Efficient Data Management Research Articles

Design and Build “Smart Key Tracking Systems with RFID and Arduino Mega” for Use in Petrochemical Engineering Department PTSN

The problem concerning key storage at Politeknik Tun Syed Nasir Syed Ismail (PTSN) systems is a lack of systematic administration, leading to uncontrolled and insecure essential management procedures. Consequently, the objective is to create an intelligent key tracking system (SKTS) utilizing radio frequency identification (RFID) and Arduino Mega to establish a secure and properly organized key storage system with efficient data management. This project introduces the monitoring system with automated and real-time data collection. SKTS design uses an Arduino Mega 2560 microcontroller and an ESP8266 Node MCU Wi-Fi module that sends data over the cloud. The project also utilizes the Telegram chat application bot to notify and monitor the keys' retrieval and return. It is also equipped with an RFID reader as a sensor. As the authorized user, once the RFID card is swept, type the selection key number; the solenoid lock for the selected key will open, and the key is free to be picked. The liquid crystal display (LCD) will show the authorized user data and selected room number. A signal will send the information to the Telegram application through a mobile phone to notify the other authorized user. This project successfully achieves its objective by utilizing the Telegram application to notify critical users and authorized individuals when keys are returned. These real-time notifications are visible to all registered group members on the Telegram application. The Likert Scale, which has two points, is established by utilizing data responses from the rubrics form, with evaluators consisting of industrial designers and researchers. The results are subsequently synthesized using qualitative percentages. The responses to the item provided via the rubric assessment form show that the SKTS meets the design, ideal, and functional aspects.

Virtual Assistant by using Large Language Model

Abstract— This paper presents the development of a virtual assistant named “Pragyan,” utilizing a Large Language Model (LLM) for natural language understanding and response generation. The virtual assistant is designed to interact with users through text and speech inputs, providing informative and conversational responses. Pragyan is built with a frontend user interface, a backend server, and a database for efficient communication and data management. The performance evaluation and result analysis demonstrate the effectiveness and user-friendliness of the virtual assistant Keywords: Virtual Assistant, Large Language Model, Natural Language Processing, Frontend, Backend, Database, Performance Evaluation, Result Analysis.

Study of web application development methodology for a medical clinic using React

The article explores in detail the methodologies for developing web applications for medical clinics using the popular React library. The main goal of this study is the analysis of modern approaches to the creation of web applications that meet the specific requirements of medical institutions. Particular attention is paid to the functional and non-functional characteristics that are critical to such systems, including data security, user experience, integration with existing systems, and compliance with regulatory standards. First of all, a comprehensive analysis of the requirements of medical clinics for web applications was carried out. Data security is one of the most important aspects as healthcare institutions work with sensitive information. The study examines methods for ensuring the confidentiality and integrity of medical data, including compliance with HIPAA and GDPR standards. React makes it easy to integrate modern security tools such as JWT (JSON Web Tokens) for authentication and authorization, and use secure HTTP connections for data transfer. In addition, special attention is paid to the user experience (UX/UI), which should ensure the convenience and efficiency of using the web application by both medical personnel and patients. React allows you to create dynamic and interactive interfaces that can adapt to different devices and screens, thus providing a high-quality user experience. This is especially important for medical applications, where the accuracy and speed of interaction can affect the quality of healthcare delivery. A detailed description of best practices and approaches to web application development using React is provided. A component architecture is considered, which provides modularity and code reuse, which is a key factor in creating scalable and easily maintainable applications. Different methods of state management in the application, such as the use of Context API and Redux, have been explored, which allows for efficient data management and stable operation of the application. Guidelines for configuring routing using the React Router library are described, allowing you to create dynamic and multi-page applications while providing smooth navigation and a good user experience. In addition, ways to ensure application security are considered, including protection against XSS attacks, CSRF and other types of vulnerabilities, which is critical in medical applications. The article provides specific recommendations for implementing a web application for a medical clinic. They include practical code examples that demonstrate the implementation of key functionalities such as user registration and authentication, medical record processing, and database interaction. The process of setting up the infrastructure for development, testing and deployment of the application is described, including the use of CI/CD tools to automate these processes, which contributes to increasing the efficiency of development and the quality of the final product. The article provides valuable information for developers who seek to create effective and secure web applications for medical clinics using modern technologies. The conclusions and recommendations presented in the study will help ensure high quality and compliance of the developed solutions with modern industry standards and requirements, which will contribute to increasing the efficiency of the work of medical institutions and the quality of medical services provided to patients

Damage identification in sandwich structures using Convolutional Neural Networks

The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

Using BIM for the development of accessibility

Accessibility in today's reality is becoming an increasingly important issue as it affects every individual. We are observing the progressive ageing of the Polish population. By applying the principles of universal design, all needs related to mobility, vision, hearing, and other issues are met, thereby creating inclusive spaces that eliminate social exclusion and enhance the quality of life. This article aims to demonstrate the usefulness of BIM in building urban accessibility. For this purpose, a point cloud acquired from laser scanning of a single-family building was utilised. Based on this, a digital BIM model of the actual building was created in Revit and subsequently modernised. This modernisation aimed to remove barriers from the building, as stipulated in the relevant regulation. The paper refers to the theory of ageing in place, a response to the steady increase in average age. The BIM model is not merely a geometric representation of the building, but a digital reconstruction of the object using elements equivalent to those used in reality, allowing for efficient data management.

