Description:FIELD OF THE INVENTION
The present invention relates to data stream mining in evolving and dynamic networks. More particularly, it concerns an auto-tuning framework that integrates traffic-aware load distribution, adaptive partitioning, and security-enhanced mining engines. The invention provides scalable, secure, and high-throughput stream processing suitable for real-time data-driven applications such as smart cities, IoT, financial networks, and healthcare systems.
BACKGROUND OF THE INVENTION
Autoimmune disorders such as Celiac disease are challenging and often misdiagnosed as they share common symptoms and signs with other ailments and even where there are no visible biomarkers. Traditional diagnosis is carried out through invasive, costly, and time-consuming serology tests and biopsies. Additionally, existing machine learning algorithms, though promising, are black-box systems that provide no explanation of decision-making and as such their clinician acceptability is low.
Poor hyperparameter optimization and feature choice in traditional AI models are among the most important issues that result in overfitting or underfitting and therefore reduce diagnostic accuracy. Moreover, current solutions are generally not disease-specific and therefore less specific for particular conditions such as Celiac disease.
This invention herein fills such gaps with highest relevance in the guise of a hybrid AI engine incorporating explainable deep learning and Harmony Search optimization. The invention allows identification of salient clinical features, output explanation via visualized explanations, and learning to manage several autoimmune diseases. It does not merely aid in improving predictability accuracy but also actionable precise knowledge to allow, all with an ultimate objective of reducing diagnostic delays, complications prevention, and allowing personalization in real-clinical applications.
The current AI systems are less worried about classification precision than understanding the reason behind a particular classification being made. Techniques like Support Vector Machines (SVMs), Random Forests, and basic Deep Neural Networks have been used in disease prediction but none of them give any indication of why the prediction was done. Transparency has prevented them from being used in the clinical environment, especially for sensitive disease classes like autoimmune diseases.
Apart from that, with hand-tuned heuristics or traditional search algorithms like Grid Search or Legacy Genetic Algorithms or Particle Swarm Optimization, model tuning and feature selection are usually performed. They are local minima-prone or computationally expensive and therefore lead to low performance with high-dimensional biomedical data.
All these models also suffer from the problem of generalization, which are learned from small datasets with no capacity to generalize to many autoimmune diseases. Further, the majority of them do not support real-time prediction or customizable interfaces for clinicians.
The foregoing invention addresses such constraints with the introduction of an HSA-optimized deep learning model for predicting autoimmune disease. It is equipped with explainable AI components, has automatic and intelligent feature tuning ability, and is conditionally modular, differing from the state-of-the-art methods both in performance as well as simplicity.
There are many variations of the invention that are being claimed, which can be made in a bid to broaden its scope of application:
1.Disease-Specific Tuning: As much as autoimmune diseases are put under the focus, the system can be re-trained to predict specific diseases like Type 1 diabetes, Multiple Sclerosis, or Lupus based on domain-specific information.
2.Other Optimization Techniques: Harmony Search being the driving concept here, other optimization techniques such as Ant Colony Optimization or Differential Evolution can be employed to attempt comparative performance or hybrid ensemble optimization.
3.Multimodal Input Integration: The platform might be so designed that it can integrate multimodal inputs, i.e., DNA samples, radiology images (e.g., endoscopy images for Celiac), and medical histories, which would further improve its diagnosis.
4.Cloud-Based Platform: A Hospital-On-Demand version of the forecasting engine could be provided over a cloud to be utilized by hospital and clinic centers that have implemented electronic health record (EHR) integration for facilitating real-time, location-independence-based diagnostics.
5.Mobility and IoT Supportability: The software can be made downloadable over mobile healthcare applications or mapped on IoT health devices such that distant monitoring and foretelling of flare-ups or disease exacerbations can be provided.
Such other deployments make the system very flexible, extensible, and configurable to greater medical and technology infrastructures.
US8435167B2: A space is set substantially in a tropical rain forest type environment to activate a human's essential brain region and realize an environment suitable for the human's brain by arranging a device for setting the tropical rain forest type environment based on characteristics of activating human's essential brain region responsive to tropical rain forest type environment information, in a space such as an urban space, a housing space or other living space. The tropical rain forest type environmental information has higher density and higher complexity than those of urban space type environmental information, and includes at least one of auditory information, visual information, and super perceptual information of aerial vibration. The tropical rain forest type environmental information is comfortable for the human with no excessive stress, and is environmental information for effecting prevention and treatment of diseases due to stress by realizing the environment comfortable for the human's brain.
US20250188542A1: The present disclosure encompasses systems, methods, and compositions for enriching a population of rare circulating cells, including progenitor cells, from peripheral blood. Specific embodiments encompass methods of analyzing rare circulating cell transcriptomic, genetic, protein expression, metabolic, epigenomic, and/or other functional assay data to identify differential gene or protein expression and/or chromatin accessibility, and/or functional characteristics. Particular methods relate to enriching and analyzing rare circulating cells in patients following COVID-19 infection, and treating the patient based on the analysis. Embodiments also relate to an enriched population of rare circulating cells from peripheral blood and uses thereof.
With the exponential growth of IoT devices, smart infrastructures, financial systems, and social platforms, vast amounts of streaming data are generated continuously. Real-time processing of this information is critical for timely decision-making. However, existing stream mining platforms are typically static in configuration, unable to adapt to fluctuating data volumes, and lack inherent security mechanisms.
Legacy systems employ rule-based or batch partitioning schemes that fail under dynamic traffic conditions. As workloads shift, nodes become overloaded while other resources remain idle, leading to high latency and performance degradation. Furthermore, most platforms prioritize throughput without integrated encryption or security protocols, leaving sensitive data vulnerable to exploitation.
Conventional frameworks such as Apache Flink, Spark Streaming, and Storm offer scalability and fault tolerance but still require manual tuning, external modules for load management, and separate security layers. These shortcomings hinder their reliability in highly dynamic and sensitive applications such as healthcare, governance, and industrial IoT.
Thus, there exists a pressing need for a self-tuning, traffic-aware, and security-embedded stream mining framework that can handle dynamic workloads, optimize resource usage automatically, and ensure data confidentiality.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention discloses a smart auto-tuning real-time data stream mining framework designed for evolving database topologies. It optimizes data processing by continuously monitoring traffic patterns and employing adaptive load-balancing techniques. Unlike static systems, the framework dynamically partitions workloads, preventing congestion and maximizing efficiency.
The system integrates traffic analyzers, auto-tuning engines, secure mining modules, and feedback-driven controllers. It provides secure, high-speed stream processing with enhanced system performance even under unpredictable workloads. A feedback loop ensures continuous adaptation and learning from past traffic patterns, enabling predictive resource allocation.
The novelty lies in combining traffic-aware partitioning, self-adjusting auto-tuning, and embedded security mechanisms into a single scalable framework. This makes the system highly suitable for real-time applications in sectors such as smart governance, finance, healthcare, and industrial IoT.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed invention outlines an explainable AI-mediated medical prediction engine on HSA-tuned DNNs to deliver transparent and accurate autoimmune disorder prediction, in our case, Celiac disease. The solution utilizes a feature selection method using HSA and explainable layers to deliver visual and textual explanations of the predictions, enabling it to be relied upon by the clinical setup. It possesses the ability to learn from heterogenous biomedical data, adaptively tuning model parameters and discovering discriminant biomarkers. It bridges the loop between high accuracy and model explainability in medical AI, providing explainable results for clinicians to enable early diagnosis and individualized treatment planning.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The main objective of this invention is to provide a scalable and secure auto-tuning framework for real-time stream mining in evolving data networks. The invention seeks to overcome the limitations of existing systems, which are rigid, pre-configured, and incapable of adapting to unpredictable workloads. A further objective is to enable traffic-aware load distribution that dynamically balances workloads across distributed nodes, ensuring maximum utilization of computing resources while minimizing latency. The framework also aims to incorporate lightweight encryption mechanisms directly into the mining process, thereby safeguarding sensitive data without compromising processing speed. Another objective is to eliminate the need for manual parameter tuning by introducing an adaptive auto-tuning engine capable of self-adjustment through reinforcement learning and historical feedback. By offering modular deployment across cloud, edge, and hybrid environments, the invention further aims to provide flexibility, scalability, and resilience. Ultimately, the invention aspires to deliver a high-throughput, fault-tolerant, and security-conscious platform suitable for critical applications in sectors such as finance, healthcare, smart cities, and industrial IoT.
