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Companies that don’t have a digitization and automation strategy will probably not survive in the next decade. Why? Because a host of technological developments are making it possible to free employees from a range of routine operations, so they can focus on the most impactful areas of business.

Enterprises that embrace automation can have happier customers, more satisfied employees, and streamlined operations. From back-office tasks to the inspection of industrial complexes and manufacturing sites, there are tools and platforms to collect and analyze various types of data, and take actions that automate repetitive tasks that previously required human effort.

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In the past two years alone, ten years’ worth of digitization has occurred across different industries as more organizations realize the need to automate their operations. This should be an alarm bell for those who do not yet have a digitization/automation strategy.

However, the huge opportunity for automation comes with its own challenges. The growing number and variety of tasks that can be automated need careful oversight and planning. This is why you should consider appointing a Chief Automation Officer (CAO), a person who will be able to trace your enterprise’s digitization and automation strategy, provide a birdseye view of the digitization journey, and make sure that your enterprise is on its path to success.

The underlying technology

The appeal of automation has been around for a long time, and enterprises have been looking for ways to automate routine tasks. But there are a few accurate trends that have lent to the explosion of opportunities for automation across industries.

“Artificial intelligence is clearly a trend,” says Dinesh Nirmal, General Manager at IBM Data, AI and Automation. “It can help with intelligent automation, whether it’s IT or line of business.”

AI is helping automate a broad range of tasks, from processing images and scanned documents, to summarizing text documents, finding meaningful correlations and clusters in large datasets, and transcribing audio files.

Enterprises are also using AI in IT operations, typically called AIOps, including anomaly detection, event correlation, and blast radius evaluation in case a service component becomes unavailable.

An interesting example is logistics and transportation company J.B. Hunt, which used IBM Turbonomic software to automate the scaling of its cloud and on-premise computing resources. For their on-premises environment, J.B. Hunt is automating all non-disruptive actions 24×7 and scaling non-production actions during a nightly maintenance window. In their public cloud environment, the team has been using a combination of recommendations and automated actions to manage their resources.

Digitization cannot happen without automation.

Over the course of 12 months, Turbonomic executed nearly 2,000 resizing actions which—assuming manual intervention requires 20 minutes per action—freed up over 650 hours of the team’s time to focus on strategic initiatives.

“Another technology is robotic process automation (RPA),” Nirmal says. “In line-of-business, a lot of technology that is being driven for automation is task related.”

If a task requires long hours of manually entering data and pressing buttons, there’s a great chance it can be automated through RPA. Today, organizations are using RPA to automate a wide range of repetitive back-office tasks, such as extracting information, moving files, and filling forms. The combination of RPA and AI is helping organizations take automation to higher levels and handle tasks that cannot be defined with explicit rules.

For example, insurance companies are using AI and RPA to automate the processing of customer reports. An application that previously took weeks to complete can now be processed in hours, thanks to RPA and AI technologies. One way is with computer vision algorithms that can assess damage from a picture of an accident and document processing tools that can extract and store the content of scanned documents.

A fourth notable trend that Nirmal talks about is “process mining.”

Process mining is about figuring out which processes are ripe for automation. What is the ROI if I were to automate this process? How much do I save in terms of time and resources by automating that process? What other touch points does the process have?

Process mining uses information from business and IT systems and user interactions to provide factual insights about how processes and workflows can be improved across an enterprise. For example, if delivering software requires several actions and file transfers across different platforms, process mining helps organizations document those steps, build them into a graph, monitor their performance, and find ways to optimize and Boost them.

An interesting case study is BlueShore Financial, a Canadian credit union that partnered with IBM to digitize and automate its operations. At first, IBM helped BlueShore digitize the files of 40,000 clients and go entirely paperless, saving the company 7,000 square feet of filing space. The digitization then led to the automation of complex workflows and business processes related to onboarding, mortgage renewals, auditing, and more.

What is the role of the Chief Automation Officer?

“Automation helps with every single aspect of enterprise operations,” Nirmal says. “This is why every enterprise needs a Chief Automation Officer to focus on digitizing the enterprise to satisfy customers and help generate more business.”

Nirmal compares the role of the CAO to the Chief Data Officer (CDO). Before the CDO, organizations held an ambiguous volume of data, scattered across different silos and systems. The CDO’s role was to devise the right tools and strategies to provide a unified view of the organization’s data. The CDO became pivotal in areas such as business intelligence, data science, and machine learning, which need to pull and process data from different sources across the enterprise.

It can really Boost employee morale and productivity.

Likewise, the CAO takes an overarching view across different verticals of the enterprise and seeks the best opportunities for digitization and automation. For example, in manufacturing, the CAO can help identify how computer vision can help automate defect detection. In financial services, the CAO can help find the right combination of tools to automate repetitive back-office tasks. According to Nirmal:

An enterprise is usually a big setup of complex processes across business and IT. We need a person who works collaboratively with the chief transformation officer, chief strategy officer, chief data officer, and others to bring digitization to the forefront. I believe that the CAO’s focus needs to be on digitizing the enterprise. Digitization cannot happen without automation.

Automation is finding its way into every department. According to IBM’s Global AI Adoption Index, companies are using automation across different verticals, including IT processes, business processes, marketing and sales, financial planning and analysis, etc.

At IBM, Nirmal has helped several customers transition through the digitization and automation process and understand the value of appointing a CAO. He sees three common denominators in the benefits that these organizations have achieved.

One is the optimization of resources within an enterprise. Two is productivity—I truly believe it drives a huge amount of productivity in the enterprise that you cannot get otherwise. And three is observability of your enterprise end-to-end, and how you’re digitizing your enterprise across different sectors.

The human side of automation

Nirmal says, “I think automation brings an enterprise into a much more stable format that will enable them to become more agile, allowing them to do more meaningful work.”

For example, from an employee productivity perspective, the right automation tools can lift the burden of repetitive tasks and enable employees to focus on more meaningful endeavors, such as innovation.

“A lot of times, automation allows employees to focus on higher value tasks,” Nirmal says. “For the employee, the huge benefit is that it releases them from routine tasks and frees them to look at things from an innovation or differentiation perspective. It can really Boost employee morale and productivity, optimization, etc.”

But obviously, the biggest goal that every enterprise will have to focus on is improving customer satisfaction.

“The end product is how you help create satisfied customers. For me, to achieve that you need automation,” Nirmal says.

He sees this as the future of the enterprise, which will make the Chief Automation Officer role key to success.

The next decade is all about intelligent automation. Enterprises will have to invest in automation to survive and succeed, or they may not exist. It’s a profound statement, but I think it’s critical because your competition will invest in it. It drives customer satisfaction. If you don’t have satisfied customers, you are clearly not in a position to win.

Tue, 06 Dec 2022 07:41:00 -0600 en text/html
Killexams : An automated way to assemble thousands of objects

The manufacturing industry (largely) welcomed artificial intelligence with open arms. Less of the dull, dirty, and dangerous? Say no more. However, planning for mechanical assemblies still requires more than scratching out some sketches, of course—it's a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies.

Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential to be riddled with errors.

In a quest to ease some of said burdens, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that's accurate, efficient and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multi-part assembly, and then searches for a physically realistic motion path for each step.

