Real Analytics Research Articles

Enhancing Real Estate Data Analytics Education with Digital Badges

Enhancing Real Estate Data Analytics Education with Digital Badges

Intelligent mission planning for autonomous distributed satellite systems

System autonomy plays a critical role in the success of next generation Distributed Satellite Systems (DSS) mission architectures, enabling the inference of external and internal environment conditions to support system adaptation in unexpected and unfamiliar situations. Artificial Intelligence (AI) based techniques show promise in achieving this desired self-adaptive capability by offering on-board predictive real time analytics, supporting an evolution towards Trusted Autonomous Satellite Operations (TASO). In parallel, TASO requires an evolution of the control and coordination of increasingly autonomous large scale DSS, achieved in the design and operation of intelligent Mission Planning Systems (MPS). In this paper, we focus on intelligent mission planning functionality problem utilising a distributed ant colony optimisation approach to achieve local task allocation and platform coordination. The approach is implemented in the context of a Space-Based Space Surveillance (SBSS) mission architecture that considers self-adaptive capabilities, global coordination and supervisory control aspects. The effectiveness of the approach is shown, demonstrating that the proposed solution based on metaheuristics supports an efficient and pervasive SBSS capability.

Clinical nSights: A software platform to accelerate real world oncology analyses.

e23316 Background: Real world data analyses are critical to assess drug safety and effectiveness, including in patient populations underrepresented in clinical trials. Significant barriers to performing such analyses remain, including: (1) incomplete access to electronic health record (EHR) data due to patient privacy concerns, (2) lack of data harmonization within and across EHRs, (3) requirements for time- and labor-intensive data curation efforts, and (4) necessity of coding or data science expertise given the absence of user-friendly platforms. We present Clinical nSights, a software platform that addresses these challenges to enable researchers and clinicians to perform rapid, robust, and scalable real world oncology analyses. Methods: With input EHR data from multiple health systems, we developed intuitive user interfaces and a scalable researcher friendly programming environment for real world clinical analytics. We used (1) nCognito, a previously described HIPAA-compliant de-identification algorithm, to de-identify EHRs across data modalities; (2) the combination of a de novo knowledge graph, vector and distribution similarity metrics, and manual review to harmonize data types including but not limited to laboratory tests, disease codes, and drugs; and (3) a series of Bidirectional Encoder Representations from Transformers (BERT) models to perform augmented curation of variables of interest (e.g., cancer staging, tumor biomarkers) from unstructured clinical records. We developed a user interface for users to create and analyze defined patient cohorts. Results: Clinical nSights facilitates intuitive cohort creation via flexible specification of inclusion and exclusion criteria, including options to perform temporal linking between criteria and propensity score matching. The ability to specify criteria documented in de-identified clinical notes expands the repertoire of available features for cohort creation beyond those captured in structured EHR fields, including but not limited to cancer stage and tumor biomarker status (e.g., HER2 status, microsatellite instability). Data harmonization enables scalable longitudinal analyses of laboratory test data across cancer types, including changes in measurements in the years leading up to and following initial cancer diagnosis. Augmented curation improves the coverage of phenotypes including immune-related toxicities of checkpoint inhibitors and symptoms experienced by patients prior to a cancer diagnosis. Using the no-code interface, users can easily quantify and visualize outcomes such as overall survival and hospitalization rates. Conclusions: Clinical nSights addresses important challenges in the collection, curation, and analysis of real world oncology data. By enabling a broad range of users to rapidly generate and test hypotheses, this platform has the potential to significantly improve care delivery and patient outcomes.

REAL ANALYTIC DISCRETE CHOICE MODELS OF DEMAND: THEORY AND IMPLICATIONS

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates nonparametric and semi-nonparametric identification, extrapolation to hypothetical counterfactuals, numerical implementation of demand inverses, and numerical implementation of the maximum likelihood estimator.

Analyticity and supershift with irregular sampling

The notion of supershift generalizes that one of superoscillation and expresses the fact that the sampling of a function in an interval allows to compute the values of the function outside the interval. In a previous paper, we discussed the case in which the sampling of the function is regular and we are considering supershift in a bounded set, while here we investigate how irregularity in the sampling may affect the answer to the question of whether there is any relation between supershift and real analyticity on the whole real line. We show that the restriction to R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {R}$$\\end{document} of any entire function displays supershift, whereas the converse is, in general, not true. We conjecture that the converse is true as long as the sampling is regular, we discuss examples in support and we prove that the conjecture is indeed true for periodic functions.

