Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association by Jessica Giovagnola, Manuel Pegalajar Cuéllar, Diego Pedro Morales Santos

“…Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. …”
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Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain by Beniamino Di Martino, Antonio Esposito, Gennaro Junior Pezzullo, Tien-Hsiung Weng

“…Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. …”
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Distributed consensus problem with caching on federated learning framework by Xin Yan, Yiming Qin, Xiaodong Hu, Xiaoling Xiao

“…Federated learning framework facilitates more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. …”
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An Integrated Supplier-Buyer Inventory Model with Conditionally Free Shipment under Permissible Delay in Payments by Chia-Hsien Su

“…Algorithm for computing the optimal policies is derived. …”
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Inverse Rendering of Near-Field mmWave MIMO Radar for Material Reconstruction by Nikolai Hofmann, Vanessa Wirth, Johanna Braunig, Ingrid Ullmann, Martin Vossiek, Tim Weyrich, Marc Stamminger

“…Drawing inspiration from physically based rendering techniques in computer graphics, we formalize an advanced inverse rendering algorithm that integrates electromagnetic wave propagation models directly into the optimization process. …”
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Contributon-Informed Approach to RPV Irradiation Study Using Hybrid Shielding Methodology by Mario Matijević, Krešimir Trontl, Dubravko Pevec

“…The complex design of a typical PWR is posing a deep penetration shielding problem for which an elaborate simulation model is needed, not only in geometrical aspects but also in efficient computational algorithms for solving particle transport. …”
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A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning by Emanuel Valerio Pereira, Wendley Souza da Silva

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Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro. by Gabriel Galeote-Checa, Gabriella Panuccio, Angel Canal-Alonso, Bernabe Linares-Barranco, Teresa Serrano-Gotarredona

“…To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. …”
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Exploring the Effects of Pre-Processing Techniques on Topic Modeling of an Arabic News Article Data Set by Haya Alangari, Nahlah Algethami

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Cooperative Mechanism for Energy Transportation and Storage in Internet of Energy by Weigang Hou, Guoda Tian, Lei Guo, Xiaojie Wang, Xu Zhang, Zhaolong Ning

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Priority Load Management for Improving Supply Reliability of Critical Loads in Healthcare Facilities Under Highly Unreliable Grids by Ndukwe Henry Ibiam, Fadi Kahwash, Jubaer Ahmed

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Energy Optimisation in Aquaponics—Integrating Renewable Source and Water as Energy Buffer for Sustainable Food Production by Abdul Aziz Channa, Kamran Munir, Mark Hansen, Muhammad Fahim Tariq

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Method of terminal control in ascent segment of unmanned aerial vehicle with ballistic phase by N. Y. Polovinchuk, S. V. Ivanov, M. Y. Zhukova, D. G. Belonozhko

“…Bellman’s principle of dynamic programming is the most reasonable from the point of view of the implementability of the computationally efficient on-board algorithms and the solution to the problems in the form of synthesis. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

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A New Method for Solving Quadratic Fractional Programing Problems by Mediya B. Mrakhan, Basiya K.Abdulrahim, Rzgar F. Mahmood, Aram Bajalan, Shwan Adnan Ali

“…Numerical examples were illustrated to show the efficiency of the method. In addition, the computer operation of the current algorithms was discussed by using MATLAB 2013a to solve a constructed numerical example. …”
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Design as Exploration by Ding Yang

“…Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms. …”
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A comprehensive review on safe reinforcement learning for autonomous vehicle control in dynamic environments by Rohan Inamdar, S. Kavin Sundarr, Deepen Khandelwal, Varun Dev Sahu, Nitish Katal

“…Reinforcement learning (RL) presents a promising approach for AV control, as it utilizes trial and error to enable optimal decision-making. However, traditional RL algorithms are unsuitable for safety-critical applications, as they may explore unsafe actions, potentially resulting in accidents. …”
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Application of microscopic smartphone attachment for remote preoperative lab testing by Kefan Song, Kefan Song, Alexander T. Adams, Alexander T. Adams

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Deep Learning-Based Keras Network Formulation for Predicting the Shear Capacity of Squat RC Walls and Sensitivity Analysis by Badie H. Sulaiman, Amer M. Ibrahim, Hadeel J. Imran

“…It is distinguished by its optimal cost and excellent performance. Nonetheless, precise assessment of the shear strength of squat shear walls is crucial for design specifications, and its computation can be exceedingly variable and intricate due to several efficient, expensive, and time-consuming constraining elements. …”
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Accelerated development of multi-component alloys in discrete design space using Bayesian multi-objective optimisation by Osman Mamun, Markus Bause, Bhuiyan Shameem Mahmood Ebna Hai

“…Our findings highlight the superior performance of the qEHVI acquisition function in identifying the optimal Pareto front across 1-, 2-, and 3-objective aluminum alloy optimisation problems, all within a constrained evaluation budget and reasonable computational cost. …”
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