Carbon Emission Prediction on the World Bank Dataset for Canada

The continuous rise in CO2 emission into the environment is one of the most crucial issues facing the whole world. Many countries are making crucial decisions to control their carbon footprints to escape some of their catastrophic outcomes. There has been a lot of research going on to project the amount of carbon emissions in the future, which can help us to develop innovative techniques to deal with it in advance. Machine learning is one of the most advanced and efficient techniques for predicting the amount of carbon emissions from current data. This paper provides the methods for predicting carbon emissions (CO2 emissions) for the next few years. The predictions are based on data from the past 50 years. The dataset, which is used for making the prediction, is collected from World Bank datasets. This dataset contains CO2 emissions (metric tons per capita) of all the countries from 1960 to 2018. Our method consists of using machine learning techniques to take the idea of what carbon emission measures will look like in the next ten years and project them onto the dataset taken from the World Bank's data repository. The purpose of this research is to compare how different machine learning models (Decision Tree, Linear Regression, Random Forest, and Support Vector Machine) perform on a similar dataset and measure the difference between their predictions.

Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis

Around 450 million people are affected by pneumonia every year which results in 2.5 million deaths. Covid-19 has also affected 181 million people which has lead to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X-ray) and covid-19 (RT-PCR) require the presence of expert radiologists and time, respectively. With the help of Deep Learning models, pneumonia and covid-19 can be detected instantly from Chest X-rays or CT scans. This way, the process of diagnosing Pneumonia/Covid-19 can be made more efficient and widespread. In this paper, we aim to elicit, explain, and evaluate, qualitatively and quantitatively, major advancements in deep learning methods aimed at detecting or localizing community-acquired pneumonia (CAP), viral pneumonia, and covid-19 from images of chest X-rays and CT scans. Being a systematic review, the focus of this paper lies in explaining deep learning model architectures which have either been modified or created from scratch for the task at hand wiwth focus on generalizability. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified, and hence they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the datasets, model architectures, and results, we aim to provide a one-stop solution to beginners and current researchers interested in this field.

Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images

Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.