Encryption Software Market Report 2020| Regional Analysis & Growth Forecast to 2026 – News by Decresearch

The encryption software market will surpass the $21 billion mark by 2026. In 2019, on-premise deployment model accounted for more than 70% of the overall encryption software market share. The growth stemmed from the preference of enterprises to implement on-premise deployment model approach owing to the high level of security required for handling sensitive data in-house.

Moreover, enterprises are preferring to operate on a cloud-based deployment model, which will foster industry demand. Cloud platforms for saving enterprise data are gaining significant traction on account of cost benefits and high scalability. Such benefits offered by cloud-based deployment model are expected to escalate encryption software market size.

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Increasing number of data breaches and cybercrimes and supportive government policies will enable encryption software market to witness a bullish growth over the coming years. This can be validated by the draft of an encryption law published by Chinas State Cryptography Administration (SCA) in November 2019. The draft was issued to bring about encryption in the private & public sectors and set guidelines on the use of cryptography for protecting national security.

Cybersecurity vendors are addressing evolving threats by offering security threats, resulting in the higher implementation of email, mobile, and disk encryption capabilities, which will spur encryption software industry growth.

Email data protection software is highly being adopted by companies as sending an email is the most general communication method used. Security software has found extensive applications to identify thefts, phishing, and protect data from malware. The software protects a multitude of aspects of email systems, such as email access, content or media attachments.

The data is encoded by the software in transit to ensure the security of sensitive data under the regulatory compliance. Estimates claim that email encryption software market will account for more than 25% of the overall market share by 2026.

The protection of customer data during online retail operations has become a crucial requirement these days. Third-party services in the retail sector are observing wide adoption to optimize customer experience on websites and support online transactions, resulting in a higher number of data breaches.

As per a report issued by Thales eSecurity, in 2018, almost 75% of the U.S. retailers have experienced a breach, which was 52% in 2017. The demand for cybersecurity solutions is expected to depict an upsurge from 2020 to 2026 to prevent theft and protect customer information.

Latin America has been witnessing increasing incidences of cyberattacks on the business-critical infrastructure. The use of digital platforms in the region for conducting business transactions has led to the regional governments introducing various initiatives to support cybersecurity.

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For example, since December 2019, numerous Mexican institutions, such as the National Defense Ministry (Sedena), Mexico Central Bank, the House of Representatives, and Mexico Supreme Court registered over 45 million tried attacks to access databases and steal information. In accordance, Latin America encryption software market is projected to witness an 18% CAGR over 2020-2026.

Table of Contents (ToC) of the report:

Chapter 1. Methodology & Scope

1.1. Methodology

1.1.1. Initial data exploration

1.1.2. Statistical model and forecast

1.1.3. Industry insights and validation

1.1.4. Scope

1.1.5. Definitions

1.1.6. Methodology & forecast parameters

1.2. Data Sources

1.2.1. Secondary

1.2.1.1. Paid sources

1.2.1.2. Public sources

1.2.2. Primary

Chapter 2. Executive Summary

2.1. Encryption software market 360 synopsis, 2015 2026

2.2. Business trends

2.3. Regional trends

2.4. Component trends

2.4.1. Software trends

2.4.2. Service trends

2.5. Deployment model trends

2.6. Application trends

Chapter 3. Encryption Software Market Insights

3.1. Introduction

3.2. Industry segmentation

3.3. Industry landscape, 2015 2026

3.4. Evolution of encryption software

3.5. Encryption software industry architecture

3.6. Encryption software industry ecosystem analysis

3.7. Technology & innovation landscape

3.7.1. Quantum cryptography

3.7.2. Honey encryption

3.7.3. Lattice based cryptography

3.8. Regulatory landscape

3.8.1. North America

3.8.1.1. ENCRYPT Act of 2019 (U.S.)

3.8.1.2. Gramm-Leach-Bliley Act of 1999 (U.S.)

3.8.1.3. Personal Information Protection and Electronic Documents Act [(PIPEDA) Canada]

3.8.2. Europe

3.8.2.1. General Data Protection Regulation (EU)

3.8.2.2. Data Protection Authority (DPA) regulations on the transmission of personal data by e-mail (Denmark)

3.8.3. APAC

3.8.3.1. National Law on Cryptography (China)

3.8.3.2. Guide to securing personal data in electronic medium (Singapore)

3.8.4. Latin America

3.8.4.1. Law No. 9,296 of July 24, 1996 (Government Access to Encrypted Communications, Brazil)

3.8.4.2. National Directorate of Personal Data Protection (Argentina)

3.8.5. MEA

3.8.5.1. Policy of Control and Licensing of Commercial Encryption Items (Israel)

3.8.5.2. Regulation of Interception of Communications and Provision of Communication-related Information Act, 2002 (RICA- South Africa)

