This article is fulfilled within the framework of Erasmus+ project "The Future is in Applied Artificial Intelligence"(FAAI) and examines the study of practical solutions implemented using applied artificial ...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techn...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.
This paper analyses the performance of the uplink multiple-input single-output (MISO) reconfigurable intelligence surface (RIS)-based system. Aiming to improve the user's uplink channel quality, we consider the sc...
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The proposed project uses a Raspberry Pi microcontroller to prevent crop losses caused by animals like dog, wild pigs, and monkeys. These animals pose a significant threat to farmers, leading to financial losses. This...
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Massive Multiple-Input Multiple-Output (MIMO) answers the exponentially increasing demand for comprehensive fixed broadband and broadcast wireless communication services. Massive MIMO is a pivotal technology in the 5G...
Massive Multiple-Input Multiple-Output (MIMO) answers the exponentially increasing demand for comprehensive fixed broadband and broadcast wireless communication services. Massive MIMO is a pivotal technology in the 5G and beyond (5GaB) wireless communication systems. This paper compares linear precoding algorithms such as Minimum Mean Square Error (MMSE), Neumann Series Approximation (NSA) with nonlinear precoders such as Tomlinson-Harashima Precoder (THP), and Lattice Reduction (LR) algorithm, the Lenstra-Lenstra-Lov'asz (LLL) precoder. The comparison was conducted using Bit-Error Rate (BER) and signal-to-noise ratio (SNR) performance measures. Simulated results prove that nonlinear precoders outperform linear precoding in high SNR regions.
A large volume of image data has been increasingly generated in every area, and it is necessary to keep confidential. Multiple image encryption is a promising solution for massive image data. Existing algorithms allow...
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In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays...
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Frequency response services have become more important than ever in an increasingly inertia-less power system. A promising way to provide such services in a photovoltaic (PV) system is by hybridizing with supercapacit...
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With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enabl...
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As fundamental components of programmable logic circuits, Lookup Table (LUT) circuits enable the implementation of arbitrary combinational logic. The volatility, standby power dissipation, and propagation delay of LUT...
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