Data Processing Research Articles

The Effect Of Variations In Dribble and Retreat Exercises Zig-Zag Dribble On Dribble Speed In Basketball Extraculicular SMP 1 Sukodono

Abstract Speed ​​ This research aims to determine the effect of variations in dribble retreat and zig-zag dribble training on dribbling speed in extracurricular basketball at SMP 1 Sukodono. The hypothesis put forward is that there are variations in dribble retreat and zig-zag dribble training on dribble speed in basketball extracurricular activities at SMP 1 Sukodono. The sample of this research were 20 students who took part in extracurricular basketball at SMP 1 Sukodono. The research design used in this study was a pretest-posttest control group design. This type of research uses quantitative research methods. The data collection method uses a ball dribbling ability test instrument which is obtained from the results of doing retreat dribble and zig-zag dribble exercises. Data analysis used to test the hypothesis is the paired sample t test. The results of the description above can be seen based on calculations from the results of SPSS 25 data processing, it is found that the data in table 14 shows that the sig. (2-tailed) using variations of dribble training using variations of retreat dribble and zig-zag dribble training was 0.00 < 0.05, and the calculated t value was 11.857, while the control group without any treatment was 0.000 < 0, 05, and the calculated t value is 9,000. So it can be concluded that variations in dribble retreat and zig-zag dribble training can increase dribble speed in basketball extracurricular activities at SMP 1 Sukodono. Keywords: Retreat dribble, zig-zag dribble, speed dribble

Edge computing: Revolutionizing data processing for IoT applications

Edge computing is emerging as a transformative solution for managing the vast amounts of data generated by the Internet of Things (IoT). By decentralizing data processing and bringing computation closer to the data source, edge computing addresses critical limitations of traditional cloud computing, including latency, bandwidth constraints, and security vulnerabilities. This review explores the key benefits of edge computing, such as reduced latency, bandwidth optimization, improved reliability, enhanced data privacy, and scalability. It discusses the architecture and components of edge computing, highlighting the roles of edge devices, edge nodes, and fog computing. The review also examines various use cases across sectors, including autonomous vehicles, smart cities, healthcare, industrial IoT, and retail. Finally, the review considers the challenges facing edge computing, including hardware limitations, network security, interoperability, and cost considerations. The future outlook suggests that advancements in 5G technology and artificial intelligence will further enhance the potential of edge computing in driving IoT innovations.

Research on Risk Control and Internal Control in Corporate Accounting

As the market economy rapidly develops, new opportunities arise for modern business growth, yet the complexity of financial risks and internal control issues that enterprises face also increases. To maintain stable development in the fierce industry competition and effectively tackle various challenges, internal managers and related staff need to transform traditional thinking, strengthening risk control and internal controls within accounting. Risk control mainly focuses on risks related to the distortion of accounting information, misappropriation of funds, and tax compliance, whereas internal control focuses on solving problems such as inaccuracies in financial data processing, deficiencies in the internal control system, and lack of internal communication. However, there is a mutual influence between risk control and internal control, indicating that they complement each other. This paper deeply analyzes the existing problems in risk control and internal control in current corporate accounting, proposes improvement measures according to specific conditions, and enhances the levels of risk control and internal control in corporate accounting through methods such as improving personnel professionalism, standardizing accounting standards, and constructing a comprehensive internal control system, thereby providing a strong guarantee for the sustainable development of enterprises.

Pengaruh Kompensasi, Kompetensi, dan Kepuasan Kerja terhadap Komitmen Organisasional (studi pada PT. Pos Indonesia cabang Erlangga Semarang)

This study was conducted to analyse effect of compensation, competence, and job satisfaction on the organizational commitment of PT. Pos Indonesia Erlangga Branch Semarang and the sample used is 102 employees. This study uses primary data and data collection methods using questionnaire. The results of data processing are in the form of respondent descriptions, variable descriptions, validity test results, reliability test results, F test results, coefficient of determination test results (R2), and hypothesis test results. The results show that compensation variable has a positive and significant effect on organizational commitment, competence variable has a positive and significant effect on organizational commitment, and job satisfaction has a positive and significant effect on organizational commitment

Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling

The insurance industry is experiencing a paradigm shift driven by the integration of big data analytics and machine learning technologies. This article presents a comprehensive framework for implementing advanced analytics in insurance operations, focusing on risk assessment optimization and predictive modeling applications. Building upon established actuarial methods, this article demonstrates how modern machine learning algorithms and real-time data processing enhance underwriting accuracy and claim processing efficiency. This article examines the integration of multiple data sources, including IoT sensors, telematics, and external databases, highlighting their collective impact on risk assessment precision. This analysis indicates significant improvements in underwriting accuracy and claims processing speed through the implementation of automated analytics workflows. This article also addresses critical challenges in data quality, regulatory compliance, and integration complexity, providing strategic recommendations for insurers pursuing digital transformation. This article contributes to the growing body of knowledge in insurance analytics by establishing a scalable framework for big data integration while maintaining regulatory compliance and operational efficiency

A conceptual framework for data-driven business optimization: Enhancing operational efficiency and strategic growth in U.S. small enterprises

Small enterprises play a vital role in the U.S. economy, yet many face significant challenges in optimizing operations and achieving sustainable growth due to resource constraints and market dynamics. This study proposes a conceptual framework for data-driven business optimization, aimed at enhancing operational efficiency and fostering strategic growth in U.S. small enterprises. Leveraging advancements in data analytics, the framework integrates predictive modeling, real-time data processing, and machine learning algorithms to enable informed decision-making and proactive strategy formulation. The framework emphasizes three core components: · Data Acquisition and Management, which involves collecting structured and unstructured data from internal and external sources to build a robust data repository; · Analytics and Insights Generation, utilizing advanced tools to identify patterns, forecast trends, and detect operational inefficiencies; and · Strategic Implementation, applying data-driven insights to streamline workflows, reduce costs, and capitalize on emerging opportunities. By aligning data strategies with organizational goals, the framework ensures a systematic approach to addressing challenges and unlocking new growth avenues. Furthermore, this study explores the role of emerging technologies, including artificial intelligence and Internet of Things (IoT) devices, in enhancing the scalability and adaptability of the framework. It also examines the challenges of adopting data-driven approaches, such as limited technological expertise and data privacy concerns, offering practical recommendations to overcome these barriers. Case studies of successful implementations in diverse sectors—retail, manufacturing, and services—highlight the framework's potential to deliver measurable outcomes, including increased productivity, improved customer satisfaction, and competitive advantage. This research provides small enterprises with actionable insights into leveraging data as a strategic asset, fostering innovation, and achieving resilience in a rapidly evolving business landscape.

Harnessing LLMs for Financial Forecasting: A Systematic Review of Advances in Stock Market Prediction and Portfolio Optimization

Abstract: This review paper examines the rapidly evolving landscape of Large Language Models (LLMs) in financial analysis, synthesizing recent advances and applications in this transformative field. We trace the progression from traditional natural language processing (NLP) methods to contemporary language models in financial applications, showing how these technologies are reshaping market analysis, risk assessment, fraud detection, and investment decision-making across various financial sectors. The paper offers an in-depth overview of LLM architectures and approaches used in finance, covering both general-purpose models adapted for financial tasks and specialized models tailored for industry needs. Through systematic analysis of the latest research and empirical studies, we assess the capabilities of LLMs in processing diverse financial data sources, including real-time market data, news articles, social media sentiment, earnings calls, and regulatory filings, enhancing insights and predictive accuracy. We identify key challenges in LLM implementation, such as the need for real-time data processing, high accuracy, and interpretability, crucial for trust and adoption in high-stakes contexts. Additionally, we explore emerging trends and future research directions, highlighting both the transformative potential and limitations of LLMs as they redefine analytical frameworks and decision support in finance. This review underscores the need for ongoing research to bridge gaps and fully realize LLMs’ potential in reshaping financial analysis and practices

Deep Learning for Personalized Healthcare Recommendations

Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in developing personalized healthcare recommendations, including the major achieved algorithms, data sources and existing issues. and future work in exploring the ways deep learning can be harnessed for the development of personalized healthcare systems are presented.

