Large Volumes Of Data Research Articles

Identifying the deficiencies of currently available CGM to improve uptake and benefit.

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI.

Data Validation Utilizing Expert Knowledge and Shape Constraints

Data validation is a primary concern in any data-driven application, as undetected data errors may negatively affect machine learning models and lead to suboptimal decisions. Data quality issues are usually detected manually by experts, which becomes infeasible and uneconomical for large volumes of data. To enable automated data validation, we propose “shape constraint-based data validation”, a novel approach based on machine learning that incorporates expert knowledge in the form of shape constraints. Shape constraints can be used to describe expected (multivariate and nonlinear) patterns in valid data, and enable the detection of invalid data which deviates from these expected patterns. Our approach can be divided into two steps: (1) shape-constrained prediction models are trained on data, and (2) their training error is analyzed to identify invalid data. The training error can be used as an indicator for invalid data because shape-constrained models can fit valid data better than invalid data. We evaluate the approach on a benchmark suite consisting of synthetic datasets, which we have published for benchmarking similar data validation approaches. Additionally, we demonstrate the capabilities of the proposed approach with a real-world dataset consisting of measurements from a friction test bench in an industrial setting. Our approach detects subtle data errors that are difficult to identify even for domain experts.

Appraisal or Critique? Analyzing Sentiments in Google Play Store Reviews with Python

In today's world of mobile applications, understanding how users feel about your app is key for developers and businesses looking to enhance their offerings. Our project takes a people-centric approach to analyzing sentiments expressed in Google Play Store reviews. We have crafted a comprehensive method using Python to delve into user feedback. We rely on the VADER sentiment analysis tool to gauge the emotional tone of reviews, sorting them into positive, negative, or neutral categories. Our code covers everything from gathering data to preprocessing, sentiment analysis, and visualization. This allows users to extract valuable insights from the feedback loop. By deciphering the sentiments within these reviews, our project becomes a potent resource for developers and businesses, empowering them to refine user experiences and make informed decisions. Our Python-based solution is not just powerful but also flexible and efficient, capable of handling large volumes of data. This approach contributes to more informed app development strategies, fostering better user satisfaction and business success.

Review of Image Quality Assessment Methods for Compressed Images

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs.

Security and privacy challenges, issues, and enhancing techniques for Internet of Medical Things: A systematic review

AbstractThe Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real‐time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, and so forth. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment's or devices, transmitters that are critical for monitoring the patients remotely and continuing the real‐time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. To ensure the Confidentiality and integrity of the IoMT devices and the sensitive medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Furthermore, the paper thoroughly investigates both cryptographic and non‐cryptographic based security and privacy‐enhancing techniques for IoMT or healthcare systems with particular emphasis on advancements in key areas such as Homomorphic Encryption, Differential Privacy, and Federated Learning.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population.

As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an "antibody score", which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect diagnosis. From an application perspective, these models have exhibited two major challenges: (1) They require large volumes of annotated data at the training stage and (2) They lack explainable factors to justify their outcomes at the prediction stage. In the present study, we developed a class activation mapping (CAM)-based ensemble model, called Ensemble-CAM, to address both of these challenges via weakly supervised learning by employing explainable AI (XAI) functions. Ensemble-CAM utilizes class labels to predict the location of disease in association with interpretable features. The proposed work leverages ensemble and transfer learning with class activation functions to achieve three objectives: (1) minimizing the dependency on strongly annotated data when locating thoracic diseases, (2) enhancing confidence in predicted outcomes by visualizing their interpretable features, and (3) optimizing cumulative performance via fusion functions. Ensemble-CAM was trained on three CXR image datasets and evaluated through qualitative and quantitative measures via heatmaps and Jaccard indices. The results reflect the enhanced performance and reliability in comparison to existing standalone and ensembled models.

Multi-dimension-precision chaotic encryption mechanism for Internet of Things

The massive amount of information processed in the Internet of Things(IoT) environment requires effective encryption schemes to ensure secure transmission. However, traditional encryption methods such as Elliptic Curve Cryptography (ECC) or Data Encryption Standard (DES) are not suitable for IoT environments where large amounts of data need to be quickly encrypted. In this paper, a multi-precision encryption mechanism based on multi-dimensional chaotic system is proposed. Firstly, the proposed scheme uses floating-point, 32-bit, 24-bit, and 16-bit data accuracy respectively, which can effectively reduce the complexity through fixed-point calculation. Secondly, aiming at the problem that traditional chaotic dynamic analysis methods such as the Lyapunov index cannot be applied, an analysis scheme based on complexity measure is proposed. Finally, a free dimension encryption mechanism based on Logistic, Tent, and PWLCM is proposed. By adding random disturbance to a one-dimensional chaotic map, the weak randomness of the single chaotic sequence is overcome effectively. Finally, the security analysis of the encryption scheme is carried out, and the result proves that the scheme can effectively resist the common attack means. In the 3D chaotic system, the encryption effect of 24-bit data accuracy is close to floating point accuracy, which is suitable for the IoT environment with large data volume, high encryption efficiency, and vulnerability.

