AI-Enhanced Safety and Emergency Response
At Kainematics, we embrace the ‘AI for Good’ usage to safeguard citizens and their way of life by enhancing public safety through advanced AI technologies. Given the recent rise of natural and man-made disasters due to climate change and radicalization, our technologies can support citizens, first responders at operational, tactical and strategic level and Regional-National Authorities to take the right decisions through early warnings, optimal resource usage, actionable insights and recommendations. We believe that every citizen deserves to live in a safe environment, free from threats and hazards. Our goal is to utilise AI to create safer public spaces, ensuring rapid and effective responses to emergencies, and protecting critical infrastructure from both man-made and natural threats, through cutting-edge AI algorithms and machine learning models to detect and mitigate risks, enhance situational awareness, and improve emergency response efforts. Our solutions integrate real-time data analysis, predictive modeling, and anomaly detection.
- With Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks we can analyze surveillance footage and sensor data to detect suspicious behavior and identify potential threats such as unattended bags or suspicious movement patterns in (near) real-time to enable proactive actions.
- Deep Convolutional Neural Networks (CNNs) are used to scan images and video feeds for firearms or explosive devices, ensuring rapid identification and alerting authorities to potential threats in public spaces like airports, train stations, and public events.
- Clustering algorithms such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can detect unusual traffic patterns or congestion, which may indicate accidents, road blockages, or suspicious activities, facilitating timely intervention.
- Sentiment Analysis using Bidirectional Encoder Representations from Transformers (BERT) can be used to analyze social media content to detect signs of radicalization and extremist behavior, providing insights to law enforcement for preventive measures.
- Isolation Forests Algorithms can be used to monitors critical infrastructure systems for unusual activity or performance issues, providing early warnings and alerts for potential threats or failures.
- Adaptive Neuro-Fuzzy Inference System (ANFIS) can be used for time series analysis and forecasting of flood levels based on historical data and edge devices
- ConvLSTM Algorithms that combine convolutional layers with LSTM units to handle spatiotemporal data (e.g., vegetation type, topography), weather data along with past fire incidents. Convolutional operations are applied within the LSTM cells to capture spatial correlation for predicting future fire spread over time.
- YOLO (You Only Look Once) models are employed in drones and robotic systems to locate victims in disaster zones, such as collapsed buildings or flood areas, enabling efficient search and rescue operations in wide area coverage.
- Decision Trees and Fuzzy C-Means (FCM) clustering techniques can be used for analyzing complex, uncertain data. In the context of risk assessment and recommending actions for first responders, by grouping similar risk factors and suggesting targeted responses for optimal routes, potential victim locations, triaging, optimal asset management and tactical decision improving effectiveness of emergency operations.