Technical Architecture Research Articles

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight.

Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

WEALTH’S SECRET

In recent years, trading applications have revolutionized the way individuals engage with the financial markets, providing unprecedented access to real-time data and advanced trading strategies. This paper presents "Wealth Secret," a cutting-edge trading application designed to cater to the needs of both novice and experienced traders. The application leverages real-time stock and index data sourced from yfinance, focusing on the top 50 stocks of Nifty-50, leading stocks in the banking sector, and a comprehensive range of mutual funds including mid-cap, small-cap, and large-cap categories. "Wealth Secret" incorporates a robust backend developed in Python and deployed on PythonAnywhere, ensuring reliable and scalable performance. The frontend, built using Kotlin, offers an intuitive and user-friendly interface. Firebase Realtime Database and Firebase Auth provide secure and efficient data management and user authentication, complemented by local storage solutions through SQLite. One of the standout features of "Wealth Secret" is its extensive coverage of options trading strategies, tailored to various market conditions—bullish, bearish, and sideways. This strategic depth equips users with the necessary tools to navigate the complexities of the financial markets effectively. Additionally, the application includes a dedicated section for purchasing, enhancing the user experience by facilitating seamless transactions within the platform. This paper delves into the technical architecture, real-time data integration, and strategic functionalities of "Wealth Secret," highlighting its potential to empower traders with actionable insights and sophisticated trading capabilities. Through this research, we aim to demonstrate how innovative technology can transform trading practices, making them more accessible and efficient for a broader audience.

School Real time Chat Application System

Abstract— The emergence of new technologies has brought about significant changes in the way people communicate with each other. One of the most popular ways of communication in today’s digital age is through messaging applications. To facilitate this need, several chat applications have been developed. In this thesis, we introduce a chat application built using the MEAN stack, which is a popular technology stack used for building web applications. This application provides users with the ability to create accounts, join chat rooms, and send messages to other users in real-time. With the use of web sockets and Angular’s two-way binding, the application allows users to see messages as soon as they are sent. Moreover, the application also includes features such as user authentication and authorization to ensure secure access to the chat rooms. Through this project, we aim to demonstrate the feasibility and effectiveness of building real-time chat applications using the Mern stack. IndexTerms – Educational Management System, Modern Web Technologies, Real Time Chat , Technical Architecture, MERN Stack..

MEDIPLUS- PROVIDE MEDICAL SERVICES

ABSTRACT: MediPlus is a web-based platform designed to streamline the provision of medical services. Utilizing core PHP for backend functionalities and HTML, CSS, and Bootstrap 4 for frontend development, MediPlus aims to improve access to healthcare by offering various services such as appointment scheduling, medical records management, and online consultations. This paper details the development process, technical architecture, features, and potential impact of MediPlus on the healthcare industry. At MediPlus, we prioritize your well-being and strive to make healthcare more accessible, convenient, and patient-centricAt MediPlus, we leverage technology and medical expertise to deliver patient-centered care, empowering individuals to take control of their health and wellbeing. Join our platform today and experience the future of healthcare.” KEYWORDS– Medical Services Platform, Appointment Scheduling, Online Consultations, Healthcare Accessibility, Scalability in Healthcare IT.

TrenchX An Integrated Marketplace for Secure E-Commerce and NFT Transactions

TrenchX stands as a groundbreaking initiative at the intersection of e-commerce and blockchain technology, dedicated to empowering artists, entrepreneurs, and users in the digital marketplace. Leveraging the decentralized and transparent nature of blockchain, TrenchX offers a user-friendly platform for creating, managing, and trading Non-Fungible Tokens (NFTs). These NFTs, powered by smart contracts, serve as unique digital assets representing ownership of various forms of digital content, including artworks, collectibles, and more. By seamlessly bridging traditional e-commerce with blockchain innovation, TrenchX provides a secure and transparent environment for individual creators and businesses to thrive alongside their conventional counterparts. The project's objectives are multifaceted, aiming to democratize access to the digital marketplace, ensure transparent and fair transactions, and foster a vibrant, community-driven ecosystem of creativity and entrepreneurship. Through a comprehensive exploration of its technical architecture, implementation strategies, and potential impacts, this research paper endeavors to shed light on the transformative potential of TrenchX in revolutionizing e-commerce and empowering creators in the digital age. Keywords: Blockchain Technology, Decentralized Platform, E-commerce, Non-Fungible Tokens (NFT) Marketplace, Ethereum, Security and Privacy, Transparent Exchanges.

