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Machine Learning in Education Market Report: 2023-2029Machine Learning in Education Market (Newly published report) which covers Market Overview, Future Economic Impact, Competition by Manufacturers, Supply (Production), and Consumption Analysis

The market research report on the global Machine Learning in Education industry provides a comprehensive study of the various techniques and materials used in the production of Machine Learning in Education market products. Starting from industry chain analysis to cost structure analysis, the report analyzes multiple aspects, including the production and end-use segments of the Machine Learning in Education market products. The latest trends in the industry have been detailed in the report to measure their impact on the production of Machine Learning in Education market products.

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Results of the recent scientific undertakings towards the development of new Machine Learning in Education products have been studied. Nevertheless, the factors affecting the leading industry players to adopt synthetic sourcing of the market products have also been studied in this statistical surveying report. The conclusions provided in this report are of great value for the leading industry players. Every organization partaking in the global production of the Machine Learning in Education market products have been mentioned in this report, in order to study the insights on cost-effective manufacturing methods, competitive landscape, and new avenues for applications.

Leading key players in the Machine Learning in Education market are IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning

Product Types: Cloud-Based On-Premise

On the Basis of Application: Intelligent Tutoring Systems Virtual Facilitators Content Delivery Systems Interactive Websites Others

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Regional Analysis For Machine Learning in EducationMarket

North America(the United States, Canada, and Mexico) Europe(Germany, France, UK, Russia, and Italy) Asia-Pacific(China, Japan, Korea, India, and Southeast Asia) South America(Brazil, Argentina, Colombia, etc.) The Middle East and Africa(Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

This report comes along with an added Excel data-sheet suite taking quantitative data from all numeric forecasts presented in the report.

Whats in the offering: The report provides in-depth knowledge about the utilization and adoption of Machine Learning in Education Industries in various applications, types, and regions/countries. Furthermore, the key stakeholders can ascertain the major trends, investments, drivers, vertical players initiatives, government pursuits towards the product acceptance in the upcoming years, and insights of commercial products present in the market.

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Lastly, the Machine Learning in Education Market study provides essential information about the major challenges that are going to influence market growth. The report additionally provides overall details about the business opportunities to key stakeholders to expand their business and capture revenues in the precise verticals. The report will help the existing or upcoming companies in this market to examine the various aspects of this domain before investing or expanding their business in the Machine Learning in Education market.

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Machine Learning in Education Market Size 2023 by Top Key ... - The Bowman Extra

The Intersection of Big Data, Machine Learning, and Web 3.0: Shaping the Future of Internet Development

The dawn of the internet brought with it a revolution in the way we communicate, share information, and conduct business. The subsequent evolution of the internet, often referred to as Web 2.0, saw the rise of social media, user-generated content, and increased interactivity between users and websites. Today, we stand at the precipice of another major shift in the digital landscape: the emergence of Web 3.0, also known as the Semantic Web. This new era of internet development is characterized by a more intelligent, personalized, and secure online experience, and it is being shaped by the convergence of big data, machine learning, and advanced algorithms.

Big data refers to the massive amounts of structured and unstructured data generated by individuals, businesses, and machines on a daily basis. This data, when harnessed and analyzed effectively, can provide valuable insights and drive informed decision-making. Machine learning, a subset of artificial intelligence, enables computers to learn from data and improve their performance over time without being explicitly programmed. Together, big data and machine learning are playing a crucial role in the development of Web 3.0, as they allow for the creation of more intelligent and responsive online systems.

One of the key features of Web 3.0 is the ability to understand and interpret the meaning behind data, rather than just processing and displaying it. This is where machine learning comes into play, as it allows computers to analyze vast amounts of data and identify patterns, trends, and relationships that would be impossible for humans to discern. By applying machine learning algorithms to big data, developers can create websites and applications that are capable of understanding natural language, recognizing images, and making predictions based on user behavior.

Another important aspect of Web 3.0 is personalization. As users increasingly demand tailored experiences and content that is relevant to their interests, big data and machine learning are helping to make this a reality. By analyzing user data, such as browsing history, location, and social media activity, machine learning algorithms can make informed recommendations and deliver personalized content. This not only enhances the user experience but also allows businesses to target their marketing efforts more effectively.

Security is also a major concern in the development of Web 3.0, as the proliferation of data and increased connectivity between devices have led to a rise in cyber threats. Machine learning can play a vital role in combating these threats by analyzing data from various sources to identify patterns and anomalies that may indicate a potential security breach. This allows for the development of more robust security systems that can proactively detect and respond to threats, rather than simply reacting to them after the fact.

In addition to these applications, big data and machine learning are also driving innovation in areas such as virtual reality, augmented reality, and the Internet of Things (IoT). These technologies are set to play a significant role in the future of internet development, as they enable more immersive and interactive experiences, as well as greater connectivity between devices and systems.

In conclusion, the intersection of big data, machine learning, and Web 3.0 is shaping the future of internet development by enabling more intelligent, personalized, and secure online experiences. As we continue to generate vast amounts of data and develop increasingly sophisticated algorithms, the possibilities for innovation and growth in this space are virtually limitless. It is an exciting time to be involved in the digital world, as we stand on the cusp of a new era that promises to revolutionize the way we interact with the internet and each other.

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The Role of Big Data and Machine Learning in Web 3 0 Development - CityLife

A novel machine learning tool, called CRANK-MS, was able to identify, with high accuracy, people who would go on to develop Parkinsons disease, based on an analysis of blood molecules.

The algorithm identified several molecules that may serve as early biomarkers of Parkinsons.

These findings show the potential of artificial intelligence (AI) to improve healthcare, according to researchers from the University of New South Wales (UNSW), in Australia, who are developing the machine learning tool with colleagues from Boston University, in the U.S.

The application of CRANK-MS to detect Parkinsons disease is just one example of how AI can improve the way we diagnose and monitor diseases, Diana Zhang, a study co-author from UNSW, said in a press release.

The study, Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinsons Disease, was published inACS Central Science.

Parkinsons disease now is diagnosed based on the symptoms a person is experiencing; there isnt a biological test that can definitively identify the disease. Many researchers are working to identify biomarkers of Parkinsons, which might be measured to help identify the neurodegenerative disorder or predict the risk of developing it.

Here, the international team of researchers used machine learning to analyze metabolomic data that is, large-scale analyses of levels of thousands of different molecules detected in patients blood to identify Parkinsons biomarkers.

The analysis used blood samples collected from the Spanish European Prospective Investigation into Cancer and Nutrition (EPIC). There were 39 samples from people who would go on to develop Parkinsons after up to 15 years of follow-up, and another 39 samples from people who did not develop the disorder over follow-up. The metabolomic makeup of the samples was assessed with a chemical analysis technique called mass spectrometry.

In the simplest terms, machine learning involves feeding a computer a bunch of data, alongside a set of goals and mathematical rules called algorithms. Based on the rules and algorithms, the computer determines or learns how to make sense of the data.

This study specifically used a form of machine learning algorithm called a neural network. As the name implies, the algorithm is structured with a similar logical flow to how data is processed by nerve cells in the brain.

Machine learning has been used to analyze metabolomic data before. However, previous studies have generally not used wide-scale metabolomic data instead, scientists selected specific markers of interest to include, while not including data for other markers.

Such limits were used because wide-scale metabolomic data typically covers thousands of different molecules, and theres a lot of variation so-called noise in the data. Prior machine learning algorithms have generally had poor results when using such noisy data, because its hard for the computer to detect meaningful patterns amidst all the random variation.

The researchers new algorithm, CRANK-MS short for Classification and Ranking Analysis using Neural network generates Knowledge from Mass Spectrometry has a better ability to sort through the noise, and was able to provide high-accuracy results using full metabolomic data.

Here we feed all the information into CRANK-MS without any data reduction right at the start. And from that, we can get the model prediction and identify which metabolites are driving the prediction the most, all in one step.

Typically, researchers using machine learning to examine correlations between metabolites and disease reduce the number of chemical features first, before they feed it into the algorithm, said W. Alexander Donald, PhD, a study co-author from UNSW, in Sydney.

But here, Donald said, we feed all the information into CRANK-MS without any data reduction right at the start. And from that, we can get the model prediction and identify which metabolites are driving the prediction the most, all in one step.

Including all molecules available in the dataset means that if there are metabolites [molecules] which may potentially have been missed using conventional approaches, we can now pick those up, Donald said.

The researchers stressed that further validation is needed to test the algorithm. But in their preliminary tests, CRANK-MS was able to differentiate between Parkinsons and non-Parkinsons individuals with an accuracy of up to about 96%.

In further analyses, the researchers determined which molecules were picked up by the algorithm as the most important for identifying Parkinsons.

There were several noteworthy findings: For example, patients who went on to develop Parkinsons tended to have lower levels of a triterpenoid chemical known to have nerve-protecting properties. That substance is found at high levels in foods like apples, olives, and tomatoes.

Further, these patients also often had high levels of polyfluorinated alkyl substances (PFAS), which may be a marker of exposure to industrial chemicals.

These data indicate that these metabolites are potential early indicators for PD [Parkinsons disease] that predate clinical PD diagnosis and are consistent with specific food diets (such as the Mediterranean diet) for PD prevention and that exposure to [PFASs] may contribute to the development of PD, the researchers wrote. The team noted a need for further research into these potential biomarkers.

The scientists have made the CRANK-MS algorithm publicly available for other researchers to use. The team says this algorithm likely has applications far beyond Parkinsons.

