Data Collection Research Articles

Ethical Concerns in Large-Scale Data Labeling and Usage: Challenges and Solutions

Abstract: The rapid adoption of artificial intelligence (AI) and machine learning (ML) has created an unprecedented demand for high-quality labeled data. Large-scale data labeling, a critical component of AI system development, often involves vast datasets sourced from diverse populations and annotated using a combination of automated processes and human labor. However, the ethical challenges associated with these practices have gained significant attention. This paper explores key ethical concerns in large-scale data labeling and usage, focusing on four critical areas: bias, privacy, labor practices, and transparency. Bias in labeled data, arising from the inherent subjectivity of annotators and the unrepresentative nature of many datasets, exacerbates the risk of unfair or discriminatory outcomes in AI applications. Privacy violations occur when sensitive information is collected or used without proper consent, often challenging the effectiveness of anonymization techniques. Furthermore, the reliance on crowdsourced labor for data annotation raises concerns about worker exploitation, low compensation, and the mental toll of labeling sensitive or explicit content. Lastly, the lack of transparency and accountability in data collection and labeling processes undermines public trust and ethical standards. Through a comprehensive review of existing practices, this paper highlights real-world case studies and controversies, including biased datasets and privacy violations. Current technological and policy-driven solutions—such as privacy-preserving techniques, labor reforms, and bias mitigation algorithms—are critically examined. Finally, the paper discusses the challenges of implementing these solutions at scale and identifies future research directions. By addressing these concerns, this work aims to promote more equitable, transparent, and ethical practices in the lifecycle of AI data management.

Association Between Functional Dyspepsia and Binge Eating Disorder: A Frequent, Often Overlooked Overlap Clinical Presentation.

This study aims to investigate the association between Binge Eating Disorder and functional dyspepsia in a Mexican population, focusing on symptomatology and demographic characteristics. We conducted a cross-sectional study on 1016 subjects, evaluating binge eating disorder (BED) and functional dyspepsia based on the Rome IV criteria. Data collection included sociodemographic information, gastrointestinal symptom severity, and anxiety/depression screening using validated tools. A multivariate logistic regression analysis with the χ2 test was conducted for comparison analysis. The prevalence of dyspepsia in BED was 53.6% (95% CI, 46-56). Postprandial fullness (OR, 1.52; 95% CI, 1.06-2.17; P = 0.021) and overlap syndrome (OR, 1.80; 95% CI, 1.25-2.60; P = 0.002) were significantly associated with BED. Patients with BED also presented more severe postprandial distress syndrome (P = 0.027). Anxiety was prevalent in BED patients, while depression was more prominent in patients with BED and dyspepsia overlap. BED patients have a high prevalence of dyspepsia with an association between postprandial fullness and this eating disorder. BED appears to be more prevalent in younger individuals and males. These findings underscore the importance of considering dyspepsia in the management of BED and highlight the need for further research on this association.

Effects of three pretreatments on seed germination and seedling growth of Mansonia altissima (A. Chev.) in a nursery, a timber species exploited in Cote d'Ivoire

Côte d'Ivoire's forests are a reservoir of biodiversity. Indeed, they offer a diverse ecosystem service. However, they are subject to strong anthropogenic pressures, which have resulted in the disappearance of several plant species. This was the case of Mansonia altissima, a species renowned for the quality of its wood. Given the importance of this species, there is an urgent need to focus on its conservation. It is in this context that this study aims to improve seed multiplication and the growth of M. altissima seedlings with a view to using them in reforestation. To achieve this, three pretreatments were applied to the seeds. These were the control (T0), shelled and unsoaked winged fruit (T1) and winged fruit soaked in cold water for 48 hours and then shelled (T2). Data collected using a data collection form were subjected to statistical analysis. Results showed that T2 treatment recorded the best germination rate (83%). In terms of seedling growth, the pretreatments had a significant effect on seedling diameter (P > 0.05). As for height, which averaged around 8 cm, and number of leaves, which averaged 6 per individual for each pretreatment, no significant effect was observed. However, for a multiplication of the species, the pretreatment (T2) where the winged fruits are soaked in ordinary water for 48 h and then shelled is recommended.