3.9. Industry impact forces

3.9.1. Growth drivers

3.9.1.1. Stringent regulations on cybersecurity and data privacy compliances

3.9.1.2. Rising concerns over securing enterprise Intellectual Property (IP) assets

3.9.1.3. Increasing proliferation of cloud and virtualization technologies

3.9.1.4. Growing trend of Bring Your Own Devices (BYOD) among enterprises

3.9.2. Industry pitfalls & challenges

3.9.2.1. Complexities in encryption key management

3.9.2.2. Easy availability of pirated and free-to-use encryption software

3.9.2.3. Regulatory restrictions on cryptography and encryption

3.10. Growth potential analysis

3.11. Porters analysis

3.12. PESTEL analysis

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Encryption Software Market Report 2020| Regional Analysis & Growth Forecast to 2026 - News by Decresearch

Verizon to use blockchain in its newsroom for comms verification – The Block – The Block

James is editor in chief of TechForge Media, with a passion for how technologies influence business and several Mobile World Congress events under his belt. James has interviewed a variety of leading figures in his career, from former Mafia boss Michael Franzese, to Steve Wozniak, and Jean Michel Jarre. James can be found tweeting at @James_T_Bourne.

Verizon has announced the launch of a blockchain-based product which aims to provide an authoritative record of changes to company news releases.

Full Transparency by Verizon is a proof of concept built with open source blockchain technology. The product is put together in partnership with AdLedger, a consortium which explores standards for blockchain and cryptography in media and advertising, authentication infrastructure provider MadNetwork, and marketing company Huge.

Verizon noted the rationale and idea behind the product:

Full Transparencys goal is to change the way corporate newsrooms provide visibility to their readers and hold themselves accountable for what they communicate to the public, the company wrote. Official news releases that incorporate Full Transparency are tracked on the blockchain ledger, so news releases or statements can be treated as authoritatively reflecting what was intended to be released.

All news releases published to the Verizon Newsroom will be secured and bound using cryptographic principles, so that subsequent changes can be tracked and contextualised, the company added.

The company cited the 2020 Edelman Trust Barometer study which found almost three in five consumers globally believed the media they consumed was in some way untrustworthy. Jim Gerace, chief communications officer for Verizon, said the company is inviting organisations around the world to adopt blockchain-verified communication practices.

Interested in hearing more in person?Find out more at theBlockchain Expo World Series, Global, Europe and North America.

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Verizon to use blockchain in its newsroom for comms verification - The Block - The Block

The Dutch central bank includes a new crypto exchange in its register. – The Washington Newsday

The Dutch central bank recently added a new stock exchange BLOX to the list of registered crypto companies.Every crypto-company that wants to operate in the country must register and prove that it complies with the legal requirements.Unfortunately, only three companies have registered so far, while the deadline is getting closer.

At the beginning of 2020, the Dutch parliament decided to pass new AML amendments, which meant that crypto exchanges must register with the countrys central bank. Those who did not want to do so were not allowed to continue operations in the country.

Since then, three crypto platforms have been added to the list of recognized exchanges, with the last one being added to the list only today.

The Dutch Central Bank recognizes its third crypto platform

The latest addition to the list of the Dutch central bank is a crypto stock exchange called BLOX. The exchange announced the news this morning via its blog and announced that it has received approval from De Nederlandsche Bank after registration.

The fact that BLOX is only the third exchange to be registered and approved by the bank is rather worrying for local crypto users, especially since the registration period is about to expire. The other two exchanges that have decided to register are Anvcoin Direct and AMDAX.

The bank recognized both, and they too received the same operating licenses as BLOX.

Those who do not register will have to stop all operations

About two months ago, in September 2020, the central bank also issued an announcement stating that crypto stock exchanges would be supervised by the bank itself under the fifth European AML Directive.