Effizienter Datenmanagement-Workflow zur Analyse eines PtGtX-Systems basierend auf Open-Source-Software und Low-Cost-Hardware

ZusammenfassungUm ein Power-to-Gas-to-X-System effizient zu optimieren, kann ein digitaler Zwilling als Simulationsmodell auf Basis experimenteller Daten für ein Laborsystem erstellt und entsprechend verändert werden. Darüber hinaus müssen für die Überwachung des realen Systems bzw. die Online-Simulation kontinuierlich Daten aus Experiment und Simulation erfasst und verarbeitet werden. Insgesamt ist ein effizienter Datenmanagement-Workflow erforderlich.In dieser Arbeit wird ein Workflow aus freier, etablierter und skalierbarer Open-Source-Software für die vorliegende Anwendung skizziert und insbesondere ein geeignetes Datenmodell entwickelt, implementiert und seine ressourcensparende Realisierung auf kostengünstiger Hardware gezeigt. Abhängig von der Datenmodellierung kann preiswerte und alte Hardware für die geforderte Aufgabe ausreichend sein.Mit Apache NiFi wird ein visueller Workflow zum Abrufen und Verarbeiten von Daten aus verschiedenen Quellen geschaffen. Die extrahierten Daten werden in Apache Cassandra aggregiert, einem Datensystem, das aufgrund seiner Leistung, Skalierbarkeit und Haltbarkeit häufig verwendet wird.Grafana wird zur visuellen Überwachung des Systems eingesetzt. Das gesamte System wird mit Hilfe von Docker-Containern aufgebaut zum Zwecke der Reproduzierbarkeit und effizienten Bereitstellung.Benchmarks und realistische Hardware- und Datenmodellierungskonfigurationen demonstrieren die Leistung der vorgeschlagenen Lösung.

Breaking Down the Barriers between the Digital and the Real: Mixed Reality Applied to Battery Manufacturing R&D and Training

AbstractIn a scenario in which the manufacturing of high‐performance, safe batteries on an unprecedented large scale is crucial for the energy transition and fight against climate change, research laboratories and cell production industries are facing challenges due to the lack of efficient data management and training tools. In this context, the use of intelligent devices plays an important role on the path towards the optimization of the manufacturing process and the enhancement of the battery performance while reducing production costs. In this Concept, we present an innovative Mixed Reality tool for efficient data collection and training in real‐time in battery research laboratories and battery manufacturing pilot lines, which runs on Microsoft HoloLens 2 glasses. We report a deep analysis on its ergonomic and usability aspects, and describe how we solved the problems found during its development. Thanks to this tool, users can collect data while keeping their hands free and receive advice in real time to design and build batteries with tailored properties. This optimizes data management in battery manufacturing environments. Now, thanks to our Mixed Reality application, users can collect data in the place of work, save this data automatically on a server and exploit it to receive advice and feedback to support their decision‐making and learning of the manufacturing process.

Integrating FAIR Experimental Metadata for Multi-omics Data Analysis

The technological advancements in bio-science research are resulting in the generation of vast amounts of complex and heterogeneous data sets from individual studies. Efficient Research Data Management solutions based on FAIR principles can guide the research groups toward standardizing and packaging the study-specific research results into uniquely identifiable digital objects that are easily traceable and identifiable, leading to knowledge discovery, collaboration, and innovation. However, to explore the inter-dependencies among data sets originating from different study disciplines, it is crucial to deploy a generic data-centric RDM solution that overcomes inherent challenges and helps manage complex data sets. This solution should respect participating groups’ data and security policies while providing an integrated data view. In this paper, we introduce ARC Registry, a cloud-native search & exploration application that integrates experimental metadata from individual research groups that have deployed PLANTdataHUB (FAIR-RDM) solution. The focus is on the parts of PLANTdataHUB and ARC Registry that facilitate near-real-time integration of experimental metadata from standardized digital objects (Annotated Research Contexts) across participating research groups.

