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The article presents the top soft computing applications that businesses should check out in 2023

Soft computing is the use of approximate calculations to provide imprecise but usable explanations for complex computational problems. Top soft computing applications enable solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware. soft computing applications are generally based on computational intelligence. Soft computing applications provide an approach to problem-solving using means other than computers. With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision, and approximation, unlike traditional computing models. The tolerance of soft computing enables researchers to deal with some serious problems that traditional computing can’t process. Companies are using soft computing applications to make them a step ahead in the competition. Therefore, Knowing the top soft computing applications for 2023 can provide a boost to one’s professional career. Here the article enlists the top 10 soft computing applications that Businesses should know in 2023.

Soft Computing in Investment and Trading

The data present in the finance field is grandiosity and traditional computing is unable to handle and process such kind of data. There are multiple soft computing techniques that help to handle those massive data. The pattern recognition approach is used to understand the pattern or behavior of the data and time series is used to predict future trading points.

Soft Computing Techniques in Bioinformatics

Soft computing applications in bioinformatics help to modify any uncertainty and indifference that biometrics data may have. Soft computing applications provide distinct low-cost solutions with the help of algorithms, databases, Fuzzy Sets (FSs), and Artificial Neural Networks (ANNs). These techniques are fantastic to deliver quality results in an efficient way.

Soft Computing based Architecture

In this process, an intelligent building gathers inputs from the sensors and controls effectors by using them. The construction industry uses the technique of DAI (Distributed Artificial Intelligence) and fuzzy genetic agents to deliver the building with capabilities that match human intelligence. The fuzzy logic of Soft Computing is used to create behavior-based architecture in intelligent buildings to deal with the unpredictable nature of the environment, and these agents embed sensory information in the buildings.

Soft Computing Techniques in Power System Analysis

Soft computing uses the method of Artificial Neural Networks to predict any instability in the voltage of the power system. Using the Artificial Neural Network, the pending voltage instability can be predicted. The methods which are deployed here, are very low in cost.

Soft Computing and Decision Support System Handwritten Script Recognition using Soft Computing

Handwritten Script Recognition is one of the most demanding parts of computer science. It has the ability to translate multilingual documents and sort the various scripts accordingly. It applies the concept of the “block-level technique” where the system recognizes the particular script from a number of script documents given. It uses a Discrete Cosine Transform (DCT), and discrete wavelets Transform (DWT) together, which classify the scripts according to their features.

Use of Soft Computing in Automotive Systems and Manufacturing

Soft computing applications have solved a major misconception about the automobile industry that it is slow to adapt. Fuzzy logic is a soft computing technique used in vehicles to develop classic control methods. It takes the example of human behavior, which is described in the form of rule – “If-Then “statements. The logic controller then converts the sensor inputs into fuzzy variables that are then defined according to these rules. Fuzzy logic has a crucial role in engine control, automatic transmissions, antiskid steering, etc.

Image Processing and Data Compression using Soft Computing

Image analysis is one of the crucial parts of the medical field. It uses a high-level processing technique that includes recognition and bifurcation of patterns. Using soft computing solves the problem of computational complexity and efficiency in classification. Soft computing techniques including Genetic Algorithms, Genetic Programming, Classifier Systems, Evolution Strategies, artificial life, and a few others are used to deliver the best result.

Soft Computing Applications in Supply Chain Management Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization

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Top 10 Cryptocurrency Influencers You Should Be Following

Many people are eager to get their hands on cryptocurrency now that it has gained popularity. Many media covers articles describing what cryptocurrencies are, how they function, and the most recent market developments, but they may be lacking in key information. Investors and others considering jumping on the cryptocurrency bandwagon should thank the internet for giving them access to views and viewpoints from industry leaders and crypto pioneers.

Top 10 cryptocurrency influencers

For individuals interested in learning more about the cryptocurrency business, we have listed several influencers that include in-depth analyses of the sector.

Nicolas Merten,

Nicolas Merten a YouTube personality who focuses on cryptocurrency trading. DataDash is the name of his channel. Numerous informative lectures are among the many crypto-related subjects he discusses. Check to study his earlier videos, especially those that teach basic concepts like the Moving Average Convergence/Divergence (MACD) and the Bollinger Bands, if you’re just getting started.

