Tag: Ai

How Artificial Intelligence in Disaster Management Software Is Saving Lives?
What if we could turn chaos into clarity during disasters? Since 1990, floods have caused $50 billion in damages and impacted millions in India. Knowing about a disaster before it strikes could give communities time to prepare and respond effectively. That’s where Artificial Intelligence is turning this possibility into a reality. From issuing early warnings for hurricanes to guiding rescue operations during floods, AI is revolutionizing disaster management.

In this blog, let’s explore how AI in disaster management software transforms predictions, responses, and recovery efforts to save lives.

How Artificial Intelligence in Disaster Management Software Is Saving Lives?

AI in disaster management software enhancing life-saving efforts

Artificial Intelligence (AI) revolutionizes disaster management by permitting more accurate predictions, speedy responses, and efficient recoveries. AI enables advanced algorithms, and real-time data is fed to disaster management software to soften the impact of natural and artificial disasters.

1. Disaster forecasting through AI

AI has come as one of the significant transformations that AI has undergone to improve disaster management systems. Through analyzing vast amounts of data and finding patterns, the chances of predicting and, thus, preparing for any disaster are primarily enhanced.

Data Collection by AI

AI collects data from different sources, and this includes:
- Weather data, which can track storms, hurricanes, and cyclones
- A seismic activity record is used to identify the initial seismic signals of an earthquake.
- Historical data to identify trends of disaster recurrences in certain areas.
This integrated analysis helps accurately predict when and where disasters might occur. For instance, AI can scan satellite images to monitor ocean temperatures and predict the cyclone’s formation.

Risk Assessment

AI evaluates the potential damage caused by disasters by assessing:
- Population density: Determining areas where the disaster would impact the most people.
- Infrastructure weaknesses: This highlights the weak points such as bridges, dams, or flood-prone neighborhoods.
- Environmental factors: These are natural features such as forests or water bodies that may affect the intensity of disasters.
This helps governments and agencies to plan better and provide more resources to high-risk areas.

Early Warning Systems

Machine learning models are trained on historical data, predicting disaster patterns and providing early warnings. These warnings:
- Give communities enough time to evacuate or prepare.
- Allowing authorities to preposition emergency supplies, including food, water, and medical kits.
For instance, AI-based flood prediction systems use rainfall, river levels, and soil saturation data to predict floods days ahead of time. This helps save lives and reduce property damage.

2. Real-Time Monitoring of Disasters

When disasters occur, the difference between life and death can be a matter of having accurate information in real-time. AI shines in monitoring unfolding events and guiding responders in real-time.

Live Data Analysis

AI processes live feeds from sources like:
- Drones: Taking aerial views of disaster-stricken areas to identify damage and locate stranded individuals.
- Satellites: Offering large-scale images to track the spread of disasters such as wildfires or floods.
- IoT Sensors: Track water levels, air quality, and structural strength in disaster areas.
Processing this information in real-time, AI provides actionable insight to the emergency teams to determine the nature of the situation and plan for it.

Anomaly Detection

AI constantly monitors the critical parameters and detects anomalies that might lead to further deterioration. Such anomalies could be:
- Rising water levels above flood safety levels.
- Rapidly rising temperatures in a forested area potentially indicate wildfires.
- Gas leaks in earthquake-damaged industrial areas.
The detection alerts the responders, who can take prompt action before further damage is done.

Situational Awareness

AI-based GIS creates comprehensive maps that outline the following:
- Storm-inundated areas
- Affected areas due to wildfires and landslides
- Safe zones for evacuation or relief operations.
These maps enable better resource allocation so that aid would first reach the most vulnerable areas. For instance, AI-enhanced drones can identify stranded victims and direct rescue boats to that area during floods.

3. Response Automation

With AI able to automate critical tasks in the response function, emergency operations become swift and efficient with fewer chances of delay and error.

Optimized Dispatch

AI orders distress calls according to priority and determines their urgency and location. It may be demonstrated as below:
- Calls from severely affected areas will be prioritized over other less urgent requests.
- AI systems scan traffic conditions to route emergency vehicles to destinations as quickly as possible.
This ensures that ambulances, fire trucks, and rescue teams reach the victims in need much faster, even in the most chaotic environment.

Traffic Management

In evacuations, traffic congestion is one of the biggest threats to lives. AI systems scan traffic patterns in real-time and recommend:
- Alternative routes to avoid gridlocks.
- Safe evacuation routes for big crowds.
AI will give the safest route to avoid danger zones during a wildfire, ensuring civilians and emergency responders stay safe.

The Future of AI in Disaster Management Software

The use of AI in disaster management is getting stronger with every passing day. Here’s what might be in store:
- Improved Predictive Models: AI will predict disasters even more accurately with better algorithms and data.
- Real-Time Adaptation: AI systems would change responses dynamically in response to real-time updates to be efficient.
- Collaboration Tools: Future AI systems enable easy data exchange among government agencies, NGOs, and AI technology companies.
- Integration with IoT: AI-based incident management systems work with IoT devices like smart sensors to monitor critical parameters like water level and air quality in real-time.
For instance, in flood-prone areas, AI, in conjunction with IoT sensors, can facilitate real-time updates that inform people in advance to evacuate in time.

