a Machine Leaning (ML) prediction is applied to generate predictions with respect to maintenance of the HPC systems.

Abstract: Optical fiber network or fiber network is used for transmitting large volumes of data with maximum speed. Fiber to home is a recent technology of the fiber network where the initial fiber rollout cost is more. Hence a proper network management is necessary to rollout the fiber network in an optimized manner. Conventional methods provides construction led approach for fiber network planning and field survey. The present disclosure receives a plurality of geocoded addresses associated with a plurality of users and an average revenue per user. A fiber rollout cost projection is performed based on the input data and a fiber network is generated based on the projected fiber rollout cost. Further, field survey is performed based on the generated network and a fiber network construction design is made. Further, a fiber network rollout is performed based on the fiber network construction design and a redline deviation markup.

Abstract: Unlike visual similarity, visual compatibility is a complex concept. Existing approaches for outfit compatibility prediction does not focus on methods with personalization. The present disclosure proposes a novel approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. The present disclosure represents the outfit as a graph and uses Graph Neural Network (GNN) with attention mechanism to capture the inter-relationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the user embedding.

Abstract: High cost of biotherapy drug makes it unaffordable for patients to seek treatments. Further, access to information related to new low-cost alternatives like Biosimilars and Interchangeable may not be available with the physician at the time of consultation. Most of the conventional approaches aims to select an alternative biosimilar for a reference drug without considering patient's information. The present disclosure recommends a list of low-cost alternatives to high-cost reference drugs thereby enabling the physician to get timely and updated information on development of Biosimilars. The solution leverages Natural Language Processing (NLP) technology to extract known adverse events for a reference drug and a relative scoring based technique to identify and optimum alternative to prescribed biologics. The capability of the solution is further extended to identify secondary adverse events due to multiple drugs, thereby providing a clinical decision support system to help physicians take an informed decision.

Abstract: State of the art data transmission approaches require all the lost data packets to be retransmitted, which may not be required in a variety of scenarios. Existing protocols lack the option to change packet semantics on the fly for individual fragments on the fly for the same stream. Also, parameters including maximum number of retransmissions permitted are set beforehand, one for entire stream. Method and system in embodiments disclosed herein provide an approach for context-based retransmission of lost packets. Method and system disclosed in the embodiments herein identifies one or more packets as lost packets based on reception status of acknowledgement (ACK) within a dynamically adaptive timeout period, and then based on value of a NRTx header field associated with the lost packet, determines whether or not to retransmit the lost packet. Value of the dynamically adaptive timeout period is recalculated dynamically, based on a determined instantaneous channel condition.