The graph represents a network of 1,081 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 04 February 2022 at 12:05 UTC. The requested start date was Friday, 04 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 21-hour, 2-minute period from Tuesday, 01 February 2022 at 03:57 UTC to Friday, 04 February 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

However, what cannot go unnoticed is the fact that artificial intelligence is the key that unleashes its power. The importance of AI in almost every field has brought in a heap of opportunities for people who are looking forward to making a promising career. You need to have a fair understanding about AI to land a decent job. Taking this into account, there are countless AI courses and programs available that one can rely on. Which one to choose among the lot has always been a question. Well, we have got you covered.

Data science is a rapidly growing field with many career opportunities. Data scientists are at the forefront of solving complex problems using data-driven approaches, from predicting market trends to developing personalized recommendations. To succeed in this field, you'll need a strong foundation in mathematics, statistics, and computer science and the ability to work with large and complex datasets. The demand for skilled data scientists is high, and the earning potential is significant. According to Glassdoor, the median salary for a data scientist is over $100,000 per year. With such promising career prospects, it's no wonder that so many people are interested in pursuing data science courses. Although most data scientist jobs require you to have a bachelor's or master's degree in a related field, several jobs in the data science domain are open to those who have the right skills or experience.

This article was published as a part of the Data Science Blogathon. This is a multiclass classification project to classify the severity of road accidents into three categories. This project is based on real-world data, and the dataset is also highly imbalanced. There are three types of injuries in a target variable: minor, severe, and fatal. Road accidents are the major cause of unnatural deaths around the world.

Deterministic solutions are becoming more critical for interpretability. Weighted Least-Squares (WLS) has been widely used as a deterministic batch solution with a specific weight design. In the online settings of WLS, exact reweighting is necessary to converge to its batch settings. In order to comply with its necessity, the iteratively reweighted least-squares algorithm is mainly utilized with a linearly growing time complexity which is not attractive for online learning. Due to the high and growing computational costs, an efficient online formulation of reweighted least-squares is desired. We introduce a new deterministic online classification algorithm of WLS with a constant time complexity for binary class rebalancing. We demonstrate that our proposed online formulation exactly converges to its batch formulation and outperforms existing state-of-the-art stochastic online binary classification algorithms in real-world data sets empirically.

This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink rate, facial actions units) and user actions (e.g., head pose, distance to the camera). The multimodal system uses the following modules based on Convolutional Neural Networks (CNNs): Eye blink detection, head pose estimation, facial landmark detection, and facial expression features. First, we individually evaluate the proposed modules in the task of estimating the student's attention level captured during online e-learning sessions. For that we trained binary classifiers (high or low attention) based on Support Vector Machines (SVM) for each module. Secondly, we find out to what extent multimodal score level fusion improves the attention level estimation. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment that contains data from 38 users while conducting several e-learning tasks of variable difficulty (creating changes in student cognitive loads).

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled massive research and development efforts in the artificial intelligence for education (AIEd) sector. In this review article, we investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety - from students admissions, course scheduling etc. in the proactive planning phase to knowledge delivery, performance assessment etc. in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 194 original research articles published in the past two decades i.e., 2003 - 2022. We discuss the paradigm shifts in the solution approaches proposed, i.e., in the choice of data and algorithms used over this time. We further dive into how the COVID-19 pandemic challenged and reshaped the education landscape at the fag end of this time period. Finally, we pinpoint existing limitations in adopting artificial intelligence for education and reflect on the path forward.

MINNEAPOLIS – Since its public launch in November, ChatGPT has taken the world by storm. The free artificial intelligence chatbot has attracted the attention of giant companies like Microsoft, who will reportedly cut a potential $10 billion deal with the company who owns ChatGPT, OpenAI. "What's unique about this is that it presents information in a way that a human would," says Gene Munster, a managing partner for Loup Ventures, a technology research and investment firm. "It essentially has the ability to think." Artificial intelligence research has been happening for decades, but only recently has it gotten good enough that OpenAI released its ChatGPT chatbot to the public via an easy-to-use website.

In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers. It will be important for individuals to stay up to date on the latest developments in AI and to consider how their skills and expertise can be leveraged in a world where machines are increasingly able to perform many tasks.

Artificial intelligence empowers increasingly complex interactions between humans and machines. This technology, currently popularized by ChatGPT, provides a potentially enormous opportunity for business professionals, for business students, and for business educators. However, these same technologies pose similarly enormous challenges for teachers attempting to illustrate, explain, and apply these ideas to students. How can teachers intentionally and openly incorporate AI into our classrooms while reducing the risk of students substituting AI output for their own (also known as "cheating")? The term "metaverse" was first popularized in 2003 by Neal Stephenson in a science fiction novel entitled "Snow Crash."