An Efficient Approach to Extract and Store Big Semantic Web Data Using Hadoop and Apache Spark GraphX

The volume of data is growing at an astonishingly high speed. Traditional techniques for storing and processing data, such as relational and centralized databases, have become inefficient and time-consuming. Linked data and the Semantic Web make internet data machine-readable. Because of the increasing volume of linked data and Semantic Web data, storing and working with them using traditional approaches is not enough, and this causes limited hardware resources. To solve this problem, storing datasets using distributed and clustered methods is essential. Hadoop can store datasets because it can use many hard disks for distributed data clustering; Apache Spark can be used for parallel data processing more efficiently than Hadoop MapReduce because Spark uses memory instead of the hard disk. Semantic Web data has been stored and processed in this paper using Apache Spark GraphX and the Hadoop Distributed File System (HDFS). Spark's in-memory processing and distributed computing enable efficient data analysis of massive datasets stored in HDFS. Spark GraphX allows graph-based semantic web data processing. The fundamental objective of this work is to provide a way for efficiently combining Semantic Web and big data technologies to utilize their combined strengths in data analysis and processing. First, the proposed approach uses the SPARQL query language to extract Semantic Web data from DBpedia datasets. DBpedia is a hugely available Semantic Web dataset built on Wikipedia. Secondly, the extracted Semantic Web data was converted to the GraphX data format; vertices and edges files were generated. The conversion process is implemented using Apache Spark GraphX. Third, both vertices and edge tables are stored in HDFS and are available for visualization and analysis operations. Furthermore, the proposed techniques improve the data storage efficiency by reducing the amount of storage space by half when converting from Semantic Web Data to a GraphX file, meaning the RDF size is around 133.8 and GraphX is 75.3. Adopting parallel data processing provided by Apache Spark in the proposed technique reduces the required data processing and analysis time. This article concludes that Apache Spark GraphX can enhance Semantic Web and Big Data technologies. We minimize data size and processing time by converting Semantic Web data to GraphX format, enabling efficient data management and seamless integration.

PENERAPAN METODE ICONIX PROCESS DALAM PENGEMBANGAN SISTEM INFORMASI KEPEGAWAIAN UNIVERSITAS CATUR INSAN CENDEKIA

Accurate and precise information plays a crucial role in producing high-quality Human Resources (HR) or employees at Universitas Catur Insan Cendekia. Higher education institutions have various needs in managing HR data, including personal information, educational history, rank classification, and other important employee details. The Iconix Process methodology is employed in the development of the Human Resource System at Universitas Catur Insan Cendekia, starting with requirements analysis, diagram creation, initial design, detailed design, coding, testing, and implementation. The result is a more structured, easily accessible, and efficient management of HR data through a website. Keywords: Iconix Process, Development, Information Systems, Personnel

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.

Peningkatan Efisiensi dan Manajemen Informasi dalam Pelayanan Unit Kesehatan Sekolah (UKS) Berbasis Website

The School Health Unit is an important component in the education system to promote the health and well-being of students. UKS aims to provide comprehensive health services to students, including disease prevention, health monitoring, health counseling, and basic medicine. But there are several obstacles that are often faced in managing data in the UKS, including limited technology infrastructure, lack of training and skills in data management, time constraints, data privacy and security issues and several other obstacles that might occur. For this reason, a website-based information system is needed that can be used easily by all people but still with useful features such as drug stock lists and student visits so as to be more efficient in inputting data. The School Health Unit Information System (SI-UKS) is a technology platform used to manage and organize student health information and support the administrative and operational activities of the School Health Unit. The School Health Unit Information System (SI-UKS) plays an important role in improving the efficiency and management of information in school health services. Through efficient management of student data, good data reporting and analysis, and integrated communication, SI-UKS helps improve the quality of health services and overall student well-being. In designing this website-based Information System using the waterfall method.

Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

Student Result Analysis and Management

Abstract: Student result analysis and management (SRAM) gives a straightforward interface to support student data. It might be utilised by instructive universities or colleges to keep up the records of students effectively. Student information system manages all sorts of student details, academic related reports results, batch details, and other resource related details too. An effective result analysis system plays a pivotal role in facilitating efficient management of student data and performance assessment. Institutions staff and administrators need to manage students’ information and academic records. For staff by offering analysis, streamlined administrative tasks and efficient records management. For students, it provides easy access to their academic performance and results metrics. This abstract delineates the design and implementation of a robust result analysis system, encompassing a PHP-based backend. The system caters to the needs of teachers, students, and administrators, offering functionalities tailored to each user category. Ultimately, the deployment of the result analysis system on a web server caters to the needs of educational institutions, facilitating efficient management of student data and fostering a conducive environment for academic assessment and improvement

A blockchain-based hybrid encryption technique with anti-quantum signature for securing electronic health records