The present invention outlines an explainable AI-mediated medical prediction engine on HSA-tuned DNNs to deliver transparent and accurate autoimmune disorder prediction, in our case, Celiac disease. The solution utilizes a feature selection method using HSA and explainable layers to deliver visual and textual explanations of the predictions, enabling it to be relied upon by the clinical setup. It possesses the ability to learn from heterogenous biomedical data, adaptively tuning model parameters and discovering discriminant biomarkers. It bridges the loop between high accuracy and model explainability in medical AI, providing explainable results for clinicians to enable early diagnosis and individualized treatment planning.
The proposed invention provides an auto-tuning framework for scalable and secure stream mining in evolving data networks. The framework is designed to process massive, continuous data streams originating from diverse sources such as IoT devices, smart city infrastructure, financial markets, and healthcare systems. At the heart of the invention lies a combination of traffic analysis, adaptive auto-tuning, secure mining, and feedback-driven optimization. Together, these modules create a resilient platform capable of handling unpredictable workloads, improving system responsiveness, and safeguarding sensitive information.
The framework begins with the ingestion of real-time data streams from heterogeneous sources. Lightweight communication protocols ensure that information from sensors, logs, and APIs is efficiently captured. This data is then analyzed by a traffic analyzer that continuously monitors the flow, classifying streams based on type, source, size, and temporal trends. By maintaining a constant watch on the nature of data traffic, the analyzer creates a comprehensive view of current and evolving workloads.
To anticipate variations in data flow, a load forecasting engine is integrated into the system. Using historical trends and predictive modeling, the engine projects upcoming workloads and provides this information to the auto-tuning mechanism. The auto-tuning engine then dynamically adjusts system parameters, such as thread allocation, memory usage, and partitioning strategies, ensuring that resources are aligned with real-time requirements. Unlike static systems, which rely on pre-configured rules, this invention provides automatic and intelligent reconfiguration without human intervention.
The traffic-aware load balancer complements this process by distributing data streams efficiently across multiple processing nodes. Informed by both real-time monitoring and workload forecasts, the balancer ensures that nodes are neither overburdened nor underutilized. This adaptive partitioning reduces bottlenecks, minimizes latency, and maximizes throughput, particularly in high-velocity data environments. The integration of the auto-tuning engine with the load balancer enables a seamless flow of tasks even under volatile traffic conditions.
Data security is a critical component of the invention. The framework incorporates a secure stream miner equipped with lightweight encryption and decryption mechanisms. Sensitive data, such as user activity logs, healthcare records, or financial transactions, is encrypted during processing to prevent unauthorized access. At the same time, the secure miner executes clustering, classification, or other mining algorithms on encrypted data with minimal latency. This dual function ensures that analytical accuracy is preserved while maintaining data confidentiality and integrity.
The mined results are securely stored in distributed databases or file systems, ensuring fault tolerance and scalability. Redundant storage mechanisms allow the system to maintain high availability even under heavy workloads or node failures. This makes the invention suitable for mission-critical applications requiring uninterrupted operations and reliable access to processed results.
A monitoring and alert system is embedded within the framework to track performance metrics such as throughput, latency, and system accuracy. When anomalies or irregularities are detected, the system automatically issues alerts for administrators, ensuring rapid response to potential overloads or security threats. These monitoring capabilities also provide continuous visibility into system health.
Another important component of the invention is the policy manager. This feature allows administrators to define policies related to security, performance, and service-level agreements (SLAs). These policies guide the auto-tuning engine and load balancer in making resource allocation decisions that align with organizational goals. In effect, the system not only adapts to workload dynamics but also respects user-defined operational rules.
Central to the adaptability of the invention is the feedback loop controller. This module continuously evaluates system outputs, compares them against defined benchmarks, and feeds performance data back into the auto-tuning mechanism. Over time, the feedback loop refines the system’s predictive models, making it increasingly accurate and efficient in anticipating workload fluctuations and optimizing system behavior.
The invention also features an intuitive user interface and API layer. Dashboards provide real-time visualization of traffic, workload distribution, and mining results, while RESTful APIs ensure seamless integration with enterprise applications. This enhances usability and allows organizations to tailor the framework’s functionalities to their specific requirements.