BUDGETTING ANALYSIS USING MACHINE LEARNING

In an era characterized by unparalleled technological progress and pervasive digital integration, the amalgamation of machine learning and mobile applications has instigated a significant transformation in the realm of financial management. This report embarks on a thorough exploration of this revolutionary convergence, elucidating its impact on budget analysis and forecasting, while also laying the groundwork for a future distinguished by heightened fiscal oversight and informed decision-making. The evolution of budget management and financial forecasting has been intricately linked to the ascent of digital technology. As the intricacies of financial challenges confronting individuals, enterprises, and governments continue to evolve, there arises an increasing need for innovative solutions capable of adapting to diverse financial profiles. At the heart of this transformative shift lies the fusion of Flutter, a versatile and user-friendly mobile app development framework, with the predictive capabilities of machine learning, heralding a paradigmatic shift in the domain of budget analysis. This report offers a comprehensive exploration of our project's multifaceted dimensions. From its conceptualization and rationale to the intricate technical architecture and methodologies underpinning its functionality, each aspect is meticulously examined. Throughout this captivating journey, we delve deeply into the realm of data-driven financial prediction, where machine learning algorithms dissect historical spending patterns, illuminating potential future financial trajectories. The Flutter application, meticulously designed to serve as an intuitive gateway to personalized financial insights and effective budget management, occupies a central role in this endeavor. It epitomizes accessibility and ease of use, empowering users from diverse backgrounds to harness the power of data-driven fiscal decision-making. Beyond the technological innovations, this report underscores the paramount importance of ethical considerations. In an era where data privacy and algorithmic fairness are of utmost concern, we adopt a proactive stance in ensuring that our project adheres to the highest ethical standards. Our endeavor transcends mere innovation; it is a commitment to shaping a more inclusive and responsible financial future’s.

MEDICAL DATA EXTRACTION

The proposed system is a user-friendly and efficient Data Extraction software for Medical Documents and Images, integrating advanced technologies such as PHP, Android, database, ML-Kit, retrofit, recycler views. The application facilitates efficient user registration, user login, insurance claims, and management of claimed users. The system aims to enhance the accessibility and accuracy of medical data extraction, thereby optimizing insurance procedures for agents, Also This paper outlines the technical architecture, functionalities, and implementation details of the application, highlighting its potential to revolutionize insurance operations within the healthcare industry. Keywords: - Machine Learning Kit(ML-Kit)

Improving emergency and disaster response management through bidirectional communication between citizens and decision-makers – A field test on the coordination of spontaneous volunteers

Cooperation between different stakeholders plays an important role in managing crises and disasters. The software tools used must also support cooperation and the mutual exchange of information. The bidirectional exchange of information between the population and official agencies poses a particular challenge. In this paper, we present a software demonstrator for the coordination of spontaneous volunteers and its evaluation as part of an overarching research project to gain design knowledge for the development of interconnected systems in disaster management. We describe how the system demonstrator was instantiated on the basis of our existing design knowledge from technical requirements and reference architecture and show the respective interfaces for the stakeholders involved. As the main contribution of the paper, we describe the implementation of a field test to evaluate the demonstrator and present the results in terms of usability and perceived usefulness, which we obtained using a methodologically sound questionnaire.

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

Research on Non-Standard Business Optimization of Insurance Companies Under Computer Big Data

This article mainly describes a lightweight framework based on J2EE (Struts+ Spring+ Hibernate). Then the paper puts forward the design and implementation scheme of the electric power enterprise insurance system based on J2EE. The technical architecture, function model, data model, information security mechanism and test specification of the system are emphatically described. The goal is to use this architecture to increase program reusability and reduce engineering complexity. The NP method is used to screen the customers in the insurance system, so as to improve the effectiveness of customer relationship management. Finally, the feasibility of the proposed algorithm is verified by computer simulation. This method can not only ensure that probability 1 converges to the optimal solution, but also improve the efficiency of customer feature selection.