The relationship between birth weight and exclusive breastfeeding on the height of toddlers aged 1-5 years in Mekanderejo village, Kedungpring district, Lamongan regency

Background: The occurrence of stunting or height that is not appropriate for age in children under five is a health issue that needs attention in Indonesia. Stunting can cause growth failure, delays in cognitive and motor development, metabolic disorders, and reduced intellectual capacity. This study aims to analyze the relationship between birth weight and exclusive breastfeeding with the height of children aged 1-5 years in Mekanderejo Village. Methods: This type of research is an observational analytic study with a cross-sectional design. Data collection was conducted using primary data through questionnaires containing the characteristics of mothers and children and secondary data from the respondents' MCH books. Data analysis used the Chi-Square test. Results: The results of the study showed that there were still 8 (10.96%) children born with low birth weight, 28 (38.36%) children with a history of not being exclusively breastfed, and 6 (8.22%) children with very short height. The bivariate Chi-Square test results showed a p-value of 0.035 (<0.05), indicating a significant relationship between birth weight and child height, and a p-value of 0.047 (<0.05), indicating a significant relationship between exclusive breastfeeding and child height. Conclusion: These data indicate a relationship between birth weight and exclusive breastfeeding with the height of children aged 1-5 years in Mekanderejo Village.

Synergy of supermarket system and human resources in manufacturing industry

This case study explores the supermarket concept to control raw material supplies and improve process flow between departments in the manufacturing industry, PT PI in the Sidoarjo industrial area, East Java province, Indonesia. This research is combination qualitative-quantitative research. Using observations and interviews with employee and management representatives. Use brainstorming to create a Failure Mode and Effects Analysis to find recommended actions and prioritize them. Annual panel data collection to determine safe stock and minimum – maximum stock levels. The research results show that the supermarket concept provides significant improvements to distribution flows, reduces inventory levels by up to 90%, improves production inventory and increases employee maturity, all of these improvements make production more reliable. Many papers only discuss the concept, model and quantity of inventory control but rarely discuss how to apply it in limited conditions (warehouse space, production space, operator behaviour, etc.). This paper suggests several approaches and actions that can provide great benefits regarding the implementation of supermarket systems in the manufacturing industry.

Advanced cloud-based real-time credit scoring models: Leveraging big data and AI

Cloud-based credit scoring systems represent a transformative shift in financial technology, leveraging artificial intelligence and machine learning to revolutionize creditworthiness assessment. These platforms integrate traditional and alternative data sources, enabling real-time processing and comprehensive risk evaluation. The evolution from conventional methods has enhanced accessibility for underserved populations while maintaining robust security measures and regulatory compliance. Through advanced technical architecture incorporating data collection, processing, and analysis layers, these systems deliver faster, more accurate credit decisions. The implementation of sophisticated fraud detection mechanisms, coupled with machine learning algorithms, has significantly reduced false positives and improved risk assessment capabilities. Industry leaders like TransUnion demonstrate the practical success of these innovations through improved operational efficiency and expanded market reach. Despite challenges in data quality management and regulatory compliance, the future of credit scoring continues to advance with blockchain integration, enhanced real-time processing, and innovative fraud detection capabilities.

Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems

This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.

The role of management elements in achieving complete basic immunization in infants

Effective efforts to prevent sickness and death of children with Immunization. Immunization protects children from several diseases. The achievement of UCI Puskesmas is still below the national target of 100%. The availability of human resources, costs, facilities infrastructure, and functions management implementation immunization base influences the success of the immunization program base complete in babies. This study aimed to determine the role of management elements in achieving complete basic Immunization in infants. Quantitative research type with cross-sectional design. The population of the study was 32 immunization program holders. The sampling technique used was Total Sampling. Data collection method by interview. Data analysis with Chi-Square Test. The results showed that the elements of poor management were 15 (46.9%), good management elements were 17 (53.1%), immunization coverage was not achieved by 13 (40.6%), and immunization coverage was achieved by 19 (59.4%). There is a relationship between management elements and immunization coverage with a p-value of 0.001. It is expected that there will be the development of immunization health workers in terms of number and competence and rewards for good health workers in the immunization program to achieve immunization coverage rates in each health center area.

A Robust Framework for Detecting Brute-Force Attacks through Deep Learning Techniques

A considerable concern arises with the precise identification of brute-force threats within a networked environment. It emphasizes the need for new methods, as existing ones often lead to many false alarms, as well as delays in real-time threat detection. To tackle these issues, this study proposes a novel intrusion detection framework that utilizes deep learning models for more accurate and efficient detection of brute-force attacks. The framework’s structure includes data collection and preprocessing components performed at the outset of the study using the CSE-CICIDS2018 dataset. The design architecture includes data collection and preprocessing steps. Feature extraction and selection techniques are employed to optimize data for model training. Further, after building the model, various attributes are extracted from the data from feature selection to be used in the training. Then, the construction of multiple architectures of deep learning algorithms, which include Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models. Evaluation results show CNN and LSTM achieved the highest accuracy of 99.995 Parsant and 99.99 Parsant respectively. It showcases its ability to detect complex attack patterns in network traffic. It indicates that the CNN network got the best optimum results with a test time of 9.94 seconds. This establishes CNN as an effective method, achieving high accuracy quickly. In comparison, we have surpassed the accuracy of current methods while addressing their weaknesses. The findings are consistent with the effectiveness of CNN in brute-force attack detection frameworks as a more accurate and faster alternative, increasing the capability of detecting intrusions on a network in real-time.

Electronic security system installation skills requisite for self-reliance of radio-television and electronic work students in technical colleges in Rivers State

The study determined the electronic security system installation skills requisite for self-reliance of radio-television and electronic work students in technical colleges in Rivers State. A descriptive Survey design was used for the study. The population for the study was 30 teachers and 185 NTC III students in technical colleges in Rivers state. The study was carried out in Rivers state. A Structured questionnaire was used as instruments for data collection. Two research questions and two null hypotheses guided the study. Cronbach Alpha method was used for the reliability of the instrument which yielded a coefficient of 0.75. Mean and standard deviation were used to analyze the data for answering research questions, while t- test was used to test the hypotheses of no significant difference at 0.05 level of significant. The study found out that: the acquisition of physical access control system skills will enable the radio-television and electronic work students to be self-reliance upon graduation and the gaining of video surveillance system skills will also make the radio-television and electronic work students in technical colleges to be more effective and efficient in the delivery of their jobs.

Comparative assessment of costs of household trips to healthcare facilities in contiguous riverine regions in Rivers State, Nigeria

Physical access to neighbourhood facilities for utilization depends on distance, time and cost factors in relation to location characteristics. In this study the costs of accessing healthcare facilities in island riverine contiguous regions of Rivers State, Nigeria were considered. The study employed the survey research design following quantitative approach for data collection and analyses. Furthermore, the multistage sampling technique employed and 30% (66) communities of total communities of 223 in the study area was purposively determined. Also sample size of (56,131) households was obtained from total population of 305, 289 of the study area as provided by 1991 National Population Census data by application of Krejcie and Morgan Model (1970) principle and 5 persons average household considered. Finally, the random sampling technique was used in selection of communities for administration of questionnaires in the study area. A total of 382 respondents (household heads) were interviewed including key informants (government agencies and professionals). Other survey tools used were photographs, observations, Stock-watch and Geographic Positioning System (GPS) Speedometer. 328 questionnaires were retrieved from the field and considered valid for analysis with the aid of Statistical Package for Social Sciences (SPSS). Hypotheses were tested by the application of the multi regression model. It was found in traveling for healthcare, an aggregate of 21.4% of household members in the region monthly made trips in communities with and without PHC facilities with multi-modal transport systems at varied costs.

Autogenic relaxation on pain reduction in mothers post caesarean section: Literature review

Postoperative pain after cesarean section is one of the main problems that affect the comfort and recovery process of mothers. Inadequate pain management can affect the physical and psychological function of patients. Autogenic relaxation therapy, which involves self-relaxation techniques, is believed to help reduce pain intensity through control of physiological and emotional responses. The purpose of this study was to analyze journals on autogenic therapy for reducing pain in mothers with post-cesarean section surgery. Data collection method using a literature review study method using the Google Scholar journal database in the last 10 years, as many as 5 appropriate journals showed that there was an effect of autogenic therapy on reducing pain given on average for 3 days after 4 to 6 hours after surgery. The conclusion from the results of the analysis was a decrease in pain after autogenic relaxation therapy was given.

The convergence of big data and SRE in modern agriculture: Transforming farming through technology

This article examines the transformative integration of Big Data analytics and Site Reliability Engineering (SRE) principles in modern agriculture. The article explores how precision agriculture leverages advanced technologies including IoT sensors, drone surveillance, and satellite imagery, to revolutionize traditional farming practices. It analyzes the implementation of sophisticated data collection infrastructures, Big Data systems, and SRE practices in agricultural settings. The article investigates the operational benefits and sustainability implications of these technological integrations, highlighting improvements in resource optimization, yield enhancement, and environmental conservation. Furthermore, it discusses future perspectives in agricultural technology, including edge computing applications, AI-driven autonomous systems, blockchain integration, and climate change adaptation strategies.

