Hello. Good day everyone. I'm very working as a senior resident computer program with Search Technology Solutions here in us. So here I am today to present my ideas on emergency response using AI driven cloud computing, robotics, and predictive analysis. With all this being said let me start my presentation. So before getting into the actual presentation, I would like to first provide you some insights on what are the present challenges that we are facing with the options that we have with the traditional emergency responses. So here I would like to emphasize on two points. The first one being the growing disaster intensity. So when I say growing disaster intensity. I am more emphasizing on the climate change that accelerated the frequency and severity of national disasters worldwide. So the traditional emergency response systems build on legacy infrastructure and manual coordination struggle to keep pace with the speed and scale of modern crisis. And that being said, these conventional approaches rely heavily on human decisions and soil communication channels and reactive rather than being proactive measures. So creating dangerous gaps in response times and resource allocation with this existing options. So the first one being growing disaster intensity. The second one is systematic vulnerabilities. So when I say systematic vulnerabilities, they are like more of the fragmented data sources preventing unified situation awareness and slow information, limited predict to capabilities and inadequate. Cross jurisdictional coordination mechanisms and also some communication bottlenecks. So these are a couple of issues that we are facing with current day options with respect to the emergency response. Let's start with a new patterning of safety and security operations. So here when I say a new paradigm, it's more of integration of artificial intelligence with cloud computing, robotics, and predictive analytics. Which represents a fundamental shift in how safety and security operation systems that is SOS functions during emergencies. And by unifying disparate technologies into cohesive frameworks, we create the system cap capable of processing vast data streams and predicting disaster trajectories and automating critical decisions and deploying resources with unprecedented precision and speed. So now for this speed and to achieve this strength, so I would like to concentrate more on three core technologies, which actually are powering our intelligence response. That is power lifting our intelligent response. The first one being machine learning. So the pattern recognition algorithms that the machine learning analyzes using historical. Disaster data to predict event likelihood, severity, and optimal intervention strategies. And the next one is visual deep learning. When I say visual deep learning, it's more about the computer vision, the systems process, like the satellites are providing as the imaginary, sorry, imagery and the drone footages and surveillance speeds to assess damage and identify hazards. And the next one being NLP. That is natural language processing here in NLP. We use engines that extract action level intelligence from emergency calls and from, say, some social media and other news channels using the filtering signal from noise during the crisis. So that being said I also want to emphasize on transforming fragmented data into actionable intelligence. Here we are using three concepts. The first one is data collection, AI processing, and actionable insights. These three actually transform the fragmented data into an actionable intelligence. So first one, as I said, is data collection. So with data collection we more rely on the satellite imaginary imagery, the IOT sensors, the social media, and some emergency communications. So coming to AI processing be more to utilize the real time data pattern detection, threat assessment, and some prediction models. And the last one being actionable insights. So here in actionable insights sorry. If we use some resource deployment, evacuation roads and situational awareness and decision support. So using all these three intelligent actionable items we can. Converge multiple data streams, each with different formats, update frequencies at reliable levels and present both technical and operational challenges. And also, AI systems must harmonize these inputs, validate information accurately. And this being said, let's go to the next slide where I want to showcase the cloud native architecture. Wherein I have utilized serverless and continuous platforms, these two. And let's get started with the architecture. So when I say serverless and content, continuous platforms still mostly I'm talking about cloud native architectures, which enable emergency response systems to scale dynamically during crisis and leveraging serverless functions for efficient data stream processing without any infrastructure management overhead. Okay. And coming to the content rights platforms so here, when I say content rights platforms, it's more likely about Kubernetes which ensures us high availability through automatic failover, load balancing, and multiple regional deployment, and also guaranteeing the operational continuity even when the local infrastructure fail. So considering these two platforms. So we can also leverage the rapid elastic scaling using automatic resource allocation during demand spikes and geographical redundancy wherein multiple region deployment for disaster resilience. And the last one being cross jurisdictional continuity wherein seamless data sharing between agencies and regions. So this is about the native cloud native architecture. And coming to next slide here, I want to showcase. A smart city flood management using AI inaction. So what I'm doing here is I consider a metropolitan area, which is facing unprecedented rainfall and rising river levels. So here the traditional systems, like we spoke about in the first slide which mostly rely on manual process, like on manual gauge ratings, weather forecasts, and some historical flood maps. All processed by human coordinators making evacuation decisions really extreme in, in, in real extreme pressure. So what is AI doing here? So AI system transforms this scenario entirely, the predictive model analysis using the rainfall patterns, soil saturation, river flow rates, and tide schedules to forecast flood extent before water arrives. So this this, all this data is leveraged by ai and the other thing is the comfort vision monitors for managing the blockages in the systems. And apart from that, we also have the NLP which uses social media posts and news channels for emerging flight reports in the real time. So these are some points that AI is leveraged. And coming to enhanced capabilities through intelligent systems. And these capabilities come with three points here. The first one being dynamic evacuation planning. So here the A optimizes evacuation rules based on realtime traffic and predicted flood parts and shelter capacity and updating recommendations as conditions evolve. And the second one being unified situation level. Yes. Here