Electronic health records (EHRs) are important for the efficient management of healthcare data. However, Healthcare data travels across an open route, i.e., the Internet, making EHR security a difficult process to do. This puts healthcare data vulnerable to cyber assaults. A possible method for protecting EHRs is blockchain technology. In this work, we develop an EHR architecture based on blockchain, which ensures all stakeholder's safety and privacy. We analyze various security architectures used for EHRs and the standard encryption system is integrated with quantum computing (QC). To safeguard the conventional traditional encrypting system against quantum assaults, we provide a hybrid signature technique that combines the Elliptic Curve Digital Signature Algorithm (ECDSA) and Dilithium within the anti-quantum lattice-based blind signature. Based on the difficulty of lattice problems over finite fields, Dilithium is a lattice-based signature method that is substantially safe against selected message assaults. The developed technique creates high entropy secret keys using the lattice basis delegation mechanism. The combination of ECDSA and Dilithium provides an efficient and secure signature system that is resilient to quantum attacks. The proposed scheme ensures that only authorized users with a defined role can use the database to access the data. We evaluate the efficiency of our scheme by comparing its performance to other state-of-the-art solutions in terms of transaction throughput, resource utilization, and communication cost. Results demonstrate that the developed technique outperforms the existing techniques in terms of efficiency and security.

PERANCANGAN STRATEGI VALIDASI DATA DENGAN PEMANFAATAN APLIKASI VALIDASI BERBASIS GMAPS UNTUK PRODUK INDIHOME

Guaranteeing the accuracy and completeness of data is a critical aspect in the scope of telecommunications services. In this context, this research aims to design an effective data validation strategy through the use of a Google Maps (GMaps) based validation application in Indihome products. The design involved an approach that combined GMaps interactive map technology, the React JS framework, and the MongoDB database. Using the React JS framework, an interactive user interface has been developed to facilitate the use of validation applications. Utilization of Google Maps allows visual integration of validation data with precise geographic locations. The use of the MongoDB database supports efficient storage and management of validation data, ensuring that the data obtained meets requirements. Application testing has been carried out to ensure the effectiveness of the designed data validation strategy. The result of this research is the design of an effective data validation strategy, through the application of GMaps, React JS, and MongoDB technology. This implementation is expected to provide a more accurate, efficient and intuitive solution in ensuring data quality in Indihome services. This research can also provide benefits for technological developments in the field of data validation and the application of map concepts in user experience

Life Integrate: Unify Your Life, Simplify Your Success

Abstract: Efficient management and organization of personal and commercial data is critical for reducing stress and increasing productivity in the fast-paced digital environment we live in. An inventive smartphone software called "Life Integrate" attempts to meet this requirement by effectively managing duties and merging different facets of life. The project aims to mimic the effectiveness of an AI personal assistant by bridging the gap between contemporary lives and efficient organizing. The software smoothly distinguishes between personal and professional data by utilizing technologies like machine learning and natural language processing, which improves the user experience and efficiency. One to three clicks can easily share data with the app, which adds to its effectiveness and user-friendliness. Aiming to streamline processes, key features include real-time editing, interactive chats, speech-activated assistants, and contextual awareness. The project's emphasis on developing a personal bot that excels at data collection and generates intelligent ideas based on collected data exemplifies an intriguing combination of customization and artificial intelligence. With its 13 primary sub-tabs and extensive questionnaires for storing both personal and professional data, the app offers an approach to organization and management that is user-centric. Effective app development and deployment are ensured by the use of the Flutter and Dart platforms. In general, "Life Integrate" seeks to transform the information landscape, making it easier to acquire and use, and facilitating more enjoyable interactions between humans and machines. With this project, we hope to create a future in which people can easily incorporate technology into their daily lives, increasing productivity and lowering stress levels

Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis.

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency.

Enhancing Contextual Masking in Reversible Linguistic Steganography with Ensemble Methods

Various cybercrimes can be prevented by text authentication that is responsible for preserving digital identities and contents. Digital signatures come in handy as a way of authenticating texts, which is an extensively used method. One approach to this problem is linguistic steganography, which allows hiding the signature in other words within the text and thereby facilitating efficient data management. However, it should be noted that there is a danger that these kinds of changes may result in inappropriate decisions being taken by automated computing systems not to mention change their final outputs (unseen). As such, many people are becoming more concerned with the possibility of reversing steganography so that it becomes possible to eliminate any distortions made during the process. This paper uses Contextual masking instead of masking randomly with BERT model. The goal behind this research was developing a natural language text specific Reversible Steganographic System. Our model uses pre-trained BERT as a transformer based masked language model and reversibly embeds messages through predictive word substitution. To quantify predictive uncertainty, we introduce an adaptive steganographic technique using Bayesian deep learning. This experiment shows us how our proposed system balances imperceptibility with capacity while maintaining near semantics at all times. Also, we integrate ensemble methods instead of Monte Carlo to balance the imperceptibility.

CADCare: Smart System for CHD Identification & Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.