Conclusion

Artificial Intelligence changes the face of disaster management software by saving lives through accurate predictions, swift reactions, and intelligent resource allocation. AI ensures people obtain information immediately by sending early warnings and real-time updates.

In countries with frequent natural disasters, we must use AI-driven tools to reduce damage and protect communities. These tools do not only help us prepare but also respond better during emergencies. Companies like SCS Tech drive these innovations to build safer and more resilient communities and tap into the power of technology to save lives.
December 17, 2024
Best security tips to avoid a cyber breach

Preventing cyber data breaches is the best defense against the nightmare and expense that comes with them. Nevertheless, you must first identify them in order to be able to stop a data breach. The sorts and costs of data breaches you could experience as a small- to medium-sized business owner are described below, along with tips on how to avoid them.

When hackers gain access to data and sensitive information, data breaches occur. These breaches are very expensive. According to a data report, the average cost of a data breach is around $3.86 million that too in addition to the irreparable harm to an organization’s reputation. It costs time as well. The identifying of the cause and reprimanding it usually takes up to 280 days.

You can use a variety of high-level security techniques, such as AI and prepared incident response teams, to stop a data breach. Let’s dig deep into that!

Limit access to your valuable data –

Every employee used to have access to all of the files on their computer back in the day. Companies today are discovering the hard way how important it is to restrict access to their most important data. A mailroom employee has no need to see a customer’s financial information, after all. By limiting who is permitted to read specific papers, you reduce the number of workers who might unintentionally click on a hazardous link. Expect to see all records partitioned off as organisations go into the future so that only those who specifically require access will have it. One of those obvious fixes that businesses probably ought to have implemented sooner rather than later.

Security policy with third party vendors –

Every firm interacts with a variety of outside vendors. The need to understand who these people are has never been greater. Even permitting visitors onto their property might expose businesses to legal action. It’s necessary to restrict the kinds of documents that these vendors can access.

Although taking such steps can be a bother for the IT department, the alternative could be a data breach that costs millions of dollars. Demand transparency from the businesses that are permitted to access your sensitive information. Don’t just assume that they are abiding by privacy regulations; verify it. Request background checks for any outside contractors entering your business.

Employee awareness training –

Employees are the weakest link in the data security chain, according to recent research. Despite training, workers read dubious emails with the potential to download malware every day. Employers make the error of assuming that one cybersecurity training session is sufficient. Schedule frequent sessions every quarter or even monthly if you’re serious about protecting your crucial data.

According to marketing studies, the majority of consumers must hear the same message at least seven times before their behaviour starts to change.

Update Software Regularly–

Experts advise routinely updating all operating systems and application software. When patches are available, install them. When programmes aren’t constantly patched and updated, your network is exposed. Baseline Security Analyzer, a software from Microsoft, may now be used to periodically check that all programmes are patched and current. This is a simple and affordable solution to fortify your network and thwart attacks before they start.

Develop a cyber breach response plan –

What would you do if you discovered a data breach when you arrived to work the following day? Surprisingly few businesses have a reliable breach response strategy in place. Both the company and the employees can understand the potential losses by creating a thorough breach preparedness strategy. Employees want to know the truth; therefore, an employer should be very open about the extent of the violation. A sound response strategy can reduce lost productivity and stop bad press.

Setting strong passwords –

One thing that security professionals will emphasise when they visit your organisation to train your staff is the importance of routinely changing all passwords. The majority of people are now aware of how crucial it is to make passwords challenging to crack. We have mastered the use of capital letters, numbers, and special characters when creating passwords, even on our home PCs. Make it as difficult as you can for hackers to enter and steal your belongings.

March 16, 2023
A complete guide on Cloud Computing

One of the technologies influencing how we work and play is cloud computing. The cloud helps businesses eliminate IT problems and promotes security, productivity, and efficiency. It also enables small enterprises to utilize cutting-edge computing technologies at a significantly lesser cost. Here is what you need to know about the cloud and how it can benefit your company.

On-Demand Computing

The term “cloud” describes online-accessible servers and software that anyone can use. You are spared from hosting and managing your hardware and software as a result. Additionally, it implies that you can use these systems from any location where you have internet access.

Every day, you encounter cloud computing. You are accessing data that is kept on a server somewhere in the world whenever you check your Gmail inbox, look at a photo on your Dropbox account, or watch your favorite shows on Netflix. Even though the emails, videos, or other files you require are not physically present on your computer, you may quickly, simply, and affordably access them owing to contemporary cloud computing technology.

Public, Private, and Hybrid Cloud

Private, public, and hybrid deployment strategies are the three main types of cloud computing. In the end, all three models will give customers access to their business-critical documents and software from any location, at any time. It all depends on how they approach the task. The kind of cloud you should use for your company depends on several variables, including the purposes for which you intend to use it, applicable laws on data storage and transmission, and other aspects.

Private Cloud

A single entity is served via private clouds. While some companies construct and manage their ecosystems, others rely on service providers to do so. In either case, private clouds are expensive and hostile to the cloud’s advantages for the economy and IT labor productivity. Private clouds, however, are their sole choice because certain organizations are subject to greater data privacy and regulatory constraints than others.

