Efficient Data Collection Research Articles

EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things

The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy.

Comparative assessment of ESG ratings methodology and results based on XBRL

This study provides a comparative analysis of Environmental, Social, and Governance (ESG) ratings methodologies and explores the potential of eXtensible Business Reporting Language (XBRL) to enhance transparency and comparability in ESG reporting. Evaluating ratings from different agencies, the research identifies significant methodological inconsistencies that lead to conflicting information for investors and stakeholders. Statistical tests and adjusted rating scales confirm substantial divergence in ESG scores, primarily due to differing data categories and indicators used by rating firms. Using a sample of 265 European companies, the study demonstrates that individual ESG agencies report markedly different ratings for the same firms, which can mislead stakeholders. It proposes that XBRL based reporting can mitigate these inconsistencies by providing a standardized framework for data collection and reporting. XBRL enables accurate and efficient data collection, reducing human error and enhancing the transparency of ESG reports. The findings advocate for integrating XBRL in ESG reporting to achieve higher levels of comparability and reliability. The study calls for greater regulatory oversight and the adoption of standardized taxonomies in ESG reporting to ensure consistent and comparable data across sectors and jurisdictions. Despite challenges like the lack of a standardized taxonomy and inconsistent adoption, the research contends that XBRL can significantly improve the reliability of ESG ratings. In conclusion, this study suggests that standardizing ESG data through XBRL could provide a viable solution to the unreliability of current ESG rating scales, supporting sustainable business practices and informed decision making by investors.

Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach

Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach

Co-designed study with the Traveller Community in Ireland to determine cancer awareness & attitudes

Abstract Background Irish Travellers are a distinct ethnic minority group (formal recognition March 2017). They experience higher mortality than the general population at all ages for all causes of death. Study Aim To determine cancer awareness and attitudes among the Traveller Community in Ireland, and to identify barriers/enablers to cancer risk reduction behaviours and early diagnosis of cancer. Methods A co-designed multi-method study was developed in collaboration with Pavee Point, a national NGO for Travellers, the National Social Inclusion Office (NSIO) and the National Cancer Control Programme (NCCP) including: 1. Consultation with Pavee Point and NSIO representatives to inform the study proposal and methodology. 2. Co-design of methodology for ethics applications and subsequent data collection plan including interview topic guides and the development of a culturally appropriate version of the NCCP’s 2022 National Survey on Cancer Awareness, adding questions on social determinants of health and screening participation. 3. Collaboration with 12 peer-led Primary Health Care for Travellers Projects (PHCTPs), to provide in-person training for Traveller Community Health Workers to undertake a cross-sectional survey of 380 adult Travellers. 4. Collaboration with PHCTPs to undertake 20 semi-structured interviews with Travellers. 5. Conduct of semi-structured interviews with healthcare professionals to explore perceptions of barriers/enablers for Travellers engaging with the health system. Lessons Learned Working closely and collaboratively with our study partners, we gained valuable insights which enabled effective and efficient data collection across 8 counties, while exceeding our survey target sample size. Implications for Research: Collaborative, trusting, and respectful relationships are critical for all studies, but particularly when working with minority groups. Conclusions The study findings will inform policy for improved cancer outcomes among Travellers in Ireland. Key messages • Formation of a collaborative and respectful relationship with the Traveller community has facilitated successful research. • Traveller Community Health Workers (peer researchers) were key to the success of data collection.

BGF-YOLOv10: Small Object Detection Algorithm from Unmanned Aerial Vehicle Perspective Based on Improved YOLOv10

With the rapid development of deep learning, unmanned aerial vehicles (UAVs) have acquired intelligent perception capabilities, demonstrating efficient data collection across various fields. In UAV perspective scenarios, captured images often contain small and unevenly distributed objects, and are typically high-resolution. This makes object detection in UAV imagery more challenging compared to conventional detection tasks. To address this issue, we propose a lightweight object detection algorithm, BGF-YOLOv10, specifically designed for small object detection, based on an improved version of YOLOv10n. First, we introduce a novel YOLOv10 architecture tailored for small objects, incorporating BoTNet, variants of C2f and C3 in the backbone, along with an additional small object detection head, to enhance detection performance for small objects. Second, we embed GhostConv into both the backbone and head, effectively reducing the number of parameters by nearly half. Finally, we insert a Patch Expanding Layer module in the neck to restore the feature spatial resolution. Experimental results on the VisDrone-DET2019 and UAVDT datasets demonstrate that our method significantly improves detection accuracy compared to YOLO series networks. Moreover, when compared to other state-of-the-art networks, our approach achieves a substantial reduction in the number of parameters.