Dual-layered micromesh stent technology for embolic prevention in carotid revascularization: technical experience and clinical outcomes from a high-volume interventional radiology center.

Carotid artery stenting (CAS) has become a cornerstone of carotid revascularization for stroke prevention. Despite the advantages of CAS, large-scale randomized trials involving prior (single-layer) generation of carotid stents demonstrated its higher risk of periprocedural cerebrovascular events compared to carotid endarterectomy (CEA). Dual-layer mesh-covered stents (DLSs) showed promising results in terms of 30-day embolic events. This study aims to evaluate 30-day clinical efficacy of DLS against a closed-cell single-layer stent, based on large-volume data. The study center is part of the Italian National Outcomes Evaluation Program (PNE). CAS procedures performed between November 2017 and September 2023 were retrospectively analyzed. Our primary endpoint was 30-day survival free of death, stroke, and myocardial infarction (MI). Periprocedural stroke rate, technical success and restenosis rate of CAS procedures performed with DLSs and first-generation stents (FGSs) were also evaluated. Over a total of 1101 CAS procedures (55 men; 745 males; mean age of 79±7.8 years), 80.2% were treated with DLS and 48.6% were symptomatic. The cumulative stroke-, MI- and death-free 30-day survival was 98.9%, Technical success was achieved in 98.9% of cases. The DLS group showed significantly lower 30-day death, stroke and death+stroke and periprocedural minor stroke rates compared to FGS group (P=0.04; P=0.04; P=0.003 and P=0.0002, respectively). The use of DLS in patients undergoing CAS in our large-volume center showed a high technical success rate and minimal cerebral embolic complications by 30 days. High volumes and an experienced interventional team may contribute to these favorable outcomes.

LINEAR ALGEBRA AS THE MATHEMATICAL FOUNDATION OF ARTIFICIAL INTELLIGENCE: CONCEPTS, APPLICATIONS, AND FUTURE PROSPECTS

Linear algebra, a fundamental tool in the field of mathematics, plays an essential part in the mathematical foundation of the groundbreaking arena of Artificial Intelligence (AI). This mathematics branch offers immense possibilities for capturing complex relationships within datasets through matrices and vectors. These mathematical tools facilitate the concise representation of large volumes of data, contributing to efficient data analysis between them, bringing immense value to AI algorithms and models. This research paper investigates the pivotal role of linear algebra as the fundamental mathematical framework underpinning artificial intelligence. This paper also explores the foundational concepts of linear algebra and its direct applications in various AI algorithms, demonstrating how matrices, vectors, and transformations provide the essential mathematical scaffolding for the development and optimization of intelligent systems. 

Analysis of the Present and Future of the Korea Online Platform Regulation Using Latent Dirichlet Allocation

Purpose: This study was conducted to provide a comprehensive understanding of online platform regulation in South Korea. It aims to illuminate key issues related to online platform regulation from 2017 to 2021, exploring the current state and future prospects. The primary goal is to foster social discourse on online platform regulation and to offer valuable insights for policymakers and academics. Design/methodology/approach: The methodology involved collecting articles from a large news database using keywords associated with online platform regulation. This data was analyzed using Latent Dirichlet Allocation (LDA) topic modeling. This technique is instrumental in identifying themes and patterns within large volumes of text data, facilitating the identification of significant themes and trends related to online platform regulation. Findings: The study unearthed a variety of themes. Among the most significant are the impact of the COVID-19 pandemic and its ramifications, political aspects, the roles of major Korean corporations (e.g., Naver and Kakao), the influence of global tech giants, the impact of news and online communities, technology and innovation, and social issues. These themes indicate that online platform regulation in Korea is influenced by a multitude of factors. Research limitations/implications: While this study offers diverse perspectives on online platform regulation within Korea, it has limitations. Reliance on data from a single news source may restrict the study's diversity and depth. Future research should employ a variety of news sources to provide a broader perspective on online platform regulation. Originality/value: This research makes a significant contribution to the academic study and policy formulation in the field by analyzing the key themes and trends related to online platform regulation over time. By exploring the complex aspects of online platform regulation, the study provides new insights for policymakers, researchers, and the general public.

Handling Unstructured Image using Generative AI and Dev-Ops

Abstract: This study explores the application of generative artificial intelligence (AI) to convert unstructured medical image data into structured formats. Medical imaging is vital for diagnosis and treatment, yet handling the large volume of unstructured data presents challenges. Utilizing generative AI, particularly convolutional neural networks (CNNs), enables the transformation of raw medical images into structured data representations. By integrating AI techniques, this approach enhances the accessibility and interoperability of medical imaging data, providing valuable insights for healthcare professionals. The proposed methodology aims to streamline the process, optimizing scalability and reproducibility without extensive reliance on DevOps practices. This research signifies a significant step towards leveraging AI for efficient utilization of medical imaging data in healthcare applications

An Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images

An Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images

NAVIGATING THE FUTURE: INTEGRATING AI AND MACHINE LEARNING IN HR PRACTICES FOR A DIGITAL WORKFORCE

As organizations navigate the complexities of the digital age, the role of Human Resources (HR) is evolving to meet the demands of a digital workforce. This review explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in HR practices to enhance efficiency, effectiveness, and employee satisfaction in the digital era. AI and ML technologies offer HR departments the opportunity to streamline operations, improve decision-making processes, and enhance employee experiences. By leveraging AI and ML, HR professionals can automate routine tasks such as recruitment, onboarding, training, and performance evaluation, allowing them to focus on more strategic initiatives that drive organizational success. One of the key advantages of integrating AI and ML in HR practices is the ability to personalize employee experiences. These technologies can analyze large volumes of data to identify patterns and trends, enabling HR professionals to tailor programs and policies to meet the unique needs of individual employees. This personalization can lead to higher levels of employee engagement, satisfaction, and retention. Furthermore, AI and ML can help HR departments make more informed decisions by providing data-driven insights. These technologies can analyze employee data to identify areas for improvement, predict future trends, and develop strategies to address challenges proactively. By leveraging these insights, HR professionals can make strategic decisions that align with the organization's goals and objectives. However, integrating AI and ML in HR practices also presents challenges, such as data privacy concerns, ethical considerations, and the need for upskilling HR professionals to use these technologies effectively. Organizations must address these challenges to realize the full potential of AI and ML in HR practices. In conclusion, integrating AI and ML in HR practices offers organizations the opportunity to enhance efficiency, effectiveness, and employee satisfaction in the digital age. By leveraging these technologies, HR departments can streamline operations, personalize employee experiences, and make more informed decisions that drive organizational success. As organizations increasingly turn to digital solutions, the role of artificial intelligence (AI) and machine learning (ML) in Human Resources becomes pivotal. This paper will focus on how AI and ML are being integrated into HR functions such as recruitment, onboarding, and employee engagement. It will also discuss the ethical implications and the challenges of maintaining human touch in an increasingly automated workplace. Case studies of companies leading in digital HR practices will be highlighted to provide real-world insights. Keywords: Digital Force, HR Practices, AI, Machine Learning, Future.

Enhancing Academic Success Prediction: An Ensemble Approach

The process of extracting meaning and knowledge from large volumes of data is termed as Data mining. It also refers to the way of inferring information from databases, which can be used in diverse fields including educational domain. Educational data mining plays a key role in finding ways to discover knowledge from data in the education sector. Educational Data Mining has evolved as a significant element in prediction of students’ academic performance. The most important goal of the paper is to analyze and evaluate the engineering students’ performance by applying stacking, an ensemble method in orange tool. Ensemble Stacking combines several base classifier models in order to create one optimal prediction model. Engineering students’ dataset was used to build predictive model using traditional classifiers SVM, Logistic Regression, Naïve Bayes and then stacking technique was implemented. The results showed that the proposed stacking technique obtains a high performance, which has a superior result compared to the other base classifiers techniques. Therefore, conclusion could be reached that the stacking performance is better than that of different algorithms.

A novel intuitionistic fuzzy rough instance selection and attribute reduction with kernelized intuitionistic fuzzy C-means clustering to handle imbalanced datasets

Due to advancement of internet and lab based technologies, large volume of high dimensional data are generated every day. These data usually consisted of several issues such as class imbalance, noise, later uncertainty, irrelevant and/or redundant features, and redundancy in size. These issues degrade the overall performance measures of the various machine learning algorithms. An efficient method to cope with such issues for a large sized datasets is to apply efficient data reduction techniques. In the recent years, numerous data reduction techniques have been presented based on fuzzy rough set theory to tackle these obstacles. However, building such a method that can handle all the above mentioned issues simultaneously is still a challenging task. In this study, we handle all these obstacles simultaneously by introducing a novel approach to obtain the reduced dataset by combining kernelized intuitionistic fuzzy C-means with intuitionistic fuzzy rough set model. Intuitionistic fuzzy rough set handles the vagueness and uncertainty in a better way than fuzzy set aided models as it takes use of membership, non-membership as well as hesitancy to capture the uncertainty of real-valued datasets. To generate the membership and non-membership grades, kernelized intuitionistic fuzzy C- means based notion is introduced. Further, an intuitionistic fuzzy rough set model is established by addressing lower and upper approximations based on a novel similarity relation. Moreover, all the necessary conditions are justified with the relevant mathematical theorems. Next, this model is employed for dimensionality reduction based on the concept of discernibility matrix by using the idea of different classes’ ratios to avoid the noise. Thereafter, positive region is defined with the help of lower approximation. The positive region information is applied to tackle problematic instances available in both minority and majority class for the imbalanced dataset after generation of artificial samples by synthetic minority oversampling technique (SMOTE). A comprehensive experimental study is added to show the effectiveness of the proposed technique. Finally, a framework is established to improve the prediction of animal toxin peptides.

A Survey of the Applications of Text Mining for the Food Domain

In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards.