Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

“…Zero-Shot Learning (ZSL) and synthetic data are used traditionally to address these challenges. …”
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Verification Technical Scheme for Deep Learning Algorithm Based on Interactive Zero Knowledge Protocol by LYU Yanfeng, LYU Xuesheng, HUANG Shenghui, LIANG Qinglei

“…Innovative research on zero knowledge protocols based on deep learning algorithms was proposed, which combines the discipline of artificial intelligence machine learning. …”
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Robust zero-watermarking for color images using hybrid deep learning models and encryption by Hager A. Gharib, Noha M. M. Abdelnapi, Khalid M. Hosny

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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…Our method exploits reinforcement learning, a sub-type of machine learning which trains agents to make decisions and take actions to maximize an approximation of some underlying cumulative reward signal and discover patterns and features within data related to zero-day discovery. …”
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Zero-Shot Sand-Dust Image Restoration by Fei Shi, Zhenhong Jia, Yanyun Zhou

“…In this paper, we propose a new zero-shot learning method based on an atmospheric scattering physics model to restore sand-dust images. …”
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Policy Similarity Measure for Two-Player Zero-Sum Games by Hongsong Tang, Liuyu Xiang, Zhaofeng He

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Zero-shot Image Classification Method Based on Discriminator Feedback by FAN Yufei, DING Bo, HE Yongjun

“…Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training.At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot.On this basis, we propose a zero-shot image classification method based on Discriminator Feedback VAEGAN (DF-VAEGAN).This method introduces a feedback module in the discriminator part, which can improve the overall performance of the model in the training stage.In the feature generation stage, it can be combined with the generator to jointly improve the quality of feature generation.Finally, the classifier is trained through high quality synthetic features to improve classification accuracy.The method also reconstructs attribute features through the decoder and uses a cycle consistency loss to ensure semantic consistency of the generated feature.Experiments on ZSL and generalized zero-shot learning (GZSL) show that our method outperforms existing methods on five classical datasets, effectively enhancing the quality of feature synthesis and reducing the goal of between categories in the zero-shot image classification task.…”
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Leveraging the Power of Zero-Shot Learning for Malware Detection Using Application Programming Interface Call Sequences by P. Meena, K. P. Rama Prabha

“…The proposed method achieved accuracies of 0.98 on the Kaggle Malware Detection dataset, 0.98 on the API Call Sequences dataset, and 1.0 on the UCI Malware Detection dataset. The research results show that deep learning is effective for malware detection and exemplify how online emerging threats can be countered with zero-shot learning.…”
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Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports by Suganya Subramaniam, Sara Rizvi, Ramya Ramesh, Vibhor Sehgal, Brinda Gurusamy, Hikmatullah Arif, Jeffrey Tran, Ritu Thamman, Emeka C Anyanwu, Ronald Mastouri, G. Burkhard Mackensen, Rima Arnaout

“…We used statistical machine learning (EchoMap) and zero-shot inference using GPT. …”
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Towards zero-shot learning in 3D change detection: improving generalization with custom augmentations and evaluation by Riccardo Contu, Valerio Marsocci, Virginia Coletta, Roberta Ravanelli, Simone Scardapane

“…The most successful augmentation combination reduces cRMSE to 5.88 m and tpRMSE to 5.34 m, from 6.33 m and 5.60 m of the baseline, respectively. Finally, a first zero-shot learning experiment is carried out on a new small dataset, achieving promising improvements towards domain generalization. …”
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Zero-Shot Prediction of Conversational Derailment With Large Language Models by Kenya Nonaka, Mitsuo Yoshida

“…Online discussion platforms often show a tendency for conversations to stray from the topic and devolve into personal attacks. …”
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“From zero to three hundred” - intensive acquisition techniques for the 300 most frequent content words in Welsh by Tess Fitzpatrick, Steve Morris

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Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer by Chang-Hun Ji, Gyeonghun Lim, Youn-Hee Han, Sungtae Moon

“…Furthermore, we also propose a PX4-ROS2 based reinforcement learning framework for achieving stable zero-shot sim-to-real transfer. …”
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Zero‐shot animal behaviour classification with vision‐language foundation models by Gaspard Dussert, Vincent Miele, Colin Van Reeth, Anne Delestrade, Stéphane Dray, Simon Chamaillé‐Jammes

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Critical raw material-free multi-principal alloy design for a net-zero future by Swati Singh, Mingwen Bai, Allan Matthews, Saurav Goel, Shrikrishna N. Joshi

“…Thermo-Calc evaluation and ML model predictions of the Vickers hardness showed excellent agreement with the experimental hardness values, which lends credence to our approach. …”
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Zero-Shot Aspect Category Sentiment Analysis Model with Enhanced PPLM Template by FENG Lizhou, LI Mengsha, WANG Youwei, YANG Guijun

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Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning by Alyaman H. Massarani, Mahmoud M. Badr, Mohamed Baza, Hani Alshahrani, Ali Alshehri

“…The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. …”
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An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid by Ying Han, Yujing Hou, Luoyi Li, Weifeng Meng, Qi Li, Weirong Chen

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Cascaded intrusion detection system using machine learning by Md. Khabir Uddin Ahamed, Abdul Karim

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Integral Reinforcement Learning-Based Online Adaptive Dynamic Event-Triggered Control Design in Mixed Zero-Sum Games for Unknown Nonlinear Systems by Yuling Liang, Zhi Shao, Hanguang Su, Lei Liu, Xiao Mao

“…In this paper, multiplayer mixed zero-sum games (MZSGs) are studied by the means of an integral reinforcement learning (IRL) algorithm under the dynamic event-triggered control (DETC) mechanism for completely unknown nonlinear systems. …”
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