This will include all services offering crypto-fiat or crypto-to-crypto transactions, including crypto-wallet providers. The bank pointed out that these companies need to prepare for this and that the bank wants them to register.

This will be necessary as the bank is still very concerned about the use of cryptography in white-collar crime.

In order to be recognized, the stock exchanges must prove that they are well organized and able to deal with money laundering and terrorist financing.

The central bank will also continue to monitor their performance and ensure that they comply with the rules after registration. All those who do not register will be forced to cease all their activities after the deadline.

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The Dutch central bank includes a new crypto exchange in its register. - The Washington Newsday

Simeio Solutions expert says: Most breaches are from exploited passwords. Let’s get rid of them. – Intelligent CIO ME

James R Quick,Director, Solutions & Advisory for Simeio Solutions, tells us its time to get rid of passwords and instead automate and secure the authentication process.

There are two things we can do to secure our corporate assets; get rid of users or eliminate passwords. I say that tongue and cheek, but theres truth to half of that statement.

Ok. We obviously need users but employees are on the front lines in a cyberwar over corporate and consumer data, battling myriad cyberattacks. Most data breaches are caused by credential theft. Thats why, our most important endpoints are users. They are the most likely to unknowingly give away the kingdom keys.

Im not being flippant about passwords. Id like to see them gone. The best way to eliminate nefarious activity from stolen passwords is to eliminate them. To secure employees, systems, applications, corporate secrets and consumer data, we must rein in repetitive and weak passwords that expose organizations to attacks.

Time to shift away from passwords

Everyone recognizes password weaknesses. Were frustrated with having to create and remember them, and where we stored them. So, we repeatedly use the same weak passwords, that are easily memorized. We know this creates a security risk but do it anyway.

Security teams are overwhelmed managing, storing and protecting credentials. They may not have the budget or resources for the most up-to-date systems. They might lack the processes and policies to consistently update software, and dont have the domain expertise to keep up with the latest technologies to protect their business. They know hackers can acquire user credentials and move laterally across their network to access anything they want. Theyre also challenged to keep up with ever-growing privacy regulations.

A password replacement must be pervasive

Our smartphones are almost another appendage. Theyre with us constantly and are ubiquitous in our personal lives and business. While there are many methods and strategies for avoiding stolen and misused passwords, there is one that scales and permeates our personal and business activities. We can harden endpoints, like smartphones, tablets, smart speakers and laptops, with standards-based public key cryptography.

How it works

Secure key-enabled user devices remove the need for passwords, eliminate user registration and login friction, and globally scale. To initiate the process, users authenticate with the website using their devices private key, which responds to the websites security challenge.

The private key can be used only after the security code has been unlocked by the user, by swiping a finger, entering a PIN etc. The device creates a new public/private key pair, unique to the online service, and the users account. The public key is sent to the online service and associated with the users account. The private key and local authentication information never leaves the device.

Passwords require human interaction which is a formula for disaster. We must automate and secure the authentication process. This means removing people from the equation. While there are many approaches to eliminating the password conundrum, standards-based public key cryptography provides strong authentication that scales and can be deployed on devices we use to register and login to online applications and services.

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Simeio Solutions expert says: Most breaches are from exploited passwords. Let's get rid of them. - Intelligent CIO ME

What Is The Legal Status Of Cryptocurrency In Nigeria? – Jollofnews

Cryptocurrency is the online money which is used with the help of secret codes is known as chromatography. Cryptography is one of the techniques by which you can put your money in a safe place online, and you can use them by using this technique. It is safe and easy to use. Cryptocurrency can be defined in many ways and very different forms. One of the definitions of Cryptocurrency is a digital currency, which regulates all the currency and verifies all the transfer funds. You can also define Cryptocurrency as the digital currency made based on the cryptography technique. This currency is secure and unknown.

Electronic money will not show any users identity using this website, one of the best Cryptocurrency features. The design of this currency is based on a beneficial blockchain technique. Cryptocurrency is not ruled by government authority. The use of the service is private and anonymous to anyone. The currency does not need any value on assets such as material resources for production; however, it is regulated by the computer program and saved on the computer.

