The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this area of research have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak that were posted on Twitter between 7 May 2022 and 3 March 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about Mpox, Updates on Cases and Investigations about Mpox, Mpox and the LGBTQIA+ Community, and Mpox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was Views and Perspectives about Mpox. This was followed by the theme of Mpox and the LGBTQIA+ Community, which was followed by the themes of Mpox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with related studies in this area of research is also presented to highlight the novelty and significance of this research work.

The digitalization of news media become a good indicator of progress and signal to more threats. Media disinformation or fake news is one of these threats, and it is necessary to take any action in fighting disinformation. This paper utilizes ground truth-based annotations and TF-IDF as feature extraction for the news articles which is then used as a training data set for Multinomial Naive Bayes. The model has an accuracy of 99.46% in training and 88.98% in predicting unseen data. Tagging fake news as real news is a concerning point on the prediction that is indicated in the F1 score of 89.68%. This could lead to a negative impact. To prevent this to happen it is suggested to further improve the corpus collection, and use an ensemble machine learning to reinforce the prediction

I have recently updated my extensive analysis of the popularity of data science software. This update covers perhaps the most important section, the one that measures popularity based on the number of job advertisements. I repeat it here as a blog post, so you don't have to read the entire article. One of the best ways to measure the popularity or market share of software for data science is to count the number of job advertisements that highlight knowledge of each as a requirement. Job ads are rich in information and are backed by money, so they are perhaps the best measure of how popular each software is now.

At RapidMiner, we all know the power that artificial intelligence has to positively shape the future--in business and in the world at large. While many enterprises are relatively new to implementing AI, I've spent years scrutinizing prominent uses cases and staying on top of the latest trends. The hype around AI has only grown in 2022, but sustaining trends are what differentiates baseless hype from cold, hard reality. In this post, I'll break down the hype and the buzzwords to walk through what I consider to be the top five current AI and machine learning trends and how I predict they'll impact data science in the years to come. As I laid out in my data science manifesto, accountability is an essential part of a data scientist's role.

Although reading books and watching lectures is a great way to learn analytics – it is best to start doing. However, it can be quite tricky to start doing when it comes to languages such as Python and R if someone does not have a coding background. Not only do you need to know what you are doing in terms of analytical procedures, but you also need to understand the nuances of programming languages which adds onto the list of things to learn to just get started. Therefore, the best middle ground between knowledge acquisition (books, videos, etc.) and conducting advanced analytics (Python, R, etc.) is by using open-source analytics software. These types of software are great for both knowledge acquisition and actually doing analysis as documentation is built into the software and you can start doing relatively complex tasks with only mouse clicks.

Machine learning (ML) is one approach for businesses to improve how they use large data to better understand their consumers' behaviour, happiness and loyalty. ML can look for patterns and abnormalities that users wouldn't think to look for on their own. Some machine learning algorithms are pre-programmed to specialise in a certain task, but in this article, we'll focus on machine learning tools that allow users to create their own machine learning methods for any data they have. Now, let's get down to the top 10 machine learning tools of 2021. Shogun toolbox, often known as Shogun, is a machine learning tool library that is independent and accessible to use.

More accessible than ever, ML will be leveraged to reshape essential processes within the industry. Since the Industrial Revolution, manufacturing has undergone several transformations to harness new technologies as they become available. Over the past decade, advancements such as complex robotic systems, IoT technology, and artificial intelligence (AI) have pushed us into yet another round of technological adoption, the Industry 4.0 Revolution. As manufacturers embrace cutting-edge technologies, old methods are being augmented to achieve better, safer, and more profitable operations. Recently, machine learning (ML)--a sub-domain of AI that provides systems with the ability to automatically learn from experience without being explicitly programmed--has become increasingly pervasive in nearly every industry, but it's only just beginning to live up to its full potential, especially in manufacturing.

Pekka Koskinen is the CEO & Founder of Leadfeeder, a lead generation software. Did you know that the number of marketers adopting AI technology grew by 44% between 2017 and 2018? At Leadfeeder, we use machine learning to filter ISPs and nonrelevant hostnames out of the lead data we provide to customers. LinkedIn's VP of artificial intelligence, Deepak Agarwal, has even gone on record declaring that "at LinkedIn, AI is like oxygen." Compared to the collective and growing enthusiasm, however, AI's actual implementation has been relatively low.

In hospitals, it helps doctors decipher X-rays, and in banks, it helps calculate financial risks. Artificial intelligence has been used in commercial relatively recently, but it is no less exciting. But how is make success in such an implementation? From 2012, when one researcher won a competition for image recognition using artificial intelligence, and to this day, when machine learning has become part of the technology industry, all experts understand that MLOps need for the successful use of AI. Cloudera's data engineer, Santiago Giraldo, estimates that only 13% of all projects go through the experimental stage, while others don't. MLOps was created as an analog of DevOps - a modern practice of creating, implementing, and running programs for corporations.

Rapid Miner is a platform for data scientists and big data analysts to quickly analyse their data. Rapid Miner has taken a huge leap in the AI community since it is most popularly used by non-programmers and researchers. The platform provides a vast number of options in terms of plugins and data analysis techniques. Apart from this, it is also compatible with iOs, Android, and web application tools like Node JS and flask. This platform is useful for anyone with an idea they would like to experiment with without spending much time or effort on it.