Abstract: The disclosure relates generally to assortment of products in a retail scenario and more particularly to variety optimization in hyper-localized assortment. In retail scenario, due to availability of limited space in the store, retailers have limited options for displaying a category based on the attribute values of important attributes of the category. In addition, due to increased customer expectations, the attribute values are increasing each day leading to challenges in variety optimization in assortment. The state-of-art techniques address the problem of identification of key attributes for a category, however identifying optimal attribute values among the numerous attribute values specific to location of stores is still challenging.

Abstract: This disclosure relates generally to system and method for identifying suitable equipment for pelletizations of particles. Conventional approaches for identifying pelletization equipment for a process are cumbersome and unmethodical. Even though, methods of identifying optimal operating conditions in a particular equipment for a desired product specification are widely discussed in literature, these research works lack in considering practical criteria like equipment cost, equipment capacity, equipment maintenance, etc., in addition to product specifications while deciding a suitable pelletization equipment. Present disclosure provides system and method that involve optimization to identify the best range of operating parameters in a pelletization equipment to achieve desired product size distribution for a given feed size distribution.

Abstract: This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

Abstract: Existing data residency compliance techniques suffer from inherent drawbacks to discover the spread of data, understanding the data residency regulations and semantics behind them and most importantly placement of data in cloud datacenters such that it is data residency compliant. Embodiments herein provide a method and system for optimizing placement of data to a cloud datacenter complying data residency regulations. The system selects one serving cloud datacenter for a user center. The selection considers three conflicting objectives such as minimum data placement cost, provide good quality of service (i.e. latency) and to comply with data residency regulations. The system essentially covers data residency compliance problem in three phases namely, violation detection, decision support and recommendation. Herein, the system trades-offs latency with data placement cost.

Abstract: This disclosure relates generally to intelligent visual reasoning over graphical illustrations using a MAC unit. Prior arts use visual attention to map particular words in a question to specific areas in an image to memorize the corresponding answers, thereby resulting in a limited capability to answer questions of a specific type. The present disclosure incorporates the MAC unit to enable reasoning capabilities and accordingly attend to an area in the image to find the answer. The present disclosure therefore allows generalizing over a possible set of questions with varying complexities so that an unseen question can also be answered correctly based on the reasoning methods that it has learned. The system and method of the present disclosure can be used for understanding of visual information when processing documents like business reports, research papers, consensus reports etc. containing charts and reduce the time spent in manual analysis.

Abstract: ROI (Region of Interest) detection is an important step in extracting relevant information from a document image. Such images are very high-resolution images in nature and size of images is in order of megabytes, which makes text detection pipeline very slow. Traditional methods detect and extract ROI from images, but these work only for specific image types. Other approaches include deep learning (DL) based methods for ROI detect which need intensive training and require high end computing infrastructure/resources with graphical processing unit (GPU) capabilities. Systems and methods of the present disclosure perform ROI extraction by partitioning input image into parts based on its visual perception and then classify the image in first or second category. Region of interest is extracted from a resized image based on the classification by applying image processing techniques. Further, the system determines whether the input image is a pre-cropped image or a normal scanned image.

Abstract: Currently available live media distribution systems have to continuously maintain multiple peer-to-peer (P2P) connections which further increases the computational load of robot. Present disclosure provides a method and a system for managing multimedia exchange in tele-robotics using dew computing. The system first identifies a dew signaling server wherein a tele-presence robot device present in enterprise network offer to act as dew signaling server. The system then establishes first P2P connection between dew interface and dew signaling server. Thereafter, system establishes second P2P connection between dew signaling server and cloud media manager present in public cloud server. Further, system identifies computing device present in enterprise network as dew media manager. The system then establishes third P2P connection between dew media manager and the cloud media manager via the cloud signaling server.

Abstract: This disclosure relates to a method and system for regime-based process optimization of various assets of industrial manufacturing and process plants or units. Operating regimes of assets are identified and a regime similarity score of each asset is computed from regime sequences of integrated industrial data and compared with a given threshold to identify regimes of operation. Operating regimes are matched with regimes of regime database and industrial assets and similar operating regimes are group together. Process optimization is carried out for each group of industrial assets to identify optimum settings in order to maximize output or minimize loss/cost considering process and equipment constraints. Anomalies are analyzed in the unmatched operating regimes and a diagnosis is carried out to identify the root cause for any detected anomalies. Remaining useful life of components in the assets with unmatched operating regimes is estimated to ensure component reliability and to prevent failure.

Abstract: Manually framing questions for research and evaluation has the disadvantage that quality and effectiveness of questions of the questions depend on knowledge and expertise of subject matter expert who is framing the questions. The disclosure herein generally relates to data processing, and, more particularly, to a method and system for generating protocol for data extraction from one or more users. The system identifies sub-objectives corresponding to an identified research objective, and then generates questions to get response that matches the objective and sub-objectives. Further, a sequence is decided for the questions, and accordingly recommendations are generated. The system also assesses whether response obtained from users has reached saturation, and accordingly generates trigger to terminate data collection.

Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model.

Abstract: This disclosure relates to optimizing an operation of an equipment by a neural network based optimizer is provided. The method include receiving, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps; training, a plurality of simulation models for each equipment instance to obtain a function (fj); processing, the external input parameters (et) to obtain a fixed-dimensional vector and passed as an input to obtain an vector (it); generating, a modified (it) from the output vector (it) based on a domain constraint value; computing, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj); and iteratively performing the steps of processing, generating, and computing reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.

Abstract: Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.

Abstract: The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

Abstract: Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.

Abstract: Enterprise software relies on numerous types and instances of mathematical and logical calculations for proper functioning, which are formally specified in spreadsheets, known as Calculation Specification (CS) sheets, that provide illustrations of the inputs and outputs of the calculation, and are expressed in inputs and formulae applied on them to get values for outputs. The values for such inputs are retrieved by executing ad hoc queries on respective application's database. Present disclosure provides systems/methods to synthesize code using CS sheets by formulating a Programming By Example (PBE) problem for each input involved in the calculation. A query synthesis technique is then implemented that uses natural language processing and enumerative program synthesis techniques to infer queries corresponding to the PBE problem. These queries are filtered based on whether their output is consistent with expected output in PBE.

Abstract: Generating reports from database is conventionally performed manually which requires skilled persons, time consuming and prone to errors. Thus, embodiments of present disclosure provide a no-code platform for generating reports as a transaction. User selects a slice object, its information attributes, report attributes, and optionally filters, grouping, splitting, and sorting conditions which are displayed by the no-code platform. Further, a database query is generated based on the user selection by the no code platform. The database query is then executed to retrieve data from a database. Finally, a report is generated based on the retrieved data and the report attributes. Thus, users can obtain reports as a transaction by just specifying their requirements. They need not know the complex structure of the database. Also, the method can be used to generate any type of report from any type of database irrespective of the internal data organization of the database.

Abstract: Conventional approaches are not efficient for the screening of active multi component alloy catalysts. Present disclosure provides systems and method that incorporate elements of materials chemistry, artificial intelligence (AI)/machine learning (ML) and optimization for the screening of such active catalysts for a target chemical reaction. Given a set of elements constituting the alloy and the target reaction, surface atomic structures of several different alloy composition and configurations are constructed. Then, adsorption energy of key reaction intermediates on the surface atomic structures are computed using quantum chemical methods (QCM). Next, an AI/ML model is trained using data obtained from QCM to predict the adsorption energy of the reaction intermediates on the alloy surface. Finally, numerical optimization is used to identify alloy compositions that maximize the activity and selectivity of the catalyst for a target chemical reaction.

Abstract: Cloud and Fog computing are complementary technologies used for complex Internet of Things (IoT) based deployment of applications. With an increase in the number of internet-connected devices, the volume of data generated and processed at higher speeds has increased substantially. Serving a large amount of data and workloads for predictive decisions in real-time using fog computing without Service-Level Objective (SLO) violation is a challenge. Present disclosure provides systems and method for inference management wherein a suitable execution workflow is automatically generated to execute machine learning (ML)/deep learning (DL) inference requests using fog with various type of instances (e.g., Function-as-a-Service (FaaS) instance, Machine Learning-as-a-service (MLaaS) instance, and the like) provided by cloud vendors/platforms. Generated workflow minimizes the cost of deployment as well as SLO violations.

Abstract: This disclosure relates generally to method and system for estimating smoking episodes from smoke puffs using a wearable device. Since the expense of treating diseases is rising, a digital smoking cessation improves healthcare systems such as cardiovascular issues. To achieve an optimum model given the platform limitations a very compact model is built specifically for the target microcontroller platform. The method of the present disclosure generates an optimum model for deployment on the wearable device using a pretrained deep neural network (DNN). A set of sensor signals are inputted to a convolutional neural network (CNN) smoke detection model to detect smoke puffs. Gesture classifier determines whether the user of the wearable device is engaged/engaging in a smoking session. Further, the method provides users of the wearable device with a cloud estimated smoking behavior analysis based on a set of smoking episodes to generate a set of user risk scores.

Abstract: This disclosure relates generally to system and method for managing dynamically adaptive supply network. The method includes simulating, by an exogenous model, the supply network in an analytical modeling language using at least a data populated from the supply network through a sensory data processing framework. The exogenous model provides a plurality of candidate analytical solutions corresponding to an event condition associated with the supply network based on the simulation. Corresponding to the event condition in a global context of the supply network, a satisfiable solution is identified. An endogenous model corresponding to the supply network is modified based on the satisfiable solution to obtain a modified endogenous model. The modified endogenous model is transformed into a programming language to obtain an updated endogenous model. The supply network is modified as directed by the updated endogenous model.