Data Analysis System Research Articles

Deep Learning Approaches for Medical Image Analysis and Diagnosis.

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way forseamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefitfrom deep learning models.

Основные аспекты цифрового развития общества

The digital development of society is an urgent and important topic in the modern world, as technologies and digital tools play an increasingly important role in our daily lives. This research topic is an urgent phenomenon that requires attention and study for the effective implementation of digital technologies in various spheres of life. The purpose of the work is to study the main aspects of the digital development of society, their use and impact on people’s lives. To achieve the goal, the following were studied: the development of digital technologies and devices, the creation of digital infrastructure, the use of digital technologies to increase productivity and efficiency in various fields, the use of digital technologies to ensure a decent human life, as well as opportunities and risks. Thus, the relevance of this work lies in describing the impact of digitalization on all processes taking place in society as a whole. The paper reflects the directions of the use of the Internet and the use of digital technologies in organizations, identifies the share of the use of digital technologies to increase productivity and efficiency in agriculture and higher education, notes the development of electronic services for public services in the field of public administration and social services, improving the efficiency of the health system, the introduction of electronic voting and digital identification, the development of data monitoring and analysis systems and more. It is noted that along with the possibilities of digitalization, risks arise. Thus, digitalization has affected the economy, education, healthcare, government, cultural and entertainment development, and security.

Enhancing Study Space Utilization at UCL: Leveraging IoT Data and Machine Learning

University College London (UCL) has more than 51,000 students along with a challenge to find available study space. UCL has improved its Internet of Things (IoT) infrastructure to better analyze space occupancy in order to solve this. Current systems offer real-time information, but they are only available in crowded places. Therefore, this study presents a data analysis system that recommends the best study areas by using Machine Learning (ML) and historical data. The main library, student center, and science library at UCL are the top three study places. The goal of this study is to transform raw occupancy data from these venues into useful insights. The development of an independent system written in Python was facilitated by research into dataset optimization, ML regressions, popularity patterns, and feature analysis. This technique uses historical trends to forecast occupancy rates for the future.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Data mining algorithm in the identification of accounting fraud by smart city information technology

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

Development of a modular structural geographic information system for spatial data analysis

This work contains the research on the development and implementation of a modular structural geographic information system (GIS) for spatial data analysis, which is based on artificial intelligence technologies, mainly machine learning. The article discusses the basic principles and concepts of modularity, and also proposes a methodology for developing a modular GIS structure using modern technologies and tools. The study has practical significance for specialists in the field of geoinformatics and spatial analysis, and can also be useful for GIS developers and scientific researchers interested in creating modular GIS structures for analyzing spatial data.

GEODESY IN SOLVING PROBLEMS OF SUSTAINABLE CITY DEVELOPMENT: ANALYSIS OF URBAN TERRITORY PLANNING

The article focuses on the creation and implementation of a geodetic planning system aimed at ensuring the sustainable development of urban areas. The main emphasis is on the use of open and accessible software, integration of full-fledged GIS and DBMS components. The process of determining the most suitable GIS platform for GSP included a detailed analysis of various GIS systems taking into account their functionality, which allowed to choose QGIS as the optimal solution due to its high functionality in the field of geodata management, support for various data formats, user interface and dynamic development. The structure of the GSP is based on two main platforms: the QGIS GIS system for cartographic data presentation and analysis, and the Microsoft Access DBMS for performing all data operations. Interaction between these platforms is provided through the eVis module built into QGIS, which allows for efficient planning of urban area surveys and land use management. The main tasks solved with the help of GSP are the planning of primary surveys to identify target plantations, detailed taxation of plantations with the creation of samples according to specified parameters, obtaining point maps of the distribution of various types of vegetation, planning sample surveys with specific geospatial conditions, as well as geo-information and tax analysis in in solving the problems of sustainable development of cities. The implementation of GSP provides significant advantages for planning the sustainable development of urban areas, allowing more efficient use of land resources, optimizing urban management processes and intensifying scientific research in this field. The development and integration of modern digital technologies in the field of urban planning play a key role in achieving the goals of sustainable development.

Detecting Halyomorpha halys using a low-power edge-based monitoring system

Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.