Analyzing top nationalities visiting Thailand in 2030

This research paper will look at and analyze the projected top nationalities visiting the beautiful and vibrant country of Thailand in the year 2030. The focus of this investigation is to point out and put great emphasis on the great significance of grasping and understanding the detailed tourist demographics. In doing so, we are creating a way towards sustainable development and growth in the Thai tourism industry, one that plays an important part in the nation's economy. To contextualize the importance of this research, it is important to bring into focus the fact that international tourism has played a vital role in the economic progress of Thailand. An escalation in the number of visitors and the considerable contribution of foreign tourists to the country's Gross Domestic Product will be explored with a keen eye for detail. This will provide us with valuable insight into what is undeniable: the impact visitors have on the economy of Thailand. This study has incorporated a mixed methods approach so that the findings can be not only reliable but also comprehensive. This will enable us to paint an accurate picture of the nationalities that are expected to dominate the tourist arrivals in the year 2030, using both qualitative and quantitative data collection techniques. The holistic research methodology will give us an understanding of the shifting landscape of incoming visitors. From the results of our in-depth research analysis, it can be deduced that some nationalities will still top the list as tourists traveling to Thailand by the year 2030, with specific countries like Japan, China, and India, for instances, remaining at the pinnacle of providing the most tourist visitors. In this paper, we will identify the intrinsic need for tailored marketing strategies and resource allocation by key stakeholders in the ever-thriving tourism sector by tapping into the cultural, economic, and behavioral characteristics of these tourists. The paper also goes beyond statistical findings and discusses implications for public policy and sustainable tourism practices. It strongly calls for the creation of a collaborative partnership between government and industry that will plan for a better visitor experience. It is through this partnership that we will be able to identify and respond to challenges, including growing issues of overtourism and degradation of the natural environment. It is a holistic approach that considers future tourism development in tandem with environmental conservation and the culture of Thailand. Basically, this research encapsulates the undeniable importance of getting a comprehensive understanding of the nationalities of visitors in Thailand. By decoding and interpreting such valuable information, insight, and understanding, Thailand can move along the path of always offering the best tourism product to make sure that the country's economic benefit is continuously reaped for many years to come. This research venture hereby also aspires to add to the well-being and prosperity of Thai tourism and to assist its movement toward international leader status in its field.

Investigation of calorimetry burned in food using image processing and IoT

Calorimetry is an avenue for exploring the energy change due to heat involved between the various reaction stages at chemical, physical, or phase transition levels. Calorimetry seeks to determine the calories contained in food items within food science. However, classical calorimetric analyses are time-consuming and require a good amount of human intervention. The concept of image processing, involving the YOLO object detection algorithm, along with IoT technologies are a completely new domain in automating calorimetric measurements in food with high accuracy. YOLO's fast and efficient detection of the object allows accurate identification and tracking of food samples during the analysis. The project aims to develop a system that will efficiently and accurately measure the caloric value of carbonized food samples using image processing using YOLO and IoT-unified data collection method to automate the whole data gathering process and achieve precision in measurement and with monitoring and analysis in real time.

Expanding horizons in the Middle East: evaluating the Hair Addict’s strategies for growth

Research methodology The case was developed using a combination of primary and secondary data collection. The main source of primary data collection was an interview with the founder of The Hair Addict, Doaa Gawish, in August 2023, with later follow-up calls to clarify some aspects of the business model and obtain more specific information. For secondary sources of information, the authors relied on the company website, as well as a couple of published interviews with the founder in an online business magazine [1] and an independent news website that covers Egyptian news, culture and lifestyle topics (Makary, 2021). The case also relies on macroeconomic and industry data obtained from various databases and research report providers, which are referenced in the case and teaching note.

The Relationship Between Neonatal Nurses' e-Health Literacy Levels and Care Behaviours: A Cross-Sectional Study.

The study aims to determine the relationship between e-health literacy level and the care behaviour of neonatal nurses. E-health literacy is becoming increasingly important, as health-related information can be obtained easily and quickly from electronic sources. A cross-sectional design was adopted in the survey. The STROBE checklist for cross-sectional studies was used in reporting this study (Data S1). The study was conducted between July and November 2023. The sample of the study consists of 384 neonatal nurses working in different hospitals in Türkiye. An introductory form, the E-Health Literacy Scale (eHEALS), and the Care Behaviours Inventory-24 (CBI-24) were used for data collection. The data collection forms were created using Google Forms. eHEALS 4.32 ± 0.551, CBI-24 5.58 ± 0.567, CBS-24 sub-dimension: Assurance 5.58 ± 0.591, CBS-24 sub-dimension: Knowledge-Skills 5.64 ± 0.561, CBI-24 sub-dimension: Respect 5.56 ± 0.565, CBI-24 sub-dimension: Connectedness 5.52 ± 0.637 was found. It was determined that there was a very weak positive relationship between eHEALS, CBI-24 and sub-dimension scores (p < 0.001). A significant relationship was found between the eHEALS and the independent variables age, female, living in the Southeastern Anatolia region, working in a public hospital, using the internet for their profession, and CBI-24 sub-dimension: assurance (F = 6.111; p < 0.001). The relationship between e-health literacy and care behaviours of neonatal nurses was examined. Neonatal nurses' was found eHEALS, CBI-24, and sub-dimension scores had a high and very weak positive relationship. A significant relationship was found between the eHEALS level and age, female, Southeastern Anatolia region, state hospital, using the internet for their profession, and assurance sub-dimension. Study determines the eHEALS of neonatal nurses and provides information about this field. Interventions aimed at eHEALS should be developed and implemented at both the institutional and individual levels. STROBE, No Patient or Public Contribution.

