Data Processing Techniques Research Articles

TRUTH OR DARE GAME MEDIA DEVELOPMENT TO PROVIDE AN UNDERSTANDING OF FAMILY FUNCTION

This research aims to develop the truth or dare game media to provide an understanding of family functions. The method used is Research and Development (R&D), using the Analysis, Design, Development, Implementation, Evaluation (ADDIE) development model. Data collection techniques by conducting expert feasibility tests on products that have been developed. The data processing technique uses a Likert scale in the form of quantitative and qualitative data. Feasibility test results with experts in the field, media experts 82.5%, material experts 93.3%, language experts 88.6%. The research subjects were 10 students from the Class of 2021 Department of which means it is very suitable for application and the truth or dare game media can increase students' understanding of family functions.

Cartographic animations, a supporting technology for education

Abstract. To date, basic theoretical and methodological ideas of animated cartography have been formed, as well as general schemes for animated maps compilation. However, the technological issue remains relevant in this domain due to the abundance of modern tools that can be used for compilation of animated maps. Particularly, most of currently implemented cartographic animation methodologies require the use of a variety of specialized software, including geographic information systems (GISs), vector and raster graphical editors, video effects editing software, and others. So the data processing and conversion techniques are extremely complicated in some cased, when data converted from one software platform to another.In our study, we established investigation of popular desktop GISs functionality application as a full-cycle animated cartography environment. In such a context, we discover approaches to improve methodology of animated maps automated compilation. To prototype a full-cycle animation in GIS environment, we developed a set of animation algorithms implemented in QGIS and documented the algorithms using UML (Unified Modeling Language) diagrams.

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.

Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.

Design Science Approach Comparing Ensemble Learning and Artificial Neural Networks for Uncertainty-Aware Helicopter Turbine Engines Health Monitoring

This work presents the development of an uncertainty-aware health monitoring system for helicopter turbine engines, focusing on improving operational availability and reducing maintenance costs. We address the critical issue of uncertainty quantification in data-driven fault detection and prognostics, essential for increasing system reliability. The project follows an iterative development cycle, incorporating multiple techniques for data processing, such as polynomial feature generation and data cleansing, and model development, including ensemble learning and artificial neural networks. Evaluation is performed using K-fold and group-fold cross-validation. The final solution consists of a cascaded ensemble learning model combining bagged linear regression built on polynomial features and random forest. This model demonstrates robust performance, achieving a test score of 0.955719 and a validation score of 0.886953, showcasing the effectiveness of uncertainty-aware machine learning methods in health monitoring systems.

Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning

This study presents a new method for optimising high-risk trading (HFT) strategies using deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural network architectures with sophisticated business data processing techniques. The framework employs a combination of convolutional neural networks for manual order analysis, short-term memory networks for time series processing, and a multi-head listening mechanism for body fusion. We formulate the HFT problem based on Markov Decision Processes and use the Proximal Policy Optimization algorithm for training. The model is evaluated using tick-by-tick data from the NASDAQ exchange, including ten liquid stocks in 6 months. The experimental results show the superiority of our method, achieving a Sharpe ratio of 3.42, outperforming the learning model and machine learning based on benchmarks up to 33%. The proposed strategy has demonstrated strong performance across a wide range of regulatory markets and has shown potential for strategic objectives. Sensitivity analysis confirms the model's stability across a range of hyperparameters. Our findings suggest that the DRL-based approach can improve HFT performance and provide better market adaptation and risk management. This research leads to the continuous evolution of algorithmic trading strategies and shows the potential of AI-driven approaches in financial markets.

MENGOPTIMALKAN KAPABILITAS INOVASI, KOMPETENSI DAN BUDAYA CATUR PURUSA ARTHA DALAM ERA DIGITAL: PERAN KINERJA UMKM

This study aims to determine the effect of innovation capability variables, competence, and catur purusa artha on the performance of MSMEs in Bali Province. The respondent data sampled in this study were MSMEs in Bali Province using a questionnaire method using 100 MSME samples calculated using the Slovin formula. In contrast, the data processing and analysis techniques used SPSS software. The findings of this study indicate that: 1) Innovation capability does not have a significant effect on the performance of MSMEs, 2) Competence has a positive and significant effect on the performance of MSMEs, 3) Catur Purusa Artha has a positive and significant effect on the performance of MSMEs.