Artificial Intelligence Integration in Biochips: Enhancing Diagnostics and Precision Medicine

Abstract: The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi- functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

Civil Liability for Clients Who Suffer Losses in Using the Services of an Attorney Using Artificial Intelligence

Artificial Intelligence has become an inseparable part of everyday life. Artificial Intelligence has been designed to help humans in various ways. included in the legal profession of an advocate to serve clients. Artificial Intelligence can be a tool in serving clients by not eliminating or changing the function and position of the advocate profession by considering, examining the data or documents provided by Artificial Intelligence. Advocates can use Artificial Intelligence to automate data document processing. This includes classifying documents, highlighting important issues, and extracting relevant information from them. By using Artificial Intelligence document processing can be done quickly and efficiently, thereby saving time and costs. With various types of Artificial Intelligence, of course there are errors and losses for users that cannot be avoided. In the use of Artificial Intelligence, it is found that the injured party is the client. With the discrepancy in the data results provided by Artificial Intelligence, this causes the client to be harmed because there is a discrepancy in the data and facts which makes the client feel that there is an error that the data processing robot should have carried out the data analysis to produce result B even though the data.

Identification of Luciferin on Europa through Data Science Analysis of Near-Infrared Mapping Spectrometer (NIMS) Data from the Galileo Orbiter

In this study, we investigated the potential presence of extraterrestrial life on Europa, one of Jupiter’s moons, by exploring the existence of Luciferin, a bioluminescent molecule. This hypothesis is based on Europa’s subsurface oceans and the geothermal conditions created by gravitational forces from Jupiter, similar to Earth’s hydrothermal vents. We reanalyzed raw data from the Near-Infrared Mapping Spectrometer (NIMS) on the Galileo spacecraft, utilizing advanced machine learning and data processing techniques to enhance data quality. This involved cleaning, preprocessing, and normalizing the multidimensional data to reduce noise and correct errors, ensuring more accurate analysis. Our primary focus was on detecting spectral signals that might indicate Luciferin, particularly in the spectral bands around 560-570nm, which are associated with Luciferin’s strong emission. The results were encouraging, as several points of interest showed spectral intensity consistent with the presence of this molecule. Although these findings are preliminary and require further validation, they offer promising evidence of bioluminescence on Europa. If confirmed, this discovery would significantly impact the fields of astrobiology and space exploration, providing crucial insights into the search for extraterrestrial life.

Efficient Integration of CNN and LSTM for Data Mining Observation Algorithms: A Systematic Review

An extensive analysis of the combination of Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) is presented in this research. within data mining applications. CNNs, optimized for spatial data processing, and LSTMs, made to manage data in a sequential fashion, are combined to leverage their respective strengths in feature extraction and temporal pattern recognition. By analyzing over 60 published studies, we evaluate the effectiveness of CNN-LSTM models across diverse domains such as medical image analysis, financial time series forecasting, stock price prediction, and sentiment analysis. The review reveals that CNN-LSTM hybrids significantly outperform traditional methods in activities requiring both temporally and spatially understanding. However, challenges such as model interpretability, hyperparameter optimization, and the need for diverse datasets remain. Future research should focus on optimizing these models for specific applications, improving scalability, and addressing limitations like data scarcity. This review underscores the significant potential of CNNLSTM integration in advancing data mining techniques and solving complex problems in various fields. We present the related work of some of the most important publication in the field and classify over 60 published papers from different perspective. This review intends to classify the major techniques, methods, algorithms in the domain of medical images, financial time series analysis, stock price and social media.