The team cooked up a spartan level large-scale dataset with thousands of physically valid industrial assemblies and motions to test their method. The proposed method is capable of solving almost all of them, especially outperforming previous methods by a large margin on rotational assemblies, like screws and puzzles. Also, it's a bit of a speed demon in that it solves 80-part assemblies within several minutes.

"Instead of one specifically designed for one specific product, if we can automatically figure out ways to sequence and move, we can use a fully adaptive setup," says Yunsheng Tian, a Ph.D. student at MIT CSAIL and lead author on the paper. "Maybe one assembly line can be used for tons of different products. We think of this as low-volume, high-mixed assembly, opposed to traditional high-volume, low-mixed assembly, which is very specific to a certain product."

Given the objective of assembling a screw attached to a rod, for example, the algorithm would find the assembly strategy through two stages: disassembly and assembly. The disassembly planning algorithm searches for a collision-free path to disassemble the screw from the rod. Using physics-based simulation, the algorithm applies different forces to the screw and observes the movement. As a result, a torque rotating along the rod's central axis moves the screw to the end of the rod, then a straight force pointing away from the rod separates the screw and the rod. In the assembly stage, the algorithm reverses the disassembly path to get an assembly solution from individual parts.

"Think about IKEA furniture—it has step-by-step instructions with the little white book. All of those have to be manually authored by people today, so now we can figure out how to make those assembly instructions," says Karl D.D. Willis, a Senior Research Manager at Autodesk Research. "You can imagine how, for people designing products, this could be helpful for building up those types of instructions. Either it's for people, as in laying out these assembly plans, or it could be for some kind of robotic system right down the line."

Credit: MIT Computer Science & Artificial Intelligence Lab

The disassemble/assemble dance

With current manufacturing, in a factory or assembly line, everything is typically hard-coded. If you want to assemble a given product, you have to precisely control or program instructions to assemble or disassemble a product. Which part should be assembled first? Which part should be assembled next? And how are you going to assemble this?

Previous attempts have been mostly limited to simple assembly paths, like a very straight translation of parts—nothing too complicated. To move beyond this, the team used a physics-based simulator—a tool commonly used to train robots and self-driving cars—to guide the search for assembly paths, which makes things much easier and more generalizable.

"Let's say you want to disassemble a washer from the shaft, which is very tightly geometrically assembled. The status quo would simply try to demo a bunch of different ways to separate them, and it's very possible you can't create a simple path that's perfectly collision free. Using physics, you don't have this limitation. You can try, for example, adding a simple downward force, and the simulator will find the correct motion to disassemble the washer from the shaft," says Tian.

While the system handled rigid objects with ease, it remains in future work to plan for soft, deformable assemblies.

One avenue of work the team is looking to explore is making a physical robotic setup to assemble items. This would require more work in terms of robotic control and planning to be integrated with the team's system, as a step toward their broader goal: to make an assembly line that can adaptively assemble everything without humans.

"The long-term vision here is how do you take any object in the world and be able to either put that together from the parts, using automation and robotics?", says Willis. "Inversely, how do we take any object in the world that's made up of many different types of materials and pull it apart so that we can recycle and get them into the correct waste streams? The step we're taking is looking at how we can use some advanced simulation to be able to begin to pull apart those parts, and eventually get to the point where we can test that in the real world."

"Assembly is a longstanding challenge in the robotics, manufacturing, and graphics communities," says Yashraj Narang, research scientist at NVIDIA. "This work is an important step forward in simulating mechanical assemblies and solving assembly planning problems. It proposes a method that is a clever combination of solving the computationally-simpler disassembly problem, using force-based actions in a custom simulator for contact-rich physics, and using a progressively-deepening search .

"Impressively, the method can discover an assembly plan for a 50-part engine in a few minutes. In the future, it will be exciting to see other researchers and engineers build upon this excellent work, perhaps allowing robots to perform the assembly operations in simulation and then transferring those behaviors to real-world industrial settings."

More information: Yunsheng Tian et al, Assemble Them All, ACM Transactions on Graphics (2022). DOI: 10.1145/3550454.3555525


Provided by MIT Computer Science & Artificial Intelligence Lab

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Wed, 07 Dec 2022 03:18:00 -0600 en text/html
Killexams : How Automation Can Free the Creative Genius

Thomas Edison is credited with the phrase: "Genius is 1% inspiration and 99% perspiration." This view may have been true in Edison's time before his inventions came to fruition and simplified everyday life for millions. Today, however, with advanced technologies that can automate everything from banking to retail to customer service, many laborious and menial tasks stealing our time have been completely eliminated.

With far less perspiration required to meet our basic needs, we have more time than ever to bring new ideas and innovation to life, enhance customer experience and Boost business ROI.

Remove Barriers to Creativity

Humans are great at pattern recognition and formulating strategies, but they don't particularly excel after staring at data for hours. Automation can categorize data, detect anomalies and extract insights, giving people a "head start" on creative brainstorming. With artificial intelligence (AI) responsible for the heavy legwork, people don't have to exhaust their intellect on monotonous tasks. Instead, they can apply their cognitive potential to handle high-level business issues and refine existing systems.

Automation can also help reduce unnecessary and legacy business processes. For example, HR leaders spend a lot of time — in one estimate, up to 40% of their time — on administrative tasks, answering phone calls, writing emails and transferring data from one system to another. However, some innovative companies use machine learning automation to streamline talent acquisition and employee recruitment. Automating tedious onboarding processes can help HR personnel to wisely spend their time and creative abilities, such as thoroughly evaluating candidates to ensure no qualified individual gets overlooked.

When people are less preoccupied with crunching numbers and replying to emails, their creativity will have more of a chance to shine — but, more importantly, they'll be more satisfied in their careers. Menial tasks can sap people of their energy and motivation — however, projects that let us exercise our cognitive potential are more rewarding, ultimately leading to better results.

Make the Consumer's Life Better

Customer experience (CX) is critical to differentiating from competitors and creating loyal clients. Today, consumers want to save time — the modern customer would often prefer not to wait for a representative to check with a manager or look up something in their system. If customers can discover the answer to their questions without speaking with an agent, they will be all the more pleased.

Automation solutions are effective means of making the consumer's life better as they can, when integrated with a contact center, provide self-service features such as chatbots and virtual assistants. These AI-powered automated communication solutions boost CX and reduce consumer stress by eliminating hold times and allowing callers to get answers to questions rapidly while quickly resolving simple requests like paying bills or password changes.

Brands can also use automation to better coordinate messaging between different channels — voice, email, SMS, and social — and create a smooth and consistent experience for the consumer. Likewise, liberating customer support from monotonous requests will enable them to use their creativity elsewhere. Notably, they will be free to fix pain points, removing friction in the customer journey. Staff will also have time to learn and implement new technologies, further enriching CX.

Improve Business ROI

Automation won't replace humans, but it will augment their efforts. By removing time-consuming and tedious tasks, individuals, teams, and company leaders can direct their energy and effort to higher ROI areas and prioritize challenges that require human intellect.

In just one month, a FinServe company leveraging automation to launch a new customer project was able to process 1.5 million new account activation requests. Similarly, automation will quickly pay for itself and other initiatives. For instance, by offloading inbound inquiries to automation and self-service, contact centers could handle twice the call volume — meaning, a company of 100 agents could see $2 million in annual savings.

Businesses can further Boost revenue by automating appointment and event reminders. A study found that missed healthcare-related appointments in the US cost the industry an absurd $150 billion annually. The benefits of automating these reminders are twofold; it will Boost the likelihood that customers will attend or reschedule on time, increasing revenue; it will also help call center agents pivot their attention to more pressing issues.