Transactions are executed over the Internet, and you will not need any gold or license for making your id. Cryptocurrency is different from other currencies in the world.

As we know, the market of cryptocurrencies is overgrowing, and it is also developing rapidly. For the expanding market, if the digital currency, they are adding new currencies at the market and helping if it develops at a high rate. The latest information about the digital currency is that at this time in the market of Cryptocurrency, there are 2000 currencies are present, which is showing the growth rate.

Many types of Cryptocurrency are present in the market like bitcoin, litecoin, z cash, dash, and ripple. Among the many forms of Crypto types, Bitcoin is the most famous Cryptocurrency throughout the world, and it is introduced to the people in the year 2009. From there, you can see the growth rate of bitcoin in the market. Following are some features of Cryptocurrency-

One of the great features of Cryptocurrency applications is a design based on cryptography, known for its secret codes. You can identify the user name or any user information from this application, the secret application with Anonymous identity.

On the Cryptocurrency, there is no charge of the government authority. It is separate and fully independent. The central government cant interfere in this situation. They also dont have the permission to look into the personal information of the users.

The best feature of this digital currency, where there is no involvement of the third party in the transaction process. There is no involvement of banks or governments. You can directly transfer coins from one account to another with the help of the Internet.

Many issues are related to Cryptocurrency is present in this world. Cryptocurrency is volatile, and many things will change in the market of this Cryptocurrency. The market of the coins is rapid, and the anonymous design of this application will become volatile. You can not predict the changes in this application. It is one of the prevalent issues of this application related to the Cryptocurrency market as we know that this application has unidentified nature. It is designed based on the cryptography technique. But because of this anonymous nature, you cant trust with the money lending and purchasing.

You cant be sure with your invested money in this application because it is rapid, and it will change suddenly. The change in the market is based on supply and demand. Because of its secretive nature, government authority cant control any transaction from this device. It does also not pay any revenue to the government. There are no regulations on Cryptocurrency, but some necessary regulations must follow in some countries. There is no restriction on using this application in the world. Many criticisms are present related to the use of Cryptocurrency.

Regulation of Cryptocurrency

The main principle of Cryptocurrency is decentralization, which means it is independent of any central government or government authority controls over this application, which is also secretive and independent. There are many opinions about the uses of Cryptocurrency in different regulatory states; therefore, many states do not have any regulations decided or rules related to Cryptocurrency. Many states are developing their regulation framework about the Cryptocurrency in their city. Some states already imposed some regulations on the use of Cryptocurrency.

Many acts are placed related to Cryptocurrency in several countries. Some countries ban the use of cryptocurrencies and denied permission for the secretive application with an anonymous feature. Many countries governments want the authority to remove this application, leading to the growth in cybercrime. All the costumers of this application are not real. Some of them are scams, and they can steal your money and data from your device.

Modern technology and all the countrys financial systems are trying to embrace this online system. When the cryptocurrencies are introduced to the people of Nigeria, Nigeria experiences a lot of negative and positive things that is important for the government of Nigeria. Investors in Nigeria invest their money in the Cryptocurrency with the hope of some profit in the future. Bitcoins are one of the popular Cryptocurrency in the world. Bitcoins are shared, and all the investors are investing their money in the Bitcoins. There is no presence of a third person in the Cryptocurrency transaction like the government and the bank.

Nigerias government has attempted a ban on the use of cryptocurrencies. Still, Nigerias legal status is questionable, unlike Morocco, which bans Cryptocurrency in their area of the country. If the government finds any used of Bitcoins in the city, they will charge more fines. The Nigerian government also takes some action on the use of cryptocurrencies. They issued a notice about the cryptocurrencies, which is explained the terms of cryptocurrencies. They also explain the benefits of the virtual currency of the government and losses of using cryptocurrencies. They also explain the risk of using cryptocurrencies instead of state currencies.

They also explain various warnings about cybercrimes related to cryptocurrencies like it will spread violence, terrorism, and other activities. They also pass the statements about the transaction of the Bitcoins by the banks in 2017. The Nigerian government also passes the statement for the investors who invest their money in the Bitcoins. They said that it is precarious and banned in Nigeria.

Conclusion

All this information will give you the idea of legal status in Nigeria related to cryptocurrencies. Currently, they are trying to build the framework of the regulation that reacted to the cryptocurrencies. They are trying to develop the rules and regulations in Nigeria.

