Web Framework Research Articles

Systemic Functional Linguistic Based on Semantic Web

This research aims to combine a functional and systematic linguistic approach with Semantic Web technology. The main aim of this research is to develop a framework that can facilitate systematized analysis and language modeling in the context of the semantic web. The research method used is a qualitative approach with focused data analysis. The data collected will involve text and web content relevant to the Semantic Web framework. The analysis was carried out using the principles and tools of systematic functional linguistic analysis to identify language structure, communicative function, and meaning in web content. The results of this research are expected to provide new insights into the understanding of how language can be organized and analyzed regularly in the context of the semantic web. Apart from that, this research is also expected to contribute to the development of Semantic Web technology by using a functional linguistic approach. It is hoped that the conclusions of this research will provide a deeper understanding of the role of language in the context of the semantic web and provide a basis for the development of more sophisticated and effective Semantic Web-based applications and systems.

Joining to the web. Predation of Heterophrynus sp. (Amblypygi: Phrynidae) on a catfish Astroblepus sp. (Siluriformes: Astroblepidae), in a premontane river of the northern Andes of Colombia

Riparian food webs are linked by cross-boundary resource fluxes, including the consumption of terrestrial invertebrates and vertebrates by fish and the consumption of aquatic vertebrates by terrestrial invertebrates, mostly arachnids. Amblypygids prey upon a wide array of vertebrates, but no records on fishes are known. Here we report an interesting predation event of an adult Heterophrynus sp., upon a catfish Astroblepus sp. in the Rio Manso, northern Andes of Colombia, and summarized the published information of trophic interactions of this river in a food web framework. A high prey-size ratio (0.87) respect to Heterophrynus sp., highlight that fishes could represent an important food item for amblypygids. This datum, besides other published before, indicates that the consumption of terrestrial and aquatic vertebrates by terrestrial invertebrates can be a substantial cross-boundary flux link in tropical montane streams deserving future ecological studies.

Gym Management System Using Python Django

An inventive software program called the Gym Management System (GMS) was created to maximize and simplify the functioning of gyms and fitness centers. In a time when fitness and health are more important than ever, effective management tools are essential. By making use of the Django web framework, this project makes it possible to create a solid, user-friendly platform that meets the various needs of gym management, employees, and patrons. The GMS includes all of the necessary features, including class scheduling, attendance monitoring, member registration, and payment processing. The initiative intends to increase member involvement and operational efficiency by combining these features into a single system. The fitness sector has grown quickly, and this has made effective gym management systems more and more necessary. Conventional methods of managing gyms, which frequently depend on manual procedures, are ineffective and prone to mistakes, especially when the number of memberships and services offered increases. The design, development, and implementation of a comprehensive Gym Management System (GMS) are presented in this paper. The GMS automates a number of operational tasks, including scheduling fitness sessions, tracking attendance, accepting payments, and managing trainers. Through the use of an intuitive interface, the suggested system seeks to increase data accuracy, optimize member experience, and streamline administrative processes.

Ontologies and Research Data

This study examines the relationship between ontologies and open research data within the framework of the Semantic Web. The objective is to investigate the interconnectivity of both subjects through scholarly works that offer enhancements to the Web ecosystem and scientific research processes. The paper reviews critical theoretical frameworks related to the Semantic Web and the significance of metadata within this model. It also delves into the function that ontologies can fulfill in the Semantic Web landscape. The paper provides a historical overview of the emergence of ontologies and definitions in both Computer Science and Information Science. It chronicles how theorists in Information Science have progressively embraced the concept of ontologies since the late 20th century and assesses the current scholarly consensus on the subject. The study also addresses the importance of open research data in modern science by doing a systematic literature review; this study sources relevant publications from the Web of Science and Scopus databases, with a temporal focus from 2000 to 2023. The findings offer a comprehensive analysis of existing literature that bridges the two domains above, aiding in the theoretical and methodological systematization of the subject matter. The discussion section elaborates on the findings, offering insights into the evolutionary trajectory of the subject matter. Emphasis is placed on the utility of ontologies as tools for the sustainable and effective utilization of research data, accentuating the value of such data as a basis for future scholarly work. In conclusion, we advocate for information science to take a leading role in initiatives that leverage ontological frameworks to manage specialized knowledge in research data sets effectively, ensuring that such data remains an asset for advancing scientific understanding.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Improving and re-developing the Clerical Resolution Online Widget (CROW): open-source software for clerical review