The architecture of the invention is inherently modular, supporting deployment in cloud, edge, or hybrid environments. In cloud-native scenarios, containerization tools like Docker and Kubernetes can be used to facilitate scalability and resource orchestration. For latency-sensitive applications, lightweight edge variants ensure faster processing closer to data sources. The system is also capable of being extended with specialized embodiments, such as GPU or FPGA acceleration for high-frequency applications or blockchain modules for auditable transaction security.
Overall, the invention addresses the shortcomings of existing stream mining systems by combining auto-tuning, traffic-aware distribution, and secure mining in a unified framework. It eliminates reliance on manual parameter adjustments, adapts seamlessly to fluctuating data streams, and introduces encryption within the mining pipeline. This unique integration ensures that the framework delivers high throughput, scalability, security, and fault tolerance, making it especially valuable for applications in smart cities, finance, healthcare, governance, and industrial IoT ecosystems.
The novelty of the invention is in combining Harmony Search Algorithm with a deep model to enhance feature selection and hyperparameters for predictive modeling of certain autoimmune diseases. In contrast to black-box deep learning, the system incorporates explainable AI (XAI) layers that describe how predictions are made. Moreover, the engine can be scalable and adaptive to a number of other distinct autoimmune disorders other than Celiac disease, i.e., lupus and rheumatoid arthritis. Optimization HSA-guided does not sacrifice learning efficiency for explainability. By adding optimization, explainability, and flexibility in this invention, it surpasses prevailing models that are inaccurate or cannot provide explanation about the outputs but instead gives an equal and doctor-centric decision-support system.
Proposal invention consists of the following key elements:
1. Data Ingestion and Preprocessing Module: Process clinical, genetic, and historical health data. Normalizes, preprocesses, and encodes data prior to sending it to the learning model.
2. Feature Selection Module with Harmony Search Algorithm (HSA): Applies metaheuristic optimization in selecting the most appropriate features in an attempt to predict autoimmune disease in a bid to enhance the learning process.
3. Explainable AI Layer: Gives explainability output in the form of SHAP values, heatmaps, or rule-based explanation so that clinicians will be able to interpret the predictions.
4. Model Training and Tuning Engine: HSA auto-tunes hyperparameters and the model is retrained using new parameters for optimal performance.
5. Prediction Interface: GUI or API-based interface to assist doctors and technicians in inputting new patient details and receiving predictions and explanations.
6. Report Generation Unit: Aggregates outputs and explanations into a well-structured diagnostic report helpful for clinical decision-making.
All the modules involved possess a smart, explainable, and accurate diagnosis engine for predicting autoimmune diseases.
TECHNOLOGY USED
Patented system leverages synergy of enabling technologies and novel software algorithms as a smart, explainable diagnostic system. Most critical technologies are:
1.Software Algorithms:
Deep Neural Networks (DNN): Used specifically to address classification problems in autoimmune disorders.
Harmony Search Algorithm (HSA): Nature-inspired optimization algorithm used to optimize feature selection and hyperparameters with high efficiency.
Explainable AI Methodologies: SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and attention mechanisms for outcome interpretability purposes.
2.Data Administration:
Etl Pipelines: Used for intake, transformation, and recovery of semi-structured (clinical notes, records), and structured data (test results).
Preprocessing Techniques: Outlier handling, imputation, normalization, encoding using Python-based pipelines (NumPy, Pandas, Scikit-learn).
3. User Interfaces:
Web/Mobile Interface: Developed on ReactJS or Flutter for adaptative prediction dashboards.
API Layer: RESTful APIs to enable EHR system compatibility.
4. Communication Protocols:
HTTPS/SSL: Secure transfer of patient data.
FHIR Standards: Health data interoperability with legacy systems.
5. Deployment and Infrastructure
Cloud-based Back-End: AWS/GCP for scalability and high availability.
Docker and Kubernetes: Microservice deployment and orchestration.
Database: PostgreSQL or MongoDB for model metadata and patient data storage.
6. Power and Hardware:
Edge AI Compatibility: Compatible on Raspberry Pi or NVIDIA Jetson for edge deployment.
