Cloud Database Research Articles

May Measurement Month 2021: an analysis of blood pressure screening results from China.

We reported findings from participants screened during the May Measurement Month 2021 in China, which aimed to raise awareness of raised blood pressure (BP), and to investigate the risk factors of BP. The study participants were adults (≥18 years), ideally in whom BP had not been measured in the previous year. Blood pressure was measured three times consecutively with 1 min intervals in the sitting position, using a validated upper-arm cuff automated BP monitor (Omron HEM-7081IT), and transmitted to a central cloud database via a smartphone app. The measurement was performed in 218 844 participants in 183 sites across 31 China provinces. The mean (standard deviation) age was 47.0 (15.7) years, and 51.8% (n = 113 466) were women. The mean systolic/diastolic BP was 120.2/77.5 mmHg. Among 57 178 (26.1%) participants with hypertension, the awareness, treatment, and control rates of hypertension were 30.4% (n = 17 354), 28.7% (n = 16 369), and 17.1% (n = 9743), respectively. After adjustment for age, sex, and use of antihypertensive medication, both systolic and diastolic BP were significantly (P ≤ 0.01) higher in current smokers (n = 22 344, +0.4/+0.7 mmHg) and with moderate (n = 4780, +1.4/+4.2 mmHg) or daily alcohol intake (n = 2427, +1.3/+2.5 mmHg). Blood pressure was lower in those reporting regular exercise (n = 32 328, -2.2/-1.4 mmHg). In addition, individuals with previous COVID-19 vaccination had lower systolic and diastolic BP (n = 88 945, -1.8/-1.5 mmHg, P ≤ 0.001). In conclusion, our study showed that long-term large-scale screening for hypertension is feasible, and there is a strong association between BP and major lifestyle factors.

A new deep learning model with interface for fine needle aspiration cytology image-based breast cancer detection

Cytological evaluation through microscopic image analysis of fine needle aspiration cytology (FNAC) is pivotal in the initial screening of breast cancer. The sensitivity of FNAC as a screening tool relies on both image quality and the pathologist’s expertise. To enhance diagnostic accuracy and alleviate the pathologist’s workload, a computer-aided diagnosis (CAD) system was developed. A comparative study was conducted, assessing twelve candidate pre-trained models. Utilizing a locally gathered FNAC image dataset, three superior models-MobileNet-V2, DenseNet-121, and Inception-V3-were selected based on their training, validation, and testing accuracies. Further, these models underwent evaluation in four transfer learning scenarios to enhance testing accuracy. While the outcomes were promising, they left room for improvement, motivating us to create a novel deep convolutional neural network (CNN). The newly proposed model exhibited robust performance with testing accuracy at 85%. Our research concludes that the most lightweight, high-accuracy model is the one we propose. We’ve integrated it into our user-friendly Android App, “Breast Cancer Detection System,” in TensorFlow Lite format, with cloud database support, showcasing its effectiveness. Implementing an artificial intelligent (AI)-based diagnosis system with a user-friendly interface holds the potential to enhance early breast cancer detection using FNAC.

Cloud computing for big data analytics How cloud computing can handle procesing large amounts of data and improve real-time data analytics

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis.

Retraction notice: Cloud-based secure data storage and access control for internet of medical things using federated learning

Retraction notice: Cloud-based secure data storage and access control for internet of medical things using federated learning

SECURITY OF DATA STORED IN CLOUD SERVICES

The main purpose of this study is to - examine the security issues of data stored in cloud services. The authors of the article discuss the main problems associated with an insecure and incorrectly configured cloud database, which can lead to leakage or distortion of information of Russian citizens and organizations. In the course of the study the authors come to the conclusion that in order to protect their data it is necessary to use cloud storage providers located on the territory of the Russian Federation (hereinafter referred to as RF), use a hidden storage system, regularly update and monitor the system to identify vulnerabilities and prevent possible attacks. In addition, ways to solve the problem and effectively protect information stored in cloud services, including at the legislative level, are proposed.