I want to talk about integrating dashboards. That is some consolidated data from all sources wherein we provide these data to the managers with comprehensive use with the present unfolding situations. The last one being intelligent resource allocation. Here we use ml, that is machine learning, which predicts resource needs by location and time. Okay. Using this data we can supply the personal the personals to that particular location before the crisis actually peaks. The situation is out of control. So this is about the intelligent systems. And in the next site I want to show you a scenario, a simulated scenario with some geospatial mapping. Using some predictive visualization. So here let's say some advanced geospatial systems create high resolution urban models integrating terrain infrastructure and population data. This enables precise flood extent predictions and impact assessments. So in this scenario, the simulation tools that analyze. Allowing the emergency managers to visualize consequences of various interventions such as opening flood barriers or issuing evacuation items based on the scenario. This simulations account for cascading effects, ensuring informed strategic planning for the managers. And the next one is to build this response. We need some real time data. And for if you want to use some real time data, we should maintain privacy and some ethical considerations. So here are three points with respect to real time data and the privacy and ethical considerations with this data. So I would like to talk about three points here. The first one being data minimization principles wherein we collect only information essential for emergency response and implement automatic data retention policies that. Purge personal details once immediate crisis results. Okay, and the second one being the algorithmic transparency. So here with the emerging emergency AI systems they provide explainable decisions, particularly when recommendations affect individual rights or safety. And the decision audit trials enable post-crisis review and accountability with respect to the algorithmic transparency. And the last one with the real time data. It's consent and community trust. So here mostly in this third point, we engage the community in the system design and governance and clear communication about data usage will be given to the community and other consent mechanisms for non-emergency periods and community oversight which helps build public trust essentially for the system being very effective with respect to. So this is what is the privacy and ethical considerations with respect to real time data. And going to the next slide here, we have some frontiers innovations looking forward for the next you next innovations. So here I want to talk about four points over here with respect to the forward looking innovations. The first one being digital wins, wherein the virtual replicas of physical infrastructure enabled continuous simulation. Stress testing and optimization of emergency response plans against evolving urban environments. So that being said, in the second point we use edge computing. So the distributed processing at network edges enables ultra latency decisions when cloud connectivity is compromised. And this is about edge computing. And the third one being federated learning. So the city's collaboratively train AI models without sharing sense to data. Wherein we improve predictive accuracy while preserving privacy and local autonomy. And the last one being autonomous robots. So here, basically what we do is at the ground and aerial situations robots perform delivery supplies wherein when I say robots perform, it's only the situation when humans by themself when they cannot get into the situation or cannot get into that environment. Of rescue operations. Then we use these robots, which do the delivery actions, the risking actions in those tough environments. So these are couple of forward looking innovations and going to the next slide. So here, with all these points, considering all these points and accumulating all these points, I built intelligent response framework with some practical strategies. And the first practical strategy being assess capabilities. So here I audit existing systems and data to identify gaps. And after that we established data infrastructure wherein we deploy sensor networks and secure cloud platforms. And after that we develop AI models. And in developing these AI models, we train and refine AI on instant data. And after the development of AI models, we integrate it and test it. And we connected it to AI, to workflows, and then we simulate it and we test it. And after simulation and testing, we train the personnel. That is what we do here, is we educate the teams on AI capabilities and views. The last point being iterate and improve. So here what we do is we collect feedback and based on the feedback, we improve, like we continuously improve and update and enhance the ai. So after all these things let's talk about some impacts life saved, resources optimized resilience strengthened. So here the first point about the impacts is like the faster response time. So the AI driven systems reduce decision making delays and accelerate resource deployment during critical windows. And the second one being improved resource efficiency. So here some predictive allocation prevents waste while ensuring adequate supplies reach affected areas in time when needed. And the third one being enhance situational awareness. So here we use some unified data platforms where we provide emergency situ, emergency information to the managers. It's significant comprehensive crisis visibility to the managers so that they can act accordingly. And beyond quantitative metrics intelligence, emergency response systems fundamentally transform urban resilience. And then cities become adaptive organisms capable of anticipating threats and acting accordingly, and coordinating complex responses across agencies and learning from each incident how better we can improve in future by providing a better performance. So this is about the impacts. And going to the next slide is here. I would like to conclude saying, building the future of emergency response is always possible when we leverage AI with cloud computing, robotics, and predictive analysis fundamentally to protect societies during this emergency crisis. So as disasters escalate, the rapid and responsible deployment of intelligent systems is crucial. The frameworks and strategies discussed offer a roadmap for emergency management professionals the city planners and technology leaders. So what I'm trying to say here is with all the suggestions and the frameworks and the technology points we can always achieve. A better intelligent adaptive AI response feature. So this is all about the emergency response using ai wherein we leverage cloud computing, robotics, and plate predictive analysis. So I would like to thank you for giving me this opportunity to put forth my ideas and give a presentation about. The emergency responses using AI platform. Thank you everyone. Have a good day.