Public Cloud

Distributed across the open internet, public clouds are hosted by cloud service providers. Customers can avoid having to buy, operate, and maintain their own IT infrastructure by using the most widely used and least-priced public clouds.

Hybrid Cloud

A hybrid cloud combines one or more public clouds with private clouds. Imagine you operate in a sector where data privacy laws are extremely rigorous. While you don’t want to host legally required data in the cloud, you do want to be able to access it there. To access data saved in your private cloud, you also want to deploy your CRM in the cloud. Using a hybrid cloud is the most sensible choice under these circumstances.

Everything as a Service

The cloud “stack” is made up of numerous levels. The collection of frameworks, tools and other elements that make up the infrastructure supporting cloud computing is referred to as a stack. Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) components are included in this. Customers that use these services have varied degrees of control and accountability over their cloud environment.

Infrastructure as a Service

The customer oversees managing everything with IaaS, including the OS, middle-ware, data, and applications. Other duties, including virtualization, servers, storage, and networking obligations, are handled by the service provider. Customers are charged by how many resources, including CPU cycles, memory, bandwidth, and others, they consume. Microsoft Azure and Amazon Web Services are two examples of IaaS products.

Platform as a Service

Customers can create, test, and host their applications using PaaS solutions. The consumer oversees managing their software and data; otherwise, the service provider takes care of everything. You don’t have to be concerned about operating systems, software upgrades, or storage requirements if you use PaaS solutions. Customers of PaaS pay for any computing resources they use. Google App Engine and SAP Cloud are a couple of examples of PaaS technologies.

Software as a Service

Customers acquire licenses to utilize an application hosted by the provider under the SaaS model. Customers often buy annual or monthly subscriptions per user instead of how much of a certain computer resource they consumed, unlike IaaS and PaaS models. Microsoft 365, Dropbox, and DocuSign are a few popular SaaS products. Small firms that lack the capital or IT resources to implement the most cutting-edge technologies would benefit greatly from SaaS solutions.

Benefits of the Cloud

Reduced IT costs: By using cloud computing services, recurrent costs for monitoring and maintaining an IT infrastructure can be greatly decreased.

Scalability: When necessary, developers can increase storage and processing capability by using cloud services. Additionally, development teams do not have to spend time or money upgrading cloud computing services.

Collaboration efficiency: For the agile technology sector, cooperation has always been a need. Professionals from all around the world may work and collaborate using current cloud services. With these functionalities, teams may communicate with clients or other teams online while collaborating in real-time and sharing resources.

Flexibility: Cloud computing can provide a great deal of flexibility in addition to helping to lower operational costs. Developers and other key stakeholders now have easier access to crucial data metrics at any time and from any location.

Automatic updates: Teams may use the most recent resources available while managing and meeting IT standards thanks to automatic updates. Cloud computing is a popular technology because it allows users to access the newest tools and resources without having to spend a fortune.

March 2, 2023
Top IT Trends to look out for in 2023

Technology is still one of the main drivers of global change. Technology advancements give businesses more opportunities to increase efficiency and develop new products. Business leaders can make better plans for the future by keeping an eye on the development of new technologies, foreseeing how businesses might use them, and comprehending the factors that influence innovation and adoption. Keep reading to know about the new tech innovation and trends which will become a critical force for change in the world.

Artificial Intelligence

Artificial intelligence has long been the subject of hype. If you are a tech professional, you might not enjoy how pervasive artificial intelligence has become. For both creative and routine activities, AI has already proven its brilliance in navigation apps, cell phones, and more. The buzz surrounding AI won’t go away anytime soon. It will become more accessible because of the expanding ecosystem of as-a-service platforms and low-code or no-code AI systems.

Synthetic content is a promising area of AI to keep an eye on. It involves using the AI’s imagination to produce brand-new, unprecedented sights, sounds, or data. We can anticipate seeing the development of AI that is useful in both business and entertainment in 2023. Precedence Research predicts that by 2030, the global AI market would be worth $1,597.1 billion. We may anticipate new opportunities in programming, development, testing, and many other sectors as AI becomes more prevalent in a variety of industries.

Metaverse

Metaverse can now be classified as “a more digital environment.” Within the next five years, it is anticipated that the experience of immersive online environments and next-level user experiences will develop significantly. The phrase “metaverse” is used to refer to a highly immersive 3D virtual world that is created by combining AR, VR, and MR technologies by developing a virtual environment in which users can engage in social interaction, play games, do commerce, and more. The online experience is enhanced by the metaverse platform.

The metaverse has the potential to open doors to new artistic, social, and professional opportunities in 2023. In the next five years, the tech giant Facebook plans to add 10,000 new, highly trained jobs for the metaverse. Talented metaverse engineers, marketers, architects, and visionaries will have more opportunities as a result.

Blockchain

Blockchain technology has become more and more popular, and many sizable firms are headed in that direction. As a result, there is a huge need for developers in the field of blockchain technology. The network’s decentralization, security, and data privacy are among blockchain’s advantages. Applications for blockchain technology go well beyond digital currency like bitcoin. Blockchain’s worth will significantly rise, reaching $176 billion by 2025 and $3.1 trillion by 2030, as predicted by Gartner. Whether they are little businesses or established corporations, everyone wants a piece of the blockchain industry.