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.

Design and Implementation of University Club Management System based on WeChat Applet

To solve the problems of delayed work processes and redundant content in the management of university clubs, digital means are used to improve the efficiency of club management. We have designed and implemented a WeChat mini program for the university club ma nagement system. The mini program uses the Java Script framework and cloud database to facilitate participation in club activities, manage information, and register for activities. Through practical application, this mini program not only simplifies the club management process, but also enhances the interactivity and social attributes of the club. Effectively solving key issues in club management, it provides strong support for the digital transformation of university clubs and the convenience of student life.

Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management

Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management

IoT Based Crop Monitoring System

The IoT-Based crop monitoring System is a cutting-edge solution designed to enhance agricultural productivity and efficiency by utilizing sensor technology and automation. The core objective is to automate the irrigation process based on real-time soil moisture data. The systememploysanESP8266 module to facilitate the seamless exchange of data between various sensors and Firebase, a real-time cloud database. This interconnected system comprises two primary fields, each equipped with soil and soil moisture sensors, temperature sensors, smoke detectors, UV light, buzzer, solenoid valves, and mini water pump. The ESP8266 module serves as the central hub for collecting data from the sensors and transmitting it to the Firebase database. The data includes crucial information such as soil moisture levels, temperature, smoke detection, and UV light intensity. Furthermore, the system integrates alarm functionality through the buzzer and precise irrigation control via the solenoid valve and mini water pump. The data stored in Firebase is accessible through a web application built using React.js and Node.js. This web interface provides users with a graphical representation of the gathered data, allowing them to monitor the agricultural conditions in real-time. The main key features are Data Visualization in which users can remotely monitor soil moisture levels and receive real-time updates on crop conditions through a web application. Resource Efficiency by automating irrigation, this model optimizes water usage, reducing waste and improving water conservation. This paper not only showcases the integration of IoT technology in agriculture but also serves as a practical and environmentally responsible solution for modern farming practices.

Cloud Data Integration Tools As Services

In this paper, a variety of data integration, cloud computing, and web services-related topics were discussed. The conversation covered the advantages of using cloud-based methods for cloud-based data integration, such as enhanced data accuracy and completeness, as well as the challenges and considerations that must be addressed. In addition, the significance of document integrity and the use of auto-enhance document tools to ensure data accuracy were emphasized. The paper also provided a broad overview of the subject, touching on a variety of aspects and providing insights into the potential of cloud-based data integration methods in the cloud industry.

A systematic literature review with bibliometric analysis of Quality 4.0

PurposeThis study aims to investigate and provide comprehensive insights into the state of research on Quality 4.0 across various sectors. The investigation focusses on studies conducted in different countries and is motivated by the perceived benefits of adopting Quality 4.0 and the existing research gap in this domain. The literature review examines two main aspects of Quality 4.0: implementation ingredients and its applications.Design/methodology/approachThis study employed a comprehensive systematic literature review (SLR) and bibliometric analysis of Quality 4.0 publications to identify the benefits, challenges and trends of adopting Quality 4.0.FindingsThe applications of Quality 4.0 are centred on six key areas, including agile product development, predictive quality algorithms, cloud databases, automation, reduced cost of quality, streamlined quality processes, accurate demand forecasting and monitoring suppliers and quality metrics using digital dashboards. Geographical disparities in research were identified, with India and the United States of America emerging as the top publishing countries. The dominance of the subjects of business, management and accounting underscored the importance of quality management across diverse sectors.Research limitations/implicationsThe limitations of this study include the novelty of the Quality 4.0 topic, which posed a challenge in finding academic material, and the analysis period of six years, which could potentially limit the coverage of recent developments.Practical implicationsThe insights and observations from this research offer valuable guidance to academics, practitioners and professionals seeking to implement Quality 4.0 in diverse sectors.Originality/valueThis study contributes a novel perspective to the field by examining the application, utilisation and evaluation of Quality 4.0, providing valuable insights for effectively managing its implementation.