The development of rules and regulations is not easy in any state or country, and the process requires a lot of time and effort. That is why people should value and appreciate their efforts and try not to harm the economy and the development process in any possible way, the only way in which any person can support his/her country at its best,

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What Is The Legal Status Of Cryptocurrency In Nigeria? - Jollofnews

Machine learning removes bias from algorithms and the hiring process – PRNewswire

Arena Analytics' Chief Data Scientist unveils a cutting edge technique that removes latent bias from algorithmic models.

Currently, the primary methods of reducing the impact of bias on models has been limited to adjusting input data or adjust models after-the-fact to ensure there is no disparate impact.

Recent reporting from the Wall Street Journal confirmed these as the most recent advances, concluding, "It's really up to the software engineers and leaders of the company to figure out how to fix it [or] go into the algorithm and tweak some of the main factors it considers in making its decisions."

For several years, Arena Analytics was also limited to these approaches, but that all changed 9 months ago. Up until then, Arena removed all data from the models that could correlate to protected classifications and then measured demographic parity.

"These efforts brought us in line with EEOC compliance thresholds - also known as the or 80% rule," explains Myra Norton, President/COO of Arena. "But we've always wanted to go further than a compliance threshold.We've wanted to surface a MORE diverse slate of candidates for every role in a client organization.And that's exactly what we've accomplished, now surpassing 95% in our representation of different classifications."

Chief Data Scientist Patrick Hagerty will explain at MLConf the way he and his team have leveraged techniques known asadversarial networks,an aspect of Generative Adversarial Networks (GAN's), tools that pit one algorithm against another.

"Arena's primary model predicts the outcomes our clients want, and Model Two is a Discriminator designed to predict a classification," says Hagerty. "The Discriminator attempts to detect the race, gender, background, and any other protected class data of a person. This causes the Predictor to adjust and optimize while eliminating correlations with the classifications the Discriminator is detecting."

Arena trained models to do this until achieving what's known as the Nash Equilibrium. This is the point at which the predictor and discriminator have reached peak optimization.

Arena's technology has helped industrious individuals find a variety of jobs - from RNs to medtechs, caregivers to cooks, concierge to security. Job candidates who Arena predicted for success include veterans with no prior experience in healthcare or senior/assisted living, recent high school graduates whose plans to work while attending college were up-ended, and former hospitality sector employees who decided to apply their dining service expertise to a new setting.

"We succeeded in our intent to reduce bias and diversify the workforce, but what surprised us was the impact this approach had on our core predictions. Data once considered unusable, such as commuting distance, we can now analyze because we've removed the potentially-associated protected-class-signal," says Michael Rosenbaum, Arena's founder and CEO. "As a result, our predictions are stronger AND we surface a more diverse slate of candidates across multiple spectrums. Our clients can now use their talent acquisition function to really support and lead out front on Diversity and Inclusion."

About Arena (https://www.arena.io/) applies predictive analytics and machine learning to solve talent acquisition challenges. Learning algorithms analyze a large amount of data topredict with high levels of accuracy the likelihood of different outcomes occurring, such as someone leaving, being engaged, having excellent attendance, and more. By revealing each individual's likely outcomes in specific positions, departments, and locations, Arena is transforming the labor market from one based on perception and unconscious bias, to one based on outcomes. Arena is currently growing dramatically within the healthcare and hospitality industry and expanding its offerings to other people intensive industries. For more information contact [emailprotected]arena.io

SOURCE Arena

https://www.arena.io/

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Machine learning removes bias from algorithms and the hiring process - PRNewswire

Using machine learning to track the pandemic’s impact on mental health – MIT News

Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online.

Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide.

We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern, says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.

The analysis also revealed varying impacts on people who already suffer from different types of mental illness. The findings could help psychiatrists, or potentially moderators of the Reddit forums that were studied, to better identify and help people whose mental health is suffering, the researchers say.

When the mental health needs of so many in our society are inadequately met, even at baseline, we wanted to bring attention to the ways that many people are suffering during this time, in order to amplify and inform the allocation of resources to support them, says Laurie Rumker, a graduate student in the Bioinformatics and Integrative Genomics PhD Program at Harvard and one of the authors of the study.

