Securing the road ahead: Machine learning-driven DDoS attack detection in VANET cloud environments by Himanshu Setia, Amit Chhabra, Sunil K. Singh, Sudhakar Kumar, Sarita Sharma, Varsha Arya, Brij B. Gupta, Jinsong Wu

“…Additionally, it leverages machine learning techniques for classification and predictive analytics with an accuracy of 99.59%. …”
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Privacy-Preserving Deep Learning: A Survey on Theoretical Foundations, Software Frameworks, and Hardware Accelerators by Eric Jahns, Milan Stojkov, Michel A. Kinsy

“…We analyze over 100 recent works in areas such as Homomorphic Encryption, Functional Encryption, Multi-Party Computation, Trusted Execution Environments, Federated Learning, and Differential Privacy. …”
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The Role of Learning-oriented Language Assessment in Promoting Interactional Metadiscourse in Ectenic and Synoptic EFL Learners by Rajab Esfandiari, Omid Allaf-Akbary

“…Nevertheless, a large body of research has been predominantly centred on the qualitative examination of potential metadiscourse markers and their associated functions. In the current study, we drew on an embedded design and followed learning-oriented language assessment (LOLA) in the use of interactional metadiscourse markers (IMMs) to better understand metadiscourse use by ectenic (n = 27) and synoptic (n = 30) learners using integrative writing tasks. …”
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Screening the grading markers and their application in the grade discrimination of Gastrodiae Rhizoma using metabolomics and machine learning by Shuang Liu, Hongjing Dong, Yanling Geng, Yuzhang Mi, Quanli Wang, Xiao Wang

“…Gastrodiae Rhizoma (GR), the tuber of the Gastrodia elata Blume, is a food with functional properties. The grades of GR are closely related to its biological activity, and are mainly identified by their appearance characteristics, mainly individual weight. …”
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Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ 0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). …”
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The acceptance, readiness, affordances, and challenges of mobile-assisted language learning: A systematic literature review by Jie Fang

“…This research classified three categories of the affordances of MALL, namely technology-based affordances which are related to the variety and application of mobile apps and the enhancing features of multimedia, the functional affordances of MALL which are related to the dynamic and flexible learning environments and the accessibility of mobile devices, and various aspects of language learning-based affordances. …”
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Intelligent Attitude Control of Hypersonic Vehicle Based on DDQN and Deep Q-Learning from Demonstrations by Liu Jingwen, Cai Guangbin, Fan Yonghua, Fan Hongdong, Wu Tong, Shang Yiming

“…Learning through interaction with the environment, so that the hypersonic vehicle can adaptively adjust its attitude according to changes in the flight environment. …”
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Optimisation of Residual Network Using Data Augmentation and Ensemble Deep Learning for Butterfly Image Classification by Diniati Ruaika, Shofwatul Uyun

“…A sequence of transformation functions was applied. The ensemble deep learning was constructed by incorporating ResNet50 with CNN. …”
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Identification of system models from potential-stream equations on the basis of deep learning on experimental data by I. E. Starostin, S. P. Khalyutin

“…The functioning of various systems (in particular technical objects, living cells, the atmosphere and the ocean, etc.) is determined by the course of physical and physico-chemical processes in them. …”
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Securing Microservices-Based IoT Networks: Real-Time Anomaly Detection Using Machine Learning by Maria Katherine Plazas Olaya, Jaime Alberto Vergara Tejada, Jose Edinson Aedo Cobo

“…These risks can result in disruptions to system functioning or data compromise. Proposed strategies to mitigate these risks include developing intrusion detection systems and utilizing machine learning to differentiate between normal and anomalous network traffic, indicating potential attacks. …”
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An improved multi-leader comprehensive learning particle swarm optimisation based on gravitational search algorithm by Alfred Adutwum Amponsah, Fei Han, Jeremiah Osei-Kwakye, Ernest Bonah, Qing-Hua Ling

“…Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. …”
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Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview by Daniel Casanueva‐Morato, Alvaro Ayuso‐Martinez, Giacomo Indiveri, Juan P. Dominguez‐Morales, Gabriel Jimenez‐Moreno

“…This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. …”
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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films by Waqas W. Ahmed, Haicheng Cao, Changqing Xu, Mohamed Farhat, Muhammad Amin, Xiaohang Li, Xiangliang Zhang, Ying Wu

“…To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. …”
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A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems by Anmol Rahujo, Daniya Atif, Syed Azeem Inam, Abdullah Ayub Khan, Sajid Ullah

“…This survey investigates the significant impact of Transfer Learning and large language models on medical systems by explaining their applications in imaging procedures, disease identification, and natural language processing functions for electronic health records analysis and medical decision-making assistance. …”
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From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing by Bashayr Alqahtani, Hanrui Li, Abdul Momin Syed, Nazek El-Atab

“…Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity, replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained challenging. …”
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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size by Yong-Hoon Byun, Juik Son, Jungmin Yun, Hyunwook Choo, Jongmuk Won

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A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods by Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan

“…In this paper, we propose a forward prediction and screening framework for functional materials, which includes database selection, attributes, descriptors, machine learning models, and prediction and screening. …”
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A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning by Jiahui Pan, Yi Wei, Lei Meng, Xiangxu Meng

“…Accordingly, we propose a dual scheduling approach based on deep reinforcement learning, which consists of two layers of Deep Q-Network algorithms to realize these functions. …”
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Measuring the effectiveness of programmed instructions (PI) to learn design thinking concepts for secondary school students by Apoorv Naresh Bhatt, Amaresh Chakrabarti

“…Self-instructional media in education has the potential to address educational challenges such as accessibility, flexible and personalised learning, real-time assessment and resource efficiency. …”
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Adaptive Reconfigurable Learning Algorithm for Robust Optimal Longitudinal Motion Control of Unmanned Aerial Vehicles by Omer Saleem, Aliha Tanveer, Jamshed Iqbal

“…The dissipative and anti-dissipative actors are augmented with state-error-driven hyperbolic scaling functions, which autonomously reconfigure the associated learning gains to mitigate disturbances and uncertainties, ensuring superior performance metrics such as tracking precision and disturbance rejection. …”
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