Developing excellent plantar pressure sensors for monitoring human motions by using highly compressible and resilient PMMA conductive iongels

Based on real-time detection of plantar pressure, gait recognition could provide important health information for rehabilitation administration, fatigue prevention, and sports training assessment. So far, such researches are extremely limited due to lacking of reliable, stable and comfortable plantar pressure sensors. Herein, a strategy for preparing high compression strength and resilience conductive iongels has been proposed by implanting physically entangled polymer chains with covalently cross-linked networks. The resulting iongels have excellent mechanical properties including nice compliance (young’s modulus < 300 kPa), high compression strength (>10 MPa at a strain of 90 %), and good resilience (self-recovery within seconds). And capacitive pressure sensor composed by them possesses excellent sensitivity, good linear response even under very small stress (∼kPa), and long-term durability (cycles > 100,000) under high-stress conditions (133 kPa). Then, capacitive pressure sensor arrays have been prepared for high-precision detection of plantar pressure spatial distribution, which also exhibit excellent sensing performances and long-term stability. Further, an extremely sensitive and fast response plantar pressure monitoring system has been designed for monitoring plantar pressure of foot at different postures including upright, forward and backward. The system achieves real-time tracking and monitoring of changes of plantar pressure during different static and dynamic posture processes. And the characteristics of plantar pressure information can be digitally and photography displayed. Finally, we propose an intelligent framework for real-time detection of plantar pressure by combining electronic insoles with data analysis system, which presents excellent applications in sport trainings and safety precautions.

Development of an intelligent system for doctor-patient interaction in a day hospital

The article discusses the issue of finding optimal digital solutions to streamline organizational processes for medical organizations when providing medical care in a day hospital setting. Based on the analysis of in-depth interviews with medical specialists, the specific features of the system of providing medical care in a day hospital setting are described, and areas for optimizing the procedure for shortterm hospitalization at the present stage are identified. Taking into account the needs of practical healthcare in digital technologies, an algorithm for the functioning of a novel intellectual product (decision support system based on the intellectual processing method) is presented, including an onpremise system for monitoring, diagnosis, and analysis of medical data (condition self-assessment, satisfaction with aspects of treatment, etc.).

Landsat 9 Transfer to Orbit of Pre-Launch Absolute Calibration of Operational Land Imager (OLI)

Landsat 9 Operational Land Imager (L9-OLI) was launched on 27 September 2021, after completing a successful radiometric pre-launch calibration and characterization phase. The radiometric math model that governs the ground system—the data processing and analysis system (DPAS)—uses various calibration parameters that had been derived based on the pre-launch tests and analysis. During the on-orbit commissioning phase, the OLI system acquired specific sets of data collects, which enabled the revalidation of the pre-launch absolute calibration scale and other associated instrument performance characteristics. The analysis results shown in this paper focus on the activities and results related to the transfer-to-orbit analysis for the SI-traceable pre-launch radiometric scale. Key topics discussed in this paper include: radiance and reflectance calibration parameters for OLI; solar diffuser collects; stimulation-lamp collects; dark response; signal-to-noise ratios; and noise characteristics; radiometric response stability and the on-orbit update to the radiance to reflectance conversion factors. It will be shown that the OLI response during the early on-orbit operation matched pre-launch results and therefore this re-validates the absolute radiometric scaling at the predicted pre-launch level within the expected level of uncertainties. The launch did not cause any significant changes to the OLI system from the perspective of the absolute radiometric calibration performance. Once the transfer to orbit of the absolute calibration was confirmed, it created a solid basis for further on-orbit refinements of the radiance calibration parameters. As such, follow-on calibration refinements are discussed in other articles within this special issue, and they address issues such as uniformity as well as cross-calibration activities.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Remote Intelligent Medical Monitoring Data Transmission Network Optimization based on Deep Learning

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the Autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi-source heterogeneity in multi-source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Performance of JT-60SA Thomson Scattering data analysis system

We have developed signal processing routines for the Thomson Scattering measurement, which is planned for use in the next campaign of the JT-60SA large-scale tokamak experiment.This paper provides the data analysis system and its performance evaluated in terms of computation time and error.The sequential routine of determining the scattered light intensity from a simulated signal, including noise data from a 1 Gs/s high-speed sampling digitizer, and determining the electron temperature and density was tested for the first time on an actual machine.The data analysis system ensures that electron temperature and density can be calculated with reasonable relative errors and within a realistic time frame for operations on JT-60SA.

