Tata Consultancy Services Patente

Conventional techniques for evaluating generated infographic images, prioritize aspects like text alignment, data adherence, aesthetics, and overall quality, but often do not comprehensively assess adherence to underlying input data. Embodiments herein provide a method and system for comprehensive assessment of infographic images using Aesthetics Adherence to Data and Text (AADaT) Score. The method calculates a plurality of AADaT scores using the plurality of attribute scores for the plurality of generated infographic images. Further a plurality of LLM scores along with a plurality of feedback instructions are generated for each of the plurality of attributes, using the associated plurality of AADaT scores for the plurality of infographic images. An infographic evaluation model is trained with the plurality of infographic images, the plurality of feedback instructions, the plurality of AADaT scores, and the plurality of LLM scores, to generate a trained infographic evaluation model.

Existing design methods rely on manual effort, trial-and-error approaches, and simplified models, which can lead to suboptimal designs and reduced efficiency. The present disclosure obtains a first set of responses for a first set of questions from a user and generates a second set of questions. A second set of responses is obtained and one or more questions specific to a problem formulation are generated using one or more autonomous programs. A first set of data corresponding to one or more questions is retrieved from a knowledge base and one or more gaps is identified. A second set of data is retrieved from one or more resources and knowledge base is updated. One or more process models and corresponding one or more optimization models are retrieved, and a relevant process model is selected. The most appropriate one or more optimization models are selected and, and feedback is obtained from user.

The growing use of wearable devices requires accurate and compact representations of high dimensional physiological signals. The present disclosure presents a UNet inspired autoencoder to represent and reconstruct multiple neurophysiological signals from single channel data. The architecture includes single-encoder and multiple decoders to obtain self-attention enabled compact embeddings of mixed ExG (EEG /ECG) signals through decaying encoder-decoder skip connections, for improved representation capability. The embeddings are separable into individual ExG components enabling simultaneous reconstruction of high-fidelity EEG and ECG sources. The pretrained encoder can be used for a complex downstream task with minimum fine-tuning. Using the present disclosure on a large corpus of single-channel mixed ExG generated from overnight Polysomnography (PSG) recordings, it is shown that subject- and class- independent EEG/ECG reconstructions validated by multiple domain-specific metrics can be obtained, and the encoded EEG embeddings can be classified into five sleep stages as a downstream task.

This disclosure relates generally to a method and system for predicting animal emotions using animal emotion knowledge graph and graph neural network. Current available methods focus only on visual and language data captured from animals and lacks real time adaptability. The method disclosed generates an animal emotion knowledge graph (AEKG) that combines human and animal neurobiological data, behavioral studies, and the human wheel of emotions. Further real time graphs are generated from multimodal input data captured from the animal. These real time graphs are used for predicting primary, secondary, and tertiary emotions of the animals using a trained graph neural network-Transformer model. This model is trained using the AEKG. Using temporal graph analysis, the method predicts future emotions and generates real-time recommendations based on generative artificial intelligence techniques. Predicting the emotions of animals in real time helps to grasp their emotional well-being to improve their care and management effectively.

The embodiments of the present disclosure address unresolved problems of precise and scalable anomaly detection, root cause analysis, anomaly severity estimation, and trend analysis for plants within controlled environments. Embodiments herein provide a method and system for estimating severity and causality for plant development anomalies in plants in a controlled environment. The system uses RGB image based analysis to identify subtle, similar looking growth anomalies and timely corrective actions. System estimates anomaly severity by analyzing real-time data and dynamically generated synthetic baseline plant data for non-linear severity growth for plant anomalies. The system performs root cause analysis with prioritized rankings, severity zone mapping, severity trend estimation, and tailored role-specific insights to enable effective decision-making and targeted interventions in plant farming environments. Finally, the system identifies and ranks potential root causes of plant-specific stress symptoms along with providing a final zone, bench and rack health index and overall health status.