Integrating Azure Services for Real Time Data Analytics and Big Data Processing

Integrating Azure services for real-time data analytics and big data processing is a transformative approach that leverages the power of cloud computing to handle vast amounts of data at scale. Azure offers a wide array of services such as Azure Synapse Analytics, Azure Data Lake, Azure Stream Analytics, and Azure Databricks, which together enable businesses to extract valuable insights from both structured and unstructured data in real time. These services provide seamless integration, allowing for real-time data ingestion, transformation, and analysis, facilitating quicker decision-making processes. Azure’s capabilities in big data processing allow for scalable and efficient storage of data, ensuring optimal performance even as data volumes grow. By utilizing tools like Azure Data Factory and Azure HDInsight, organizations can automate complex workflows and apply machine learning algorithms for predictive analytics, driving innovation and competitive advantage. The integration of Azure's real-time analytics services also supports the use of advanced technologies like artificial intelligence (AI) and Internet of Things (IoT), enabling companies to monitor live streams of data from various devices and platforms. This infrastructure not only enhances operational efficiency but also ensures a more proactive approach to problem-solving by identifying trends and anomalies as they occur. In conclusion, Azure’s comprehensive suite of services offers a robust platform for managing and analysing big data in real time, empowering organizations to leverage data for better decision-making and improved business outcomes.

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

ТЕХНОЛОГІЇ ЦИФРОВОГО МЕНЕДЖМЕНТУ В УПРАВЛІННІ ПІДПРИЄМСТВАМИ

The article delves into the elements of management technologies, in the corporate world. In today’s paced era of advancements and heightened market competition digital management technologies have emerged as vital assets for efficient enterprise administration. These innovative tools enable the automation and optimization of business management technologies have emerged as vital assets for efficient enterprise administration. These innovative tools enable the automation and optimization of business operations empowering organizations to adapt to changes and make well informed managerial choices. A key advantage of employing management technologies is the enhancement of staff productivity and efficiency. Through process automation employees are ableto execute their tasks accurately resulting in superior quality products or services and reduced production expenses. Supplementary advantages of integrating management in enterprises encompass decision making processes. By granting access to real time data and analytics digital technologies empower managers to promptly make decisions fostering improved planning and resource allocation that ultimately boosts overall operational efficiency within the enterprise. Moreover digital management technologies foster enhanced. Collaboration within settings. Utilizing tools such as project management software and communication platforms enables employees to collaborate irrespective of their physical locations thus cultivating a more united and productive work environment that enhances employee satisfaction levels and retention rates.Another advantage lies in the ability to enhance customer experience. Enterprises can gain an understanding of their customers needs and preferences by utilizing customer relationship management (CRM) systems and data analytics. This strategy enables them to customize products and services to align with customer requirements fostering loyalty, among customers. In addition, digital management technologies can improve risk management and compliance.

A nanobody based ultrasensitive electrochemical biosensor for the detection of soluble CTLA-4 –A candidate biomarker for cancer development and progression

A soluble isoform of cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) has been found in the serum of healthy individuals and alterations in its expression level have been linked with the development and progression of various cancers. Conventionally, soluble CTLA-4 (sCTLA-4) has been quantified by techniques such as ELISA, western blot, and flow cytometry, which however are time-consuming, highly expensive and require large sample volumes. Therefore, rapid, cost-effective and real-time monitoring of soluble CTLA-4 levels is much needed to facilitate timely diagnosis of a worsening disease and help patient selection for immunotherapeutic interventions in cancer. Here, for the first time, we report an ultrasensitive, highly selective electrochemical nanobody (NAb) based biosensor for the quantitative detection of soluble CTLA-4 employing poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and gold nanoparticles modified electrode with attomole sensitivity. Incorporating nanomaterials with conductive polymers enhances the sensitivity of the electrochemical biosensor, while the nanobody's stability, specificity and ease of production make it a suitable choice as a bioreceptor. The proposed NAb-based sensor can detect sCTLA-4 from pure recombinant protein in a wide concentration range of 100 ag mL−1– 500 μg mL−1, with a limit of detection of 1.19 ag mL−1 (+3σ of the blank signal). The sensor's relative standard deviation for reproducibility is less than 0.4% and has effective real sample analytics for cell culture supernatant with no significant difference with pure recombinant protein (p < 0.05). Our proposed nanobody based sensor exhibits stability for up to 2 weeks (<3% variation). Moreover, this nanobody-based sensor presents a future opportunity for quantitative, ultrasensitive, and economical biosensor development that can be adapted to monitor the immune landscape of cancer patients to provide a larger therapeutic window.

