CLOUD-BASED MACHINE LEARNING AND SENTIMENT ANALYSIS

The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second research study involved the use of text-mining social media, specifically Reddit. We mined up to 100,000 comments in multiple subreddits and tested for hate speech via a custom designed version of the Python Vader sentiment analysis package. Our work integrated standard sentiment analysis with Hatebase.org and we demonstrate our new method can better detect hate speech in social media. Following sentiment analysis and hate speech detection, in the third research project, we applied statistical techniques in evaluating the significant difference in text analytics, specifically the sentiment-categories for both lexicon-based software and cloud-based tools. We compared the three big cloud providers, AWS, Azure, and GCP with the standard python Vader sentiment analysis library. We utilized statistical analysis to determine a significant difference between the cloud platforms utilized as well as Vader and demonstrated that each platform is unique in its analysis scoring mechanism

The combined effect of PDX1, epidermal growth factor and poly-L-ornithine on human amnion epithelial cells’ differentiation

Comparison of transduction efficiency of various adenoviral titres. (a) hAECs were transduced with adenovirus harbouring an mPdx1 vector at various MOIs (multiplicity of infection). Cells were stained with an mPdx1-specific antibody (Texas-Red conjugate) at 24 h and 48 h post infection to determine the transduction efficiency. Nuclei were counter stained with DAPI (blue). (b) Calculation of transduction efficiency from two different microscopic fields of cells 24 h after transduction with 50 MOI of the mPdx1-harbouring adenovirus. Cells were viewed using the 10X objective of an Olympus inverted fluorescence microscope. Purple nuclei are those that are stained by both DAPI and Texas-Red conjugated secondary antibody. (ZIP 1201 kb

Historical Perspectives and Current Challenges in Cell Microencapsulation

The principle of immunoisolation of cells is based on encapsulation of cells in immunoprotective but semipermeable membranes that protect cells from hazardous effects of the host immune system but allows ingress of nutrients and outgress of therapeutic molecules. The technology was introduced in 1933 but has only received its deserved attention for its therapeutic application for three decades now.In the past decade important advances have been made in creating capsules that provoke minimal or no inflammatory responses. There are however new emerging challenges. These challenges relate to optimal nutrition and oxygen supply as well as standardization and documentation of capsule properties.It is concluded that the proof of principle of applicability of encapsulated grafts for treatment of human disease has been demonstrated and merits optimism about its clinical potential. Further innovation requires a much more systematic approach in identifying crucial properties of capsules and cellular grafts to allow sound interpretations of the results

Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms

Machine Learning can be used with cybersecurity data to protect organizations by using artificial intelligence (AI) to generate rules and models for thread detection. Cloud platforms offer the ability to scale AI efforts as well as automatically generate machine learning models (Auto ML). Adoption of Auto-ML is increasing which is resulting in rapid improvements of the technology. The objective of this paper is to demonstrate the Auto ML functionalities against cyber security threat detection using available tools in free tier accounts created on three different cloud platforms (Microsoft Azure, Google, and IBM). We determined the performance of these tools by the evaluating the optimization speed and accuracy results. A comparison of the advantages of each of the results from the different platforms are presented. Overall, all three platforms performed greater than 70% accuracy with the IBM Cloud Platform having the strongest performance