Weve built the model in such a way that its fit for purpose, Zhang said. Whats exciting is that CRANK-MS can be readily applied to other diseases to identify new biomarkers of interest. The tool is user-friendly where on average, results can be generated in less than 10 minutes on a conventional laptop.

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Novel machine learning tool IDs early biomarkers of Parkinson's |... - Parkinson's News Today

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

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by European School of Management and Technology (ESMT)

Credit: Pixabay/CC0 Public Domain

New research from ESMT Berlin shows that utilizing machine-learning in the workplace always improves the accuracy of human decision-making, however, often it can also cause humans to exert more cognitive efforts when making decisions.

These findings come from research by Tamer Boyaci and Francis de Vricourt, both professors of management science at ESMT Berlin, alongside Caner Canyakmaz, previously a post-doctoral fellow at ESMT and now an assistant professor of operations management at Ozyegin University. The researchers wanted to investigate how machine-based predictions may affect the decision process and outcomes of a human decision-maker. Their paper has been published in Management Science.

Interestingly, the use of machines increases human's workload most when the professional is cognitively constrained, for instance, experiencing time pressures or multitasking. However, situations where decision makers experience high workload is precisely when introducing AI to alleviate some of this load appears most tempting. The research suggests that using AI, in this instance, to make the process faster can backfire, and actually increase rather than decrease the human's cognitive effort.

The researchers also found that, although machine input always improves the overall accuracy of human decisions, it can also increase the likelihood of certain types of errors, such as false positives. For the study, a machine learning model was used to identify the differences in accuracy, propensity, and the levels of cognitive effort exerted by humans, comparing solely human-made decisions to machine-aided decisions.

"The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks," says Professor de Vricourt. "However, when used alongside human rationale, machines can significantly enhance the complementary strengths of humans," he says.

The researchers say their findings clearly showcase the value of collaborations between humans and machines to the professional. But humans should also be aware that, though machines can provide incredibly accurate information, often there still needs to be a cognitive effort from humans to assess their own information and compare the machine's prescription to their own conclusions before making a decision. The researchers say that the level of cognitive effort needed increases when humans are under pressure to deliver a decision.

"Machines can perform specific tasks with incredible accuracy, due to their incredible computing power, while in contrast, human decision-makers are flexible and adaptive but constrained by their limited cognitive capacitytheir skills complement each other," says Professor Boyaci. "However, humans must be wary of the circumstances of utilizing machines and understand when it is effective and when it is not."

Using the example of a doctor and patient, the researchers' findings suggest that the use of machines will improve overall diagnostic accuracy and decrease the number of misdiagnosed sick patients. However, if the disease incidence is low and time is constrained introducing a machine to help doctors make their diagnosis would lead to more misdiagnosed patients, and more human cognitive effort needed to diagnosedue to the additional cognitive effort needed to resolve due to the ambiguity implementing machines can cause.

The researchers state that their findings offer both hope and caution for those looking to implement machines in the work. On the positive side, the average accuracy improves, and when the machine input tends to confirm the rather expected all error rates decrease and the human is more "efficient" as she reduces her cognitive effort.

However, incorporating machine-based predictions in human decisions is not always beneficial, neither in terms of the reduction of errors nor the amount of cognitive effort. In fact, introducing a machine to improve a decision-making process can be counter-productive as it can increase certain error types and the time and cognitive effort it takes to reach a decision.

The findings underscore the critical impact machine-based predictions have on human judgment and decisions. These findings provide guidance on when and how machine input should be considered, and hence on the design of human-machine collaboration.

More information: Tamer Boyac et al, Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations, Management Science (2023). DOI: 10.1287/mnsc.2023.4744

Journal information: Management Science

Provided by European School of Management and Technology (ESMT)

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Study finds workplace machine learning improves accuracy, but also increases human workload - Tech Xplore

Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms toanalyzevast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.

Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID radio frequency sensor, using machine learning algorithms from Edge Impulse.

One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.

Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.

Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.

As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.

Already, early uses of machine learning in diagnosis and treatment have shownpromiseto diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.

Withreportsindicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals,savingapproximately $20 billion annually.

Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)s ability to reduce errors and save lives.

Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.

One significant area this could benefit is monitoring blood glucose non-invasively withoutpricking the fingerfor blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.

Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc. AAPL, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc. GOOGL ) that measure heart rate, blood oxygen levels, and a host of other metrics.

But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.

The Seattle-based companyhaspartneredwithEdge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietaryBio-RFIDtechnology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.

The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.

Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based onAutoML and TinyMLto make AI more accessible, enabling quick and efficient machine learning modeling.

The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.

Featured Photo by JiBJhoY on Shutterstock

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Non-Invasive Medical Diagnostics: Know Labs' Partnership With Edge Impulse Has Potential To Improve Healthcare ... - Benzinga