31 skills found · Page 2 of 2
AnnaDiMauro / WDDreview[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets.
reviverkid / DeepFakeDetectionSystem🚀 Welcome to the Deepfake Detection System project! This project detects deepfake videos using advanced machine learning techniques, leveraging pre-trained CNNs for feature extraction and LSTM networks for temporal modeling. The frontend is built with React, and Firebase is used for database management and user authentication.
pjungwir / Postgres Temporal TalkMy talk about Postgres and Temporal Databases
Yiding00 / SpatioTemporalCausalLearningA modularized repository for Spatio-Temporal Causal Learning under ADNI database.
Nexus-Mods / NexusMods.MnemonicDBA simple, fast, and type-safe in-process temporal database for .NET applications.
Negiamit034 / EDA On Global Terrorism This project delved into the Global Terrorism Database spanning four decades, unearthing insights from over 180,000 incidents. Analyzing temporal trends, attack tactics, and geographical hotspots, we illuminated patterns and correlations. Our findings propelled strategic recommendations, from enhanced security measures.
AlbericByte / ArqonDBAI-native distributed database for agent memory and real-time state. Unifies KV, vector search, and temporal graph in a single Rust engine with Raft consensus
Sakuraaa0 / TVAA temporal graph database system
VladislavAntonyuk / SourceGeneratorsDemoTrack database operations with AuditNet, SourceGenerators, Temporal
RWLinno / Awesome SpatioTemporal TrafficA list of classical and popular paper about spatio-temporal data mining and traffic flow forecasting collected used Notion database site.
sandhyamurali23 / Web Service Recommendation Using Improved Collaborative FilteringDesigned and developed a recommendation system to recommend the most suitable web services to the user by applying multicriteria decision making technique on the spatial and temporal information that are available in the PostgreSQL database. The system provides personalized QoS prediction by computing direct user and service similarity using Pearson correlation coefficient as well as use the Random walk algorithm to exploit the indirect similarities for users and services.