This disclosure relates generally to screening large data-sets with multi-level clustering. The State-of-the-art methods leveraging LLMs with generative capabilities focus on scanning an extraneous and peripheral contents in text documents to retrieve contextually relevant information. This leads to hallucinations and uncertainty in response and results in complicating the generation of actionable insights. The method of the present disclosure performs a two-level clustering at both an intra-document and an inter-document levels to extract context relevant data. The method screens semantically similar sentence chunks at intra-document level to create a concise intra-document embeddings with contextually relevant text data. Further, an inter-document level clustering is performed by computing a cluster integrity and relevancy (CLIAR) index on a plurality of intra-document embeddings to isolate inter-document cluster having semantic representation of contextually relevant contents.

Conventionally, to automate these laboratory tasks, a robotic device has been used which is not enough as the sequence of actions like pipetting and dipping are more complex than just having grasping capabilities. Present disclosure provides a liquid handling system (LHS) and a method that determines quantity for formulation to be prepared using liquids associated with recipe. An error between a set point and actual volume of the liquids is calculated for determined motor speed to generate torque and rotational motion that are used to generate a linear motion for movement of one or more plungers associated with one or more syringes mounted to the LHS. The movement of plungers enables aspiration and dispensation of the liquids by the LHS at a required amount for preparing the quantity of the formulation using the viscosity of the liquids.

This disclosure relates generally to a method and system for conducting an offline token-based digital currency payment. State-of-the-art methods facilitating offline transaction lacks validity of each token involved in the transaction. Moreover, a separate record is required for the transactions processed in an online mode and for the transactions processed in an offline mode. Further, managing multi-hop offline transactions are not yet achieved. The disclosed method involves offline transfer of set of tokens from a payer's wallet to a payee's wallet. The method captures one or more transaction dependencies associated with each token and assigns a token-proof to each token in the set of tokens. The method executes the transaction having cleared or no prior transaction dependency by transmitting the set of tokens from the payer's wallet to the payee's wallet using a payer's WSP, a central authority, and a payee's WSP.

Existing microservices identification techniques focused on clustering the cohesive components of the applications, ignoring their alignment to the business domain that they service. The present disclosure analyzes a plurality of inputs and generates a plurality of outputs based on the analysis. A plurality of application elements and one or more associated relations of different types between the plurality of application elements is created using the generated plurality of outputs. A filtered set of application elements and the associated relations of different types between the set of application elements are enriched. An application architecture model is created using an enriched set of application elements and an architecture meta model. A weighted graph is created using the application architecture model and a domain description map. One or more communities specific to the created weighted graph are grouped and evaluated. One or more candidate microservices are identified based on the evaluation.

Existing methods have attempted 2D reconstruction of current for individual layers but not for 3D Integrated Circuits (ICs) non-invasively. Hence, embodiments of present disclosure provide a method and system of reconstructing electric current from 2-D magnetic field maps of 3D ICs. Initially, magnetic field maps of the 3D IC are measured from a certain distance using a sensor. The volume of the 3D IC is discretized into a multiple layers of equal width. Among these layers, the layers having a current flow are detected by firstly calculating current density maps associated with each of the layers using an iterative optimization technique, computing quality measures for each layer based on the current density maps and then eliminating the layers having the quality measures less than a predefined threshold value. Once the layers having current flow are detected, the current density maps for these layers are estimated using the iterative optimization technique.

Existing approaches for determining user interest in advertisements being displayed have the disadvantages related to complexity, scalability, and so on. Method and system disclosed herein obtain a video of a user, and further processes to generate an attention series data representing a plurality of visual attention values of the subject, based on an Eye Aspect Ratio (EAR) and a Blink Rate Variability (BRV) score. Further, a fluctuation matrix comprising a plurality of attention states of the subject is generated. Further, an attention stability frequency score representing frequency of oscillations in the attention series data is generated, and then an attention stability power score representing an intensity of occurrence of attention values in the attention series data is generated. The attention stability frequency score and the attention stability power score are fused to generate a like-dislike score indicating an interest of the subject in the advertisement being played.

This disclosure relates generally to method and system for generating emotional talking head video using disentangled pose and expression-flow guidance. Generating realistic one-shot emotional talking head animation on arbitrary faces is a challenging problem, as it requires realistic emotions, head movements, identity preservation, and accurate lip sync. The method receives a plurality of inputs comprising an identity image, a speech audio input data and an emotion input data to generate emotional talking head video. Further, the plurality of inputs are utilized to generate one or more pose landmarks using the pose generation network, and one or more pose landmarks are generated using the expression generation network. Finally, an image generation network generates emotional talking head video using disentangled optical flow computation for pose-guided and expression-guided motion using one or more expression-invariant pose landmarks and pose-invariant emotion landmarks. Additionally, the method generates accurate facial emotions and emotion accuracy on benchmark datasets.

This disclosure relates generally to a method for automatic knowledge refinement and task execution in robotic agents using a domain-agnostic knowledge base. Conventional Large Language Model (LLM) based methods break down complex tasks into sub-tasks but may generate incorrect steps or refer to unavailable objects or actions. While the robotic agents leverages prior-domain specific-knowledge, the unavailability of such structured knowledge in many practical domains and difficulty in updating data reliably makes agent-based task execution problematic. The integration of the domain-agnostic knowledge base with a Multi-modal Language Models (MLM) or LLM enables the robotic agent to perform tasks that span multiple domains without the constraints imposed by traditional, domain-specific knowledge bases. Combining the domain-agnostic knowledge base and the MLM allows for the generation of refined action sequences that are adaptable to the nuances of complex tasks, thereby enhancing the robotic agent's operational versatility and effectiveness.

The disclosure relates generally to methods and systems for automatic generation of an editable user interface (UI) screen from application screen image. Conventional techniques for automatic generation of the editable user interface screen from application screen image are limited to specific UI page type and challenges with the conversion accuracy. The methods and systems of the present disclosure predict the bounding boxes of different graphical components and their labels present in the given UI image screen using a trained object detection model. Then, the one or more group boxes and the one or more nested group boxes are identified based on their properties and next one or more UI widgets present in both the one or more group boxes and the one or more nested group boxes are identified. Lastly the editable UI screen image is automatically generated from the identified UI widgets.

The emergence of Sixth Generation (6G) wireless networks presents unprecedented opportunities for revolutionizing smart manufacturing environment through TeraHertz (THz) communication band. However, the associated unique characteristics pose significant challenges for reliable indoor communication. The present disclosure addresses these challenges by providing a context aware spectrum allocation system and method that capture user requirements/intents which are then translated to priority and fairness objectives. Based on mobility of Mobile-edge Entities (MEs), THz channel modelling and dynamic channel state updation are performed and context-based spectrum allocation is then done based on fairness and priority objectives. Further, dynamic mobility and updation of distance of users is captured with respect to a Base Station (BS). Q-learning technique is employed for adaptive decision making based on the reward function. Further, the system integrates Hungarian technique with the Q-learning for global optimization for dynamic sub-band allocation at infrastructure plane level.

When it comes to disaster event analysis and prediction, one modality of data which is often overlooked in research is news. Current research uses NLP modules to process the news stream, this approach has the disadvantage that the new articles, being from different sources, may have less reliability, and hence the inference and prediction that is generated from this data may not be entirely reliable. In the method and system disclosed herein, for the disaster event analysis and prediction, a first severity information is generated from text input data from various sources, and a second severity information is generated from image data. The first severity information and the second severity information are fused to generate a combined severity information represents a predicted severity of a disaster event, by performing a decision level fusion of the first severity information and the second severity information.

In a competitive retail world, store specific competitive strategies need to be formed by understanding unique characteristics of assortment followed by each specialty competitor within a trade area. Retailers face challenges to get intensive knowledge about store specific competitor assortment due to practical difficulties. Embodiments herein provide a method and system for a generative artificial intelligence (GenAI) based assistant to implement one or more competitive retail strategies associated with specialization of a specialty retail store. Locating competitive position of a specialty retail store and identifying gap in assortment is achieved by capturing unique characteristics of a specialty competitor store which is enabled by applying dynamic enrichment of content in which direction for the enrichment provided by approximating existing patterns that occur among the specialty competitive stores.

The present disclosure addresses energy overheads and latency challenges of architecture design of conventional gated recurrent unit (GRU) approaches by providing a Resistive Random-Access Memory In-Memory Computing-based gated recurrent unit (RRAM IMC-based GRU) network architecture for efficient implementation of GRUs. In the present disclosure, the RRAM IMC-based GRU network architecture is used which performs Multiply-Accumulate (MAC) operations using Ohm's law for multiplication and Kirchhoff's current law for accumulation. A plurality of GRU wight parameters are mapped as device conductance in a Resistive Random-Access Memory (RRAM) memristor array structure in a skewed arrangement. An input vector is applied as voltage pulses to wordlines of the RRAM memristor array structure corresponding to values which should be multiplied and accumulated. Outputs of the MAC operation are obtained as bitline currents, which are then sampled and converted to digital values using an Analog-to-Digital Converters for interfacing with other digital post-processing units.

Computational analysis is essential for efficient design, evaluation, and prediction of processes in industrial systems. However, it requires significant expertise, iterative refinement, and labor-intensive efforts. Embodiments herein provide a method and system for computational analysis of industrial entities using a multimodal Large Language Model (LLM). A plurality of final assumptions required for a plurality of workflows of the computational analysis of the industrial entity are generated by the multimodal LLM. Further the method creates a plurality of prompts based on the generated plurality of final assumptions. A Computer-Aided-Design (CAD) file, a final mesh file, a solver results file, and a plurality of computational analysis result insights are generated using the plurality of prompts, via the multimodal LLM. The method of the present disclosure minimizes user involvement, utilizes a knowledge base to generate relevant outputs, and offers flexibility for seamless integration with various software platforms and tools for the computational analysis.

Recommending an optimal combination of PQC schemes, and the PQC libraries for replacing the classical cryptographic schemes of a cryptographic application is a challenging decision problem. Embodiments herein provide a method for selection of the PQC schemes and the PQC libraries for cryptographic applications. The method first generates one or more candidate PQC libraries, for each of the plurality of cryptographic applications based on a plurality of stringent requirement attributes and a plurality of non-stringent requirement attributes, using a plurality of first compatible PQC libraries, and a plurality of second compatible PQC libraries. Further a schemelibrary pair of the one or more candidate PQC libraries is obtained, for each the plurality of classical cryptographic schemes based on a plurality of performance objective metrics. Then one or more final candidate PQC libraries obtained using the schemelibrary pair which is recommended for the plurality of cryptographic applications of an enterprise.

A purchase order is a commercial document issued by a buyer. Conventional methods fails to categorize the purchase orders accurately by using item description alone. The present disclosure receives purchase orders associated with an organization and normalizes by expanding a plurality of acronyms using a large language model. A first category pertaining to normalized purchase orders is determined. A second category of normalized item descriptions are further obtained by selecting a local guided categorizer or a global guided categorizer based on the categorizer selection flag. Further, a final category is obtained based on the first category and the second category and a dynamic threshold value. Finally, the plurality of item descriptions of the plurality purchase orders are grouped based on an associated final category and vector embeddings using a grouping technique. The grouped plurality of item descriptions are updated in training dataset pertaining to a local categorizer.

Cuffs for BP measurement fail to adjust to optimal pressure to observe Korotkoff sound. A smart cuff technology comprising a MR fluid at middle layer controlled by electromagnets arranged into an outer layer of the elastic cuff is provided. MR fluid under influence of active electromagnets automatically detects the shape/curves of a subject to provide clean fit on arm of a subject. The piezoelectric sensor array senses pressure exerted on arm of the subject and electromagnets actuate MR fluid in such a way that cuff takes form of the shape of the subject. A cuff inflation control system calculates and applies enough pressure to stop blood flow so that there is no excess pressure on the upper arm and artery. The system receives feedback from microphone sensor array, attached to the sleeve, and detects the blood flow if present, not exceeding the desired pressure to stop the flow of blood.