Mortality associated with polymyalgia rheumatica in the United States in the 1999-2020 period: a multiple-cause-of-death study.

Multicause-of-death methods were used to analyze mortality and leading causes of death associated with polymyalgia rheumatica (PMR) in the United States from 1999 to 2020. We analyzed mortality data from the Centers for Disease Control and Prevention (CDC) Data analysis system and selected death certificates that listed PMR as the cause of death based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10) category code. Relevant mortality rates, number of deaths and historical trends were analyzed. The number of PMR-related deaths and age-standardized mortality rate (ASMR) trend charts were made using Excel 2010 version and trend lines were added. Over the last 22 years, the total number of PMR-related deaths in the United States was 15,421 women (89.8%), a ratio of about 1:9 men to women. When PMR is listed as the underlying cause of death, the ASMR for women and men (per 100,000 people) is approximately 1.8-5.1:1, and when it is listed as the non-underlying cause of death, it is 1.8-3.3:1. PMR deaths are more frequent in individuals aged 70 years and above, with patients aged 80 years and above being most affected. Among different ethnicities, the highest number of deaths was found in Caucasians, followed by Black or African American. When it comes to causes of death, heart disease still ranks first, followed by cancer. In addition, we also found that when PMR combined with malignant tumors as a multiple cause of death, the number of female deaths was higher than that of male deaths, the overall number of deaths of both showed an upward trend, and the overall ASMR of both showed a downward trend. In the past 22 years, we have observed a low mortality rate of PMR in the United States. However, for patients with PMR, especially elderly women, medical workers should be vigilant and pay attention to whether they are combined with other complications, such as malignant neoplasms, and make timely diagnosis and treatment to further reduce the mortality rate of patients with PMR.

Ambulatory Long Block: A Model of Precision Education and Assessment for Internal Medicine Residents.

High-quality precision education (PE) aims to enhance outcomes for learners and society by incorporating longitudinal data and analytics to shape personalized learning strategies. However, existing educational data collection methods often suffer from fragmentation, leading to gaps in understanding learner and program performance. In this article, the authors present a novel approach to PE at the University of Cincinnati, focusing on the Ambulatory Long Block, a year-long continuous ambulatory group-practice experience. Over the last 17 years, the Ambulatory Long Block has evolved into a sophisticated data collection and analysis system that integrates feedback from various stakeholders, as well as learner self-assessment, electronic health record utilization information, and clinical throughput metrics. The authors detail their approach to data prioritization, collection, analysis, visualization, and feedback, providing a practical example of PE in action. This model has been associated with improvements in both learner performance and patient care outcomes. The authors also highlight the potential for real-time data review through automation and emphasize the importance of collaboration in advancing PE. Generalizable principles include designing learning environments with continuity as a central feature, gathering both quantitative and qualitative performance data from interprofessional assessors, using this information to supplement traditional workplace-based assessments, and pairing it with self-assessments. The authors advocate for criterion referencing over normative comparisons, using user-friendly data visualizations, and employing tailored coaching strategies for individual learners. The Ambulatory Long Block model underscores the potential of PE to drive improvements in medical education and health care outcomes.

Determination of Gain Scheduling Parameters for Loss of a Feedwater Pump Transient Mitigation Using Neural Networks

Transient mitigation for nuclear power plants is essential for safe operation. The fourth industrial revolution brings with it the potential for data-based predictive maintenance and identifying remaining time of life for degrading components. An improvement to predictive maintenance would be to address continued operation with faulty components between the time of identification and eventual replacement. The ability to perform data analysis and contemporary digital control systems allows for data-driven control system actions. A methodology is developed herein to train a neural network that can map desired system performance and current plant component capability to control system settings. Simulations of plant transients were recorded and used to train a neural network. This neural network was tested with different target performance goals. The results show that the trained neural network recommended settings that affected the control system response so as to meet the target performance goals.

Ocean Temperature Profiling Lidar: Analysis of Technology and Potential for Rapid Ocean Observations

Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis (‘Nature’) and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels.

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.