Integrated Two-Level New Energy Monitoring and Big Data Centers: A Comprehensive Approach for Urban Sustainability

With the help of the quick development of big data, the Internet of Things, and other related technologies, the construction of new energy monitoring and big data centres aims to enhance the control capability of urban new energy stations. Inefficient operation and maintenance of new energy stations, as well as chaotic production control, are the reasons behind this action. Given the limitations of the current stage of new energy big data platform construction, the construction of group-level new energy monitoring and big data centres to achieve a high degree of integration and sharing of resources and experience has become an inevitable development of the new energy industry. This project aims to achieve multi-level integration in decision-making from the bottom up by using the background of the construction of the Wenzhou New Energy Two-Level Centre as a guide, organising the business processes for new energy production and equipment control, designing the technical architecture of the platform for cloud-side collaboration, providing guidance for the heterogeneous data processing and analysis of new energy assets in the cloud, and acting as a model for the development of the group-level intelligent platform for the new energy industry.

Design and Analysis of a Mobile Automation Testing Framework: Evidence and AI Enhancement from Chinese Internet Technological Companies

This present study mainly describes the implementation and ideas of the mobile automation framework, which supports iOS and Android mobile automation testing technology. This study mainly uses a combination of qualitative and documentary analysis methods, designs some technical architecture diagrams, and writes some open-source code to implement respectively. Meanwhile, The automation code is not made public due to mobile automation security and privacy concerns. The paper is mainly driven by the so file inside Android and the ADB command, while iOS uses some class libraries based on the iOS system. The research framework also uses the Appium framework for encapsulation and research and carries out secondary encapsulation and call based on the internal Appium framework. It is convenient for the internal quality team's automated testing staff to use and execute and improve the efficiency and speed of automated software testing, which is the contribution of this research. Beside, The limitation of this research is that the framework is based on the secondary development and encapsulation of Appium framework, so this study are inevitably some imperfections and bugs that need to be continuously improved and used. Moreover, these contribution results demonstrate that it can combine the businesses of different china internet enterprises to complete. Next, the paper discusses specific cases of mobile automation testing framework and artificial intelligence in mobile framework design and evaluates their effects and impacts. Finally, the paper summarizes the roles and challenges of mobile automation testing framework design and AI enhancement elements and looks ahead to the future.

Leveraging Blockchain and IPFS for Secure and Privacy-Preserving Patient Medical Record Management: A Government-Controlled Approach

Abstract: In recent years, the integration of blockchain technology with healthcare systems has garnered considerable attention due to its potential to enhance security, privacy, and interoperability in managing patient medical records. This paper proposes a novel approach to patient medical record management by leveraging Ethereum blockchain and InterPlanetary File System (IPFS) for storage, within a government-controlled framework. The system ensures secure and immutable storage of patients' medical records, accessible only by verified medical professionals, thus facilitating informed diagnosis and treatment. Additionally, the platform provides mechanisms for patient recourse in case of inaccuracies, as well as potential integration with insurance agencies. Furthermore, the proposed system envisages a future extension to facilitate anonymized data sharing with the scientific community, thereby contributing to advancements in medical research. This paper provides a comprehensive academic description of the proposed approach, discussing its technical architecture, security measures, regulatory framework, and potential impact on healthcare delivery and research.

A Survey of Wireless Charging System for Electric Vehicles and Fire Protection

Wireless charging for electric vehicles offers convenient power replenishment without physical connections. Fire protection measures are integral to ensure safety during wireless charging, mitigating potential risks of overheating or electrical malfunctions. The integration of wireless charging systems for electric vehicles (evs) alongside effective fire protection measures presents a promising avenue towards enhancing the sustainability and safety of urban transportation. In this survey work, we meticulously examine the state-of-the-art advancements, methodologies, and challenges pertaining to the fusion of wireless charging technologies with robust fire protection mechanisms for evs. In this survey encapsulates a synthesis of methodologies, technical architectures, and regulatory frameworks, providing researchers and practitioners with valuable insights to navigate the intricate landscape of wireless charging systems for evs and fire protection

Live Supplier Catalog and Pricing for Omnichannel

This paper proposes a technical architecture for an Omni-channel Supplier Catalog that provides real-time price updates. This system leverages a combination of open-source technologies, including Apache Camel, Spark Connector, Kafka Topics, Cassandra database, and Kubernetes, deployed on a resilient and elastic cloud platform. This paper details the data pipeline design, outlining how each component contributes to efficient data ingestion, processing, and dissemination. Additionally, the paper highlights the benefits of using this architecture, including improved decision-making, enhanced operational efficiency, and a seamless omnichannel experience for customers.