Power Supply: Common USB-C or battery modules for use in edge deployment.
All of these technologies make the system very capable, secure, scalable, and suitable for application in clinical-grade products.
STEPWISE WORKING FUNCTIONALITY
Working procedure of the invention described as follows:
1.Lab test results, patient complaints, and clinical data are fed into the system via a secure interface or are imported from electronic health records (EHRs).
2.Data Preprocessing
Data is preprocessed by missing value imputation, category feature transformation to numeric, and numeric attribute normalization. Outliers are identified and normalized.
3.Feature Selection Using HSA:
Harmony Search Algorithm identifies input features and identifies the most informative subset relative to fitness functions like classification accuracy and feature importance.
Optimal feature set and optimal hyperparameters are utilized for training the DNN model.
4.Model Training:
The DNN is trained from labeled data sets using backpropagation with the help of the selected features. Training can be local or cloud.
5.Expectation Layer Activation:
After the model has produced a prediction (e.g., Celiac disease positive/negative), the explainable module generates text and visual-based explanations identifying the reasons for it.
6.Prediction and Output Generation:
The trained model is presented with new patient data. The system generates the prediction, confidence levels, and interpretability report.
7.Report and Interface Display:
The output is presented on a dashboard or PDF report. It includes the prediction, feature analysis, and suggested medical insights.
8.The model is recertified on new data in a periodic cyclical manner. This is for achieving very high accuracy with population-level adjustment.
Incremental nature provides assurance automation, accuracy, and transparency of medical predictions for autoimmune diseases.
EXACT FUNCTIONALITY
The model classifies autoimmune diseases such as Celiac disease based on clinical and genetic data with an interpretable and optimized deep neural network, where feature engineering and model optimization are obtained via the Harmony Search Algorithm. The model provides real-time diagnosis results with interpretable visualizations to support clinicians' decision-making.
ADVANTAGES OF THE INVENTION
•Society: Facilitating early and accurate diagnosis of autoimmune diseases and reducing chronic complications and improving the quality of life of patients.
•Healthcare System: Supporting overburdened physicians with smart diagnostic support and improving care delivery.
•Country: Boosts the country's health system with AI-powered adaptive solutions and enables the development of medical technology.
•Environment: Averts paper diagnosis and manual labor, accelerating digitalization and sustainability.
, C , Claims:1. An auto-tuning framework for scalable and secure stream mining in evolving data networks, comprising a traffic analyzer configured to monitor real-time data streams, an auto-tuning engine configured to dynamically adjust system parameters, a load balancer configured to distribute workloads across processing nodes, and a secure stream miner configured to perform encrypted stream mining, wherein the framework integrates a feedback loop to continuously optimize performance under varying traffic conditions.
2. The framework as claimed in claim 1, wherein the traffic analyzer classifies data streams based on type, size, source, and temporal patterns to support predictive workload management.
3. The framework as claimed in claim 1, wherein the auto-tuning engine automatically adjusts system parameters including memory allocation, thread distribution, and partition sizes in response to workload changes.
4. The framework as claimed in claim 1, wherein the load balancer distributes tasks using predictive forecasting models derived from historical traffic data to minimize latency and prevent bottlenecks.
5. The framework as claimed in claim 1, wherein the secure stream miner integrates lightweight encryption and decryption mechanisms to ensure confidentiality and integrity of sensitive data during mining.
6. The framework as claimed in claim 1, wherein the feedback loop controller evaluates system performance metrics including throughput, latency, and accuracy, and dynamically updates tuning parameters without human intervention.
7. The framework as claimed in claim 1, wherein the storage manager securely stores mining outputs in fault-tolerant distributed databases or file systems to ensure data availability under heavy workloads.
8. The framework as claimed in claim 1, wherein the policy manager enforces administrator-defined security, performance, and service-level agreement (SLA) policies to guide auto-tuning decisions.
9. The framework as claimed in claim 1, wherein the user interface and API layer provide dashboards and RESTful APIs for visualization, monitoring, and integration with enterprise applications.
10. The framework as claimed in claim 1, wherein the architecture is modular and deployable in cloud, edge, or hybrid environments, enabling scalability, fault tolerance, and mission-critical real-time data analysis.