This review synthesizes advances in deep learning and machine learning for oral disease diagnosis, orthodontic treatment prediction, pediatric oral health management, adolescent psychological-oral health interactions, and precision care for special-needs populations, drawing on evidence published predominantly between...
Yuan-Qi Zhang, Tao Wen, Zheng-Rou Wang et al.· International Journal of Art...· 0 citations
CIVILIZATION EVOLUTIONARY LEARNING, UNKNOWN CARTOGRAPHY & SELF-REVISING FUTURES AT THE LIMIT Meta-Learning Across Lineages, Research Phylogenies, Adaptive Commons,Multi-Speed Residents, Deep-Time Memory, Successor Autonomy, and Handoff Feng Cheng-en (33) x Starli When can a civilization-of-civilizations learn from its...
Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables : art. 54(2) CBE (Convention sur le brevet européen), art. L...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Abstract: The rapid proliferation of artificial intelligence and machine learning has enabled organizations to achieve unprecedented levels of hyper-personalization, transforming how brands interact with consumers. However, this capability intensifies the privacy paradox, a phenomenon where consumers express profound c...
Dhanraj Kalgi, Akshay Shende· Zenodo (CERN European Organi...· 0 citations
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Abstract: The rapid proliferation of artificial intelligence and machine learning has enabled organizations to achieve unprecedented levels of hyper-personalization, transforming how brands interact with consumers. However, this capability intensifies the privacy paradox, a phenomenon where consumers express profound c...
Dhanraj Kalgi, Akshay Shende· Zenodo (CERN European Organi...· 0 citations
Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...
Sourish Dey -, Anish Pandey, Shreyanjan Neogi et al.· Natural Sciences and Applied...· 0 citations
Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...
Sourish Dey -, Anish Pandey, Shreyanjan Neogi et al.· Natural Sciences and Applied...· 0 citations
Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: art. L 611-11 CPI (French Intellectual Property...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
The growing scale and heterogeneity of Internet of Things (IoT) environments are shifting machine learning (ML) from centralized cloud infrastructures toward distributed intelligence across the IoT–edge–cloud continuum [...]
A reproducible research framework for vertical federated learning on heterogeneous tabular data with explicit privacy boundaries, provenance and communication auditing.
Saurav Singla· Zenodo (CERN European Organi...· 0 citations
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.