Quantum Computing

There is a contemporary competition taking place on a worldwide basis to develop quantum computing. Quantum computing, which uses subatomic particles to develop new ways to process and store data, is expected to make it possible for computers to be a trillion times faster than the fastest regular processors currently available. The concern around quantum computing is that it might make our current encryption methods obsolete.

Therefore, any country that makes significant investments in the development of quantum computing will have the ability to decrypt the encryption used by other countries, corporations, security systems, and other entities. In 2023, keep an eye on this development as nations including the US, UK, China, and Russia invest heavily in the creation of quantum computing technology.

Hyper-automation

Hyper-automation enables the automatic completion of repetitive tasks without manual or human input. It modifies both new and existing gear and processes using robotic process automation (RPA), machine learning, and artificial intelligence (AI). By using digital transformation to improve cost and resource efficiency, a business can prosper in a more competitive world. To succeed in the current market, businesses must increase production, cut costs, and run more effectively. With the aid of hyper-automation services, you can advance.

Datafication

The transition of all the physical items in our lives into data-driven digital gadgets is known as datafication. In conclusion, it converts labor-intensive, manual processes into data-driven technologies. Data has been a part of everything for longer than we can remember, from our cell phones to our workplace software, industrial machinery, and AI-powered products.

As a result, handling our data in a secure and safe manner has become a sought-after skill in our sector. Datafication can turn unprocessed data into knowledge if done correctly. This has already been advantageous to numerous businesses.

Industry Cloud Platforms

Businesses can increase the flexibility of their workload management by using industrial cloud platforms. They can expedite adjustments to data analysis, corporate operations, and compliance processes. To enhance adaptability, quicken time to value, and meet the demands of vertical industry sectors, they combine platforms, software, and infrastructure as a service.

Sustainable Tech

In the future, we’ll start to see a move toward sustainable technology. We all rely on technology, including computers, cell phones, and tablets, but where are the parts needed to make these devices come from? The public will be more interested in where rare earth materials come from and how we use them. Additionally, we use cloud services like Netflix and Spotify, which continue to operate in big data centers that use a lot of electricity. As consumers demand energy-efficient goods and services supported by more sustainable technologies, efforts to boost supply chain transparency will probably continue in 2023.

February 1, 2023
AI in Retail: The Ideal Tool to Deliver Seamless Customer Experience and Drive Profitable Growth

Traditional analytics methods have worked perfectly fine for the data-driven retail industry for decades. However, Artificial Intelligence (AI) and Machine Learning (ML) have established an entirely new level of data processing which leads to richer business insights and customer relations. Data scientists could open a new world of opportunities to business owners to extract correlations and variance from thousands of AI /ML models. The importance of AI in the retail industry is finally being recognized. According to surveys, the retail business currently spends roughly $49 million. By 2023, when AI is factored in, it is predicted to reach $12 billion. In terms of spending, AI in the retail business is expected to grow by more than 200 percent by 2023. The growth in the global artificial intelligence in the retail market is driven by factors such as eternally growing smart device and internet users, increasing awareness about AI and big data and analytics, government initiatives on digitalization, and supply chain optimization. Furthermore, enterprises’ demands for restructuring business processes, adoption of the multichannel or omnichannel retailing strategy, mainlining inventory accuracy, and growing need to enhance end-user experience are fuelling the growth of this market.

The use cases of AI in the retail sector are quite compelling. AI empowers both backends and frontend of e-commerce business. It can help retail businesses to estimate demand forecasts to drive higher margins, can increase the efficiency of the supply chain, give valuable insights for making smart business decisions. On the customer side, AI can personalize customers’ shopping experience by providing personal recommendations, 24×7 chatbots, etc.

Because of the rapid rise in online retail businesses, the retail industry was less affected by COVID-19 than other industries. Before the COVID-19 outbreak, over 80% of purchasers were accustomed to purchasing things in physical storefronts. However, due to quarantine orders, customers purchased their products more online.

AI solutions, whether for online or offline commerce, have a lot of room to grow in the future. For the time being, we can show you several real-world AI applications that have shown business value.

Why Does Your Retail Business Need AI Optimization?

Today customer experience has surpassed product and pricing as the one major brand differentiator. Brands that fail to deliver the best possible customer experience will likely deliver less than optimal returns regardless of having a world-class product. To seamlessly create a more engaged business-to-consumer interaction and a great experience for customers, retailers need more tools at their disposal, with AI as their first choice.

Here are the few benefits businesses can gain from boosting customer engagement, experience, and success by utilizing AI:

Everyday operations become efficient

Creating a seamless customer experience and profitable business starts behind the scenes. Retailers must find efficient and profitable ways to deal with these mundane and routine tasks like:

AI in the retail supply chain can be used for quick product delivery and improved inventory control like restocking — forecasting the demand for a particular product by considering a sales history, weather, location, trends, promotions, and other parameters.

Picking and packing delivered by the robots is relatively fast ROI when compared to the manual processes. Moreover, robots can collaborate and are designed to open, fill, seal, label, and pack orders together.

Machine Learning models help in recognizing and classifying millions of items from various sellers and categorizing them according to customer requirements.

