Instant Data Analysis Research Articles

Finansal Piyasalarda ChatGPT Kullanımının Swot Analizi İle İncelenmesi

ChatGPT is a derivative of the GPT model and is an artificial intelligence technology used in the field of natural language processing. This model is used to generate appropriate responses to the user's text-based input by pre-training large amounts of text. The aim of this study is to examine the advantages, disadvantages, opportunities and threats of using ChatGPT in financial markets by performing a SWOT analysis. As a result of the analysis, it was determined that ChatGPT has potential advantages in financial analysis and decision-making processes. ChatGPT offers fast and direct communication, instant data analysis and personalized investment recommendations. These features can help investors track market movements and create personal investment strategies. At the same time, predicting future price movements by analyzing large amounts of data can ensure effective and efficient use in financial markets.

Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology

Data mining (DM) is an in-depth approach to data analysis by mining useful information from large amounts of data, and this technique is now being used in an increasing number of fields. In this paper, the authors present the design of a real-time data analysis system for sports training based on DM technology and use the corresponding mining tools of DM technology to discover relevant patterns or laws hidden in the data. Therefore, using the real-time data analysis system for sports training based on DM technology, useful information and patterns for improving examination performance can be obtained, which can improve targeted teaching methods and help students overcome learning difficulties, providing rational teaching, synchronizing courses, establishing preparation, effectively guiding students in course selection, and improving course quality and educational effectiveness.

Real-Time Measurements of Calcium and Contractility Parameters in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) represent a powerful tool for studying mutation-mediated changes in cardiomyocyte function and defining the effects of stressors and drug interventions. In this study, it is demonstrated that this optics-based system is a powerful tool to assess the functional parameters of hiPSC-CMs in 2D. By using this platform, it is possible to perform paired measurements in a well-preserved temperature environment on different plate layouts. Moreover, this system provides researchers with instant data analysis. This paper describes a method for measuring the contractility of unmodified hiPSC-CMs. Contraction kinetics are measured at 37 °C based on pixel correlation changes relative to a reference frame taken at relaxation at a 250 Hz sampling frequency. Additionally, simultaneous measurements of intracellular calcium transients can be acquired by loading the cell with a calcium-sensitive fluorophore, such as Fura-2. Using a hyperswitch, ratiometric calcium measurements can be performed on a 50 µm diameter illumination spot, corresponding to the area of the contractility measurements.

Assessing a Smartphone App (AICaries) That Uses Artificial Intelligence to Detect Dental Caries in Children and Provides Interactive Oral Health Education: Protocol for a Design and Usability Testing Study.

BackgroundEarly childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year worldwide. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. With more than 85% of lower-income Americans owning a smartphone, mobile health tools such as smartphone apps hold promise in achieving patient-driven early detection and risk control of ECC.ObjectiveThis study aims to use a community-based participatory research strategy to refine and test the usability of an artificial intelligence–powered smartphone app, AICaries, to be used by children’s parents/caregivers for dental caries detection in their children.MethodsOur previous work has led to the prototype of AICaries, which offers artificial intelligence–powered caries detection using photos of children’s teeth taken by the parents’ smartphones, interactive caries risk assessment, and personalized education on reducing children’s ECC risk. This AICaries study will use a two-step qualitative study design to assess the feedback and usability of the app component and app flow, and whether parents can take photos of children’s teeth on their own. Specifically, in step 1, we will conduct individual usability tests among 10 pairs of end users (parents with young children) to facilitate app module modification and fine-tuning using think aloud and instant data analysis strategies. In step 2, we will conduct unmoderated field testing for app feasibility and acceptability among 32 pairs of parents with their young children to assess the usability and acceptability of AICaries, including assessing the number/quality of teeth images taken by the parents for their children and parents’ satisfaction.ResultsThe study is funded by the National Institute of Dental and Craniofacial Research, United States. This study received institutional review board approval and launched in August 2021. Data collection and analysis are expected to conclude by March 2022 and June 2022, respectively.ConclusionsUsing AICaries, parents can use their regular smartphones to take photos of their children’s teeth and detect ECC aided by AICaries so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children’s caries risk. Data from this study will support a future clinical trial that evaluates the real-world impact of using this smartphone app on early detection and prevention of ECC among low-income children.International Registered Report Identifier (IRRID)PRR1-10.2196/32921

A real-time acoustic drifter for active and passive sonar applications

A low-cost, long-duration acoustic drifter has been developed to measure directional ambient noise and man-made acoustic signals in both shallow and deep oceans. Traditional acoustic and oceanographic measurements are typically performed by deploying fixed moorings that would limit retrieving the data only after the recovery. With the advancements in low-power CPUs and satellite telemetry, real-time oceanographic and acoustic measurements are possible with in-buoy data reduction and satellite transmission of processed data. A Raspberry Pi computer with a 16-channel data acquisition board is used for both active and passive sonar applications, including directional ambient measurements, seabed characterization, and ocean acoustic tomography. A specific version of the acoustic drifter was built for on-ice deployments in the Arctic as an ice-tethered acoustic buoy. Several examples of its applications from recent sea-going experiments are given. Successful deployments and instant data analysis would indicate a practical alternative to more traditional acoustic and oceanographic measurement techniques by providing a real-time monitoring capability. [Work supported by the ONR.]

The use of think-aloud and instant data analysis in evaluation research: Exemplar and lessons learned

While health information technologies have become increasingly popular, many have not been formally tested to ascertain their usability. Traditional rigorous methods take significant amounts of time and manpower to evaluate the usability of a system. In this paper, we evaluate the use of instant data analysis (IDA) as developed by Kjeldskov et al. to perform usability testing on a tool designed for older adults and caregivers. The IDA method is attractive because it takes significantly less time and manpower than the traditional usability testing methods. In this paper we demonstrate how IDA was used to evaluate usability of a multifunctional wellness tool, discuss study results and lessons learned while using this method. We also present findings from an extension of the method which allows the grouping of similar usability problems in an efficient manner. We found that the IDA method is a quick, relatively easy approach to identifying and ranking usability issues among health information technologies.