Simulation-Based Evaluation of Big Data Caching Mechanisms

This paper provides a simulation-based evaluation that addresses memory management problems throughout Big Data processing. A significant problem occurs with in-memory computing when there is not enough available memory for processing the whole chunk of data, and hence some data must be selected for deletion to make room for new ones. The selected research strategy is to use different cache selection and replacement algorithms, such as Adaptive Replacement Cache (ARC) and Low Inter-Reference Recency Set (LIRS) algorithms, besides the default one, which is Least Recently Used (LRU). A simulator was built by the authors to assess the use of different caching approaches on Big Data platforms. The evaluation showed that the LIRS and the ARC algorithms gave a better hit ratio for different workloads than the LRU algorithm.

Distributed data processing and task scheduling based on GPU parallel computing

Distributed data processing and task scheduling based on GPU parallel computing

THE role of Artificial Intelligence in industry 5.0: Enhancing human-machine collaboration

The emergence of Industry 5.0 marks a transformative shift in the manufacturing landscape, emphasizing a synergistic relationship between humans and machines. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing human-machine collaboration within this paradigm. By leveraging AI technologies, industries can foster a more personalized, efficient, and innovative work environment. AI systems facilitate seamless communication and decision-making processes, thereby augmenting human capabilities rather than replacing them. This collaboration allows for the optimization of workflows, reduction of operational risks, and enhancement of product quality through advanced predictive analytics and real-time data processing. Furthermore, the integration of AI in Industry 5.0 supports sustainability initiatives by minimizing waste and energy consumption, aligning with the global push for greener manufacturing practices. Case studies demonstrate the successful implementation of AI-driven solutions across various sectors, showcasing improvements in productivity and employee satisfaction. As Industry 5.0 continues to evolve, the interplay between AI and human labour will redefine traditional roles, empowering workers with augmented intelligence tools. The findings indicate that embracing AI not only enhances operational efficiency but also contributes to a more resilient and adaptive workforce. Ultimately, this paper posits that the future of industry lies in the harmonious collaboration between human intellect and artificial intelligence, which together will drive innovation, productivity, and sustainable practices in the manufacturing sector.

Conceptual framework for data-driven reservoir characterization: Integrating machine learning in petrophysical analysis

Reservoir characterization plays a critical role in optimizing oil and gas production by accurately determining subsurface properties such as porosity, permeability, and fluid saturation. Traditionally, this process has relied on the interpretation of well logs, seismic data, and core samples using manual or physics-based methods. However, these approaches often face limitations when dealing with complex datasets and heterogeneous reservoir properties. This review develops a conceptual framework for integrating machine learning (ML) into petrophysical analysis to revolutionize reservoir characterization. By utilizing advanced ML algorithms such as supervised learning, unsupervised learning, and deep learning, this model demonstrates the potential of data-driven methods to improve the accuracy and efficiency of reservoir analysis. The framework focuses on how ML techniques can automate the interpretation of well logs, enhance seismic data processing, and predict rock properties from core samples, leading to more accurate identification of reservoir boundaries and productive zones. This approach not only reduces interpretation errors but also accelerates decision-making in reservoir management. Moreover, machine learning can handle the vast amounts of data generated in reservoir studies, enabling real-time analysis and adaptive decision support. The study also addresses challenges such as data quality, model interpretability, and computational scalability, offering insights into future applications of ML in reservoir characterization. By combining petrophysical expertise with machine learning capabilities, this framework aims to significantly advance the field of reservoir characterization and contribute to more efficient resource management in the oil and gas industry.

Pengaruh Struktur Modal, Profitabilitas, Dan Price To Book Value Terhadap Harga Saham

The purpose of this research is to analyze the correlation between capital structure, profitability, and stock market value, proxied by the price-to-book ratio. The capital structure variable is measured using DER, while profitability is measured using the ROA. In this study, the data used comes from the financial statements of property and real estate companies listed on the IDX during the period of 2020 to 2023. This study applies multiple linear regression analysis with the help of SPSS to process data obtained from 22 purposively selected companies over four years. The research concludes that capital structure and profitability are crucial factors that can influence stock prices. These results indicate that companies need to focus on improving profitability and maintaining healthy debt management to enhance their value in the eyes of investors. Additionally, investors should not rely solely on the PBV when evaluating investment prospects.