Global food systems face a paradox: rising demand and widespread hunger coexist with massive food waste, especially fresh produce. Short shelf life, poor forecasting, and lack of transparency lead to unnecessary disposal, contributing to greenhouse gas emissions and sustainability challenges. Consumers also lack reliable information on freshness and nutritional value, further worsening waste. Hence, embodiments of present disclosure provide a method and system for traceability, quality assessment and end of life recommendations of a food item. Initially an image of the food item is captured and a machine-readable tag on the food item is scanned to retrieve traceability data. Then, using trained Visual Language and physics-based models, visual and biochemical features are analyzed to compute ripeness, shelf life, and freshness scores. Further, usage recommendations and end-of-life options are recommended, including recycling guidance for packaging and upcycling opportunities for leftovers. This integrated approach ensures transparency, optimizes consumption, and supports sustainability.

The disclosure relates generally to method and system to generate decision-fused classification map using different modalities in remote sensing applications. In remote sensing applications, the problem of missing data from satellite images occurs due to various environmental factors and spatial resolution provides inconsistent class labels during segmentation. The method receives from each satellite among a plurality of satellites a plurality of remote sensing images capturing a land use and land cover (LULC) geographical area on earth. Each pretrained classifier obtains the plurality of remote sensing images to generate a segmentation map. Further, Kolmogorov Arnold networks (KAN) decision fusion combines two or more closest segmentation maps using an ontological knowledge tree to determine a minimal common parent class based on at least one satisfying criteria. Finally, a fused classification map is generated to obtain finer grained LULC classes for each remote sensing image using the ontological knowledge tree.

Embodiments herein provide a method and system for splitting an artificial neural network (ANN) between edge devices (edge) and a user equipment (UE) under supervision of an application cloud (AC) through a unified splitting AI framework (SAF). In AI operation splitting, a few layers of the artificial neural network (ANN) are computed at the endpoint itself. The intermediate result is transferred to the Edge/ Cloud for further computation. Besides distributing the computation load, this has another important advantage. If the first few layers of the ANN are computed at the endpoint itself, then that saves the resources required for transmission of the entire input signal because the intermediate result needs much less bandwidth than the actual input. Also, the ANN could start in the offloaded node only after receiving the entire input. So, splitting the ANN computation is beneficial in terms of the total computation latency as well.

This disclosure relates generally to a method and system for predicting cardiologist level clinical explanations for electrocardiogram signal classification using deep learning. Conventional methods for explainability of artificial intelligence models are influenced by data distribution and do not provide clinical level explanations in cardiac healthcare. The method disclosed provides clinical level explanations for rhythm diagnosis from ECG signals. The method validates that clinical concepts in ECG signals can be extracted by generating a set of synthetic ECG signals. The set of synthetic ECG signals are generated using trained generative adversarial networks and statistical parameterization techniques. Further the set of synthetic ECG signals are validated by classifying and comparing them with a set of real ECG signals. Then a clinical concept identifier model is trained for predicting the clinical concepts present in an ECG signal classified into a set of signal classes. These clinical concepts predicted are cardiologist level explanations.

The disclosure relates generally to methods and systems for generating design component knowledge graph and design component retrieval from the same. Conventional techniques that utilize customizable knowledge graphs for retrieval of the design components are very limited in the digital design domain. The methods and systems of the present disclosure generate the design component knowledge graph using filtered captions generated for the design component objects. The design component objects are of the design components and are selected using an instruction triplet generated based on a user requirement query of the digital design that the user is interested in. Then, a plurality of captions is generated and filtered using a semantic filtering to obtain the filtered captions. Then the design component knowledge graph is queried with the user input to retrieve the design components that best suit the design requirement of the user.

Usage of generative artificial intelligence (GenAI) models require appropriate prompts that describe a task to be completed. Conventionally complexities of large sizes and monolithic nature of input code are difficult for handling. The present disclosure resolves problems of conventional approaches by providing a system and method for generation of prompts for GenAI model using contextual information from legacy source code. The method of the present disclosure extracts relevant information from the legacy source code as context in natural language form which is provided as input to create the prompts required to enable successful usage of the GenAI model in multiple tasks of code analysis. In the present disclosure, information from legacy application code about business domain is extracted. Input legacy source code is parsed, and technical explanation is provided by handling the syntax and semantics of language. The legacy source code is split logically into blocks of manageable units.

In traditional ensemble modeling multiple individual models are trained on the same set of input variables without considering variations in spatial and temporal resolutions. A method and system for dynamic forest fire prediction and source prediction is proposed using dynamic weighting-based ensemble modeling and probabilistic cause determination. The adaptive ensemble model dynamically re-weights multiple base models, each optimized for different data scales, to provide accurate predictions based on the spatial and temporal granularity of incoming data. The method enables adjusting fire predictions probabilities due to shifts in the feature space caused by phenological and environmental changes. Key environmental variables are continuously monitored for recalibrating model predictions using shift detection algorithms, ensuring long-term accuracy without frequent retraining. Probabilistic determination is applied for likely causes of forest fires, which integrates data from various sources to provide a nuanced, data-driven understanding of the factors contributing to fire outbreaks.

The Range Migration Algorithm (RMA) is widely used for SAR image reconstruction from backscattered signals. Traditional RMA relies on the Fast Fourier Transform (FFT), which is unsuitable for irregular scanning trajectories. An interpolator based reconstruction method, referred as Local Interpolation Function-based Range Migration Algorithm (LIF-RMA), is provided for SAR imaging. The LIF-RMA efficiently addresses challenges associated with irregularly sampled trajectories. The LIF-RMA utilizes a complex-valued encoder-decoder network to transform irregularly sampled, complex-valued backscattered data into uniformly sampled complex raw data. The complex-valued encoder maps the distorted raw data into a feature space, while the complex-valued decoder interpolates and predicts the interpolated complex raw data at missing spatial coordinates. This data is then processed through a 2D FFT block before being fed to RMA for final SAR image reconstruction. In this process, as the LIF is applied prior to RMA, it aids in preserving phase integrity.

The embodiments of the present disclosure herein address unresolved problems of communication from the semantic aspects. Embodiments herein provide a method and system for a semantic communication with networked AI in sixth-generation technology for wireless communications (6G). Here, rather than encoding the input and transmitting the encoded symbols, the semantically relevant information is inferred from the input as per the context of the application and that information is encoded through conventional source encoding and transmitted. Conversion of the actual input to semantic information allows a huge reduction in the bandwidth requirement for the end-to-end channel. On the receiver side, these symbols are first decoded to extract semantic information. The semantic information is further used to predict the semantically relevant output to carry out the intended meaning at the receiver side.

This disclosure relates generally to a method and system for measuring social awareness of crowd based on ignorance index. Conventional methods for measuring social awareness through social experimentation are conducted by collecting data manually. This manual approach takes more time and incurs more money. The disclosed method counts the number of people using object detection techniques whenever preferred action towards sustainability is performed by people. The disclosure uses camera-based computer vision techniques to identify the objects in the scene and detect sustainable activity. Further the number of subjects is counted who care about sustainability and who do not perform sustainable actions. Then an ignorance index is calculated from the number of subjects who gazed at the device but have not performed any sustainable action. The disclosed method is used for social awareness measuring through social experimentation to understand the human behavior in the social gathering.

Conventional risk estimation techniques perform dynamic analysis of application or use models which require training data. Present disclosure provides method and system to estimate risk for a software application due to quantum threat by static analysis. A set of records pertaining to the application is received and parsed to obtain application, crypto and platform parameters. In addition, list of quantum vulnerable algorithms, number of Qubits required to break a cryptographic algorithm used by the application and a current Qubit number are also received. Then, value of Quantum Day is determined based on the current Qubit number and the number of Qubits required to break the cryptographic algorithm used by the application. Further, SOD (Severity, Occurrence, Detection) scores are calculated for each parameter, and they are multiplied to determine Risk Priority Number (RPN) for each parameter. Finally, RPNs of all parameters are summed up to estimate overall risk of the application.

Conventional traffic classification methods mainly depend on pre-defined criteria from known data, highlighting demand for advanced methods that can handle new types of traffic. The present disclosure receives an unlabeled data set comprising a plurality of data points from one or more Downlink Control Information messages and pre-processes the received unlabeled data set. A set of relevant features is selected from plurality of data points using a correlation matrix. One or more hyper-parameters of Gaussian Mixture Model (GMM) are evaluated for the unlabeled dataset with selected set of relevant features using Component-wise space Expectation Maximization technique (CEM) method. An optimal number of clusters is estimated using the evaluated one or more hyper-parameters of GMM. Each of the plurality of data points are labeled using the estimated optimal number of clusters to obtain a labelled data set. A classifier model is created to perform traffic classification of the obtained labelled dataset.

