863 papers
This paper proposes the Valuation Data Ledger (VDL), a permissioned blockchain framework that integrates hybrid storage, access control, and token incentives to securely unify fragmented real estate valuation data, demonstrating through synthetic evaluation that it enhances automated valuation accuracy, enables effective fraud detection, and supports privacy-preserving collaborative learning.
This paper introduces Mixed Neural Rendering (MiNeR), an interactive Mixed Reality pipeline using HoloLens 2 that integrates real-time capture, in-situ inspection, and promptable segmentation to overcome traditional acquisition limitations and enable robust, object-centric neural 3D reconstruction.
HashGuard-FL is a novel federated learning framework that integrates a learnable hash-based GAN discriminator for fine-grained, multi-class attack detection with a post-quantum audit layer using SHAKE-256 and CRYSTALS-Dilithium signatures to ensure tamper-evident, quantum-resistant integrity across diverse benchmarks.
This paper diagnoses the severe performance gap in Oracle bone character detection caused by extreme long-tailed class distribution and proposes FAR-YOLO, a decoupled baseline that significantly improves few-shot recognition accuracy through frequency-aware sampling and morphology-preserving augmentation without compromising overall localization performance.
This paper proposes DP-DCGAN, a differential privacy-enhanced Deep Convolutional Generative Adversarial Network that employs adaptive gradient clipping and noise injection to achieve a superior balance between privacy protection and high-quality image synthesis on standard datasets.
This paper introduces an AI-driven adaptive load balancing feature for CloudSimPlus that utilizes a pre-trained Decision Tree model to dynamically select the most suitable strategy from a unified framework of policies based on real-time metrics and user-defined objectives, thereby enhancing efficiency and decision-making in dynamic cloud environments.
This paper argues that prioritizing strong model generalization to prevent overfitting is a more effective strategy for mitigating membership and attribute inference attacks in SDN-IoT intrusion detection systems than relying solely on differential privacy, proposing a hybrid AOPRF-XGBoost cascade model to achieve high detection accuracy while preserving privacy.
This paper proposes LA-MOCHS, a lifetime-aware evolutionary framework that addresses multi-objective cluster-head selection in underwater wireless sensor networks by modeling service capability as a submodular influence maximization problem, thereby achieving superior long-term network sustainability through burden-aware deployment strategies rather than single-round optimality.
This paper presents a privacy-preserving, resource-constrained framework for early student risk prediction that integrates calibrated XGBoost models, SHAP-based explainability, and a budget-aware agentic intervention layer to ensure accurate, interpretable, and compliant decision-making while supporting machine unlearning.
This paper introduces CerebroTune, a novel bio-inspired hybrid metaheuristic that integrates Cellular Automata, a modified Artificial Bee Colony algorithm, and Reinforcement Learning with Hopfield networks to dynamically optimize deep learning hyperparameters, achieving superior accuracy, reduced computational costs, and enhanced stability across diverse domains compared to existing methods.