Blockchain-Based Solution for Supply Chain Data Integrity

Data plays a crucial role in today's world, guiding business decisions across various computer- assisted activities. Maintaining the integrity of data is vital, as any tampering could have serious consequences for important business decisions. This concern is particularly significant in cloud computing settings, where data owners have limited control over key aspects like physical storage and access control. Blockchain technology has emerged as an intriguing solution to address data integrity concerns. With its inherent properties, blockchain offers promising avenues for ensuring data integrity. However, some challenges need to be overcome, such as low throughput, high latency, and stability issues, which currently limit the practical implementation of blockchain-based solutions. we focus on a case study from the European SUNFISH project, which aims to develop a secure cloud federation platform for the public sector. We examine the specific data integrity requirements in cloud computing environments and identify the research questions that need to be addressed to implement blockchain-based databases effectively. We start by outlining the open research questions and the inherent difficulties associated with addressing them. Then, we propose a preliminary design for a blockchain-based database tailored to cloud computing environments. This design aims to leverage the strengths of blockchain technology while addressing the challenges unique to cloud computing. By addressing these research questions and proposing practical solutions, we aim to pave the way for the adoption of blockchain-based databases in cloud computing environments. This has the potential to enhance data integrity and security, ultimately benefiting organizations operating in the cloud.

QOTUM: The Query Optimizer for Distributed Database in Cloud Environment

Abstract: In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters.

DNA Steganography: Embedding the Secret Messages Using Glass Stack Method and Detecting Errors Made by Different Attacks

Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. Steganography is a well-known solution to this problem. Recent studies have limitations, such as not reconstructing the cover or enlarging the original sequence, increasing personal data security risks. In this study, we provide a novel data-hiding technique that uses a secret key, bit padding, and exclusive OR (XOR) operation to insert secrets into a pre-processed DNA sequence. The message encryption and pre-processing of the cover are performed using a new glass stack pattern technique. The message encryption and pre-processing processes using the new glass stack pattern method increase the security and robustness of the system. The method embeds 3 bits per nucleotide which is greater than the existing schemes. It has a relatively small DNA expansion which is 50% and has a low risk of breaking security. The method also preserves the biological properties of DNA sequences. Additionally, a parity check approach further improves security by thwarting attacks like man-in-the-middle, chosen stego, and modification attacks that alter the stego DNA. These advances improve the system's overall security capabilities. The experimental findings demonstrate that the suggested method reconstructs the cover, healthcare data, and DNA sequence properly and error-free, and outperforms competing algorithms on all performance metric dimensions. Highlights The scheme implants a maximum of 3 bits per nucleotide with a small expansion. Its cover pre-processing and message encryption using a glass stack pattern makes strong security. The scheme can detect and fix changed bits caused by attacks. A very low cracking probability shows strong security and robustness. The biological properties of the stego DNA sequence are preserved by this scheme.

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing

With the widespread utilization of sensors and the rapid development of Mobile Crowd Sensing (MCS), the framework of distributed data storage, which makes use of the limited storage capacity of sensing devices, is providing an alternative to cloud-based data storage. Since the construction of the distributed storage framework for MCS has to be completed by Compressed Sensing (CS) theory, a reasonable measurement allocation becomes a no-brainer if we want to obtain more recovery precision of data without wasting measurement resources. To more efficiently address the above problems, we present a novel strategy of Volatility-Based Diversity Awareness for CS measurement allocation called VBDA, which collectively achieves lower computational complexity, adaptive sampling rate allocation for non-visual data, and high-quality reconstruction. We first process the target monitoring region in blocks. Then, we calculate the degree of data diversity of each area using the volatility, which is used to assess the importance of the different areas. Worthy note, based on the volatility calculation, we can obtain the magnitude of the variation in diversity that is only present in the real data, even when using very ambiguous recovered data. This is primarily due to the volatility unique design concept, which attempts to offset partially identical errors in adjacent recovered data using volatility calculations. Finally, to rationally allocate measurement resources, a volatility-based sampling rate allocation scheme is proposed. We further provide an implementation for practical deployments of VBDA in various related contexts. Numerous experimental show that the VBDA performs excellently. In comparison to the current state-of-the-art strategy, it improves data recovery accuracy by 12.2% without the use of any prior knowledge, while being adaptable to many different distribution types of data.

