Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage [0.03%]
Asha Aruna Sheela Matta,Venkata Purna Chandra Sekhara Rao Manukonda
Asha Aruna Sheela Matta
Anomaly detection in video surveillance remains a challenging problem due to complex human behaviors, temporal variability, and limited annotated data. This study proposes an optimized spatiotemporal deep learning (DL) framework that integr...
Jie Li,Yunbao Wen,Jingxin Liu et al.
Jie Li et al.
Improving software efficiency is crucial for maintenance, but pinpointing runtime bottlenecks becomes increasingly difficult as systems expand. Traditional dynamic profiling tools require full build-execution cycles, creating significant la...
A genetic algorithm-based framework for online sparse feature selection in data streams [0.03%]
一种基于遗传算法的数据流稀疏特征选择框架
Guanyu Liu,Jinhang Liu,Guifan He et al.
Guanyu Liu et al.
High-dimensional streaming data implementations commonly utilize online streaming feature selection (OSFS) techniques. In practice, however, incomplete data due to equipment failures and technical constraints often poses a significant chall...
Federated learning for teacher data privacy protection: a study in the context of the PIPL [0.03%]
《联邦学习助力教师数据隐私保护：PIPL合规语境下的研究》
Shanwei Chen,Xiu Zhi Qi,Xue Hui Han et al.
Shanwei Chen et al.
Background: The Personal Information Protection Law (PIPL) in China imposes strict requirements on personal data handling, particularly in educational contexts where teacher data privacy is critical. Traditional centraliz...
Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime prediction [0.03%]
基于共现引导的多源融合的动态迁移学习在城市时空犯罪预测中的应用研究
Chen Cui,Ziwan Zheng,Hao Du et al.
Chen Cui et al.
Spatio-temporal crime prediction is crucial for optimizing police resource allocation but faces challenges including data sparsity, which hinders models from extracting effective patterns and limits robustness-and the underutilization of cr...
Modeling household adoption of IoT-based home security in Dhaka: a PLS-machine learning framework [0.03%]
基于PLS机器学习框架的物联网家庭安全在达卡的采纳模型研究：以住户为视角
Arif Mahmud,Ashikur Rahman,Fahmid Al Farid et al.
Arif Mahmud et al.
Introduction: Despite several strategies, Bangladesh has a poor rate of internet of things (IoT) deployment. This study therefore seeks to investigate the factors shaping IoT adoption for residential security in Dhaka and...
Algorithmic recourse in sequential decision-making for long-term fairness [0.03%]
序列决策中的算法补救以实现长期公平性
Francisco Gumucio,Lu Zhang
Francisco Gumucio
Long-term fairness in sequential decision-making is critical yet challenging, as decisions at each time step influence future opportunities and outcomes, potentially exacerbating existing disparities over time. While existing methods primar...
Depression detection through dual-stream modeling with large language models: a fusion-based transfer learning framework integrating BERT and T5 representations [0.03%]
基于融合迁移学习框架的抑郁症检测：结合BERT和T5表示的双模态建模方法
Na Wang,Weijia Zhang,Raja Kamil et al.
Na Wang et al.
Millions of people around the world suffer from depression. While early diagnosis is essential for timely intervention, it remains a significant challenge due to limited access to clinically diagnosed data and privacy restrictions on mental...
Adaptive core-enhanced latent factor model for highly accurate QoS prediction [0.03%]
自适应核心增强潜在因子的高精度QoS预测模型
Siqi Ai,Peixin Li,Hao Fang et al.
Siqi Ai et al.
Accurate prediction of Quality of Service (QoS) plays a crucial role in service recommendation and selection across large-scale distributed environments. Latent factor (LF) models have become a mainstream solution for QoS prediction owing t...
EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare [0.03%]
基于双重安全框架的隐私保护联邦学习集成方法用于可持续医疗保健
Bela Shrimali,Jenil Gajjar,Swapnoneel Roy et al.
Bela Shrimali et al.
Recent advances in Artificial Intelligence have highlighted the role of Machine Learning in healthcare decision-making, but centralized data collection raises significant privacy risks. Federated Learning addresses this by enabling collabor...