Additionally, automation will reduce human error, which is common when people perform highly repetitive tasks. These mistakes are also the result of poor collaboration. One study found that US companies lose $1.2 trillion yearly due to ineffective communication. However, automating internal communication channels helps team members share information, maintain compliance and work more cohesively.

Key Features to Look for When Implementing Automation

Understandably, employees might be hesitant and even antagonistic toward automation efforts. Be sure to implement gradual shifts to promote adoption. Also, businesses must explain why certain processes are getting automated in the first place and train staff accordingly.

Likewise, businesses can achieve greater buy-in by bringing employees into the company's automation transition. Articulate that the company is not automating the employees but giving them the tools to automate their own work. Ultimately, automation can help make their roles more rewarding, improving personal and professional satisfaction.

When deciding which processes will lead to the greatest innovation and creativity once automated, companies should look to eliminate those duties that are repetitive and simple. Again, utilize a crawl, walk, run approach over an impromptu overhaul. In the same way, you should leverage platforms that allow for incremental, iterative change that empowers individual creativity to flourish.

Moreover, the software or service enabling automation capabilities should be easy to use; some leading platforms come with intuitive builders equipped with no-code and low-code applications. Investing in such software will allow anyone to create, edit or add automation to workflows — no coding experience required. Additionally, the ideal software should integrate with other platforms and enhance your existing infrastructure.

Edison is famous for having 1,000 unsuccessful attempts at inventing the light bulb. While his perseverance is commendable, most businesses don't have 1,000 chances to get automation right, so set your priorities before comparing options.

Sun, 27 Nov 2022 23:35:00 -0600 en text/html
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Killexams : Best Automation Companies in 2023

Automation is changing the world as we know it. As technology advances and companies grow, the use of automation increases constantly. As a result, companies are increasingly seeking to automate their day-to-day processes to save time, money, and resources.

Also see: Top Business Intelligence Software 

What is Automation?

Automation is the process by which machines perform tasks without human involvement. This is achieved through robots and other automated devices or computer software. Companies automate processes to increase efficiency and productivity as well as reduce errors.

The four most common types of automation include:

Fixed automation

This type of automation provides a predetermined response to predetermined input values. For example, an assembly line might be set up so that every time one product part is inserted at one end, it travels down the assembly line automatically until it comes out the other side with all of its pieces assembled.

Programmable automation

This automation allows users to set rules for decisions based on input values. So, instead of having a single preset response to input values, programmable automation lets you set different responses depending on other inputs.

Flexible automation

This type of automation combines the best features of both fixed and programmable automation. It allows you to use preset options and enable changes to them like with programmable automation.

Integrated automation

Integrated automation is when software and hardware are seamlessly integrated to produce an automated system that can perform various tasks. These systems rely on predefined data, rules, and procedures to complete functions without human intervention or time delay.

Also see: Top AI Software 

10 Best Automation Companies

Automation companies provide ways to balance human creativity with machine-driven efficiency. With the help of automation, humans can do more in less time. The best automation companies offer various services, such as industrial equipment, environmental engineering, and marketing for worldwide businesses.


Honeywell logo

Honeywell International Inc. is a Fortune 100 company that offers industrial and consumer products, technologies, and services. Mark Honeywell founded the company in 1906 in Wabash, Indiana, to manufacture and market his invention, the mercury seal generator.

The company operates four main business groups, including aerospace, home and building technologies (HBT), safety and productivity solutions (SPS), and performance materials and technologies (PMT).

Honeywell provides various automation solutions across its business groups, such as Automatic Identification System (AIS) and RF Receivers for aerospace; access systems; biometric readers; building operation experts (BOE); intrusion detection systems; heating, ventilating, and air conditioning control systems for residential and commercial buildings; safety systems for homes, schools, businesses, plants, and public spaces. It also provides specialty materials with innovative properties used to create engineered products from advanced materials found in everything from aircraft engines to home appliances.

Siemens Automation

Siemens logo

Siemens is an industrial company that provides automation products and services. With a global workforce of more than 303,000 employees worldwide, and offices in over 100 countries, Siemens is one of the largest engineering companies in the world. Many industries use their products, including power generation and energy production, healthcare and medical engineering, automotive manufacturing, process industry (like petrochemicals or chemicals), aerospace, transportation systems including trains, and vehicles.

Siemens offers several automation products, including industrial automation systems (SIMATIC), motion control systems (SIMOTION), CNC automation systems (SINUMERIK), and process control systems like SIMATIC PCS 7 and SIMATIC PCS neo.

Totally Integrated Automation (TIA) is Siemens’ industrial automation tool that integrates hardware, software, and services to provide customers with a complete digital enterprise solution. In addition, TIA leverages connectivity and cloud technologies to make automation solutions available on demand.

Siemens’ portfolio includes solutions for factory operations optimization, predictive maintenance, quality assurance, smart buildings and city management, project management support tools (PMST), service operations excellence, plus other solutions tailored to specific customer needs. It also offers a 3D simulation environment called Simcenter, which allows users to perform design, simulation, test, and data management.

Also see: AI vs. ML: Artificial Intelligence and Machine Learning


ABB logo

ABB is one of the world’s leading automation technology providers, products, and services. With a presence in over 100 countries, ABB employs around 105,000 people. ABB has four main divisions: electrification, power grids division, robotics division, and process automation division.

ABB process automation and digitalization solutions make processes more efficient and productive by automating typically time-consuming and labor-intensive tasks. It offers controllers for pneumatic valves and actuators, programmable logic controllers (PLCs), servo amplifiers, and DC drive systems as well as Fieldbus protocols such as Profibus, Ethernet/IP, or DeviceNet.

ABB robotics division develops, manufactures, and markets robots for heavy manufacturing to light assembly. Their product portfolio includes industrial robots, collaborative robots, autonomous mobile robots, controllers, equipment and accessories, OmniVance application cells, functional modules, and machine automation solutions.

In addition to robots themselves, the robotics division also provides their customers with application software, connected services, robot control mate, robotstudio, and wizard easy programming tools. The company has production facilities worldwide, including Finland, Italy, the U.S., and India.

OMRON Industrial Automation

OMRON logo

OMRON Industrial Automation is a leading global manufacturer of industrial automation equipment. Headquartered in Kyoto, Japan, it has approximately 150 offices in 40 countries worldwide and employs over 30,000 people. It is one of the world’s largest PLCs and SCADA (supervisory control and data acquisition) software producers.

The company offers comprehensive systems solutions for semiconductors, medical, food, beverage, oil and gas, pulp, and paper. Its product range includes programmable logic controllers, servo motors, sensors, switches, robot arms and grippers, and machine automation controllers.

Their products are used in various fields, from manufacturing to robotics to heavy industry. They employ many technologies like wireless communications, artificial intelligence, and big data for their product development.

Mitsubishi Electric

Mitsubishi Electric logo

Mitsubishi Electric is a Japanese multinational electronics and electrical equipment company founded in Tokyo, Japan, in 1921. The company’s major focus is manufacturing electronic parts such as semiconductors, electric and hybrid cars, electric trains, aircraft parts, and household appliances.

Mitsubishi Electric employs more than 100,000 people worldwide. It provides automation systems for power plants; robotics for factories and warehouses; air-conditioning units for buildings; and I&C systems for monitoring, protecting, and controlling nuclear power plants.

Mitsubishi Electric factory automation tools include industrial computer MELIPC, human-machine interfaces (HMIs)-GOT, SCADA, industrial/collaborative robots-MELFA, programmable controllers MELSEC, simple application controllers, motion controllers, computerized numerical controllers(CNCs), and data logging analyzer-MELQIC.

Also see: Best Machine Learning Platforms 

Rockwell Automation

Rockwell Automation, Inc. is a global automation company headquartered in Milwaukee, Wisconsin. The Fortune 500 company was founded in 1903 by Lynde Bradley and Stanton Allen as the Compression Rheostat Company and changed its name to Allen-Bradley in 1909. It later merged with Rockwell in 1985 and changed its name to Rockwell International.

Rockwell Automation’s products include programmable logic controllers (PLCs), motion control solutions, power systems, and software. This makes it one of the best companies for industrial automation because it provides systems that can be used in various industries such as energy, food and beverage, and pharmaceuticals.

Many devices are available on the company’s website, including PlantPAx distributed control systems, actuators, positioners, and conveyors. These products can be utilized in various ways, and Rockwell also offers complete solutions through its design service.

Emerson Process Management

Emerson logo

Emerson Process Management is an engineering and consulting services company focusing on industrial automation, process control, and power delivery. The business offers a wide range of products and services to help clients Boost their production processes.

Founded in 1890, the Fortune 500 company’s headquarters are located in St. Louis, Missouri. The company operates through two segments: automation solutions and commercial & residential solutions.

Emerson Process Management also provides solutions for smart buildings, energy management systems, industrial motors and drives, specialty chemicals plants, food processing plants, pharmaceutical plants, refineries, and petrochemical plants. In addition, the company has recently seen growth in sectors such as oil and gas, healthcare, and water treatment.

In addition to designing automated equipment for clients across various industries, Emerson Process Management provides automation and control software solutions such as SCADA software, data acquisition, human-machine interface software, data historian software (DeltaV), and configuration tools.

Its current product portfolio includes components like distributed slice I/O systems, programmable logic controllers, actuators and valves, variable frequency drives (VFD), energy meters, sensing and protection devices, motor controls and relays, and transformers.

Also see: Data Analytics Trends 

Fortra’s Automate

Fortra logo

Fortra’s Automate is the leading automation company specializing in building and designing custom automated programs for various industries. Formerly known as HelpSystems, LLC, the company was founded in 1982 to develop solutions to automate complex business processes.

In 2022, it rebranded as Fortra to create a new image for its cybersecurity and automation solutions. Fortra currently employs more than 3,000 people worldwide, with offices in 18 countries.

Fortra’s Automate can automate business processes in various industries, including IT, banking, health, accounts payable, human resources, and user provisioning.

Yokogawa Electric

Yokogawa Electric logo

Yokogawa Electric is a Japanese multinational company headquartered in Tokyo, Japan. It was founded in 1915 by Tamisuke Yokogawa and was incorporated in 1920 as Yokogawa Electric Works Ltd.

This company offers services and solutions for process control technology, industrial automation, and energy management. It has many well-known products, such as PLC, distributed control system, SCADA, and HMI.

Yokogawa works with various industries, including oil and gas, LNG supply chain, chemical, power, pharmaceutical, and food and beverage.

Schneider Electric

Schneider Electric logo

Schneider Electric is a French multinational corporation headquartered in Rueil-Malmaison, France. The company operates in the energy and automation markets. Schneider Electric provides various industrial automation and control products for field, factory, and office use to Boost customer operations’ reliability, efficiency, and profitability.

Schneider Electric’s offerings include telemetry and remote SCADA systems, PLC and PAC-dedicated controller, motor starters, motion control, robotics, and HMIs. In addition, Schneider Electric’s EcoStruxure product line provides edge control and apps, analytics, and services to homes, buildings, data centers, infrastructure, and industries.

Also see: Top Data Visualization Tools 

Benefits of Automation Companies

Automation is a vital part of business development in the current day and age. Automation has become ubiquitous, with many industries adopting it for various purposes. For example:

  • The finance industry often uses automation to reduce human error when calculating complex mathematical formulas.
  • Retailers use it to manage inventory and perform data analysis for future marketing campaigns.
  • The healthcare sector employs automation to keep up with demand from aging populations and new technologies that require constant monitoring.
  • Manufacturing companies had used automation since the 1980s when robot arms started replacing assembly line workers on their production lines.
  • AI has also become an essential component of automation, especially in customer service jobs. Chatbots can answer common questions more efficiently than humans, freeing time for customer service representatives to focus on more complicated problems.

Automation companies come in all shapes and sizes, but they share two things in common: They make life easier for people through reduced labor needs and improved efficiency, and they offer a wide range of services related to specific industries.

A good automation company will know what its customers want, how to give it to them efficiently, and how it can provide more benefits than competitors. It also should have excellent technical capabilities, so customers can rely on them for maintenance and support over the long term.

How to Choose the Best Automation Provider

When it comes to automation, various considerations come into play. From the specific tasks that need to be automated to the level of investment you’re willing to make, many factors exist to consider when choosing an automation company.

Industry experience

When determining which automation company is right for you, you must ensure they have experience with your industry. The best way to do this is by asking for references from people in your industry or looking at the portfolio of their completed projects. If they don’t have any testimonials or past work in your field, you may want to move on and find another company with more relevant experience.

Range of services and pricing

Before deciding which automation company to hire, take some time to explore what they offer and how much they each cost. Some companies provide one service, while others offer many solutions. Make sure you research each type of service they provide before deciding on a particular company, so you can efficiently compare prices and offerings.

Dedicated team

Working with an automation company with dedicated teams ensures every project gets the attention it deserves. Dedicated teams also allow for faster turnarounds, so if speed is vital, then this factor could be essential to consider.

Customer satisfaction rate

You should always look at customer satisfaction rates when researching different automation companies. Reviews are usually available online and give valuable insight into how well a company performs. Another way to assess the quality of a company is by looking at the number of times they’ve been awarded contracts and accolades. Doing your due diligence upfront can help you avoid future problems and costly mistakes.

Company certification

Look for certifications such as ISO 9001 (Quality Management) and ISO 14001 (Environmental Management). These certifications speak to a company’s commitment to providing high-quality products and sustainable practices. Choosing a company with these certifications is wise because they demonstrate good intentions in providing green solutions with little to no environmental impact.

Also see: Best Data Analytics Tools 

History of Automation

Automation has been developing for a surprisingly long time. In the late 1800s, factories used primitive automation to replace human labor and make production more efficient. Automation has come a long way from steam to internal combustion engines.

The invention of automated flour mills in 1785 by Oliver Evans helped push this movement even further, as they could produce continuously without any human intervention. Then, in the 1950s, Joseph F. Engelberger co-founded Unimation, an American company that developed and manufactured robotic machines.

The first machine it built was the Unimate, which could automatically perform tasks like picking up and moving parts on a production line. George Devol invented the first-ever industrial robot arm with six degrees of freedom. His company, Unimation, would license its technology to major U.S. industries, including General Motors.

After that, automation grew into new industries like manufacturing, retailing, transportation, and communication. Today, robots are everywhere, from automobile manufacturing plants to hospitals performing procedures such as brain surgery or installing computer chips into electronic devices. Automation has advanced from solely mechanical and electrical systems to incorporating robotics and artificial intelligence.

Thu, 24 Nov 2022 06:08:00 -0600 en-US text/html
Killexams : Automated Machine Learning (AutoML) Market 2023 Expected to Witness the Highest Revenue Growth Over Forecast to 2028

The MarketWatch News Department was not involved in the creation of this content.

Nov 25, 2022 (The Expresswire) -- Final Report will add the analysis of the impact of COVID-19 on this industry.

"Automated Machine Learning (AutoML) Market" Insights 2022 - By Applications (Banking, Financial Services, and Insurance (BFSI), Information Technology (IT) and Telecom, Healthcare, Government, Retail, Manufacturing), By Types (On-Premises, Cloud), By Segmentation analysis, Regions and Forecast to 2028. The Global Automated Machine Learning (AutoML) market Report provides In-depth analysis on the market status of the Automated Machine Learning (AutoML) Top manufacturers with best facts and figures, meaning, Definition, SWOT analysis, PESTAL analysis, expert opinions and the latest developments across the globe., the Automated Machine Learning (AutoML) Market Report contains Full TOC, Tables and Figures, and Chart with Key Analysis, Pre and Post COVID-19 Market Outbreak Impact Analysis and Situation by Regions.

Automated Machine Learning (AutoML) Market Size is projected to Reach Multimillion USD by 2028, In comparison to 2021, at unexpected CAGR during the forecast Period 2022-2028.

Browse Detailed TOC, Tables and Figures with Charts that provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

Considering the economic change due to COVID-19 and Russia-Ukraine War Influence, Automated Machine Learning (AutoML), which accounted for % of the global market of Automated Machine Learning (AutoML) in 2021


Moreover, it helps new businesses perform a positive assessment of their business plans because it covers a range of syllabus market participants must be aware of to remain competitive.

Automated Machine Learning (AutoML) Market Report identifies various key players in the market and sheds light on their strategies and collaborations to combat competition. The comprehensive report provides a two-dimensional picture of the market. By knowing the global revenue of manufacturers, the global price of manufacturers, and the production by manufacturers during the forecast period of 2022 to 2028, the reader can identify the footprints of manufacturers in the Automated Machine Learning (AutoML) industry.

Automated Machine Learning (AutoML) Market - Competitive and Segmentation Analysis:

As well as providing an overview of successful marketing strategies, market contributions, and accurate developments of leading companies, the report also offers a dashboard overview of leading companies' past and present performance. Several methodologies and analyses are used in the research report to provide in-depth and accurate information about the Automated Machine Learning (AutoML) Market.

The Major players covered in the Automated Machine Learning (AutoML) market report are:

● SAS Institute Inc
● dotData Inc
● Determined AI
● DataRobot Inc
● EdgeVerve Systems Limited
● Squark
● Aible Inc
● Big Squid Inc
● Inc
● Google LLC
● Microsoft Corporation
● Amazon Web Services Inc

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Short Description About Automated Machine Learning (AutoML) Market:

The Global Automated Machine Learning (AutoML) Market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2020, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

This report focuses on global and United States Automated Machine Learning (AutoML) market, also covers the segmentation data of other regions in regional level and county level.

Due to the COVID-19 pandemic, the global Automated Machine Learning (AutoML) market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a Impressive CAGR during the review period. Fully considering the economic change by this health crisis, by Type, Automated Machine Learning (AutoML) accounting for % of the Automated Machine Learning (AutoML) global market in 2021, is projected to value USD million by 2028, growing at a revised % CAGR in the post-COVID-19 period. While by Application, Automated Machine Learning (AutoML) was the leading segment, accounting for over percent market share in 2021, and altered to an % CAGR throughout this forecast period.


The global Automated Machine Learning (AutoML) market is projected to reach USD million by 2028 from an estimated USD million in 2022, at a magnificent CAGR during 2023 and 2028.

Report Scope

This report aims to provide a comprehensive presentation of the global market for Automated Machine Learning (AutoML), with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Automated Machine Learning (AutoML).

The Automated Machine Learning (AutoML) market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue (USD millions), considering 2021 as the base year, with history and forecast data for the period from 2017 to 2028. This report segments the global Automated Machine Learning (AutoML) market comprehensively. Regional market sizes, concerning products by types, by application, and by players, are also provided. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.

For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.

The report will help the Automated Machine Learning (AutoML) manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, product type, application, and regions.

Key Companies and Market Share Insights

In this section, the readers will gain an understanding of the key players competing. This report has studied the key growth strategies, such as innovative trends and developments, intensification of product portfolio, mergers and acquisitions, collaborations, new product innovation, and geographical expansion, undertaken by these participants to maintain their presence. Apart from business strategies, the study includes current developments and key financials. The readers will also get access to the data related to global revenue, price, and sales by manufacturers for the period 2017-2022. This all-inclusive report will certainly serve the clients to stay updated and make effective decisions in their businesses.

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Automated Machine Learning (AutoML) Market 2022 is segmented as per type of product and application. Each segment is carefully analyzed for exploring its market potential. All of the segments are studied in detail on the basis of market size, CAGR, market share, consumption, revenue and other vital factors.

Global Automated Machine Learning (AutoML) Market Revenue Led By Product Type Segment:

● On-Premises
● Cloud

Global Automated Machine Learning (AutoML) Market Leading End-Use Segment:

● Banking, Financial Services, and Insurance (BFSI)
● Information Technology (IT) and Telecom
● Healthcare
● Government
● Retail
● Manufacturing

Automated Machine Learning (AutoML) Market is further classified on the basis of region as follows:

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

This Automated Machine Learning (AutoML) Market Research/Analysis Report Contains Answers to your following Questions

● What are the global trends in the Automated Machine Learning (AutoML) market? Would the market witness an increase or decline in the demand in the coming years? ● What is the estimated demand for different types of products in Automated Machine Learning (AutoML)? What are the upcoming industry applications and trends for Automated Machine Learning (AutoML) market? ● What Are Projections of Global Automated Machine Learning (AutoML) Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export? ● Where will the strategic developments take the industry in the mid to long-term? ● What are the factors contributing to the final price of Automated Machine Learning (AutoML)? What are the raw materials used for Automated Machine Learning (AutoML) manufacturing? ● How big is the opportunity for the Automated Machine Learning (AutoML) market? How will the increasing adoption of Automated Machine Learning (AutoML) for mining impact the growth rate of the overall market? ● How much is the global Automated Machine Learning (AutoML) market worth? What was the value of the market In 2020? ● Who are the major players operating in the Automated Machine Learning (AutoML) market? Which companies are the front runners? ● Which are the accurate industry trends that can be implemented to generate additional revenue streams? ● What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Automated Machine Learning (AutoML) Industry?

Customization of the Report

Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.

Inquire more and share questions if any before the purchase on this report at -

Detailed TOC of Global Automated Machine Learning (AutoML) Market Insights and Forecast to 2028

1 Study Coverage
1.1 Automated Machine Learning (AutoML) Product Introduction
1.2 Market by Type
1.2.1 Global Automated Machine Learning (AutoML) Market Size by Type, 2017 VS 2022 VS 2028
1.3 Market by Application
1.3.1 Global Automated Machine Learning (AutoML) Market Size by Application, 2017 VS 2022 VS 2028

1.4 Study Objectives
1.5 Years Considered

2 Global Automated Machine Learning (AutoML) Production
2.1 Global Automated Machine Learning (AutoML) Production Capacity (2017-2028)
2.2 Global Automated Machine Learning (AutoML) Production by Region: 2017 VS 2022 VS 2028
2.3 Global Automated Machine Learning (AutoML) Production by Region
2.3.1 Global Automated Machine Learning (AutoML) Historic Production by Region (2017-2022)
2.3.2 Global Automated Machine Learning (AutoML) Forecasted Production by Region (2023-2028)
2.4 North America
2.5 Europe
2.6 China
2.7 Japan

3 Global Automated Machine Learning (AutoML) Sales in Volume andamp Value Estimates and Forecasts
3.1 Global Automated Machine Learning (AutoML) Sales Estimates and Forecasts 2017-2028
3.2 Global Automated Machine Learning (AutoML) Revenue Estimates and Forecasts 2017-2028
3.3 Global Automated Machine Learning (AutoML) Revenue by Region: 2017 VS 2022 VS 2028
3.4 Global Automated Machine Learning (AutoML) Sales by Region
3.4.1 Global Automated Machine Learning (AutoML) Sales by Region (2017-2022)
3.4.2 Global Sales Automated Machine Learning (AutoML) by Region (2023-2028)
3.5 Global Automated Machine Learning (AutoML) Revenue by Region
3.5.1 Global Automated Machine Learning (AutoML) Revenue by Region (2017-2022)
3.5.2 Global Automated Machine Learning (AutoML) Revenue by Region (2023-2028)
3.6 North America
3.7 Europe
3.8 Asia-Pacific
3.9 Latin America
3.10 Middle East andamp Africa

4 Competition by Manufactures
4.1 Global Automated Machine Learning (AutoML) Production Capacity by Manufacturers
4.2 Global Automated Machine Learning (AutoML) Sales by Manufacturers
4.2.1 Global Automated Machine Learning (AutoML) Sales by Manufacturers (2017-2022)
4.2.2 Global Automated Machine Learning (AutoML) Sales Market Share by Manufacturers (2017-2022)
4.2.3 Global Top 10 and Top 5 Largest Manufacturers of Automated Machine Learning (AutoML) in 2022
4.3 Global Automated Machine Learning (AutoML) Revenue by Manufacturers
4.3.1 Global Automated Machine Learning (AutoML) Revenue by Manufacturers (2017-2022)
4.3.2 Global Automated Machine Learning (AutoML) Revenue Market Share by Manufacturers (2017-2022)
4.3.3 Global Top 10 and Top 5 Companies by Automated Machine Learning (AutoML) Revenue in 2022
4.4 Global Automated Machine Learning (AutoML) Sales Price by Manufacturers
4.5 Analysis of Competitive Landscape
4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)
4.5.2 Global Automated Machine Learning (AutoML) Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
4.5.3 Global Automated Machine Learning (AutoML) Manufacturers Geographical Distribution
4.6 Mergers andamp Acquisitions, Expansion Plans

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5 Market Size by Type
5.1 Global Automated Machine Learning (AutoML) Sales by Type
5.1.1 Global Automated Machine Learning (AutoML) Historical Sales by Type (2017-2022)
5.1.2 Global Automated Machine Learning (AutoML) Forecasted Sales by Type (2023-2028)
5.1.3 Global Automated Machine Learning (AutoML) Sales Market Share by Type (2017-2028)
5.2 Global Automated Machine Learning (AutoML) Revenue by Type
5.2.1 Global Automated Machine Learning (AutoML) Historical Revenue by Type (2017-2022)
5.2.2 Global Automated Machine Learning (AutoML) Forecasted Revenue by Type (2023-2028)
5.2.3 Global Automated Machine Learning (AutoML) Revenue Market Share by Type (2017-2028)
5.3 Global Automated Machine Learning (AutoML) Price by Type
5.3.1 Global Automated Machine Learning (AutoML) Price by Type (2017-2022)
5.3.2 Global Automated Machine Learning (AutoML) Price Forecast by Type (2023-2028)

6 Market Size by Application
6.1 Global Automated Machine Learning (AutoML) Sales by Application
6.1.1 Global Automated Machine Learning (AutoML) Historical Sales by Application (2017-2022)
6.1.2 Global Automated Machine Learning (AutoML) Forecasted Sales by Application (2023-2028)
6.1.3 Global Automated Machine Learning (AutoML) Sales Market Share by Application (2017-2028)
6.2 Global Automated Machine Learning (AutoML) Revenue by Application
6.2.1 Global Automated Machine Learning (AutoML) Historical Revenue by Application (2017-2022)
6.2.2 Global Automated Machine Learning (AutoML) Forecasted Revenue by Application (2023-2028)
6.2.3 Global Automated Machine Learning (AutoML) Revenue Market Share by Application (2017-2028)
6.3 Global Automated Machine Learning (AutoML) Price by Application
6.3.1 Global Automated Machine Learning (AutoML) Price by Application (2017-2022)
6.3.2 Global Automated Machine Learning (AutoML) Price Forecast by Application (2023-2028)

7 Automated Machine Learning (AutoML) Consumption by Regions
7.1 Global Automated Machine Learning (AutoML) Consumption by Regions
7.1.1 Global Automated Machine Learning (AutoML) Consumption by Regions
7.1.2 Global Automated Machine Learning (AutoML) Consumption Market Share by Regions

8.1 North America
8.1.1 North America Automated Machine Learning (AutoML) Consumption by Application
8.1.2 North America Automated Machine Learning (AutoML) Consumption by Countries

9.2 United States
9.2.1 Canada
9.2.2 Mexico

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10.1 Europe
10.1.1 Europe Automated Machine Learning (AutoML) Consumption by Application
10.1.2 Europe Automated Machine Learning (AutoML) Consumption by Countries
10.1.3 Germany
10.1.4 France
10.1.5 UK
10.1.6 Italy
10.1.7 Russia

11.1 Asia Pacific
11.1.1 Asia Pacific Automated Machine Learning (AutoML) Consumption by Application
11.1.2 Asia Pacific Automated Machine Learning (AutoML) Consumption by Countries
11.1.3 China
11.1.4 Japan
11.1.5 South Korea
11.1.6 India
11.1.7 Australia
11.1.8 Indonesia
11.1.9 Thailand
11.1.10 Malaysia
11.1.11 Philippines
11.1.12 Vietnam

12.1 Central and South America
12.1.1 Central and South America Automated Machine Learning (AutoML) Consumption by Application
12.1.2 Central and South America Automated Machine Learning (AutoML) Consumption by Countries
12.1.3 Brazil

13.1 Middle East and Africa
13.1.1 Middle East and Africa Automated Machine Learning (AutoML) Consumption by Application
13.1.2 Middle East and Africa Automated Machine Learning (AutoML) Consumption by Countries
13.1.3 Turkey
13.1.4 GCC Countries
13.1.7 Egypt
13.1.6 South Africa

14 Corporate Profiles

14.1.1 Corporation Information
14.1.2 Overview
14.1.3 Automated Machine Learning (AutoML) Sales, Price, Revenue and Gross Margin (2017-2022)
14.1.4 Automated Machine Learning (AutoML) Product Model Numbers, Pictures, Descriptions and Specifications
14.1.7 accurate Developments

15 Industry Chain and Sales Channels Analysis
15.1 Automated Machine Learning (AutoML) Industry Chain Analysis
15.2 Automated Machine Learning (AutoML) Key Raw Materials
15.2.1 Key Raw Materials
15.2.2 Raw Materials Key Suppliers
15.3 Automated Machine Learning (AutoML) Production Mode andamp Process
15.4 Automated Machine Learning (AutoML) Sales and Marketing
15.4.1 Automated Machine Learning (AutoML) Sales Channels
15.4.2 Automated Machine Learning (AutoML) Distributors
15.7 Automated Machine Learning (AutoML) Customers

16 Market Drivers, Opportunities, Challenges and Risks Factors Analysis
16.1 Automated Machine Learning (AutoML) Industry Trends
16.2 Automated Machine Learning (AutoML) Market Drivers
16.3 Automated Machine Learning (AutoML) Market Challenges
16.4 Automated Machine Learning (AutoML) Market Restraints

17 Key Finding in The Global Automated Machine Learning (AutoML) Study

18 Appendix
18.1 Research Methodology
18.1.1 Methodology/Research Approach
18.1.2 Data Source
18.2 Author Details
18.3 Disclaimer

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Fri, 25 Nov 2022 00:51:00 -0600 en-US text/html
Killexams : Beautiful Soup vs. Scrapy vs. Selenium: Which Web Scraping Tool Should You Use? © Provided by MUO

Want to learn web scraping with Python but are confused about whether to use Beautiful Soup, Selenium, or Scrapy for your next project? While all these Python libraries and frameworks are powerful in their own right, they don't cater to all web scraping needs, and hence, it's important to know which tool you should use for a particular job.

Let's take a look at the differences between Beautiful Soup, Scrapy, and Selenium, so you can make a wise decision before starting your next Python web scraping project.

1. Ease of Use

If you're a beginner, your first requirement would be a library that's easy to learn and use. Beautiful Soup offers you all the rudimentary tools you need to scrape the web, and it's especially helpful for people who've minimal experience with Python but want to hit the ground running with web scraping.

The only caveat is, due to its simplicity, Beautiful Soup isn't as powerful as compared to Scrapy or Selenium. Programmers with development experience can easily master both Scrapy and Selenium, but for beginners, the first project can take a lot of time to build if they choose to go with these frameworks instead of Beautiful Soup.

To scrape the title tag content on using Beautiful Soup, you'd use the following code:

url = ""

res = requests.get(url).text

soup = BeautifulSoup(res, 'html.parser')

title = soup.find("title").text


To achieve similar results using Selenium, you'd write:

url = ""

driver = webdriver.Chrome("path/to/chromedriver")


title = driver.find_element(By.TAG_NAME, "title").get_attribute('text')


The file structure of a Scrapy project consists of multiple files, which adds to its complexity. The following code scrapes the title from

import scrapy

class TitleSpider(scrapy.Spider):

name = 'title'

start_urls = ['']

def parse(self, response):

yield {

'name': response.css('title'),


If you wish to extract data from a service that offers an official API, it might be a wise decision to use the API instead of developing a web scraper.

2. Scraping Speed and Parallelization

Out of the three, Scrapy is the clear winner when it comes to speed. This is because it supports parallelization by default. Using Scrapy, you can send multiple HTTP requests at once, and when the script has downloaded the HTML code for the first set of requests, it's ready to send another batch.

With Beautiful Soup, you can use the threading library to send concurrent HTTP requests, but it's not convenient and you'll have to learn multithreading to do so. On Selenium, it's impossible to achieve parallelization without launching multiple browser instances.

If you were to rank these three web scraping tools in terms of speed, Scrapy is the fastest, followed by Beautiful Soup and Selenium.

3. Memory Usage

Selenium is a browser automation API, which has found its applications in the web scraping field. When you use Selenium to scrape a website, it spawns a headless browser instance that runs in the background. This makes Selenium a resource-intensive tool when compared with Beautiful Soup and Scrapy.

Since the latter operate entirely in the command line, they use fewer system resources and offer better performance than Selenium.

4. Dependency Requirements

Beautiful Soup is a collection of parsing tools that help you extract data from HTML and XML files. It ships with nothing else. You have to use libraries like requests or urllib to make HTTP requests, built-in parsers to parse the HTML/XML, and additional libraries to implement proxies or database support.

Scrapy, on the other hand, comes with the whole shebang. You get tools to send requests, parse the downloaded code, perform operations on the extracted data, and store the scraped information. You can add other functionalities to Scrapy using extensions and middleware, but that would come later.

With Selenium, you get a web driver for the browser you want to automate. To implement other features like data storage and proxy support, you'd need third-party modules.

5. Documentation Quality

Overall, each of the project's documentation is well-structured and describes every method using examples. But the effectiveness of a project's documentation heavily depends on the reader as well.

Beautiful Soup's documentation is much better for beginners who are starting with web scraping. Selenium and Scrapy have detailed documentation, no doubt, but the technical jargon can catch many newcomers off-guard.

If you're experienced with programming concepts and terminologies, then either of the three documentation would be a cinch to read through.

6. Support for Extensions and Middleware

Scrapy is the most extensible web scraping Python framework, period. It supports middleware, extensions, proxies, and more, and helps you develop a crawler for large-scale projects.

You can write foolproof and efficient crawlers by implementing middlewares in Scrapy, which are basically hooks that add custom functionality to the framework's default mechanism. For example, the HttpErrorMiddleware takes care of HTTP errors so the spiders don't have to deal with them while processing requests.

Middleware and extensions are exclusive to Scrapy but you can achieve similar results with Beautiful Soup and Selenium by using additional Python libraries.

7. JavaScript Rendering

Selenium has one use case where it surpasses other web scraping libraries, and that is, scraping JavaScript-enabled websites. Although you can scrape JavaScript elements using Scrapy middlewares, the Selenium workflow is the easiest and most convenient of all.

You use a browser to load a website, interact with it using clicks and button presses, and when you've got the content you need to scrape on screen, extract it using Selenium's CSS and XPath selectors.

Beautiful Soup can select HTML elements using either XPath or CSS selectors. It doesn't offer functionality to scrape JavaScript-rendered elements on a web page, though.

Web Scraping Made Easy With Python

The internet is full of raw data. Web scraping helps convert this data into meaningful information that can be put to good use. Selenium is most probably your safest bet if you want to scrape a website with JavaScript or need to trigger some on-screen elements before extracting the data.

Scrapy is a full-fledged web scraping framework for all your needs, whether you want to write a small crawler or a large-scale scraper that repeatedly crawls the internet for updated data.

You can use Beautiful Soup if you're a beginner or need to quickly develop a scraper. Whatever framework or library you go with, it's easy to start learning web scraping with Python. ​​​​​​

Sun, 04 Dec 2022 08:00:15 -0600 en-US text/html
Killexams : Data-driven, automated machine-learning system for detecting emerging public health threats

A dire threat to public health can emerge from a huge variety of sources—for example, infectious diseases, a spate of drug overdoses, or exposures to toxic chemicals. Federal, state, and local health departments must respond rapidly to disease outbreaks and other emerging bio-threats. While the current automated systems for "syndromic surveillance" can help by monitoring health data and detecting disease clusters, they are not able to detect clusters with rare or previously unseen symptomology.

