Machine Learning in Education Market research report is the new statistical data source added by A2Z Market Research. "Machine Learning in Education Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market". Machine Learning in Education Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.

Kay Firth-Butterfield is Head of AI & ML at World Economic Forum, and a humanitarian with a strong sense of social justice. Kay talks to us about why AI Ethics matter during her presentation at the RE•WORK Applied AI Virtual Summit. Read the full transcript below and watch the video here. It's really great to be with you, and thanks to RE.WORK for making it happen. My title is, Does AI Ethics Matter?

Distance learning and remote teaching have increased reliance on tech making it a reality, and able to traverse borders with less regard for physical geo-locations. There are numerous restrictions that prevent online learning from being ubiquitous such as internet accessibility, access to learning platforms, adequate attention for learners individually, and language barriers. Video-based learning could be enough for urban pupils, but for rural areas, connectivity becomes low, less reliable, and interrupted lessons. For international students, pursuing higher education or probably taking vocational courses, a lack in fluency in English or any other intermediary languages can play a significant role in limiting proper online learning. Learning a new language is the objective for work or to further studies, but the bigger question is how technology can bridge the language learning divide.

In this paper, we propose and prove the theorem regarding the stability of attributes in a decision system. Based on the theorem, we propose the LRA framework for accelerating rough set algorithms. It is a general-purpose framework which can be applied to almost all rough set methods significantly . Theoretical analysis guarantees high efficiency. Note that the enhancement of efficiency will not lead to any decrease of the classification accuracy. Besides, we provide a simpler prove for the positive approximation acceleration framework.

In recent years the increase of machine learning applications to water resources have allowed us to propose new solutions to complex problems. Alumni from the Hydroinformatics program have explored new areas that in many cases have led to implementations at different places in the world, and have shown to be able to compete with ongoing traditional solutions. For this seminar we will make an overview of some of the most recent ideas of applications of machine learning in Hydroinformatics. These presentations will be divided into two sessions that will cover forecasting problems. An introduction in both sessions to a variety of machine learning basic concepts will be given to introduce the topics, limitations and a friendly way to see the theory.

A stellar flare is a sudden flash of increased brightness on a star. Young stars are prone to these flares which can incinerate everything around them, including the atmospheres of nearby planets starting to form. Finding out how often young stars erupt can help scientists understand where to look for habitable planets. But until now, searching for these flares involved poring over thousands of measurements of star brightness variations, called'light curves', by eye. Now, an international team of scientists based in Australia and the USA have used machine learning to make the search faster and more effective.

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods.

In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task. First, we present a simplified case in which the goal is to estimate the means of two Gaussian variables, for the purpose of gaining some insights on the advantage of the proposed cross-learning strategy. Then we provide a stochastic projected gradient algorithm to perform cross-learning over a generic loss function. If the number of parameters is large, then the projection step becomes computationally expensive. To avoid this situation, we derive a primal-dual algorithm that exploits the structure of the dual problem, achieving a formulation whose complexity only depends on the number of tasks. Preliminary numerical experiments for image classification by neural networks trained on a dataset divided in different domains corroborate that the cross-learned function outperforms both the task-specific and the consensus approaches.

Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, shown that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained both feature scenarios.

The global AI-based fever detection camera market size is USD1.28 billion by 2020 and is projected to reach USD 2.19 billion by 2027, exhibiting a CAGR of 8.0% during the forecast period. The worldwide surge in the growth of corona infected people has led to the emergence of advanced artificial intelligence-based fever detection cameras to monitor and detect human body temperature. Vaccine for coronavirus is still in its development stage and hence, the only way to reduce the spread of this pandemic is to isolate the infected person from the crowd. This type of camera is being considered as an efficient and effective device to identify a person with high temperature as fever is one of the symptoms of coronavirus. An individual with high temperature is further screened with virus-specific tests.