A novel soft sensor modeling method based on gated stacked target-supervised VAE with variable weights

The variational autoencoder (VAE) has garnered extensive attention in the field of soft sensor modeling due to its superior capabilities in probabilistic data description and feature extraction. However, a single-layer VAE is challenging to extract higher-level features in the face of strong nonlinear process data. This paper proposes a gated stacked target-supervised VAE with variable weights (W-GSTVAE) to improve the modeling prediction performance of VAE. First, a stacked VAE is employed to enhance the feature extraction capability. In the pretraining phase, to enhance the correlation between the features and the target variable, feature learning is guided by incorporating the prediction error of target values into the loss function as well as calculating the maximum information coefficient between input and target variables. Meanwhile, in the fine-tuning phase, to make full use of shallow features, gated linear units are used to integrate the output features of each layer, fully exploiting the information from all layers. Finally, the effectiveness and superiority of the proposed model is demonstrated through two real industrial cases.

Facial recognition technology in law enforcement: Regulating data analysis of another kind

Facial recognition technology (FRT) has emerged as a powerful tool for law enforcement, enabling the automated identification of individuals based on their unique facial features. Authorities have more and more made use of the technology to enhance criminal investigations through the analysis of images and video footage. In view of its increased use in Europe, this paper explores the legal implications of FRT in law enforcement under EU law and evaluates approaches to regulation. FRT use constitutes biometric data processing and comes with a particularly sensitive analysis of data. Its specific nature is grounded in the creation of a new (biometric) quality of data in order to subsequently compare for matches. Due to its impact on fundamental rights, this approach differs from conventional forensic analyses and must be appropriately regulated. Such regulation should consider the multiple data processing steps and reflect each step's impact on fundamental rights. From this procedural stance, the shortcomings of the EU Artificial Intelligence Act (AI Act) become evident. The AI Act contains specific rules for biometric AI systems but does not provide the necessary legal bases to justify FRT use by law enforcement. Without a comprehensive legal framework, such use is not permitted. This article provides concrete guidelines for addressing such regulation.

Applications of Vedic Mathematics in Computer Science

The convergence of ancient wisdom and modern computation is an intriguing frontier explored in this research paper, which investigates the potential application of Vedic Mathematics in the domain of Computer Science. Vedic Mathematics, originating from ancient Indian texts known as the Vedas, offers a distinct approach to mathematical problem - solving that emphasizes mental agility, pattern recognition, and elegant algorithms. This paper delves into the historical context of Vedic Mathematics, tracing its origins and key principles, and highlights its relevance in contemporary Computer Science. The intersection of Vedic Mathematics and Computer Science is illuminated through the identification of shared principles, such as pattern recognition and optimization. The paper explores specific Vedic Mathematics techniques that have the potential to enhance computational efficiency, including the "Nikhilam" method for subtraction and the "Vertically and Crosswise" method for multiplication. Case studies are presented to demonstrate the practical application of Vedic Mathematics techniques in Computer Science, spanning areas such as data processing, algorithmic optimization, and error detection and correction. However, this integration is not without its challenges and ethical considerations. The paper addresses concerns about accuracy and reliability, particularly when integrating unconventional mathematical techniques into modern computational paradigms. Cultural sensitivity and proper acknowledgment of the origins of Vedic Mathematics are paramount, as is the need to ensure that the techniques are accessible to a diverse audience. The ethical implications of cross - cultural knowledge exchange are also explored, emphasizing the importance of collaboration and respect. Looking ahead, the paper outlines potential research directions and implications for education in Computer Science. The integration of Vedic Mathematics offers opportunities for developing novel algorithms, enhancing problem - solving skills, and fostering cross - cultural understanding. Collaborations between mathematicians and computer scientists are highlighted as a promising avenue for innovation in algorithmic design. In conclusion, the fusion of Vedic Mathematics and Computer Science represents a dynamic interplay of ancient traditions and modern advancements. As we navigate the complexities of integration, we embark on a journey that transcends disciplinary boundaries, fosters cultural appreciation, and holds the potential to reshape computational methodologies in the digital age.