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Aerial LIDAR Processing to Prevent Wildfires
Get PDFThis project looks at the use of Aerial LIDAR data to prevent wildfires. Aerial LIDAR can be captured by a drone or airplane and is much more efficient then using ground crews. Using LIDAR forest floors are exposed and through processing using GIS software large areas can be mapped and have proper analysis conducted on them
The Application of Design Thinking on Evaluating a User Self-Service Data Analytics/Science Platform
Get PDFThis thesis is aimed at utilising design thinking and the first half of the double diamond framework to i) set-up a research and select the appropriate participants, ii) gather requirements and define user personas from those eligible participants, and then iii) define the framework for evaluating a user self-service data analytics/science platform. Derived from the author’s own experiences, both as a Business Analyst (BA) and Citizen Data Scientist, with no-, low-, and code-based data analytics and science platforms are being implemented for enabling user self-service analytics – for users who are completely new to the space of data analysis and science as well as those who are experienced analysts and data scientists across a variety of industries and global regions – and there has been a need to outline an enablement process for this space. Through this research, the current state of the marketplace is researched, analysed, and evaluated alongside user research carried out on the feasibility and applicability of a UI- and UX-centric framework for ensuring human-centred design. A literature review showcases the benefits of human-centred design for humans when it comes to usability and techniques for such an application in various other fields. The key aspects of this research are to understand the users’ capabilities, needs, and wants, then categorise those users into personas, analyse and segment the requirements, create functional and non-functional requirements for platform capabilities, and then, ultimately, provide an evaluation framework for any organisation and/or individual looking for a user self-service data analytics/science platform by carrying out a pilot research study on ten (10) participants
How the Power of Machine – Machine Learning, Data Science and NLP Can Be Used to Prevent Spoofing and Reduce Financial Risks
Get PDFThis paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024.
Keywords: Machine Learning, NLP, Financial Risks, Python programming, MongoDB Atlas, Spoofing, Cyber Securit
What Interactive Web Features are Most Used
Get PDFThe use of interactive features in websites has become common place on the internet. People use these tools to help navigate and understand the content related to that website. However, due the large variety of websites it can be tricky to understand what features are best to utilize based on the topic of your site. This paper seeks to address this issue by researching how users interact and utilized different features on different websites. Research is gathered via scholarly articles and direct data gathered from volunteers. This data shows users tend to favor more interactive tools to help with navigation, but this can vary depending on different situations
The Revenue Operations (RevOps) Framework: A Qualitative Study of Industry Practitioners.
Get PDFIn recent years Revenue Operations or RevOps has emerged in professional circles as a new approach to manage Sales, Marketing and Customer Success teams in the context of b2b sales. In practitioner circles, RevOps definitions range from the increased collaboration of the three job functions to an all-out creation of job function within organizations. While the subject of interdepartmental alignment has been covered extensively in academia (albeit not exhaustively), RevOps as a term and set of practices has received no attention and industry practitioners struggle to find a unified set of best practices that isn’t coming from organizations trying to pitch a product or service. As a first step and to provide some background we decided to perform a Multivocal style Literary Review to take advantage of grey literature such as blogs and industry reports. Following, a more formalized literature review serves to give a background in issues around organizational integration and alignment along with an exploration of the concepts of Sales, Marketing and Customer Success within organizations and how these are changing. We then performed an exploratory based interview study involving multiple RevOps professionals using the grounded theory approach to help guide our line of questioning as we interviewed practitioners and learned new concepts. As a main objective, we aim to produce a standardized framework to help practitioners understand the key tenets of Revenue Operations, how it may be implemented, what challenges organizations can face and provide researchers with a basis to explore the concept in further detail
What Twitter Tells Us about Online Education During the COVID-19 Pandemic
No full textThe Covid-19 pandemic caused schools to shut down in at least 191 countries, resulting in challenges for students and educators. While the instructional technology community was already researching online education prior to Covid-19, this pandemic has resulted in mainstream discussions about the effectiveness of online learning as an alternative to traditional teaching methods. Unlike traditional instructional technology conversations that are followed by a niche set of experts, Covid-19 brought online learning to the forefront involving social media discourse across an array of influential voices. With so many new voices joining the dialogue, questions have emerged about the types of conversation that took place on Twitter about Covid-19 and online education. This study utilizes Twitter as a research tool to understand the pandemic and online education
Anonymized location data reveals trends in legal Cannabis use in communities with increased mental health risks at the start of the COVID-19 pandemic
No full textBackground: The coronavirus disease 2019 (COVID-19) pandemic has led to increases in felt negative affect for many. This is concerning as individuals at increased risk for mental health issues are often more likely to use substances to cope with stressors. Objectives: The aim of the current study is to examine whether communities reporting an increased risk for developing mental health issues showed differential patterns of legal cannabis use as the pandemic began. A secondary goal is to examine the feasibility of using anonymized location data to uncover community consumption patterns of potential concern. Methods: Anonymized location data from approximately 10% of devices in the United States provided a count of the number of visitors to 3,335 cannabis retail locations (medical and recreational) each day from December 1st 2019 through April 2020. Visitor counts were merged with the average number of mentally unhealthy days (aMUDs) reported in the Federal Information Processing Standard (FIPS) county the retailer was located along with FIPS county population and poverty rate estimates. A Poisson spline regression predicting visitors by day, aMUDs, as well as their interaction was performed, entering population and poverty rate as covariates. Results: As the pandemic began communities reporting a greater aMUDs showed greater visitation to cannabis retailers. Conclusions: These results suggest that the COVID-19 pandemic may have led to increased legal cannabis use in at risk communities. They also highlight the value anonymized location data can provide policymakers and practitioners in uncovering community level trends as they confront an increasingly uncertain landscape
Comparison of Major Cloud Providers
Get PDFThis paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing
