Mediaboss Marketing

Westford, USA, Aug. 24, 2022 (GLOBE NEWSWIRE) -- As the world becomes increasingly automated, businesses are turning to industrial automation solutions to help increase efficiency and productivity. By automating routine tasks and processes, businesses can free up workforce time to focus on more important duties. This growth of the industrial automation market is mainly due to the increasing demand for safe, reliable, and efficient manufacturing systems. These systems help manufacturers achieve increased output and reduce costs. As per SkyQuests findings, businesses save from 15 to 60% on worker costs, making it one of the most cost-effective investments a business can make.

In addition to reducing labor costs, industrial automation can also reduce environmental impact. For example, if a factory is using manual tasks to produce products, the production process often involves a lot of waste created from the work processes. With industrial automation, these tasks can be automated, leading to a decrease in waste and a reduction in environmental impact.

Get sample copy of this report:

https://skyquestt.com/sample-request/industrial-automation-market

There are a number of different industrial automation technologies available, so businesses can find the right solution for their specific needs. Some of the most common types of industrial automation include: robots, machine learning algorithms, computer-aided manufacturing (CAM), and wireless technology.

As per SkyQuest analysis, some of the leading companies in the industrial automation market are ABB Ltd., Siemens AG, Fanuc Corporation, Mitsubishi Electric Corporation, Kawasaki Heavy Industries Ltd., and Rexnord Corp. These companies offer a range of products and services that include controllers, drives, processing units, sensors, and software. They prefer to partner with larger manufacturers who can leverage their resources to develop and deploy advanced technology solutions across their entire manufacturing operations.

Increasing adoption of advanced manufacturing technologies, such as 3D printing, and recent shift in production to Asia are driving the growth of this industry. Additionally, surging demand for smart machines that can automatically optimize processes and reduce variability is boosting the growth of industrial automation market.

SkyQuest has published a report on global industrial automation market. The provides a detailed understanding about market trends, consumer analysis, demand and supply gap, pricing analysis, top players in the market and their market share, competitive landscape, value chain analysis, and market dynamics. It will help the market participants in identifying lucrative growth opportunity, targeting potential consumers, devising growth strategies, finding what competition are doing and where the opportunity lies to incentives on the weaknesses of others. For more details.

Browse summary of the report and Complete Table of Contents (ToC):

https://skyquestt.com/report/industrial-automation-market

Industrial Automation to Account for a Whopping 37% of the Global Workforce Says Analyst at SkyQuest

As industrial automation technologies continue to develop, so too is the industrys adoption of them. A recent study found that automation is growing even more rapidly than anticipated, and by 2025, it is predicted that industrial automation will account for a whopping 37% of the workforce.

According to a recent survey conducted by SkyQuest on industrial automation market, the chief executive of companies that have invested in industrial automation say that the technology has been key to their success. In fact, almost 2/3 of respondents reported that industrial automation has helped them boost production and improve efficiency. Furthermore, nearly 50% of these businesses say that the technology has increased their competitiveness and allowed them to attract new customers.

AI and Blockchain Technology are Trending in Industrial Automation Market

One of the most pressing issues facing industrial automation today is reliability. With a growing number of devices and systems interacting with one another, it's critical that these systems work as intended and without issue. Here are some of the top trends happening in industrial automation today:

Smart Manufacturing is Gaining Grounds in Industrial Automation Market

Manufacturing is not just a physical process. It's also a digital process. The rise of smart manufacturing technologies means that factories can now control and monitor their processes in real time, which enables more efficient production and improved safety. Today, automotive, electronics and FMCG sectors is contributing around 65% of the revenue to the global industrial automation market. Automotive sector is witnessing steady growth owing to soaring demand for safety features, enhanced functionality, efficient fuel economy and elevated adoption of intelligent mobility solutions. In addition, automotive industry is also witnessing rise in popularity of hybrid and electric vehicles which is adding to the growth momentum of this sector.

The most common types of smart manufacturing technologies are robotics, sensors, and machine learning algorithms. Robotics help factories automate tasks and functions so that they can be performed faster and with greater accuracy. Sensors enable factories to monitor conditions inside and outside the factory, and they can transmit this information to processors for analysis.

As per SkyQuest analysis, machine learning is being used to improve a variety of processes and operations. For example, it can be used to optimize production lines, predict the needs of customers and even determine when products need to be replaced. Additionally, it can be used to improve predictive maintenance and forecasting. Furthermore, it can also be used to develop autonomous systems.

Today, manufacturers across the global industrial automation market are opting for smart manufacturing due to following key factors:

Speak to Analyst for your custom requirements:

https://skyquestt.com/speak-with-analyst/industrial-automation-market

Top Players in Global Industrial Automation Market

Related Reports in SkyQuests Library:

Global Metaverse Infrastructure Market

Global Micro Mobile Data Center Market

Global Machine Learning Market

Global Location Based Services Market

Global Virtual Events Market

About Us:

SkyQuest Technologyis leading growth consulting firm providing market intelligence, commercialization and technology services. It has 450+ happy clients globally.

Address:

1 Apache Way, Westford, Massachusetts 01886

Phone:

USA (+1) 617-230-0741

Email:sales@skyquestt.com

LinkedInFacebookTwitter

Go here to see the original:
Industrial Automation Market to Generate Revenue of $289 Billion by 2028 | Growing Adoption of AI and Machine Learning to Play Key Role -...

Dr Birthe Nielsen discusses the role of the Methods Database in supporting life sciences research by digitising methods data across different life science functions.

Reproducibility of experiment findings and data interoperability are two of the major barriers facing life sciences R&D today. Independently verifying findings by re-creating experiments and generating the same results is fundamental to progressing research to the next stage in its lifecycle - be it advancing a drug to clinical development or a product to market. Yet, in the field of biology alone, one study found that 70 per cent of researchers are unable to reproduce the findings of other scientists and 60 per cent are unable to reproduce their own findings.

This causes delays to the R&D process throughout the life sciences ecosystem. For example, biopharmaceutical companies often use an external Contract Research Organisation (CROs) to conduct clinical studies. Without a centralised repository to provide consistent access, analytical methods are often shared with CROs via email or even by physical documents, and not in a standard format but using an inconsistent terminology. This leads to unnecessary variability and several versions of the same analytical protocol. This makes it very challenging for a CRO to re-establish and revalidate methods without a labour-intensive process that is open to human interpretation and thus error.

To tackle issues like this, the Pistoia Alliance launched the Methods Hub project. The project aims to overcome the issue of reproducibility by digitising methods data across different life science functions, and ensuring data is FAIR (Findable, Accessible, Interoperable, Reusable) from the point of creation. This will enable seamless and secure sharing within the R&D ecosystem, reduce experiment duplication, standardise formatting to make data machine-readable and increase reproducibility and efficiency. Robust data management is also the building block for machine learning and is the stepping-stone to realising the benefits of AI.

Digitisation of paper-based processes increases the efficiency and quality of methods data management. But it goes beyond manually keying in method parameters on a computer or using an Electronic Lab Notebook; A digital and automated workflow increases efficiency, instrument usages and productivity. Applying a shared data standards ensures consistency and interoperability in addition to fast and secure transfer of information between stakeholders.

One area that organisations need to address to comply with FAIR principles, and a key area in which the Methods Hub project helps, is how analytical methods are shared. This includes replacing free-text data capture with a common data model and standardised ontologies. For example, in a High-Performance Liquid Chromatography (HPLC) experiment, rather than manually typing out the analytical parameters (pump flow, injection volume, column temperature etc.), the scientist will simply download a method that will automatically populate the execution parameters in any given Chromatographic Data System (CSD). This not only saves time during data entry, but the common format eliminates room for human interpretation or error.

Additionally, creating a centralised repository like the Methods Hub in a vendor-neutral format is a step towards greater cyber-resiliency in the industry. When information is stored locally on a PC or an ELN and is not backed up, a single cyberattack can wipe it out instantly. Creating shared spaces for these notes via the cloud protects data and ensures it can be easily restored.

A proof of concept (PoC) via the Methods Hub project was recently successfully completed to demonstrate the value of methods digitisation. The PoC involved the digital transfer via cloud of analytical HPLC methods, proving it is possible to move analytical methods securely between two different companies and CDS vendors with ease. It has been successfully tested in labs at Merck and GSK, where there has been an effective transfer of HPLC-UV information between different systems. The PoC delivered a series of critical improvements to methods transfer that eliminated the manual keying of data, reduces risk, steps, and error, while increasing overall flexibility and interoperability.

The Alliance project team is now working to extend the platforms functionality to connect analytical methods with results data, which would be an industry first. The team will also be adding support for columns and additional hardware and other analytical techniques, such as mass spectrometry and nuclear magnetic resonance spectroscopy (NMR). It also plans to identify new use cases, and further develop the cloud platform that enables secure methods transfer.

If industry-wide data standards and approaches to data management are to be agreed on and implemented successfully, organisations must collaborate. The Alliance recognises methods data management is a big challenge for the industry, and the aim is to make Methods Hub an integral part of the system infrastructure in every analytical lab.

Tackling issues such as digitisation of methods data doesnt just benefit individual companies but will have a knock-on effect for the whole life sciences industry. Introducing shared standards accelerates R&D, improves quality, and reduces the cost and time burden on scientists and organisations. Ultimately this ensures that new therapies and breakthroughs reach patients sooner. We are keen to welcome new contributors to the project, so we can continue discussing common barriers to successful data management, and work together to develop new solutions.

Dr Birthe Nielsen is the Pistoia Alliance Methods Database project manager

Read more here:
Tackling the reproducibility and driving machine learning with digitisation - Scientific Computing World

Digital mental health company Cerebral is using a machine learning algorithm to help pinpoint patients in crisis. And the recently unveiled initiative is just the beginning of Cerebrals use of machine learning-enabled solutions, according to the company.

Machine learning (ML) and artificial intelligence (AI) are critical tools in the advancement of mental health care, but these benefits are only possible at scale, a team of Cerebral researchers explained in a company post. Both technologies require many data points to test and validate hypotheses in order to prove that the systems are working effectively.

Dubbed the Crisis Message Detector 1 (CMD-1), the newly touted tool is designed to identify messages from patients experiencing a mental health crisis, then refer those patients to a crisis response specialist.

Specifically, the tool was trained to spot signs of suicidal ideation, homocidal ideation, non-suicidal self-injury or domestic violence, according to the company post. If a patients messages to Cerebral have been flagged, a specialist will reach out directly and assess the patients risk level.

This mental health professional will then be able to call emergency contacts or local responders, if needed.

Cerebral is pitching this as an alternative to relying on patients to call 911 if there is an emergency. The company claims the tool is accurate in properly identifying individuals in crisis.

During a week-long pilot, CMD-1 screened over 60,000 EMR messages and flagged more than 500 potential crises, the Cerebral researchers wrote. The model successfully detected over 99% of all crisis messages and, as a result, crisis specialists were able to respond to patients in less than 9 minutes on average.

Cerebral receives several thousand patient messages each day via its online chat system or mobile app. The company says that somebody from the patients care team reviews and addresses those messages, but that human-led process isnt always as fast as it needs to be.

The company plans to expand its machine learning initiatives in the upcoming months and focus on issues such as response times for medication concerns, scheduling issues and general support requests.

Because of Cerebrals experience serving a quarter-million people (and counting), we are uniquely suited to develop and implement cutting edge ML/AI tools to supplement the expertise of our clinicians and help improve clinical outcomes, the companys post added.

CMD-1 is being rolled out nationally and will be available 24/7.

In addition to demonstrating the companys interest in ML and AI, the patient-identification tool is also reflective of its previously discussed commitment to quality control moving forward.

Earlier this year, Cerebral came under fire for its prescribing practices of controlled substances. In June, news surfaced that the Department of Justice (DOJ) launched an investigation into the company based on a potential violation of the Controlled Substance Act.

In turn, the digital health company changed up its leadership team. Founder Kyle Robertson stepped down as CEO, and Chief Medical Officer Dr. David Mou took on the role.

I will say that we have made mistakes, Mou said at the American Telemedicine Association conference in May. And Ill also admit that we will continue to make mistakes and learn.

Cerebral also announced layoffs for July 1. In June, Mou told BHB that the layoffs were reflective of the companys priorities to keep behavioral health front and center while moving into value-based care.

As for the future, the company is looking to treat serious mental illness (SMI) and developing its value-based care proposition, Mou previously told BHB.

Original post:
Cerebral Uses Machine Learning to Identify Patients in Mental Health Crisis, Vows 'Just the Beginning' for AI Investments - Behavioral Health Business

Artificial intelligence and ML are making considerable inroads in finance. They are the critical aspect of variousfinancial applications, including evaluating risks, managing assets, calculating credit scores, and approving loans.

Businesses use AI and ML:

Taking the above points into account, its no wonder that companies like Forbes and Venture beat are usingAI to predict the cash flow and detect fraud.

In this article, we present the financial domain areas in which AI and ML have a more significant impact. Well also discuss why financial companies should care about and implement these technologies.

Machine learning is a branch of artificial intelligence that allows learning and improvement without any programming. Simply put, data scientists train the MI model with existing data sets and automatically adjust its parameters to improve the outcome.

According to Statista, digital payments are expected to show an annual growth rate of 12.77% and grow to 20% by 2026. This vast number of global revenues, done online requires an intelligent fraud system.

Source: Mordor Intelligence

Traditionally, to check the authenticity of users, fraud-detection systems analyze websites through factors like location, merchant ID, the amount spent, etc. However, while this method is appropriate for a few transactions, it would not cope with the increased transactional amount.

And, analyzing the surge of digital payments, businesses cant rely on traditional fraud-detection methods to process payments. This gives rise to AI-based systems with advanced features.

An AI and ML-powered payment gateway will look at various factors to evaluate the risk score. These technologies consider a large volume of data (location of the merchant, time zone, IP address, etc.) to detect unexpected anomalies, and verify the authenticity of the customer.

Additionally, the finance industry, through AI, can process transactions in real-time, allowing the payment industry to process large transactions with high accuracy and low error rates.

The financial sector, including the banks, trading, and other fintech firms, are using AI to reduce operational costs, improve productivity, enhance users experience, and improve security.

The benefits of AI and ML revolve around their ability to work with various datasets. So lets have a quick look at some other ways AI and ML are making roads into this industry:

Considering how people invest their money in automation, AI significantly impacts the payment landscape. It improves efficiency and helps businesses to rethink and reconstruct their process. For example, businesses can use AI to decrease the credit card processing (gettrx dot com card processing guide for merchants) time, increase automation and seamlessly improve cash flow.

You can predict credit, lending, security, trading, baking, and process optimization with AI and machine learning.

Human error has always been a huge problem; however, with machine learning models, you can reduce human errors compared to humans doing repetitive tasks.

Incorporating security and ease of use is a challenge that AI can help the payment industry overcome. Merchants and clients want a payment system that is easy to use and authentic.

Until now, the customers have to perform various actions to authenticate themselves to complete a transaction. However, with AI, the payment providers can smooth transactions, and customers have low risk.

AI can efficiently perform high volume; labor-intensive tasks like quickly scrapping data and formatting things. Also, AI-based businesses are focused and efficient; they have minimum operational cost and can be used in the areas like:

Creating more Value:

AI and machine learning models can generate more value for their customers. For instance:

Improved customer experience: Using bots, financial sectors like banks can eliminate the need to stand in long queues. Payment gateways can automatically reach new customers by gathering their historical data and predicting user behavior. Besides, Ai used in credit scoring helps detect fraud activity.

There are various ways in which machine learning and artificial intelligence are being employed in the finance industry. Some of them are:

Process Automation:

Process automation is one of the most common applications as the technology helps automate manual and repetitive work, thereby increasing productivity.

Moreover, AI and ML can easily access data, follow and recognize patterns and interpret the behavior of customers. This could be used for the customer support system.

Minimizing Debit and Credit Card Frauds:

Machine learning algorithms help detect transactional funds by analyzing various data points that mostly get unnoticed by humans. ML also reduces the number of false rejections and improves the real-time approvals by gauging the clients behavior on the Internet.

Apart from spotting fraudulent activity, AI-powered technology is used to identify suspicious account behavior and fraudulent activity in real-time. Today, banks already have a monitoring system trained to catch the historical payment data.

Reducing False Card Declines:

Payment transactions declined at checkout can be frustrating for customers, putting huge repercussions on banks and their reputations. Card transactions are declined when the transaction is flagged as fraud, or the payment amount crosses the limit. AI-based systems are used to identify transaction issues.

The influx of AI in the financial sector has raised new concerns about its transparency and data security. Companies must be aware of these challenges and follow safeguards measures:

One of the main challenges of AI in finance is the amount of data gathered in confidential and sensitive forms. The correct data partner will give various security options and standards and protect data with the certification and regulations.

Creating AI models in finance that provide accurate predictions is only successful if they are explained to and understood by the clients. In addition, since customers information is used to develop such models, they want to ensure that their personal information is collected, stored, and handled securely.

So, it is essential to maintain transparency and trust in the finance industry to make customers feel safe with their transactions.

Apart from simply implementing AI in the online finance industry, the industry leaders must be able to adapt to the new working models with new operations.

Financial institutions often work with substantial unorganized data sets in vertical silos. Also, connecting dozens of data pipeline components and tons of APIS on top of security to leverage a silo is not easy. So, financial institutions need to ensure that their gathered data is appropriately structured.

AI and ML are undoubtedly the future of the financial sector; the vast volume of processes, transactions, data, and interactions involved with the transaction make them ideal for various applications. By incorporating AI, the finance sector will get vast data-processing capabilities at the best prices, while the clients will enjoy the enhanced customer experience and improved security.

Of course, the power of AI can be realized within transaction banking, which sits on the organizations usage. Today, AI is very much in progress, but we can remove its challenges by using the technology. Lastly, AI will be the future of finance you must be ready to embrace its revolution.

Featured Image Credit: Photo by Anna Nekrashevich; Pexels; Thank you!

View original post here:
AI and Machine Learning in Finance: How Bots are Helping the Industry - ReadWrite

It is hard to classify documents. At least manually.

Imagine this: you head into a standard bookstore where pieces are supposed to be classified as genres like thriller, romance, science fiction, and more. You want to pick Andy Weirs Hail Mary a novel with thriller/mystery and science fiction elements.

While the book choice seems on point, the question is: which genre should you head towards? The book can be on the science fiction shelf or on the thriller counter. It can be anywhere. And that is when the manual document classification becomes troublesome.

Sweating already? Fret not, as machine learning is here to help. Not to throw shade at the manual document classification, but they can be tedious if you plan on looking at a world outside books including inventories and databases.

Yet, document classification with machine learning can be a game changer, courtesy of the relevant and available technologies like NLP, Robots, Sentiment Analysis, OCR, and more.

Lets take a deeper dive into all of these.

Simply put, document classification is the automation process where relevant/classifying documents are stacked into relevant classes or even categories.

Often regarded as one of the sub-domain of text classification, an oversimplified version of document classification means tagging the docs and setting them right into predefined categories for the purpose of easy maintenance and efficient discovery.

In hindsight, the process is simple. Its all about extracting and retrieving information. Yet, due to the sheer size of data sets, companies often need to rely on deep learning and machine learning technologies to get ahead of document classification, albeit with a focus on speed, accuracy, scalability, and cost-effectiveness.

And just to mention, document classification can be considered a sub-domain of IDP or intelligent document processing. But more on that later.

As for the approach, document classification takes the text and visual classification techniques into consideration primarily for analyzing the document-specific phrases and also the visual structure.

Visual and text classification can help companies classify every kind of document (stills, pictures, large data modules, and more) with ease.

Short story: intelligent models scan through structured, unstructured, and even semi-structured documents to match them with the corresponding categories.

Long story: The following machine learning techniques are put to use for classifying documents according to categories:

Regardless of the approach, businesses need to find a good way to classify documents as going manual can be time-consuming, erroneous, and obviously hard.

However, if you are looking for broader shades in regards to the process, here are the steps associated with an automated and efficient document classification process:

Theoretical discourse is all cool, but what about the use-cases for document classification. We have it all sorted for you.

Opinion Classification: Businesses use this feature to segregate positive reviews from negative ones.

Spam Detection: Have you ever thought about how your email provider separates standard emails from spam emails? Well, document classification is the answer.

Customer support classification: A random day in the life of a customer support executive can be stressful. Document classification helps them understand the tickets better, especially when the request volume far exceeds their patience.

In addition to the mentioned use cases, document classification can also be used for social listening, document scanning, and even object recognition.

Every organization is information-dependent. Yet, every kind of information isnt meant for everyone. This is the reason why document classification becomes all the more important helping organizations collect, store, and eventually classify details as per requirements. And if you are still a manual evangelist, remember one thing: automation is the key to the future.

About the author: Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives. Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/

You might also like

Read the original here:
What is document classification, and how can machine learning help? - Robotics and Automation News