ObjectivesClerical review is an integral part of linkage and is fundamental to quality assurance. CROW is a low dependency, adaptable, clerical matching tool that has been deployed across 40 linkage projects. Due to limitations of the interface, with the initial version (including the limited ability to view large clusters); this project sought to re-develop the tool as a flask application. ApproachOur approach was to transform the existing CROW desktop application into a Flask (web based) application. Flask was chosen because it is a flexible web framework that enabled the development of a more dynamic interface, as well as better integration with the infrastructure of secure environments. We also engaged with accessibility experts to ensure the inclusivity of our product. Once the development was completed, testing was carried out by a range of users and stakeholders, to identify further issues and bugs for resolution. ResultsThe successful redevelopment of CROW into a Flask application improved functionality, usability, parametrisation, and accessibility of the tool. Feedback from users indicates that because of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency and leads to better decision making. ConclusionThe successful redevelopment of CROW is a significant improvement to the clerical review process for data linkage projects. ImplicationsCROW is open-source and low dependency so could be adapted to work in most environments. Therefore, it could be adapted and used by the wider data linkage community.

Empowering Research With the American Heart Association Get With The Guidelines Registries Through Integration of a Database and Research Tools.

The American Heart Association's Get With The Guidelines (GWTG) has emerged as a vital resource in advancing the standards and practices of inpatient care across stroke, heart failure, coronary artery disease, atrial fibrillation, and resuscitation focus areas. The GWTG registry data have also created new opportunities for secondary use of real-world clinical data in biomedical research. Our goal was to implement a scalable database with an integrated user interface (UI) to improve GWTG data management and accessibility. The curation of registry data begins by going through a data processing and quality control pipeline programmed in Python. This pipeline includes data cleaning and record exclusion, variable derivation and unit harmonization, limited data set preparation, and documentation generation of the registry data. The database was built using PostgreSQL, and integrations between the database and the UI were built using the Django Web Framework in Python. Smaller subsets of data were created using SQLite database files for distribution purposes. Use cases of these tools are provided in the article. We implemented an automated data curation pipeline, centralized database, and UI application for the American Heart Association GWTG registry data. The database and the UI are accessible through a Precision Medicine Platform workspace. As of March 2024, the database contains over 13.2 million cleaned GWTG patient records. The SQLite subsets benefit researchers by optimizing data extraction and manipulation using Structured Query Language. The UI improves accessibility for nontechnical researchers by presenting data in a user-friendly tabular format with intuitive filtering options. With the implementation of the GWTG database and UI application, we addressed data management and accessibility concerns despite its growing scale. We have launched tools to provide streamlined access and accessibility of GWTG registry data to all researchers, regardless of familiarity or experience in coding.

Real-Time Human Profiling: Unveiling Age, Gender and Emotions Using Deep Learning

This paper introduces the development of a real-time system, which deploys an integrated use of Flask, deep learning, Convolutional Neural Networks (CNNs) and cascade classifier approach to detect age, gender, and emotion from facial images. While its numerous applications—ranging from marketing and medical services to security surveillance—immediately catches the eye, the facial recognition technology is fast becoming the hot topic. Our suggested system aims at absolutely determining the age, gender or emotional state of a person instantaneously in real life. Flask, Python micro web framework, is at the basis of the software, providing the desired functionalities for the exchange of information between the processing engine and the front end. Convolutional Neural Networks (CNNs) which are a part of deep learning for tasks involving feature extraction and classification are the main tool used by most learning algorithms. CNNs are widely used in face recognition systems mainly due to the fact that they perform very well at such tasks as image processing. On the one hand, the model uses cascade classifiers for superior face detection, thereby finding and separating face regions in input images or video streams. Unlike some of the approaches that require heavy computation, these classifiers are computationally light solutions that can run in real-time even on resource- limited devices. The system capacity to identify age, gender, and emotion accurately in real-time is exemplified through: performance evaluation. We have used various means to stress the system and ensure that it is precise and offers timely results over the given period. By means of varied testing stages, we have discovered that the system usually brings high levels of precision and validity for the numerous data sets and trial situations. In this regard, be it differentiating between individuals’ age or readily identifying the exact gender or recognizing nuanced emotional clues, the system performs at the optimum level.

An Explainable Feature Selection Framework for Web Phishing Detection with Machine Learning

In the evolving landscape of cyber threats, phishing attacks pose significant challenges, particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy. Conventional and machine learning (ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics. Furthermore, newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable. Hence, effective detection depends on identifying the most critical features. Traditional feature selection (FS) methods often struggle to enhance ML model performance and instead decrease it. To combat these issues, we propose an innovative method using eXplainable AI (XAI) to enhance FS in ML models and improve the identification of phishing websites. Specifically, we employ SHapley Additive exPlanations (SHAP) for global perspective and aggregated local interpretable model-agnostic explanations (LIME) to determine specific localized patterns. The proposed SHAP and LIME-aggregated feature selection (SLA-FS) framework pinpoints the most informative features, enabling more precise, swift, and adaptable phishing detection. Applying this approach to an up-to-date web phishing dataset, we evaluate the performance of three ML models before and after FS to assess their effectiveness. Our findings reveal that random forest (RF), with an accuracy of 97.41% and XGBoost (XGB) at 97.21% significantly benefit from the SLA-FS framework, while KNN lags. Our framework increases the accuracy of RF and XGB by 0.65% and 0.41%, respectively, outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset, showcasing its potential.

Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system

ObjectiveThe worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data. LimitationA significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach. MethodInitially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR). ResultA simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture. ConclusionThe study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.

EasyLearnify - A Student Study Portal

EasyLearnify is web-based online research developed using Django framework of Python. Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. The research is designed to facilitate and enhance student's learning experience. The research mainly provides 8 different features for the students. It consists of Notes, Homework, YouTube, To Do List, Books, Dictionary, Wikipedia and Conversions. The main objective of EasyLearnify is to provide all in one study portal where students can manage all their study related works. The admin can also add and assign task to the students. Admin can upload notes which the students can download and assign homework for the students. Basically, it is a centralized hub for students to access various educational resources.

Sign Language Recognition

Abstract: Communication through signs has consistently been a significant way for communication among hearing and speech impaired humans, generally called deaf and dumb. It is the only mode of communicating for such individuals to pass on their messages to other human beings, and hence other humans need to comprehend their language. In this project, a sign language detection or recognition web framework is proposed with the help of image processing. This application would help in recognizing Sign Language. The dataset used is the Sign Language dataset. This application could be used in schools or any place, which would make the communication process easier between the impaired and non-impaired people. The proposed method can be used for the ease of recognition of sign language. The method used is Deep Learning for image recognition and the data is trained using Random Forest. Using this method, we would recognize the gesture and predict which sign is shown

Python's Role in Accelerating Web Application Development with Django

Efficient, secure, and scalable web services are critical in today's digital environment. This study explores the field of web service development and addresses issues such system efficiency, project duration, and changing requirements. The goal of this study is to ensure reliable and efficient web services by streamlining the development process and utilizing the Django framework. The study highlights the application of Django's Model-Template-View (MTV) design pattern, which is customized for a listing management system. Smooth data interaction and effective system performance are ensured by the study's seamless integration of Django's capabilities with MySQL for database management. To further improve system performance, focus is given to standardizing data sharing protocols and expediting user authentication procedures. The automation of web page construction with Python, HTML, and CSS modules, which increases system efficacy, is a crucial component of the research. Furthermore, the study shows how crucial security measures are, such as encryption for data confidentiality and integrity, which are made possible by the REST API interaction with the front end and the Django REST framework. Along with discussing key attacks and weaknesses that are frequent in web technology, the article also offers strategies to reduce these risks. Through the use of suitable SDLC models and testing procedures, it recognizes the crucial role that software engineering processes play in the development process. Focusing attention to Django's feature set, scalability, and wide library support, the study proves its advantages over competing web frameworks. The goal of the research is to provide developers and students with the skills they need to fully utilize Django's capabilities, making it easier to create dependable, successful and safe online applications. This study advances knowledge in the field of web technology and service development by lighting the development, difficulties, and current solutions in these fields. The study's ultimate goal is to open the door for comprehensive, modern, and efficient web service creation in order to satisfy the growing need for effective web solutions in the current digital era.