Real time visual analytics of moving features: A case of vessel movement

Real time visual analytics of moving features: A case of vessel movement

Advancing Enterprise Data Strategies: Integration and Optimization of SAP HANA for Backup and Disaster Recovery

This research paper delves into the integration and optimization of SAP HANA, in enterprise data centers with a focus on strategies to ensure backup and disaster recovery (DR). SAP HANA, which is an in memory database brings about a transformation in how data's stored and processed. It offers real time analytics, predictive modeling and advanced analytics capabilities. The paper discusses the utilization of SAP HANA in both on premise and cloud environments while highlighting the need to strike a balance between speed, scalability and challenges such as data security and system integration. Emphasizing the importance of backup and DR protocols it underscores their role in protecting data integrity and ensuring uninterrupted business operations even during high risk situations. Through exploration of methods and technologies for backup and recovery this study aims to enhance efficiency in handling data within the realm of SAP HANA. By shedding light on implications related to deployment of SAP HANA – regarding data security measures and operational resilience – this research contributes to the ongoing discourse surrounding, in memory databases within enterprise settings.

Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels

Although the effect of hyperparameters on algorithmic outputs is well known in machine learning, the effects of hyperparameters on information systems that produce user or customer segments are relatively unexplored. This research investigates the effect of varying the number of user segments on the personification of user engagement data in a real analytics information system, employing the concept of persona. We increment the number of personas from 5 to 15 for a total of 330 personas and 33 persona generations. We then examine the effect of changing the hyperparameter on the gender, age, nationality, and combined gender-age-nationality representation of the user population. The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. The hyperparameter selection for the 990 total personas results in an average deviation of 54.5% for gender, 42.9% for age, 28.9% for nationality, and 40.5% for gender-age-nationality. A repeated analysis of two other organizations shows similar results for all attributes. The deviation occurred for all organizations on all platforms for all attributes, as high as 90.9% in some cases. The results imply that decision makers using analytics information systems should be aware of the effect of hyperparameters on the set of user or customer segments they are exposed to. Organizations looking to effectively use persona analytics systems must be wary that altering the number of personas could substantially change the results, leading to drastically different interpretations about the actual user base.

Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier

Machine learning is the subset of Artificial Intelligence and it is used for prediction various real time data analytics applications. Health care monitoring is the major area to analyse the result and make effective decisions. We need intelligent and automated process for predicting diseases using medical dataset. Machine learning methods are proposed to handle the dataset. Smart healthcare prediction is proposed to identify the user or patient information or symptoms as an input. Our system has forecasting accuracy index based on likelihood of the disease and health information. We use Naive bayes classifier algorithm for handling classification, prediction and accuracy index of dataset. Our algorithm measures the disease percentage and train the dataset. Once the prediction result will appears based on effective decision to be taken. In our work, we are taken 20000 train dataset and 7500 test data set for evaluation. TensorFlow simulator is used to simulate the system and measure accuracy. In this system achieves 95% accuracy and performance result compared with existing methods.

An interpolation problem in the Denjoy–Carleman classes

AbstractInspired by some iterative algorithms useful for proving the real analyticity (or the Gevrey regularity) of a solution of a linear partial differential equation with real‐analytic coefficients, we consider the following question. Given a smooth function defined on and given an increasing divergent sequence of positive integers such that the derivative of order of f has a growth of the type , when can we deduce that f is a function in the Denjoy–Carleman class ? We provide a positive result and show that a suitable condition on the gaps between the terms of the sequence is needed.

Efficient Information Fusion from Environmental Sensors for Near Real-Time IoT Analytics

This research paper explores the world of nearly real time analytics by focusing on methods of combining information obtained from Environmental Sensor data. The study utilized a customized setup consisting of three arrays of sensors connected to Raspberry Pi devices. It. Analyzed a dataset that encompassed various environmental conditions. By utilizing the Random Forest algorithm this research investigated how sensor readings, including temperature, humidity, LPG concentrations, smoke, light intensity and motion detection can be fused together. The methodology used cross-validation to ensure model training while visually presenting the intricate relationships, between environmental parameters. The results demonstrated the performance of the Random Forest model through visualizations showing Out of Bag (OOB) error rates and a comparative analysis of machine learning classifiers. The findings shed light on the potential of combining information from sensors to enable reliable predictions using Environmental Sensor data. This provides a foundation for advancements in analytics and applications related to environmental monitoring.