The number of products returned can be reduced due to the early analysis of future purchase trends. Algorithms analyze previous searches, purchases, transactions, and even seasonal trends.

Personalized customer experience

With an increasingly digitized world, today, brands are realizing that AI is the real gamechanger in personalizing the customer experience. As customers’ expectations are changing dramatically, retailers are mounting to the challenges with the help of AI, Machine Learning, and Big Data.

To gauge customer interests and preferences, retailers use advanced Machine Learning algorithms to analyze browser history, past purchases, page views and clicks, social interactions (impressions, likes, shares, and comments), the duration of a page viewed, location, etc. In-store, AI can further enrich the shopping experience with:

Navigation: AI-enabled kiosks or robots can improve the in-store experience of customers. In-store kiosks and robots use natural language voice commands or touch screen interfaces to provide useful information to customers regarding product location.

Smart checkouts: Shopping carts with ARM-based cameras can automatically recognize the customer’s purchases, prepare orders, and enable payments through a mobile device.

AI-driven AR: Many retail stores offer virtual trial rooms, make-up, or product apps, which use AI-driven Augmented Reality interfaces to let customers test products without physically trying them on.

Visual search and recommendations: Image recognition technology allows people to upload images of the item they are interested in and get products of similar patterns, colors, and shapes. AI recommends products related to the one reviewed by the user and based on the previous purchases.

Better pricing strategy

Retail pricing is a core aspect of any business, several studies show that the price that customers pay for an item is almost always among their top concerns. Also, most of the customers wait for the time for a price drop, and they wait for arriving of the product at an ideal cost. Retailers attract customers by forecasting the price of a product based on demand, seasonal trends, competitors’ price, various product characteristics, the release date of new models of the same item in the market, etc.

AI helps businesses alter the prices of their products, by envisioning the likely outcomes of various pricing strategies. To be able to execute this, systems collect information about other products, promotional activities, sales figures, customer preferences, product locations, and additional data. Businesses can exhibit the best offers to increase customers’ footfall and boost companies’ bottom line.

Data security efforts have become accurate

Retailers are combating data leaks, cyber threats, malicious activities, and the prevention of shoplifting by adopting various advanced security measures such as IoT, AI, and Machine Learning. Also, the amount of information that the retailers collect, and process are immense. While it’s not possible to eliminate retail shrinkage, AI excludes time-consuming research tasks and provides curated analysis of risks, reducing the amount of time security analysts take to make the critical decision. Retailers, considering the vast amount of consumer and employee data they possess, should pull out all stops to ensure a robust data security platform to avoid the devastating effects of cybercrime. For this reason, most retailers use AI abilities to provide solid data security.

Retail requires these data-centric solutions, that result in highly personalized experiences and product recommendations, accurate forecasts, inventory efficiencies, and overall smarter business. Our AI, ML, Data, and Analytics Engineering Services power their Digital Next transformation actions by providing transparency, agility, and flexibility with retail-driven advanced analytics through flexible and scalable solutions.

We being an AI platform-led digital transformation, product engineering, and solutions company, conversational AI is an area of focus for us. Our solutions enable enterprises to engage customers and ensure speedy resolution of issues with the use of natural language processing, AI, and analytics to intelligently recognize and respond to customer text and voice queries.

May 5, 2022
4 ways AI can help us enter a new age of Cybersecurity
- AI is helping companies recover from the pandemic more efficiently.
- The growing uptake of AI is causing concern for data security in a time of escalating cyberthreats.
- Well-deployed AI can be used to counter these cybersecurity threats.
Global catastrophes have historically brought moments of truth for all fields of business. In such times, their inner workings, strengths and weaknesses are laid bare for the whole world to see, as organizations rapidly alter their processes to come to terms with the new reality.

Businesses that can make bold moves during such challenging times can quickly turn the misfortune into a benefit. So early indications are that businesses that value information as a currency, and have been quick to adapt machine learning and advanced data analytics, have emerged better from the economic aftermath of the pandemic.

AI and business optimization

The coronavirus pandemic that continues to ravage the world has forced small businesses into building online ventures. It has also compelled them to adopt AI-enabled platforms that offer consumer insights and help enterprises to deliver “hyper-personalized” products to online buyers.

Additionally, AI has so far helped companies that are struggling to create safe and contagion-free work environments by setting up on-demand online labour forces.

In a business context, AI has the potential to perform automated, repetitive tasks that would otherwise have been left for humans. It helps improve efficiency, reduces cost and saves time that could be invested in other business functions.

However, AI requires massive amounts of data to learn about consumer trends, predict consumer behaviour and find the “next best action” to enhance customer satisfaction and boost sales. But this predictive intelligence can also foresee demand and supply behaviours and help in quality control processes in manufacturing facilities

The trouble is that gathering and storing this data securely while safeguarding the interests of your stakeholders is no easy task. In the age of digital transformation, where everything is interconnected and shared online, internet of things (IoT) security poses a significant risk for users.

In recent years, there has been a dramatic increase – 15% to 21%, according to various estimates – in security breaches. Leading platforms such as Facebook, Twitter, and Yahoo have become victims, compromising millions of dollars’ worth of users’ data.

This does not only mean people must set better passwords, but also mandates that these platforms have higher standards for cybersecurity. Since data science and AI will be shaping the next stages of IoT development, data-rich companies must create efficient and trustworthy approaches to turn data into useful and actionable insights.

Cybersecurity in the age of AI

Data collection and AI algorithms are becoming the cornerstone of the cybersecurity industry. Automated decision-making and evaluation processes provide a wider range of protection from malicious activity than legacy solutions. For instance, AI can be proactive and monitor devices for suspicious activity, instead of relying on slowly updated malware databases.

AI developments in cybersecurity should emphasize on making systems safer and more secure for consumers to use. Let’s take a look at how to do this right:
1. Identify threats early
Combine conventional threat intelligence (a list of all known cyberthreats to date) and use machine learning to understand risks. This should result in a better, more efficient system of threat detection and prevention.

This can also help to identify any loophole or threat present in the data.

In fact, machine learning can also be used to spot any abnormality or potential vulnerability in the midst of “normal” activity and warn users of a threat before it could compromise essential data. With the right systems in place, your hackers won’t even realize that you know of their presence, so you can take immediate measures to ensure the safety of your digital infrastructure.
1. Prevent credit-card fraud
Unusual activity, such as purchases made from a different device or unusual transactions, can be instantly detected using AI-powered services that help verify the credit-card holder.

Machine learning can also help users choose passwords by warning them if a password is not safe enough.
1. Build on the blockchain
In recent years, cryptocurrencies like Bitcoin and Ethereum have been rising in popularity. These cryptocurrencies are built upon blockchain, an innovative technical solution to store a secure, decentralized record of transactions.

Blockchain can be used to enable medical records and help in security management by identifying criminal identity loopholes in the system.

With blockchain technology, verification keys wouldn’t be required anymore. If someone tries to hack the data, the system analyzes the whole mass of data chains. Even if one data node is left uninterrupted by the hacker, the entire system can be restored successfully.

This makes the entire system far more secure, ensuring that there is no discrete way of tampering with blocks in the chain, and the stored data can remain safe.
1. Go deep into the data
One of the main areas where AI can help cybersecurity is by responding to threats almost immediately. For example, in 2016 Google listed around 20,000 sites for having malware within their system. While humans can’t scrutinize millions of websites, machine learning can. It is possible to use relevant AI solutions to analyze every visit to the site, categorize visitors based on their threat level, and deal with them accordingly.

We are entering an era of hypercomplexity, where all of our information is interconnected
April 22, 2022
Smart Factory: the Way to Smarter Manufacturing
What is a smart factory?

The National Institute of Standards and Technology of the United States of America (NIST) defines smart manufacturing as “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs”.

A smart factory is a product of Industry 4.0, the fourth industrial revolution, where technologies like big data, Industrial Internet of Things (IIoT), and AI/machine learning are the driving force of digital manufacturing changes. A smart factory is the end goal of digitization in manufacturing.

Smart factories are characterized by a high degree of complex manufacturing automation, which entails running manufacturing processes with minimal or no human intervention. Such automation is powered by industrial IoT technologies comprising hardware (sensors, actuators) and software (big data, machine learning tools).

Benefits of a smart factory
- Agile production process
A smart factory allows manufacturers to quickly adapt to changing client needs, budget, product quality requirements, due to the connectivity of multiple systems, (e.g., an IIoT solution, ERP, MES, SCM) and powerful data analytics capabilities.
- Improved efficiency of manufacturing operations
The network of sensors allows for collecting data about the production process, environment, and equipment. This data is analyzed by the cloud software in near-real time, allowing manufacturers to make quick adjustments, for example, in equipment operating parameters. Further analysis of sensor-generated data helps spot trends and improvement opportunities through the entire production process.
- Improved reliability of manufacturing operations
In smart factories, the probability of human error in manufacturing operations is reduced due to high-level automation.
- Improved product quality
In smart factories, AI technologies are used for quality control. For example, cameras with computer vision algorithms can detect defects immediately, and advanced analytics software can help identify the cause of a problem.
- Improved visibility into shop floor operations
IIoT provides greater visibility into shop floor operations by providing manufacturers with continuous real-time updates on production operations and the status of industrial assets.
- Information security
Data security is ensured with the help of at-rest and in-transit data encryption, access control, AI-powered detection of abnormal user activity within a smart factory, and more
- Predictive maintenance
With the help of IIoT, data on various equipment parameters determining its health and performance is transmitted to the cloud in near-real time. There, combined with metadata, it is fed to machine learning algorithms, which help determine abnormal patterns. Thus, it becomes possible to predict potential equipment breakdown and take timely measures.
- Improved worker safety
Robots can replace human workers for dangerous tasks.

