Climate Change Analytics: Predicting Carbon Price and CO2 Emissions

The focus of this research is to predict the greenhouse gas emissions and the funding to help combat this global problem. There must be consistent funding to support and sustain the planet ecosystems. This research is motivated by the global concern of climate change caused by greenhouse gas emissions and the need to consider a multinational strategy to provide funding to combat it. The goal of the funding is to provide adequate financial backing and support for innovations needed to combat this problem. This research leverages the capabilities of machine learning found in Weka and forecasting and visualization in Tableau. The models are expected to predict a carbon tax rate that could be used multi-nationally. The results and performance measures will be scrutinized to identify the model that is the best fit for the proposed solution. The economic, population, land temperature, current multinational carbon tax rates and reverse carbon initiatives data will be interrogated by supervised machine learning models or classifiers (Frank et al., 2011). The CO2emissions for China, India and the United States will also be predicted to show expected increases in emission based on historical data through Tableau forecasting. This study concluded that a carbon rate can adequately be created and predicted using machine learning models. And, CO2emissions can also be predicted using public open data sources that provide economic, population and surface temperature features

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Tissue segmentation using medical image processing chain optimization

Surveyed literature shows many segmentation algorithms using different types of optimization methods. These methods were used to minimize or maximize objective functions of entropy, similarity, clustering, contour, or thresholding. These specially developed functions target specific feature or step in the presented segmentation algorithms. To the best of our knowledge, this thesis is the first research work that uses an optimizer to build and optimize parameters of a full sequence of image processing chain. This thesis presents a universal algorithm that uses three images and their corresponding gold images to train the framework. The optimization algorithm explores the search space for the best sequence of the image processing chain to segment the targeted feature. Experiments indicate that using differential evolution to build Image processing chain (IPC) out of forty-five algorithms increases the segmentation performance of basic thresholding algorithms ranging from 2% to 78%