Governmental data analytics: an agile framework development and a real world data analytics case study

Governmental data analytics: an agile framework development and a real world data analytics case study

Enabling real time big data solutions for manufacturing at scale

Today we create and collect more data than we have in the past. All this data comes from different sources, including social media platforms, our phones and computers, healthcare gadgets and wearable technology, scientific instruments, financial institutions, the manufacturing industry, news channels, and more. When these data are analyzed in a real-time nature, it offers businesses the opportunity to take quick action in business-development processes (B2B, B2C), gain a different perspective, and better understand applications, creating new opportunities. While changing their sales and marketing strategies, businesses are now able to manage the data they collect in real-time to transform themselves, to record them in a healthy way, to analyze and evaluate data-based processes, and to determine their digital transformation roadmaps, their interactions with their customers, sectoral diffraction, application, and analysis. They want to accelerate the transformation processes within the technology triangle. Thus, big data, recently called as small and wide data, is at the center of everything and becomes an important application for digital transformation. Digital transformation helps companies embrace change and stay competitive in an increasingly digital world. The value of big data in manufacturing, independent from sectoral variations, comes from its ability to combine both in an organization’s efforts to both digitize and automate its end-to-end business operations. In this study, the current digitalization and automation applications of one of the plastic injection-based manufacturing companies at scale will be discussed. Presented open-source-based big data analytics platform, DataCone, that increases data processing efficiency, storage optimization, encourages innovation for real time monitorization and analytics, and support new business models in different industry segments will be demonstrated and discussed. Thus, development and applied ML solutions will be discussed providing important prospects for the future.

The continuation method and the real analyticity of the accessory parameters: the parabolic case

We give the proof of the real analyticity of the accessory parameters in Liouville field theory as a function of the position of the sources in the case in which in addition to elliptic sources, parabolic sources are present. The method is a non trivial extension of the elliptic case as it requires in an intermediate step the introduction of a regulator. The treatment holds also in the case of the torus. A discussion is given of the extension to higher genus surfaces.

Abstract 13930: The Impact of a Remote Patient Monitoring Program on Blood Pressure Control, Glycemic Control, and Lipids in Patients With Hypertension: A 3.5-year Retrospective Analysis Across 39 Physician Practices

Introduction ​​Self-measured blood pressure monitoring (SMBP) is recommended for hypertension (HTN) management and remote patient monitoring (RPM) can empower clinicians to manage HTN more efficiently. We examined the impact of a digital health program integrating RPM, SMBP, and nutrition/lifestyle coaching for patients with HTN. Methods 37 primary care practices and 2 cardiology practices across 10 US states enrolled patients with HTN diagnosis (regardless of their baseline BP control status) into the iHealth Unified Care program from 2018 to 2022. Bluetooth connected devices with mobile app or WIFI connected BP monitors, a web-based clinical care platform, and real time data analytics were utilized. Physicians and their care team, including registered dietitians, provided assessment, goal setting, continuous monitoring, and follow up visits if needed. The 7-day average BP value at baseline and the most recent measurement 7-day period were calculated to analyze BP control status. Results 3364 patients (age: 65.7 ± 12.1 years, 48.7% female) with baseline and endpoint BP data were included. Enrollment was continuous with an average duration in the program of 1.5 years. The baseline average BP was 138/81mmHg and the overall SBP/DBP reduction was -5.3 ± 15.1 / -3.1 ± 8.4 mmHg (P&lt;0.0001) at the endpoint. The proportion with HTN controlled to &lt;130/80 mmHg was increased from 21.8% to 36.0% (P&lt;0.0001), and those controlled to &lt;140/90 mmHg increased from 53.9% to 63.7% (P&lt;0.0001). In a subgroup analysis on patients with Stage II HTN at baseline (n=1552), the SBP/DBP reduction was -11.9 ± 15.4 /-6.3 ± 8.7 mmHg (p&lt;0.0001) and more significant BP reductions were observed after 6 weeks, and further improved at 3 months, 6month, and 12months respectfully. Of patients with diabetes, HbA1c was reduced by 0.5 ± 1.4% (n=337, p&lt;0.001) and of those with hyperlipidemia (LDL-C &gt;100mg/dL) at baseline (n=264), LDL-C also was reduced significantly (-19.6 ± 38.4 mg/dL, P&lt;0.001). Conclusions: The digital BP monitoring program significantly improved the BP control in patients with HTN as well as HbA1c and LDL-C in those who also had diabetes and hyperlipidemia, respectively. Long term and randomized control trials are needed to further evaluate the impact on cardiovascular risk.