Satrajit Ghosh, a principal research scientist at MITs McGovern Institute for Brain Research, is the senior author of the study, which appears in the Journal of MedicalInternet Research. Other authors of the paper include Tanya Talkar, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT; John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center; and Guillermo Cecchi, a principal research staff member at the IBM Thomas J. Watson Research Center.

A wave of anxiety

The new study grew out of the MIT class 6.897/HST.956 (Machine Learning for Healthcare), in MITs Department of Electrical Engineering and Computer Science. Low, Rumker, and Talkar, who were all taking the course last spring, had done some previous research on using machine learning to detect mental health disorders based on how people speak and what they say. After the Covid-19 pandemic began, they decided to focus their class project on analyzing Reddit forums devoted to different types of mental illness.

When Covid hit, we were all curious whether it was affecting certain communities more than others, Low says. Reddit gives us the opportunity to look at all these subreddits that are specialized support groups. Its a really unique opportunity to see how these different communities were affected differently as the wave was happening, in real-time.

The researchers analyzed posts from 15 subreddit groups devoted to a variety of mental illnesses, including schizophrenia, depression, and bipolar disorder. They also included a handful of groups devoted to topics not specifically related to mental health, such as personal finance, fitness, and parenting.

Using several types of natural language processing algorithms, the researchers measured the frequency of words associated with topics such as anxiety, death, isolation, and substance abuse, and grouped posts together based on similarities in the language used. These approaches allowed the researchers to identify similarities between each groups posts after the onset of the pandemic, as well as distinctive differences between groups.

The researchers found that while people in most of the support groups began posting about Covid-19 in March, the group devoted to health anxiety started much earlier, in January. However, as the pandemic progressed, the other mental health groups began to closely resemble the health anxiety group, in terms of the language that was most often used. At the same time, the group devoted to personal finance showed the most negative semantic change from January to April 2020, and significantly increased the use of words related to economic stress and negative sentiment.

They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders. The researchers hypothesize that without their usual social support systems in place, due to lockdowns, people suffering from those disorders found it much more difficult to manage their conditions. In those groups, the researchers found posts about hyperfocusing on the news and relapsing back into anorexia-type behaviors since meals were not being monitored by others due to quarantine.

Using another algorithm, the researchers grouped posts into clusters such as loneliness or substance use, and then tracked how those groups changed as the pandemic progressed. Posts related to suicide more than doubled from pre-pandemic levels, and the groups that became significantly associated with the suicidality cluster during the pandemic were the support groups for borderline personality disorder and post-traumatic stress disorder.

The researchers also found the introduction of new topics specifically seeking mental health help or social interaction. The topics within these subreddit support groups were shifting a bit, as people were trying to adapt to a new life and focus on how they can go about getting more help if needed, Talkar says.

While the authors emphasize that they cannot implicate the pandemic as the sole cause of the observed linguistic changes, they note that there was much more significant change during the period from January to April in 2020 than in the same months in 2019 and 2018, indicating the changes cannot be explained by normal annual trends.

Mental health resources

This type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused by not only the Covid-19 pandemic but other mental health stressors such as controversial elections or natural disasters, the researchers say.

Additionally, if applied to Reddit or other social media posts in real-time, this analysis could be used to offer users additional resources, such as guidance to a different support group, information on how to find mental health treatment, or the number for a suicide hotline.

Reddit is a very valuable source of support for a lot of people who are suffering from mental health challenges, many of whom may not have formal access to other kinds of mental health support, so there are implications of this work for ways that support within Reddit could be provided, Rumker says.

The researchers now plan to apply this approach to study whether posts on Reddit and other social media sites can be used to detect mental health disorders. One current project involves screening posts in a social media site for veterans for suicide risk and post-traumatic stress disorder.

The research was funded by the National Institutes of Health and the McGovern Institute.

