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26 articles for “hybrid statistics”
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Reducing Environmental Impact through Optimized Non-Renewable Resource Management: A Systematic Review of Technological Advances
Abstract: The term "optimized use of non-renewable resources" describes the prudent management and optimization of limited natural resources that are difficult to replenish or regenerate quickly. Minerals, nuclear fuels, and fossil fuels (coal, oil, and natural gas) are examples of resources that are not renewable. The abstract idea is to minimize waste, the impact on the environment, and the pace of depletion while optimizing the utility obtained from these resources. Advanced …
Published in Journal of Nuclear Engineering & Technology · Vol. 14, Issue 3, 2024 · pp. 12–19 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Comparative Assessment of Conversion Degree, Resin Tag Depth, and Mineral Deposition in Adhesive Resin Enhanced with Inorganic Nanofillers such as Cerium Dioxide and Tantalum Oxide Nanoparticles
Abstract: The World Health Organization (WHO) characterizes dental caries as the deterioration of tooth enamel resulting from acids generated by microbial activity on carbohydrates. Untreated dental caries can lead to pain, eating and sleeping difficulties, and systemic health issues, ultimately diminishing the quality-of-life Composite restorative materials moderately became one of the most used restorative materials for anterior restorations and also is used more extensively for restoring posterior teeth which are having …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 110–118 Read article
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Interfacial and Tribo-Mechanical Performance of a TiO₂–Castor Oil Polymeric Nanofluid During Sustainable Machining of AISI 316L Stainless Steel Under MQL Conditions
Abstract: This research examines the tribo-mechanical performance and interfacial film characteristics of a TiO₂-reinforced castor-oil polymeric nanofluid during the turning of AISI 316L stainless steel under minimum-quantity lubrication (MQL). A Taguchi L9 orthogonal array was utilized to assess the synergistic effects of cutting speed, depth of cut, and coolant composition on surface integrity, while machining experiments were performed under dry, conventional, and TiO₂-nanofluid lubrication techniques. ANOVA and multiple-regression modeling were used …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 901–914 Read article
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Radio in the Age of AI
Abstract: Artificial intelligence (AI) is changing the traditional world of radio broadcasting very quickly. It is changing the way material is made, curated, shared, and listened to. This article talks about how AI technologies like machine learning, natural language processing, and automated voice synthesis can be used in radio production and operations. It looks at how AI-powered solutions may make listening more personalised, give real-time audience statistics, automatically generate news, and …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article