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Gotowe bibliografie tematyczne / Real-timeData

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Artykuły w czasopismach

Gotowa bibliografia na temat „Real-timeData”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Real-timeData"

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Pavlychev, A. V., and K. V. Kuzminetc. "DETECTION OF PHISHING INTERNET DOMAINS USING MACHINE LEARNING ALGORITHMS IN REAL-TIMEDATA STREAMING." Voprosy kiberbezopasnosti 2, no. 66 (2025): 141–53. https://doi.org/10.21681/2311-3456-2025-2-141-153.

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Objective: the aim of this research is to develop an effective method for detecting phishing Internet domains using machine learning algorithms in real-time data streaming. Methodology: the work involves an analysis of features characterizing arbitrary Internet domains, and the development of a software complex that allowed collecting a custom dataset containing a set of features for over 250,000 domains. Several machine learning models were )trained on the obtained dataset and compared in terms of accuracy and speed. The selected classifier was used to develop a software prototype that was te

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Mohammed Sahir Awais, Syed Turab Ullah, Rafeeq Ahmed, and Dr. Mohammed Abdul Bari. "Crop Yield Prediction Using Bidirectional Lstm With Real-Time Data Visualization." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 612–17. https://doi.org/10.62647/ijitce2025v13i2spp612-617.

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Crop yield prediction plays a pivotal role in ensuringfood security and optimizing agricultural planning.Traditional methods, such as statistical regressionor simple machine learning algorithms, often fail toaccount for the nonlinear and temporal nature offarming data. In this project, we introduce anadvanced deep learning approach usingBidirectional Long Short-Term Memory (BiLSTM)networks to enhance the accuracy of crop yieldprediction. The BiLSTM model is trained on acombination of historical weather data, soil metrics,and agricultural practices, enabling it to learn bothpast and future cont

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Timur, Ni Putu Vidia Tiara, Bangkit Luthfiaji Syaefullah, Susan Carolina Labatar, and Ebit Eko Bachtiar. "Cattle Disease Studies Via Geographical Information System in Bowi Subur Village, Masni District, Manokwari Regency, West Papua Province." Jurnal Ilmu-Ilmu Peternakan 33, no. 1 (2023): 109–15. http://dx.doi.org/10.21776/ub.jiip.2023.033.01.013.

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Geographical Information System(GIS) and remote sensing provide real-timedata to stakeholders.GIS is new and modern tool that are essential for mapping, monitoring, and surveillance of animal diseases.This study aims to provide a digital map of cattlepopulation and diseasedistribution. Usingsatellite imaging as mapping apparatus, this study map the distribution of cattle diseases.Animal health is key to livestock production and productivity. This study can be used as prevention and treatment measures efficiently and effectively.Based on theresults of the study, the cattle population in Bowi Su

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Simola, Jussi, and Jouni Pöyhönen. "Emerging Cyber risk Challenges in Maritime Transportation." International Conference on Cyber Warfare and Security 17, no. 1 (2022): 306–14. http://dx.doi.org/10.34190/iccws.17.1.46.

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Maritime security and surveillance have become one of the main areas in managing overall situational awareness.For example, the growing importance of maritime traffic in cross-border trade has created new pressures to develop newtechnologies for accident prevention, especially in the ports. Maritime safety is also a matter of concern for continuitymanagement. Automatic ship alarm systems, coastal radars and coastal cameras are not alone sufficient equipment to buildmaritime awareness. The Universal Shipborne Automatic Identification System (AIS) is a ship transponder system that is aglobally u

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Oluwademilade, Aderemi Agboola, Chukwuemeke Uzoka Abel, Oluwaseun Ajayi Olanrewaju, Chibunna Ubanadu Bright, Ifesinachi Daraojimba Andrew, and Elizabeth Alozie Chisom. "Transforming Supply Chain Analytics with Real-Time Data and Cloud Data Warehousing: A Strategic Framework." Engineering and Technology Journal 10, no. 05 (2025): 5029–39. https://doi.org/10.5281/zenodo.15461534.

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This paper explores the transformative role of real-time data and cloud data warehousing in optimizing supply chain analytics. As supply chains grow increasingly complex, businesses are increasingly turning to data-driven strategies to enhance operational efficiency, reduce costs, and improve responsiveness to market demands. Real-time data provides the foundation for proactive decision-making, enabling organizations to monitor supply chain activities continuously and make informed adjustments to minimize disruptions. The integration of cloud data warehousing offers scalable, flexible, and cos

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Fernandez, Pallarés Victor, Virgilio Pérez, and Rosa Roig. "Optimizing Energy Supply for Full Electric Vehicles in Smart Cities: A Comprehensive Mobility Network Model." World Electric Vehicle Journal 16, no. 1 (2024). https://doi.org/10.3390/wevj16010005.

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The integration of Full Electric Vehicles (FEVs) into the smart city ecosystemis an essential step towards achieving sustainable urban mobility. This study presentsa comprehensive mobility network model designed to predict and optimize the energysupply for FEVs within smart cities. The model integrates advanced components such asa Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamicuser interface. Important aspects include analyzing power consumption, forecasting urbanenergy demand, and monitoring the State of Charge (SoC) of FEV batteries usinginnovative algorith

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NiravNarendrakumar, Modh, and Fnu Himani. "THE DIGITAL FUTURE OF HEALTH INSURANCE: HOW AI AND CLOUD ARE MERGING." International Journal of Engineering Technology Research & Management (ijetrm) 08, no. 04 (2024). https://doi.org/10.5281/zenodo.14633998.

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Health insurance industry’s digital transformation involves the adoption of two important revolutionarytechnologies namely Artificial Intelligence (AI) and cloud computing to transform the traditional insuranceprocesses. AI helps insurers to process large volumes of data for rationalisation of activities including claims,frauds, and customer services. Using an advanced machine learning technique, patterns can be identified as wellas outcomes predicted and various policies can be sold based on a confirmed individual customer need (Smith etal., 2020). On the same note, cloud computing offe

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