Revolutionizing urban emission tracking: Enhanced vehicle ratios via remote sensing techniques

This study presents research that examines the use of several remote sensing methods to quantify automobile emissions in China. The research provides a unified data processing technique, using methodologies such as plume chasing (PCH) and point sampling (P-sampling) to measure vehicle emissions accurately. This method accurately calculates the ratios of gaseous combustion, namely NOx/CO2, in different driving scenarios and verifies the technique using controlled vehicle emissions measurements. The results demonstrate the method’s ability to identify variations in emission levels linked to engine operation and alterations in after-treatment systems. This provides significant information for monitoring emissions in metropolitan areas such as Wuhan, China. The suggested method has substantial promise for improving air quality initiatives in China.

The method of driver fatigue detection in intelligent driving

Abstract. Fatigue driving is one of the main causes of traffic accidents, and currently available methods for detecting driver fatigue include sensor detection and intelligent driving assistance. The importance of driver fatigue detection lies in timely detection of the driver condition, which helps reduce fatigue driving accidents. The research on fatigue detection technology has also provided more sensors and data processing techniques for intelligent driving systems, promoting the improvement and optimization of intelligent driving systems. This article reviews the technologies available for fatigue detection, summarizes existing research on driver fatigue detection, analyzes various modal detection methods and the use of AI in fatigue monitoring, and proposes future research and development trends for fatigue detection systems. This paper discussed three parts of fatigue monitoring technology: sensor technology, data processing technology, and algorithm optimization, and analyzed their advantages, disadvantages, and future development trends. The conclusion is that fatigue monitoring technology should use the advantages of different sensors and combine them with intelligent driving. The accuracy of this technology still needs to be improved.

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

Integrated Analysis of Utilization Efficiency of Intelligent Sports Venues Based on Data Mining Algorithms

With the rapid development of information technology and the increasing prosperity of the sports industry, the construction and management of intelligent sports venues have become an important way to improve the efficiency of sports resource utilization and meet diverse fitness needs. This paper focuses on the integrated analysis of the utilization efficiency of intelligent sports venues based on data mining algorithms. Aiming to reveal the inherent laws and potential value in the operation of sports venues through advanced data processing and analysis techniques, and provide a scientific basis for optimizing resource allocation and improving service quality. Intelligent sports venues utilize modern information technologies such as the Internet of Things, big data, and cloud computing to achieve comprehensive and intelligent monitoring and management of venue facilities, personnel activities, environmental conditions, and more. This new management model not only improves the operational efficiency of the venue but also enhances the user experience and meets personalized and diverse fitness needs. This paper focuses on the current situation of the opening of university sports venues to the outside world, aiming to reveal the pain points and bottlenecks in their operation process through detailed investigation and analysis. This study introduces advanced concepts of middleware architecture and constructs a multi-level data integration framework that includes local models, global models, user models, and mapping rules. This framework can effectively integrate distributed heterogeneous data sources, achieving seamless integration and efficient interaction of data. At the same time, with the help of distributed programming technology in cloud computing, it is possible to conduct in-depth mining and analysis of integrated massive data, discover potential value, and provide a scientific basis for decision support.

Comparative Analysis of Fall Detection Using Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) Algorithms

Fall detection is a critical area of research due to its implications for public health, particularly among older adults more vulnerable to fall-related injuries. This study explores the application of pervasive computing and machine learning algorithms in detecting falls using accelerometer and gyroscope sensors. Leveraging a dataset obtained from smartphone sensors, the study employs data processing techniques and classification algorithms, including K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN), to differentiate fall events from other activities. Evaluation metrics such as accuracy, precision, recall, and F1-score are utilized to assess the performance of the models. The results demonstrate the effectiveness of both the ANN and KNN models in accurately predicting fall detection with high precision and recall scores. The study highlights the potential of pervasive computing systems and machine learning methods to enhance fall detection capabilities, contributing to the development of proactive measures for ensuring the safety and well-being of individuals at risk of falls. Future research directions include exploring alternative algorithms and sensor modalities to improve fall detection systems further.