Examining the Views of non-e-Tutoring Students on Potential Benefits and Drawbacks of Incorporating e-Tutoring into Their Learning Experience

In recent years, technology integration in education has transformed the learning environment in online institutions. One technological advancement that has gained popularity is e-tutoring which offers personalised academic support to students through online platforms. While e-tutoring has become well-known and has been adopted to promote collaborative learning, there are still students who do not use these services for various reasons. This study explored the perspectives of students who do not utilize e-tutoring services in online education settings, shedding light on their perceptions. A quantitative research approach with a descriptive design was adopted to gather and analyse data on the perspectives of non-e-tutoring students. Online questionnaires were employed as the primary data collection method, allowing for the efficient collection of data from many respondents. The data was analysed using the Statistical Package for the Social Sciences (SPSS). Ethical concepts such as informed consent, anonymity of responses and protection of respondents against harm were maintained. Findings indicate that non-e-tutoring students perceive a sense of control over their own pace of learning, suggesting a preference for self-directed learning and the ability to tailor their educational experience to their individual needs and learning styles. They also perceive a lack of clarity in the roles of e-tutors, leading to uncertainty about their responsibilities. In terms of communication, students feel overwhelmed by the volume of announcements and find repetitive information frustrating. Additionally, some students face challenges with their internet connection and associated cost, which can hinder their participation in online activities. Notably, a desire for peer interactions, teamwork, and a sense of belonging in a community highlights the importance of social aspects in online learning. This study recommends that students seek alternate support systems by contacting academics and facilitators for guidance and clarification. Developing self-directed learning skills is essential, empowering students to take charge of their learning through setting objectives, creating their study plans, and utilising resources. For higher education institutions (HEIs), it was recommended that they ensure that a variety of support services are available to cater to the needs of all students, including non-e-tutoring students.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

Outcomes of Patients With Early and Locally Advanced Lung Cancer: Protocol for the Italian Lung Cancer Observational Study (LUCENT).

Lung cancer, predominantly non-small cell lung cancer (NSCLC), remains a formidable challenge, necessitating an in-depth understanding of evolving treatment paradigms. The Italian Lung Cancer Observational Study (LUCENT) addresses this need by investigating the outcomes of patients with early and locally advanced lung cancer in Italy. With a focus on real-world data and patient registries, this study aims to provide comprehensive insights into clinical, psychosocial, and economic impacts, contributing to informed decision-making in health care. LUCENT is a prospective observational multicenter cohort study enrolling patients eligible for minimally invasive manual, robot-assisted, or traditional open surgery. The study will develop a web-based registry to collect longitudinal surgical, oncological, and socioeconomic outcome data. The primary objectives include performance assessment through the establishment of national benchmarks based on risk-adjusted outcomes and processes of care indicators. The secondary objectives encompass economic and psychosocial impact assessments of innovative technologies and treatment pathways. The multicenter design ensures a diverse and representative study population. The evolving landscape of NSCLC treatment necessitates a nuanced approach with consideration of the dynamic shifts in therapeutic strategies. LUCENT strives to fill existing knowledge gaps by providing a platform for collecting and analyzing real-world data, emphasizing the importance of patient-reported outcomes in enhancing the understanding of the disease. By developing a web-based registry, the study not only facilitates efficient data collection but also addresses the limitations of traditional methods, such as suboptimal response rates and costs associated with paper-and-pencil questionnaires. Recruitment will be conducted from January 01, 2024, to December 31, 2026. Follow-up will be performed for a minimum of 2 years. The study will be completed in the year 2028. LUCENT's potential implications are substantial. Establishing national benchmarks will enable a thorough evaluation of outcomes and care processes, guiding clinicians and policymakers in optimizing patient management. Furthermore, the study's secondary objectives, focusing on economic and psychosocial impacts, align with the contemporary emphasis on holistic cancer care. Insights gained from this study may influence treatment strategies, resource utilization, and patient well-being, thereby contributing to the ongoing refinement of lung cancer management. ClinicalTrials.gov NCT05851755; https://clinicaltrials.gov/study/NCT05851755. ISRCTN 67197140; https://www.isrctn.com/ISRCTN67197140. PRR1-10.2196/57183.