Supervised Toothbrushing Programmes: Understanding Barriers and Facilitators to Implementation.

Supervised toothbrushing programmes (STPs), whereby children brush their teeth at nursery or school with a fluoride toothpaste under staff supervision, are a clinically and cost-effective intervention to reduce dental caries. However, uptake is varied, and the reasons unknown. The aim was to use an implementation science approach to explore the perspectives of key stakeholders on the barriers and facilitators at each level of implementation of STPs. This qualitative study involved individual interviews and focus groups with a purposive sample of stakeholders involved at all levels of implementation of STPs: (1) policymakers; (2) providers of STPs; (3) nursery/school staff; (4) parents/carers; and (5) children (aged 2-6 years old) across England. Data collection and analysis were guided by the Consolidated Framework for Implementation Research (CFIR). A total of 159 stakeholders were interviewed (40 individual interviews and 17 focus groups) across all levels of implementation. Barriers and facilitators to STP implementation were identified across 35 of the 39 CFIR constructs. Four themes were identified that determined STP implementation: (1) acceptability of STPs; (2) external 'make or break' conditions; (3) the importance of engagement across the system; and (4) desire for centralised support. This is the first study to qualitatively explore the barriers and facilitators to STP at all levels of implementation underpinned by an implementation science framework. The findings have strong implications for policymakers who wish to implement STPs, highlighting the need for careful consideration of the adaptability of the programmes, the role of formal and informal engagement systems, and the need for centralised support. This work has facilitated the co-design and piloting of a supervised toothbrushing implementation toolkit, which provides a central hub of resources and good practice to optimise implementation of STPs at scale.

Machine learning for predicting pavement roughness and optimising maintenance

This study examines four machine learning algorithms for predicting short-term and long-term International Roughness Index (IRI) changes in pavement performance. Using over 2,700 samples from the Long-Term Pavement Performance database, models are developed to forecast IRI changes under various maintenance scenarios. The approach accounts for immediate IRI changes caused by treatments and includes separate models for predicting weather and traffic variables. Random Forest models demonstrated the best performance, achieving R² values of 80–96% for long-term IRI changes and 85–92% for short-term changes across different maintenance strategies. Subsequently, Support Vector Machine (SVM) models with radial, linear, and polynomial kernels presented R² values as high as 70–84%, 60–92%, and 60–83%, respectively. The methodology enables accurate prediction of pavement roughness, facilitating treatment prioritisation and offering time and cost savings in data collection. It provides a comprehensive schedule for treatment execution, including types, timing, locations, and costs, thereby enhancing pavement management efficiency.

Systemic inequities and sources of resilience: challenges faced by Indigenous women living with HIV during COVID-19 in the Canadian prairies

ABSTRACT This study explored the challenges faced by, and resilience of First Nations, Métis, and Inuit women living with HIV in Manitoba and Saskatchewan during the COVID-19 pandemic. Through a decolonizing, community-based research approach, guided by a Community Guiding Circle (CGC), interviews were conducted with 45 Indigenous women living with HIV. Participants were recruited via community outreach, peer networks, and social media. Data collection and analysis utilized Indigenous storywork and inductive thematic analysis. The study revealed significant barriers related to housing instability, childcare, and access to healthcare, all exacerbated by systemic inequities rooted in colonialism, patriarchy, and capitalism. Despite these challenges, Indigenous cultural practices, ceremonies, community support, and family ties emerged as crucial sources of resilience, though often disrupted during the pandemic. The findings underscore the urgent need for culturally safe, women-centered care models that integrate Indigenous knowledge and practices. For health and social care practitioners, this research emphasizes the importance of advocating for systemic change to address the unique needs of Indigenous women living with HIV and calls for the development and implementation of culturally safe health and social care tailored to their unique needs and resilience.