This disclosure relates generally to method and system for predicting pregnancy complications using a ranked incremental cumulative risk (RICR) scoring. During pregnancy, undetected medical or obstetric events may result in complications that pose risks to both the mother and the neonate. The method displays a questionnaire comprising a plurality of predefined questions on a clinical device to be answered by a subject. In response from the subject a plurality of answers and one or more unanswered questions for the questionnaire are received to compute a plurality of risk score. Then, a ranked incremental cumulative risk score is computed for the plurality of risk scores using a ranked incremental cumulative risk (RICR) scoring technique to generate a rank index. Finally, the risk index, an action plan, a risk tier, and an action plan is displayed on the clinical device to notify a clinician with a machine-generated alert.

This disclosure relates generally to a method and system for the automated farm management by utilizing connected graphs to navigate through unexpected future events affecting the farm. State-of-art methods for prediction of the unexpected future events is based a vast asynchronous data distributed across various complex data analytics tools. And it becomes difficult to choose most suitable actions to be taken to navigate the future event based on the asynchronous data. The present disclosure addresses these problems through a method that utilizes a graphical form of a plurality of sensor data collected from the farm to derive refined multi-contextual intelligent queries. The refined multi-contextual intelligent query is provided to a remotely located LLM model to generate a set of recommendations. The most suitable recommendation is transmitted to an associated autonomous farm machinery to take a suitable action. The method utilizes a feedback mechanism to improve future query generation.

This disclosure relates generally to a method and system for enhancing fine-motor skills based on adaptive thresholding. The disclosure relates to an assistive system to improve the fine motor skills of specially abled children through a gamified format. The conventional methods are repetitive and disengaging, which use rule-based threshold changes which do not help in developing fine-motor skills. The disclosed system uses rotational and translational movements detected via accelerometer and gyroscope sensors comprised in a wearable device and applies adaptive thresholding to perform various actions in a first-person controller (FPC) game environment. The disclosed method uses dynamic thresholding concept for adjusting maximum and minimum sensor readings. The method computes threshold values which are computed based on previous readings and predicted user improvement values from a regression model. The disclosed method is used for improving the fine-motor skills of specially abled children who struggle with daily activities and personal independence.

An affordable compact and ergonomic bulk container handling apparatus is disclosed. A machine support structure supports a plurality of dual pair of guide rods which form the guide profile through the plurality of support brackets, a dual drive arm assembly, that further holds container holders. Dual drive arm assembly holds each container holder at a first end and a second end of each container holder via a pair of guide follower driver roller system at each drive arm of the dual drive arm assembly. Each of guide follower driver roller system couples each drive arm to a plurality of dual pair of guide rods which form the guide profile, through the plurality of support brackets at the first end and the second end of each container holder. A drive motor is attached on side of the dual drive arm assembly to power the dual drive arm assembly along guide profile.

One of the primary challenges in the current data analysis landscape is the ability to efficiently capture and simplify complex datasets. A one-size-fits-all approach to data transformation is often ineffective, as it fails to accommodate the diverse needs and styles of different users. Embodiments disclosed herein provide a method and system for cognitive transformation of data into human-perceptions. In this approach, during data processing, the system reasons through impact of removing one or more data points in the summarized information by simulating removal of each of the one or more data points evaluating changes in remaining data's semantic integrity. Further, the summarized information is modified by fitting a new set of requirements to adapt the summarized information for one or more applications, while taking into consideration semantic integrity of the data.

The ability to accurately estimate the Lines of Codes (LoC) needed to complete migration of application has been a challenge over the past decades. Embodiments disclosed herein provide a method and system for estimation of efforts in migrating applications to post quantum cryptography (PQC) state. The system, based on a determined cyclomatic complexity for one or more components present in a refined slicing output and a refined coupling output is estimated. Further, an effort of migration is estimated using the cyclomatic complexity and number of LoC impacted by the one or more cryptographic APIs.