Historical Documents and Automatic Text Recognition: Introduction

With this special issue of the Journal of Data Mining and Digital Humanities (JDMDH), we bringtogether in one single volume several experiments, projects and reflections related to automatic textrecognition applied to historical documents.More and more research projects1 now include automatic text acquisition in their data processing chain,and this is true not only for projects focussed on Digital or Computational Humanities but increasinglyalso for those that are simply using existing digital tools as the means to an end. The increasing useof this technology has led to an automation of tasks that affects the role of the researcher in the textualproduction process. This new data-intensive practice makes it urgent to collect and harmonise the corporanecessary for the constitution of training sets, but also to make them available for exploitation. Thisspecial issue is therefore an opportunity to present articles combining philological and technical questionsto make a scientific assessment of the use of automatic text recognition for ancient documents, itsresults, its contributions and the new practices induced by its use in the process of editing and exploringtexts. We hope that practical aspects will be questioned on this occasion, while raising methodologicalchallenges and its impact on research data.The special issue on Automatic Text Recognition (ATR) is therefore dedicated to providing a comprehensiveoverview of the use of ATR in the humanities field, particularly concerning historical documentsin the early 2020s. This issue presents a fusion of engineering and philological aspects, catering to bothbeginners and experienced users interested in launching projects with ATR. The collection encompassesa diverse array of approaches, covering topics such as data creation or collection for training genericmodels, reaching specific objectives, technical and HTR machine architecture, segmentation methods,and image processing.

Design of Multi-Time Scale Power Trading Platform in Regional Spot Market

In order to improve the reliability of power trading under the background of regional power spot market, a multi-time scale power trading platform is designed. The transaction status of foreign power spot markets, the general situation of China's power market construction and the particularity of China's market transactions are analyzed. A multi-time-scale trading platform in the spot market is constructed, the overall structure and technical architecture of the platform are established, the multi-time-scale transaction process is analyzed, and a clearing mechanism framework for cross-regional transactions is proposed. According to the needs of market connection, the basic plan for cross-regional transactions is proposed. Finally, the application effect analysis of the platform proposed in this paper is carried out, which shows the validity of the platform design.

Data Analytics: Unlocking Multivision’s Future Perspectives

This research examines the problem of making data analysis understandable to non-technical consumers in today’s world of extensive data. We present a project that combines a user-friendly web application built on the Django framework with simple data analysis using linear regression. Through the use of the simple statistical technique of linear regression, even non-technical people may obtain valuable insights from large-scale datasets. Easy-to-use online application that makes data uploading, analysis, and interpretation a breeze. Our method uses built-in linear regression methods in the Django framework to automate dataset analysis. In this paper, the user interface design and the use of linear regression are explained, together with technical architecture insights. Facilitating inclusion across several areas, the major objective is to democratize data analysis. Our goal is to empower non-technical individuals and enable a wider audience to understand and apply data for informed decision-making by providing a tool that combines the efficiency of linear regression with an easy-to-use online interface.

IoT-Enabled Health Monitoring Systems: Transforming Healthcare Delivery

Abstract: The Internet of Things (IoT) technology integrated with healthcare has revolutionized the way we monitor and manage health. This research paper presents a comprehensive study and exploration of a Health Monitoring System (HMS) built upon using IoT technologies and principles. The HMS offers numerous advantages, which includes, reduced healthcare cost increased accessibility to healthcare services, enhanced patient outcomes, ultimately improving the quality of life. The core features of the HMS include a diverse array of IoT devices such as wearable sensors, smart medical equipment, and smartphones, which collect real-time health data. This data is then seamlessly transmitted, using advanced communication protocols, to centralized servers for analysis and interpretation. The system employs sophisticated data analytics and machine learning algorithms, providing healthcare professionals with invaluable insights into patient health. The technical architecture of the HMS is clarifying, illuminate on how these components synergic operate to create a robust system. However, we also address potential challenges and limitations, encompassing security concerns, data privacy, and scalability issues. These challenges underscore the importance of a balanced approach to ensure patient data confidentiality and system reliability. In conclusion, this research highlights the transformative potential of IoT-driven Health Monitoring Systems. The implications of integrating these two field helps not only improved healthcare outcomes but also hefty cost savings and increased access to healthcare services.

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.