Securing the Digital Frontier: Strategies for Cloud Computing Security, Database Protection, and Comprehensive Penetration Testing

This research paper explores the effectiveness of integrated cybersecurity strategies, focusing on the amalgamation of cloud computing security, database protection, and penetration testing into a unified risk management framework. The primary aim is to evaluate how such integration impacts the overall cybersecurity posture of organizations, offering insights into mitigating cyber threats, unauthorized access, and data breaches. Employing a survey-based methodology, the study gathered data from 365 professionals across cloud computing, database administration, and cybersecurity fields. Through descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM), the research explored the interrelations between various cybersecurity strategies and their collective influence on organizational resilience against cyber threats. The findings underscore the significant benefits of a holistic cybersecurity approach, revealing that penetration testing, robust database security measures, and strict adherence to cloud computing security requirements significantly reduce vulnerabilities and incidents of data breaches. Moreover, the study established that a unified risk management framework substantially enhances an organization's cybersecurity posture, highlighting the critical role of integrated security measures in fostering organizational resilience. The research confirms the hypothesis that incorporating cybersecurity strategies across different domains leads to a synergistic enhancement of security defenses, offering a more robust mechanism against the multifaceted nature of cyber threats. In conclusion, the study advocates for the adoption of a comprehensive, integrated approach to cybersecurity, emphasizing regular penetration testing, stringent database security protocols, and adherence to cloud computing security standards as essential components of a robust cybersecurity framework. This approach not only mitigates the risk of cyber threats but also strengthens organizational resilience, ensuring a secure digital environment for future challenges.

EFFICIENT MULTI-AUTHORITY ACCESS CONTROL USING CIPHERTEXT POLICY -HIERARCHICAL ATTRIBUTE BASED ENCRYPTION IN CLOUD STORAGE

The goal of this research is to improve the security of cloud-based data storage systems by presenting the Cloud Secure Storage Mechanism based on Data Dispersion and Encryption (CPHABE). The User Frame, Authority Login, Cloud Server Login, and Data Owner Login are the four separate components that make up the system. By providing file names and requesting transformation keys from the authority for decryption, users start downloads. Requests for transformation keys, upload information, and key creation are handled by the authority. Owner identities, file names, encrypted content, gate types, and storage size graphs are all managed by cloud servers. Partially decrypted data, such as user identities, file names, transformation keys, data owner IDs, and partially decrypted content, is sent in response to user requests. Data owners may use the system to explore files, obtain keys, and apply hybrid encryption methods including Authenticated Encryption (AE) and Key Encapsulation Mechanism (KEM). This all-encompassing strategy guarantees strong encryption, data dispersion, and security measures, offering a stable foundation for cloud storage systems. Keywords: Cloud Storage, Security Mechanism, Encryption

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience.

Modern virtual reality (VR) devices record six-degree-of-freedom kinematic data with high spatial and temporal resolution and display high-resolution stereoscopic three-dimensional graphics. These capabilities make VR a powerful tool for many types of behavioural research, including studies of sensorimotor, perceptual and cognitive functions. Here we introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies such as WebXR to enable browser-based VR, without compromising on experimental design. Ouvrai's features include easy installation, intuitive JavaScript templates, a component library managing front- and backend processes and a streamlined workflow. It integrates with Firebase, Prolific and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Ouvrai is not limited to VR studies; researchers can also develop and run desktop or touchscreen studies using the same streamlined workflow. Through three distinct motor learning experiments, we confirm Ouvrai's efficiency and viability for conducting remote VR studies.