A new study from New York University's Machine Learning for Good Laboratory (ML4G Lab), with colleagues from Carnegie Mellon University and the New York City Department of Health and Mental Hygiene (NYC DOHMH), addresses this critical gap in public practice by presenting a new machine-learning approach for "pre-syndromic" surveillance.

The method is incorporated in an automated system that can enable public health practitioners to respond more quickly and effectively in the future to fast-emerging threats, including those that are unusual or novel.

"Existing systems are good at detecting outbreaks of diseases that we already know about and are actively looking for, like flu or COVID," comments NYU professor Daniel B. Neill, the senior author of the study and director of the ML4G Lab. "But what happens when something new and scary comes along? Pre-syndromic surveillance provides a safety net to identify emerging threats that other systems would fail to detect."

The study was published in Science Advances.

The authors' approach to disease surveillance is known as pre-syndromic surveillance because it relies on digitally communicated textual data on all patient conditions, rather than classifying case data under existing disease syndromes (such as "influenza-like" or "gastro-intestinal" illness). The new system enables rapid identification of newly emerging syndromes that health departments are not yet aware of.

To accomplish this, the machine learning technology uses anonymized "chief complaint" data from hospital Emergency Department (ED) visits. A chief complaint is usually provided by the patient in their own words (for example, "I've had a bad headache for the last three days and now my ear hurts") and is recorded by an ED triage nurse.

The method is capable of identifying trends and patterns in the words and phrases of the chief complaints, enabling detection of a localized case cluster. It can incorporate practitioners' feedback in the service of automatically distinguishing between relevant and irrelevant case clusters. It gives personalized and actionable decision support to hospitals and local and state health departments.

Blinded evaluations and case studies by the city health department of the new system—which the researchers dubbed MUSES, or Multidimensional Semantic Scan, after designing, developing and testing it—demonstrate that the pre-syndromic monitoring identifies more events of interest and achieves a lower false positive rate in comparison to traditional methods, according to the study authors.

MUSES, then, offers three significant methodological advances to hospitals and local and state health departments nationally, as it:

  • Eliminates the need for pre-defined syndrome categories.
  • Identifies localized case clusters through multi-dimensional scan statistics, enabling detection of emerging bio-threats that may affect certain spatial areas or demographic groups of patients.
  • Uses a "practitioner in the loop" approach to incorporate user feedback, hone in on relevant patterns, reduce , and provide local users with actionable insights based on their own criteria for what is, and is not, relevant.
More information: Mallory Nobles et al, Presyndromic surveillance for improved detection of emerging public health threats, Science Advances (2022). DOI: 10.1126/sciadv.abm4920

Citation: Data-driven, automated machine-learning system for detecting emerging public health threats (2022, November 14) retrieved 13 December 2022 from

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Mon, 14 Nov 2022 02:29:00 -0600 en text/html
Killexams : Looking to Automation as a Weapon in Curbing Inflation

There is no doubt that the current inflation figures are troubling. Our inflation rates are now higher than in the last four decades. The reasons underlying such unprecedented inflationary pressures are diverse and well-entrenched. Supply chain pressures that started snowballing with the long drawn-out pandemic compounded with labor shortages, global energy shortages, the ongoing Ukraine crisis and wage inflation have resulted in a perfect storm of rising costs.

Although the pandemic witnessed a massive push for business digitization, supply chain digitization has lagged starkly in comparison. That, coupled with the red-hot labor market, means inflation is rising. We have more job vacancies than workers, and employers are in a wage war to attract talent. But as companies pay more, so do consumers. The costs get passed on, and we have inflation — resulting in increasingly lower purchasing power over time.

As bleak as the economic picture may seem, we also have an unprecedented technological advantage available to us now that was never there before. I believe automation could provide a historical opportunity for businesses to cut short the inflationary pressures and retain margins without drastically impacting labor markets. Robots and autonomous systems can address endemic process inefficiencies in many industries and soften the workforce crunch.

Automation Can Help Reduce Inflation by Increasing Supply

Currently, we are in the early stages of automation. We've seen how it can take over routine tasks and assist humans in different factors of work and life, but automation can go further. We are already trending toward not having to put humans in harm's way because automation is capable of doing jobs that can expose humans to carcinogens, fires or other dangerous tasks. Low-code and no-code environments in software development indicate the same trend toward automation. On the other hand, the promise is human workers will be able to move up to do higher-value tasks rather than grunt work.

Beyond obvious cost, quality, and speed improvements, automation can help Boost or enhance current workforce competencies and allow businesses to reduce their dependency on wage inflation. Just imagine what automation for automobile production has done and could mean for our country. We will be able to produce more and even better quality cars and, more importantly, make them here rather than offshore thanks to cost benefits ensured by automation. Now imagine the same principle applied to industries across the board, from software, logistics and pharmaceuticals to retail — automation can help reduce inflation as it results in increased supply. Instead of the central banks relying mostly or solely on increasing the price of borrowing to slow down demand, we can help balance out the equation by increasing supply.

Automation Will Not Leave People Jobless

Wages can become inflated when there are not enough people around to fill the job vacancies. Automation can help companies fill vacant job openings as well as take the place of hazardous jobs where people typically need to risk their lives. Conversely, raising interest rates has been proven to create unemployment. To me, the notion of the Fed deliberately trying to create unemployment seems completely backward. We should try to provide jobs to everyone who wants one. If people are fully employed, and we need more (like in the current scenario of high inflation), we can either let foreign workers temporarily come in or add more automation so that workers here can earn more.

Automation will likely enable people to get back to their core specialties. This means that if you are a plumber or carpenter, you will still be able to make good money as those skills are not yet automated. But if you are in a position that has the potential to be either fully or partially automated any time soon, you will need reskilling or upskilling.

In the automotive industry, for instance, plenty of hazardous roles require people to move heavy objects and operate between moving machine parts. It's a good thing that automation can potentially save lives by fulfilling such chores. The workers can, in turn, become skilled robot operators. In jobs that cannot be fully automated, robots can work as assistants to human workers and help offset the workforce crunch. All of this can ultimately lead to reduced inflation with better efficiencies and an increased supply of goods.

Most importantly, I think this will make America more competitive in the global economy. Traditionally, we had companies outsource jobs to offshore units due to cheaper labor costs, etc. With automation, this disparity can come down significantly and put us on a more even footing.

How Companies Might Start Out With Automation

As automation's benefits become clearer, more companies are eager to start automating. But many lack a sense of direction when it comes to starting with automation. It can look different for your organization, but in our business, we have started simply by focusing on process automation for healthcare, bill and payments. The pandemic forced us to switch up our workstyle for the sake of our employees, which led us to focus on those three areas first. It's not that people don't want to work, but people have very real reasons why they cannot or may not be able to travel to the office. To address this, we built a platform that enables us to recognize and pre-process documents (such as bills, complaints, forms, etc.) to a certain extent before employees receive them. Now, they don't have to start from scratch. This has been a very successful deployment for us and is expected to cross 200 million transactions annually.

We are invested in creating a better work environment for our customers and employees, especially addressing where, how and when they have to work. And we are bringing automation to play in what I call creating 'good tech' — technology that genuinely improves the quality of life — so people can focus on important things. I think automation has a key role in realizing this future. It could also be a secret weapon to keep rampant inflation at bay.

Mon, 21 Nov 2022 23:32:00 -0600 en text/html
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