Technologies used in a smart factory
- Cloud computing
Popular cloud platforms (e.g., AWS, Azure) allow processing, storing, and analyzing large amounts of data securely.
- Radio Frequency Identification (RFID)
RFID can help track industrial equipment and machinery, inventory, finished goods as well as objects and workers in smart factories.
- Big data
This technology is used for continuous collecting, storing, and analyzing large amounts of production-related data.
- Artificial intelligence (AI) and machine learning (ML)
AI and ML are employed for end-to-end automation of the production process, equipment monitoring, and more. What’s more, these technologies enable advanced analytics insights (e.g., predictive maintenance, detection of quality improvement opportunities)

Make your manufacturing process smarter

Switching to the smart factory model is an ambitious initiative that requires substantial time and money investments. To make the transformation smooth and get value early, SCS Tech suggests going in iterations. For example, it may be viable to start with introducing a cloud-based big data storage that will later become the basis for enterprise-wide analytics and provide insights for production planning and management, industrial asset management, and more. If you need advice on where to start or if you are ready to embark on the digital transformation journey, SCS Tech team is always ready to help.
March 25, 2022
Current trends in Artificial Intelligence (AI) Application to Oil and Gas Industry

In recent years, artificial intelligence (AI), in its many integrated flavors from neural networks to
genetic optimization to fuzzy logic, has made solid steps toward becoming more accepted in the mainstream of the oil and gas industry.On the basis of recent developments in the field of Oil & Gas upstream, it is becoming clear that petroleum industry has realized the immense potential offered by intelligent systems. Moreover, with the advent of new sensors that are permanently placed in the wellbore, very large amounts of data that carry important and vital information are now available.

To make the most of these innovative hardware tools, an operator intervention is required to handle the software to process the data in real time. Intelligent systems are the only viable techniques capable of bringing real-time analysis and decision-making power to the new hardware.

An integrated, intelligent software tool must have several important attributes, such as the ability to integrate hard (statistical) and soft (intelligent) computing and to integrate several AI
techniques. The most used techniques in the Oil and Gas sector are:

• Genetic Algorithm (GA), inspired by the biological evolution of species in natural
environment, consists of a stochastic algorithm in which three key parameters must be
defined:
1. Chromosomes, or better, vectors constituted by a fixed number of parameters
(genes).
2. A collection of chromosomes called genotype, which represents the individuals of
a population.
3. The operations of selection, mutation, and crossover to produce a population from
one generation (parents) to the next (offspring).

• Fuzzy Logic (FL) is a mathematical tool able to covert crisp (discrete) information as
input and to predict the correspondent crisp outlet by means of a knowledge base
(database) and a specific reasoning mechanism. To achieve such goal, the crisp
information is firstly converted into a continuous (fuzzy) form, secondly processed by an
inference engine and at least re-converted to a crisp form.

• Artificial neural network (ANN) is constituted by a large number simple processing
units, characterized by a state of activation, which communicate between them by sending
signals of different weight. The overall interaction of the units produces, together with an external input, a processed output. The latter is also responsible of changing the state of
activation of the units themselves.

AI applications in Oil and Gas industry

Exploration & Production (E&P) sector

Most of the resources in the Oil and gas field is centered in drilling operation in which artificial
intelligence finds natural application. Drilling success and safety are related to an accurate
prediction of the likely performance of different factors such as:
• Pre-drilling settings (rig, logistics and associated drilling risks)
• Drilling equipment (casing and tubing pipes, drilling mud)
• Downhole machinery behavior (vibrations, torque limits)

The development of models, implemented by AI systems, permits to avoid the necessity of
disposing of real-time data and to produce smart outcomes in order to quickly re-establish optimum operating conditions.

Relatively to the selection of Drill bits, trained artificial neural networks (ANNs) have been used:
they are able to suggest the best drill bit to select (roller cone, diamond insert or a hybrid) analyzing a user defined database. The latter should include information relative to the IADC bit codes correlated with specific geological data.

Neural and network system (commonly GRNNs) gave accurate results in the prediction of mud
the fracture gradient. As input parameters to the model, the depth of the well, the overburden
gradient and the Poisson ratio must be provided. It is important to keep in mind that the results will strictly depend upon the range of the data set, and that extrapolations may loss in accuracy.

In the planning stages of a well, drilling engineers are responsible for the establishment of the
different depths at which the well must be cased to ensure an overall desired perforation depth. To avoid casing collapse, a neural network approach adopting a BPNN based spreadsheet program can be used. Back-propagating neural networks (BPNN) are constituted by a defined number of “layers”. Each layer is interconnected with the other: in particular, the input layer is connected with hidden layers which are in turn connected to the output layer. This neural net, provided of an historical well archive, is fed (input layer) with specific data of the well under consideration (i.e. location, depth, casing strength). Furthermore, the BPNN is able to estimate an “experienced”casing case probability.

Another example of AI application is given by the real time drilling optimization in which
artificial intelligence system are adopted to improved monitoring of downhole parameters
optimizing the drilling operation.

A crucial real-time operation is the Estimation of hole cleaning efficiency in terms of cutting
concentration. During the drilling process, the wellbore is filled with many rock fragments
(cuttings) generated by the mechanical action of the drill bit. In order to remove those cuttings
from the well, a drilling fluid, or drilling mud, is pumped from the drill bit and exits from the
wellhead: the cuttings are lifted and carried on the top of the well. According to this, the cutting concentration (expressed as a %) is the residual amount of rock fragments into the well after the cleaning action of the mud (Figure 5 gives a visual idea of the situation described). Inefficient removal of the drilled cuttings may lead, in some severe cases, to the loss of the well due to stuck pipe.