A Clinical Outcomes Data Archive for a Comprehensive Fetal Diagnosis and Treatment Center.

Data on near- and long-term clinical outcomes are critical for the care of all maternal-fetal patients presenting to a fetal center. This is especially important since physiologic and neurodevelopmental attributes do not manifest until later childhood when multilevel (e.g., individual, family, policy) factors have a direct influence on health outcomes. Electronic health records (EHRs) create opportunity for efficient data collection. However, documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences can affect resultant output. Therefore, EHR derived datasets have limited ability to accurately characterize the clinical presentation and care trajectory of patients with congenital anomalies. In addition, in most systems, the fetus lacks a digital identity and requires relinking fetal attributes documented in the maternal chart to those from the pediatric EHR. This conundrum amplifies in the setting of multiple gestation, returning maternal patients, and pregnancies with fetal demise. Moreover, current data capture systems result in incomplete abstraction of variables that may confound, mediate, or moderate critical associations. Our objective was to develop and implement a prospective data capture platform to transform EHR data into an analytic-grade database for multipurpose use. A unified platform for longitudinal follow-up of maternal-child dyads cared for at our fetal center, named the Clinical Outcomes Data Archive (CODA), was constructed. CODA was designed using a data dictionary based on multidisciplinary and interprofessional expert input, a relational identity for each patient, fetus, and pregnancy, and a process by which EHR-sourced and chart-abstracted data are validated by a well-trained team. Descriptive analyses were performed for data acquired between July 2022 and July 2023, and a comparison of studies before and after implementation of CODA is presented. 5,394,106 data points were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable or undocumented. 91% of data points were sourced from the EHR. Eighty-five percent of condition-specific variables required manual chart abstraction. The study conducted with CODA was able to contribute to 18 other studies. CODA successfully merges EHR-sourced and manually abstracted documentation for longitudinal study of the maternal-child dyad.

Enhancing land cover classification via deep ensemble network

The rapid adoption of drones has transformed industries such as agriculture, environmental monitoring, surveillance, and disaster management by enabling more efficient data collection and analysis. However, existing UAV-based image scene classification techniques face limitations, particularly in handling dynamic scenes, varying environmental conditions, and accurately identifying small or partially obscured objects. These challenges necessitate more advanced and robust methods for land cover classification. In response, this study explores ensemble learning (EL) as a powerful alternative to traditional machine learning approaches. By integrating predictions from multiple models, EL enhances accuracy, precision, and robustness in UAV-based land use and land cover classification. This research introduces a two-phase approach combining data preprocessing with feature extraction using three advanced ensemble models DenseNet201, EfficientNetV2S, and Xception employing transfer learning. These models were selected based on their higher performance during preliminary evaluations. Furthermore, a soft attention mechanism is incorporated into the ensembled network to optimize feature selection, resulting in improved classification outcomes. The proposed model achieved an accuracy of 97 %, precision of 96 %, recall of 96 %, and an F1-score of 97 % on UAV image datasets. Comparative analysis reveals a 4.2 % accuracy improvement with the ensembled models and a 1 % boost with the advanced hybrid models. This work significantly advances UAV image scene classification, offering a practical solution to enhance decision-making precision in various applications. The ensemble system demonstrates its effectiveness in remote sensing applications, especially in land cover analysis across diverse geographical and environmental settings.

Active learning for efficient data selection in radio‐signal‐based positioning via deep learning

AbstractThe problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data‐collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. Therefore, an active learning approach for efficient data collection is proposed. It is first shown that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. Then, a practical method is proposed to approximate this genie.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Implementation of a low-cost comprehensive pavement inspection system

Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.