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Using machine learning to track the pandemic's impact on mental health - MIT News

The consistency of machine learning and statistical models in predicting clinical risks of individual patients – The BMJ – The BMJ

Now, imagine a machine learning system with an understanding of every detail of that persons entire clinical history and the trajectory of their disease. With the clinicians push of a button, such a system would be able to provide patient-specific predictions of expected outcomes if no treatment is provided to support the clinician and patient in making what may be life-or-death decisions[1] This would be a major achievement. The English NHS is currently investing 250 million in Artificial Intelligence (AI). Part of this AI work could help to identify patients most at risk of diseases such as heart disease or dementia, allowing for earlier diagnosis and cheaper, more focused, personalised prevention. [2] Multiple papers have suggested that machine learning outperforms statistical models including cardiovascular disease risk prediction. [3-6] We tested whether it is true with prediction of cardiovascular disease as exemplar.

Risk prediction models have been implemented worldwide into clinical practice to help clinicians make treatment decisions. As an example, guidelines by the UK National Institute for Health and Care Excellence recommend that statins are considered for patients with a predicted 10-year cardiovascular disease risk of 10% or more. [7] This is based on the estimation of QRISK which was derived using a statistical model. [8] Our research evaluated whether the predictions of cardiovascular disease risk for an individual patient would be similar if another model, such as a machine learning models were used, as different predictions could lead to different treatment decisions for a patient.

An electronic health record dataset was used for this study with similar risk factor information used across all models. Nineteen different prediction techniques were applied including 12 families of machine learning models (such as neural networks) and seven statistical models (such as Cox proportional hazards models). It was found that the various models had similar population-level model performance (C-statistics of about 0.87 and similar calibration). However, the predictions for individual CVD risks varied widely between and within different types of machine learning and statistical models, especially in patients with higher CVD risks. Most of the machine learning models, tested in this study, do not take censoring into account by default (i.e., loss to follow-up over the 10 years). This resulted in these models substantially underestimating cardiovascular disease risk.

The level of consistency within and between models should be assessed before they are used for treatment decisions making, as an arbitrary choice of technique and model could lead to a different treatment decision.

So, can a push of a button provide patient-specific risk prediction estimates by machine learning? Yes, it can. But should we use such estimates for patient-specific treatment-decision making if these predictions are model-dependant? Machine learning may be helpful in some areas of healthcare such as image recognition, and could be as useful as statistical models on population level prediction tasks. But in terms of predicting risk for individual decision making we think a lot more work could be done. Perhaps the claim that machine learning will revolutionise healthcare is a little premature.

Yan Li, doctoral student of statistical epidemiology, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Matthew Sperrin, senior lecturer in health data science, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Darren M Ashcroft, professor of pharmacoepidemiology, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester.

Tjeerd Pieter van Staa, professor in health e-research, Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester.

Competing interests: None declared.

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The consistency of machine learning and statistical models in predicting clinical risks of individual patients - The BMJ - The BMJ

Free Webinar | Machine Learning and Data Analytics in the Pandemic Era – MIT Sloan

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Free Webinar | Machine Learning and Data Analytics in the Pandemic Era - MIT Sloan

Google Introduces New Analytics with Machine Learning and Predictive Models – IBL News

IBL News | New York

Google announcedthe introduction of its new Google Analytics with machine learning at its core, which is privacy-centric by design. They are built on the foundation of the App + Web propertypresentedlast year.

The goal of the giant searching company is to help users to get better ROI and improve their marketing decisions. It follows what a survey from Forrester Consulting points out that improving the use of analytics is a top priority for marketers.

The machine learning models include will allow the ability to alert on trends in data, like products seeing rising demand, and help to anticipate future actions from customers. For example, it calculates churn probability so you can more efficiently invest in retaining customers at a time when marketing budgets are under pressure, says in a blog-postVidhya Srinivasan,Vice President, Measurement, Analytics, and Buying Platforms at Google.

It also adds new predictive metrics indicating the potential revenue that can be earned from a particular group of customers. This allows you to create audiences to reach higher-value customers and run analyses to better understand why some customers are likely to spend more than others, so you can take action to improve your results, wroteVidhya Srinivasan.

The new Google Analytics providescustomer-centric measurement, including conversion from YouTube video views, Google and non-Google paid channels, search, social, and email. The setup works with or without cookies or identifiers.

They come by default for new web properties. In order toreplace the existing setup, Google encourages tocreate a new Google Analytics 4 property (previously called an App + Web property). Enterprise marketers are currently using a beta version with an Analytics 360 version with SLAs and advanced integrations with tools like BigQuery.

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Google Introduces New Analytics with Machine Learning and Predictive Models - IBL News