For the estimation of the hole cleaning efficiency, artificial feed-forward neural network with
back-propagation (BPNN) can be used. As input to the model all the parameters which affect the cutting concentration must be given. The latter are divided in specific parameters of the drilling (rate of penetration, inclination angle of the wellbore) or in parameters regarding the rheology conditions of the mud (viscosity, density).

Future-proof your operations, onshore or offshore

Whether you focus on exploration, extraction, transportation, storage, or production, at SCS Tech, we cater to all sectors of the oil and gas industry. From crude oil to natural gas and natural gas liquids, from refineries to gas treatment and petrochemical production, from pipelines to storage facilities, our service solutions and expertise give you the competitive edge. We ensure and boost the performance of the turbomachinery that lies at the heart of your value-adding process.

February 4, 2022
Why Conversational AI and Chatbots are Perfect for Digital Health

Over the last decade, the continued evolution of digital healthcare and the impact that next-generation technology will have on patient wellness and diagnostic outcomes has been a recurring conversation. Providers have been looking to make the most of the tech-centric tools that are increasingly available, while those in need of care or the right advice at the right time now expect the medical sector to be investing in digital solutions to physical problems.

The integration of tech into public health and wellness is nothing new, but the demands of the connected society have only raised awareness of what is possible but also flagged up the opportunities for engagement in the digital space.

Healthcare has never shied away from technology, albeit some recent improvements can be hit-and-miss – electronic health records are only as useful as the data that has been input, for example. However, there is a consensus among providers and medical professionals that the industry is willing to accept some disruption if it has a positive impact on the overall patient experience.

In terms of technology with the power to both disrupt and improve the way that people engage with digital health, there is an argument to be made that conversational AI (which includes Machine Learning) and Natural Language Processing (NLP) can be the missing link. After all, if Alexa et al can answer questions about the weather, start your car in the morning and tell jokes, then there is no reason why virtual assistants can’t provide you with the medical advice that you need, when you need it.

The Chatbot will See You Now

According to the World Economic Forum, virtual assistants – and, by association, chatbots – are not only being used in a diverse set of industries (healthcare, education, retail, tourism, and more), but also offer opportunities for companies to integrate NLP into routine or mundane activities. For instance, Amazon’s Alexa is the chatbot that the average person associates with the tech, but the key element in every interaction is the ability of NLP to understand what it is being asked and respond with the appropriate information.

The problem, the WEF said in a November 2021 blog post, is that building the chatbot is the easy part. Often, it is the conversational aspect that throws a digital spanner into the works. When you are talking to a finance bot or trying to return a pair of shoes, the stilted nature of these exchanges is rarely an issue. In a sector such as healthcare, it is the conversation and the information that is being dispensed that is a key part of how comfortable an end-user is with knowing that they are not engaged with a human being.

In rural parts of the world, the use of so-called health bots has already helped alleviate some of the pressure on localized health providers.

This is not limited to first-world or so-called developed countries, but their integration takes on a particular resonance in regions where access to tech is not easily available. In Rwanda, for example, there is often only one doctor and six healthcare workers per 10,000 people, with the WEF noting that these tools are allowing patients to have access to doctors, nurses, and relevant healthcare information.

The caveat is that there is still some concern that a well-documented stigma surrounding chatbots in general could prevent people from interacting with them in the first place – for the record, this is also not unique to developing countries, there seems to be a global mistrust of chatbots as an accurate or effective engagement tool!

Conversational AI and chatbots are not new per se, but they can suffer from falling under a broad definition of what they actually are. Simply put, a “chatbot” is AI software that simulates a human conversation with end-users – this can be text or voice – with the aim being to leverage machine learning algorithms and NLP to deliver required outcomes.

The healthcare sector has been experimenting with these solutions for some time, with Mobile Health News reporting in May 2020 that a leading healthcare provider was integrating bots into its Emergency Department EHR system. More recently, the World Health Organization launched a women’s health chatbot dedicated to breast cancer messaging – for context, this new digital experience follows hot on the heels of bots that delivered information on COVID-19, mental health and smoking cessation.

So, the bots are already here, but the question that we need to address is how we can use conversational AI and/or chatbots in addressing not only patient concerns but also alleviate the worry or concerns that a person has. Additionally, we should consider both the challenges to overcome and the advantages of using virtual assistants to deliver care and wellness.

The Future is Already Here

We all interact with digital experiences every day of our lives, so it makes perfect sense that the technologies we often use without thinking should be mirrored in the healthcare sector. And while the industry still relies far too much on legacy technology (disparate IT systems, pagers instead of wearables), the integration of digital health solutions is picking up pace.

Conversational AI has been part of the digital landscape for at least five years, although the average person is likely to only know these virtual assistants as Alexa, Siri and Google Assistant (useful tool, unimaginative name). Chatbots fit into the same category, but their integration into the connected society is less heralded and, arguably, overlooked in terms of the access to knowledge and the assistance they can provide.

However, these tools are here to stay and there is a defined need for them to become not only part of the digital health landscape but also a familiar resource for people to turn to. The effective use of chatbots to provide the right medical or clinical information at the right time is not a futuristic concept, these solutions are here now. What matters is how healthcare and life sciences companies choose to use them.

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January 28, 2022