Data-driven Prediction of Underwater Navigation Fitness Zone Classification

This article presents a pioneering approach to enhancing underwater navigation accuracy and stealth through the utilization of gravity anomaly data. Developed by researchers from leading universities in China, the study aims to revolutionize autonomous marine navigation systems in unknown sea areas. By integrating advanced data processing, feature extraction, model construction, and optimization techniques, a cutting-edge navigation system has been formulated, showcasing remarkable improvements in navigation precision.The core innovation lies in leveraging bicubic interpolation to refine data resolution, enabling a meticulous evaluation of regional suitability and segmentation of sea zones. Subsequently, a gradient-based suitability prediction model is constructed, which is validated through migration prediction, underscoring its effectiveness and reliability. This research not only addresses the limitations of traditional terrain matching navigation methods, plagued by data accuracy and resolution constraints, but also paves the way for advancements in oceanic exploration and technological competitiveness.The introduction of gravity anomaly data, coupled with machine learning and visualization tools, marks a significant step forward in the development of adaptive navigation zones. This innovative system holds immense potential to enhance the autonomy, accuracy, and concealment of underwater vehicles, thereby facilitating safer and more efficient oceanic missions in the future.

A Survey of Multimodal Perception Methods for Human–Robot Interaction in Social Environments

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

Perkembangan Penerapan Teknologi Artificial Intelligence di Indonesia

Different employee backgrounds cause differences in their readiness to use AI. One of the important factors that causes doubts among potential AI users is data security. This research focuses on the application of AI technology in corporate environments in Indonesia, with the aim of understanding the attitudes of potential AI users and the factors that influence their intention to use this technology. This study aims to determine the application of AI technology in daily work from the perspective of prospective AI users. The 100 prospective AI users who were sampled in the study were employees from various levels in several fields of the company. The data processing technique uses regression tests for quantitative data which are complemented by thematic analysis for qualitative data. The results of the regression test show that attitude has a considerable influence on the intention to use AI.

ANALISIS EFEKTIVITAS PENGGUNAAN MEDIA POWER POINT DALAM PEMBELAJARAN FIQIH KELAS XII DI SMA MUHAMMADIYAH PROGRAM KHUSUS SAMBI TAHUN AJARAN 2023/2024

The purpose of this study is to determine how the effectiveness of the use of power point media in learning fiqh class XII and to find out what are the supporting and inhibiting factors in the use of power point media in learning fiqh class XII at SMA Muhammadiyah Special Program Sambi in the 2023/2024 Academic Yaer. The type of research in writing this thesis is qualitative research. Where this research aims to nderstand the phenomena experienced by the subject holistically and descriptively in using different natural phenomena. Data processing techniques and data analysis used through three stages, namely data reduction, presentation of information and conclusion drawing. The result showed that the use of power point media in learning fiqh class XII at SMA Muhammadiyah Special Program Sambi is effective with the increase in learning facilities/resouurces in fiqh learning subjects, learning methods used by fiqh teachers do not make students passive. Supporting factors in the use of power point media include the support of the principal and a conducive learning environment while the inhibiting factor in the lack of availability of lcd projectors so that it can hinder learning activities and lack of creativity of education in presenting so that students are monotonous to the teacher’s delivery.

FLEXIBLE WORKING ARRANGEMENT: DOES REALLY GIVE JOB SATISFACTION TOWARD EMPLOYEE?

This study aims to examine the impact of Flexible Working Arrangement (FWA) on employee job satisfaction with wellbeing as a mediating variable. Data processing techniques used SMART PLS 3.0, with mediation testing based on Hayes' process. The total number of respondents in this study was 108. The results of the study revealed that FWA does not have a direct influence on employee job satisfaction. However, after the mediation process, FWA does have an impact on job satisfaction through employee wellbeing. Based on this, the practical implication is that companies need to pay attention to the limitations of working hours and workload to ensure that flexible working hours do not make employees feel like they are working endlessly with unlimited time, so that employee wellbeing can be maintained, and job satisfaction can be achieved.

Three-dimensional visualization of large-area, nanoscale topography measurements

High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.