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Task                                             #Creativity & Innovativeness




                                               Case Study: Cleaning Oceans
                The world's oceans are increasingly polluted  with  plastic waste,  which  poses a  severe threat  to marine  life
                and ecosystems. Identifying the location and density of plastic waste patches is challenging due to the vast
                and dynamic nature of the ocean. Traditional methods of detecting and removing plastic waste are inefficient
                and labour-intensive. Coastal communities and environmental organisations lack the advanced tools needed to
                monitor and clean up these plastics effectively, leading to continued environmental degradation and harm to
                marine biodiversity.
                Can we solve this problem with AI? How?

                Now that you are aware of AI concepts, plan to use them in accomplishing your task. Start with listing down all
                the factors which you need to consider saving the oceans. This system aims to:






                  Problem Scoping: Define the problem statement, objectives, and constraints of the AI project. Identify
                  stakeholders, understand the business context, and outline the scope of work to be undertaken.

                Define the scope to develop AI models for identifying and removing ocean plastic waste. Identify key challenges
                such as ocean dynamics, plastic detection limitations, and scalability of cleanup efforts.

                  Data Acquisition: Gather relevant datasets required for training and evaluating AI models. Ensure data
                  is collected from reliable sources, adheres to legal and ethical standards, and is prepared in a format
                  suitable for analysis.


                Gather satellite imagery, drone footage, and oceanographic data from various sources to create a comprehensive
                dataset. Utilise data from environmental organisations, governments, and research institutions.

                  Data  Exploration:  Explore  and  analyse  the  acquired  data  to  understand  its structure,  quality,  and
                  relationships. This phase involves data cleaning, visualisation, and statistical analysis to uncover patterns
                  and insights that inform subsequent modelling.

                Clean and preprocess the data to extract relevant features such as plastic concentrations, ocean currents, and
                marine life habitats. Explore visualisation techniques to understand spatial and temporal patterns of plastic
                waste.

                  Modelling: Develop and implement AI models  using appropriate algorithms and techniques. This
                  phase includes feature selection, model training, parameter tuning, and validation to optimise model
                  performance and accuracy.

                Develop AI models such as convolutional neural networks (CNNs) for image recognition to detect and classify
                plastic debris in ocean images. Implement reinforcement learning algorithms for optimising cleanup strategies
                based on real-time data.

                  Evaluation:  Assess  the  performance  of  trained  AI  models using evaluation  metrics and  validation
                  techniques. Validate model predictions against ground truth data to ensure reliability and generalisation
                  ability across different scenarios.



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