DiSA-CF: A distance-integrated self-attention model for collaborative filtering in web service recommendation

The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality of Service (QoS) emerges as a critical imperative. While numerous studies have proposed methodologies focusing on resource optimization, network management, and data processing techniques, several previous approaches to QoS prediction may face constraints in the Internet of Things (IoT) environment, where the dynamic nature of IoT environments demands predictive models capable of adapting to varying conditions. Integrating user and service location data into QoS prediction models is paramount, as it enables personalized service delivery tailored to the user’s specific context, thus enhancing the user experience and overall system performance. This paper presents a novel approach to QoS prediction in IoT, harnessing self-attention and collaborative filtering (CF) techniques while incorporating location-based features such as distance from the user to the service. Experimental evaluations on benchmark datasets reveal that our proposed model improves prediction accuracy by up to 7 % compared to existing methods, underscoring its efficacy in enhancing QoS provisioning in IoT environments.

An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in Drosophila melanogaster.

The compound eye of Drosophila melanogaster has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult Drosophila and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. When data collection is distributed among many individuals or extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing. To address these limitations we developed a method for multiplexed quantitative analysis of adult Drosophila melanogaster phenotypes. Efficient data collection and analysis of up to 60 adult flies in a single image with standardized conditions eliminates inter-operator variability and enables precise quantitative comparison of morphology. Semi-automated data analysis using ImageJ and R reduces image manipulations, facilitates reproducibility, and supports emerging automated segmentation methods, as well as a wide range of graphical and statistical tools. These methods also serve as a low-cost hands-on introduction to imaging, data visualization, and statistical analysis for students and trainees.

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

Missing data recovery based on temporal smoothness and time-varying similarity for wireless sensor network

Wireless Sensor Networks (WSN) play a vital role in the Internet of Things (IoT) and show great potential in monitoring applications. However, due to harsh environmental conditions and unreliable communication links, WSN often encounter partial data loss during data collection, which inevitably affects the quality of service. To address this challenge, researchers have employed matrix completion techniques to recover missing data by exploiting the low-rank features in the data, but its accuracy is not satisfactory. This paper argues that the spatiotemporal characteristics of the data underlie its low-rank nature, enabling a more accurate capture of the intrinsic patterns within the data. Drawing on this insight, we propose a missing data recovery algorithm based on Temporal Smoothness and Time-Varying Similarity (TSTVS). Unlike traditional low-rank methods, the TSTVS algorithm directly utilizes the structural features of data in the spatiotemporal domain to establish a missing data completion model. Subsequently, the model is converted into an unconstrained optimization problem using the penalty function method, and the gradient descent method is applied to solve it, reconstructing the complete data matrix. Finally, simulation experiments were conducted on three real-world monitoring datasets, comparing the TSTVS with three low-rank methods, Efficient Data Collection Approach (EDCA), Matrix factorization with Smoothness constraints (MFS) and Data Recovery Based on Low Rank and Short-Term Stability(DRLRSS). The experimental results indicate that the proposed TSTVS algorithm consistently outperforms the three low-rank based algorithms in terms of recovery accuracy across different datasets and missing rate scenarios.

Development of a Big Data Platform for the Collection and Utilization of Korea Biobank Network Clinical Information.

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

Computer vision-based model for detecting turning lane features on Florida’s public roadways from aerial images

ABSTRACT Efficient collection of roadway geometry data is crucial for effective transportation planning, maintenance, and design. Current methods involve land-based techniques like field inventory and aerial-based methods such as satellite imagery. However, land-based approaches are labor-intensive and costly, prompting the need for more efficient methodologies. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. This study proposes a computer vision-based approach to detect turning lane markings from aerial images in Florida. The method aims to identify aged or faded markings, compare lane locations with other features, and analyze intersection crashes. Validation in Leon County achieved 80.4% accuracy, detecting over 13,800 turning lane features in Duval County, Florida. This data integration offers valuable insights for policymakers and road users, highlighting the significance of automated extraction methods in transportation planning and safety.