RESEARCH ON SOFTWARE FOR ERROR PROBABILITY PREDICTION IN BUSINESS PROCESS MODELS USING LOGISTIC REGRESSION

Business process modeling allows to graphically represent organizational activities and related events. It allows to identify areas for improving the organizational performance, define requirements for software solutions, and, in general, to facilitate communication between IT and business parties within or between different organizations. Therefore, at the stage of representing the activity in the form of a model, it is necessary to understand how likely it is that errors will occur during the implementation of the depicted business process. Thus, this study aims to improve the quality of business process models by solving the problem of predicting the error probability of business process execution. In order to assign error probabilities to each business process model from the training dataset, it is proposed to use one of the complexity metrics – the coefficient of network connectivity. To predict the error probability in business process execution, it is proposed to use the simplest and most intuitive machine learning model – logistic regression. As independent variables, it is proposed to choose the basic metrics of business process modeling – the number of nodes and arcs. Thus, the algorithm for solving the task includes steps related to calculating probabilities for the training data set, preparing training and test sets, determining regression parameters, visualization, and evaluation of training results. For the software that implements the proposed approach, a client-server architecture was chosen due to its flexibility and scalability. When developing software components, the Scikit-Learn machine learning library and the Python programming language were used to build a logistic regression mathematical model. The software tool is implemented as a web application based on MySQL, the Node JS platform, and the Express JS web framework. The quality assessment results of the developed prediction model indicate the suitability of the software tool for solving the problem of predicting the error probability of business process execution.

Crop Recommendation and Crop Yield Estimation Using Machine Learning and IoT

In the contemporary landscape of agriculture, the amalgamation of cutting-edge technologies such as machine learning (ML) and Internet of Things (IoT) has emerged as a transformative force, revolutionizing traditional farming practices. This paper introduces a comprehensive framework for crop recommendation and yield estimation, leveraging the power of ML algorithms and IoT devices to optimize agricultural operations. The dataset utilized in this study was meticulously curated from reputable sources including Kaggle and Google, encompassing a vast array of agricultural parameters ranging from soil characteristics to climate conditions. This rich dataset serves as the foundation for training and validating ML models, enabling robust analysis and prediction. A diverse range of ML algorithms was employed to process and interpret the dataset, with the objective of identifying the most proficient algorithm for crop recommendation and yield estimation tasks. Through rigorous experimentation and comparative analysis, the algorithm exhibiting superior performance across multiple evaluation metrics was meticulously chosen. Moreover, to bridge the gap between sophisticated ML techniques and practical application in the field, a user-friendly interface was developed using Flask, a micro web framework for Python. This intuitive interface empowers stakeholders to seamlessly access the trained model, facilitating real-time crop recommendations and yield estimations based on input parameters. The culmination of this research endeavor presents a paradigm shift in agricultural management, offering stakeholders unprecedented insights into crop selection and productivity optimization. By harnessing the potential of ML and IoT technologies, this framework not only enhances decision-making processes but also contributes to sustainable agricultural practices and global food security.

FastAPI vs. The Competition: A Security Feature Showdown with a Proposed Model for Enhanced Protection

API security is of paramount importance in modern web applications, as it protects sensitive data and ensures authorized access to resources. FastAPI, a Python-based web framework, offers various security features to developers for building secure APIs. This paper examines the security features provided as well as supported by FastAPI, The provided features consists of OAuth2 authentication, Dependency injection and Security Schemas and Scopes, whereas the supported security features includes Json Web Token (JWT) authentication, Cross Site Request Forgery (CSRF) token support and HTTPS support. The supported features can be implemented by using python libraries and middlewares. The proposed model combines the JWT token authentication, OAuth2 authentication, and Security Scopes and schema security features to enhance the security in the FastAPI application. The proposed model defines scopes for users and uses JWT to generate tokens and using OAuth2 authentication service to only allow a user who has the specific permissions and scopes to access and perform actions in the scope specific API endpoints. The model hence allows secure and safe working of applications by eliminating the threat of unauthorized users to corrupt the application code. The focus on performance and security makes FastAPI an excellent choice for developers seeking to build secure APIs. Overall, this paper highlights the importance of API security and showcases FastAPI's security features, demonstrating how developers can leverage FastAPI to build robust, performant, and secure APIs. Key Words: FastAPI, Flask, Django, OAuth2, JWT (JSON Web Token), CSRF (Cross Site Request Forgery)

