Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks by Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano

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Next-Gen UAV-Satellite Communications: AI Innovations and Future Prospects by Sherief Hashima, Ahmad Gendia, Kohei Hatano, Osamu Muta, Mostafa S. Nada, Ehab Mahmoud Mohamed

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UAV selection for high-speed train communication using OTFS modulation by Ehab Mahmoud Mohamed, Sherief Hashima

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The intersection of AI and learning analytics: Enhancing institutional performance by Thabisa Maqoqa

“…Underpinned by computational learning theory, which emphasises understanding the performance and resource needs of machine learning algorithms, this study focuses on a sample from a rural university in the Eastern Cape. …”
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Quantum-classical hybrid algorithm for solving the learning-with-errors problem on NISQ devices by Muxi Zheng, Jinfeng Zeng, Wentao Yang, Pei-Jie Chang, Quanfeng Lu, Bao Yan, Haoran Zhang, Min Wang, ShiJie Wei, Gui-Lu Long

“…Abstract The Learning-With-Errors (LWE) problem is a fundamental computational challenge with implications for post-quantum cryptography and computational learning theory. Here we propose a quantum-classical hybrid algorithm with Ising model to address LWE, transforming it into the Shortest Vector Problem and using variable qubits to encode lattice vectors into an Ising Hamiltonian. …”
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OTFS-Based Handover Triggering in UAV Networks by Ehab Mahmoud Mohamed, Hany S. Hussein, Mohammad Ahmed Alnakhli, Sherief Hashima

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Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles. by Arne F Meyer, Jan-Philipp Diepenbrock, Max F K Happel, Frank W Ohl, Jörn Anemüller

“…The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. …”
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Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions by Ke-Lin Du, Bingchun Jiang, Jiabin Lu, Jingyu Hua, M. N. S. Swamy

“…As a kernel-based method, support vector machine (SVM) is one of the most popular nonparametric classification methods, and is optimal in terms of computational learning theory. Based on statistical learning theory and the maximum margin principle, SVM attempts to determine an optimal hyperplane by addressing a quadratic programming (QP) problem. …”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

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