HEALTHCURE-Disease Diagnosis using NLP

HealthCure is an innovative medical project designed to provide an all-in-one solution for the detection of seven critical diseases using advanced machine learning, computer vision, and deep learning technologies. The project aims to revolutionize healthcare by enabling users to obtain immediate diagnostic results from the comfort of their homes, making medical testing more accessible and efficient. The backend of HealthCure is powered by Flask, a lightweight and flexible web framework that facilitates rapid development and seamless integration of various machine learning models. The core of the diagnostic capabilities relies on Convolutional Neural Networks (CNNs), particularly for image-based disease detection tasks. Custom CNN architectures and pre-trained models like VGG-16 are utilized to achieve high accuracy in detecting conditions such as Covid-19, brain tumors, and pneumonia. For numerical data-based diseases, such as diabetes and heart disease, algorithms like Random Forest and XGBoost are implemented, leveraging their robustness and precision in handling structured data. Data storage and management are efficiently handled using SQLite, ensuring quick access and retrieval of user data and diagnostic results. The backend also employs RESTful API design, enabling smooth and efficient communication between the front-end and back-end, providing real-time data processing and immediate feedback. On the front-end, technologies like HTML, CSS, and JavaScript are used to create a responsive and user-friendly interface. Frameworks such as React.js and Bootstrap ensure a dynamic, interactive, and visually appealing user experience. Users can easily input data and receive diagnostic results with just a few clicks, making the application highly accessible across various devices. HealthCure also emphasizes future scalability and improvement. As more data becomes available, the models will be continually refined to enhance accuracy and include additional disease detection capabilities. Plans for integrating features such as personalized health advice and real-time monitoring are also underway. This comprehensive approach makes HealthCure a powerful, reliable, and scalable platform for real-time medical diagnostics, significantly contributing to improved health outcomes and patient care.

DEVELOPMENT OF A SOFTWARE SYSTEM FOR SEARCHING FOR LOST ITEMS

The article presents an approach to developing a software system to facilitate the efficient search for lost items. As part of the study, the authors designed an integrated system that includes server, web, and mobile versions, which provides broad opportunities for identifying and returning lost property. In large urban agglomerations, the loss of personal items becomes a common problem. Existing solutions are often limited to preventive measures and do not provide sufficient support after the item’s loss. The lack of an effective feedback and identification system complicates the lost property search process. The developed system uses modern technologies such as Django for the backend, JavaScript for the web interface, and Flutter for creating cross-platform mobile applications. This approach makes it possible to expand the list of system users to the maximum extent. Django, a capable web framework, facilitates reliable data management and integration with various interfaces. The study involved the development of a database to provide storage and processing of all the necessary information. Using REST API ensured efficient interaction between the system’s server, web, and mobile parts. This approach allows different system components to exchange data in a unified format. The system lets users register individuals and legal entities, which makes it possible to use it in a wide range of public spaces. It helps to effectively search for and return lost items on the street and in public places, including restaurants, theatres, and transport hubs where lost items often occur. The software system created in this study addresses the critical problem of losing personal items. Widespread use of this system across various sectors could dramatically improve managing lost and found operations, turning a typically disorganised and ineffective process into an efficient, technology-driven one. The system’s implementation has the potential to significantly reduce the time and resources spent on searching for lost property and increase the productivity of lost property offices. Keywords: software system, item search, location, lost property office.

Diabetes Prediction Using Flask and Decision Tree Classifier with Cross-Validation

Diabetes is a chronic medical condition that impairs the body's ability to process blood sugar, leading to elevated levels of glucose in the blood. This condition can cause serious health complications if not managed properly. Early detection and intervention are crucial in preventing these complications. This study aims to develop a user-friendly web application using Flask, a lightweight Python web framework, to predict the type of diabetes based on symptoms reported by users. The Machine Learning model utilized for this purpose is the Decision Tree Classifier, chosen for its simplicity and interpretability. The model's performance was evaluated through cross-validation to ensure reliability and accuracy. The results demonstrate that the developed application can effectively predict the type of diabetes, providing valuable insights and assisting users in seeking timely medical advice. This tool has the potential to enhance public awareness about diabetes and facilitate early diagnosis, ultimately contributing to better health outcomes for individuals at risk of this condition.

Document Similarity Analysis and Template Matching in Health Insurance Using Python Flask

In recent days, a primary challenge faced by healthcare insurance organizations is the reliance on a large number of files to process insurance claims and make coverage decisions. With the gradual increase in medical insurance in India, the number of people buying medical insurance plans is rising. Document processing plays a pivotal role in the efficient management of health insurance policies and claims. This proposed study presents an innovative approach to document template matching specifically tailored for the health insurance domain, implemented using the Python Flask web framework. Integration with Python Flask offers scalability, flexibility, and ease of deployment, making it suitable for a wide range of insurance applications. This solution represents a significant advancement in document processing technology, empowering health insurance professionals with a comprehensive tool to effectively manage and analyze documents.