Brian Jung

Brian Jung is a renowned YouTube content maker whose channel focuses on the bitcoin market. You won’t want to miss out on anything he posts since he has around a million followers.

Anthony Pompliano

You may be familiar with Anthony Pompliano if you listen to crypto-related podcasts. It’s estimated that there are 200,000 weekly listeners to his Pomp Podcast. He alone may boast over a million Twitter followers.

Some of the most nuanced discussions of bitcoin trends have taken place on his broadcasts. If you tune in, you may hear the thoughts and opinions of several prominent business owners and investors. It’s worth watching Pompliano’s show since he often gives away free bitcoin prizes.

Maria Pennanen

She is the current CEO of the groundbreaking innovation business Mindclip Behavior, which she just launched. In 2023, Pennanen was also chosen as one of the most powerful women in startups and venture capital in the European Union.

Ben Horowitz

This successful businessman and the author believe that your actions define you. Check out his latest #1 New York Times bestseller for additional information. He has also written many additional works on corporate leadership and management.

Andressen Horowitz is a venture capital business created by Horowitz, who has supported and invested in cryptocurrencies since 2013. He’s a shareholder in some of the most prominent companies, including Airbnb, Facebook, and Twitter. Horowitz has just made public a crypto financing venture valued at over $2 billion. If nothing else, it proves he gives a damn about these coins.

Elon Musk

You can recognise Elon Musk even if you don’t own a Tesla. You need to log in to your Twitter account once to recognise our subject immediately. When it comes to making headlines with his tweets, this guy is second only to the 45th President of the United States.

Besides his immense wealth, Musk has many opinions on cryptocurrencies, such as his endorsement of Dogecoin. He has, in the past, single-handedly changed the direction of bitcoin and a few other currencies with his statements.

Roger Ver

Many individuals look up to Roger Ver and consider him a kind of crypto “Jesus.” Ver started two firms in the early to mid-2000s, including Memorydealer, an early adopter of bitcoin payment integration. It may be said that he has contributed to the mainstream acceptance of cryptocurrency by businesses. He anticipated bitcoin’s growth and now owns more than $170 million worth of the cryptocurrency. During his career, Ver has contributed to and sponsored various cryptocurrency and startup endeavours.

Changpeng Zhao

Changpeng Zhao, or CZ, is the man behind Binance, one of the biggest cryptocurrency exchanges online. Binance has grown to process transactions worth billions of dollars despite persistent judicial scrutiny.

Like many other successful businesspeople on this list, Zhao came from modest beginnings. If you just saw him, you may believe the saying “don’t judge a book by its cover” was coined to describe him. However, putting all his savings into bitcoin after selling his home was the wildest and smartest move he ever made.

Charlie Lee

Litecoin, created by Charlie Lee, is a playable character in the crypto-verse. For a good reason, Bitcoin, Ethereum, and Litecoin are great places to start for novice investors. They’re steady and rebound well from setbacks, so you can count on making money with them.

Lee, a software worker at Google, came upon bitcoin during his employment there. Since then, he’s been thinking about making a better, more refined version of it. Because of such, Litecoin was created and is now widely held by traders.

Vitalik Buterin

You probably already follow Vitalik Buterin if you have any knowledge of cryptography at all. However, if you haven’t heard of him, you should since it’s likely that you’re utilizing his crowning achievement, Ethereum.

Although Buterin’s interest in bitcoin began at an early age, he didn’t put it to use until he launched the Bitcoin magazine some years later. The experience inspired him to dedicate time and energy to learning about cryptocurrency, which led to his founding of the world’s second-largest coin.


Even though the people on this list didn’t all grow up in the same circumstances, they all had to begin somewhere, and they all stuck with it until they achieved success. So why wait if you have what it takes to get going? Maybe one day you’ll find yourself here.

Top 10 Data Science Software Businesses Should Use In 2023

Data is huge and complex at the same time. The requirement for software that makes the best use of the data available always persists. This is exactly where data science software comes into play. As organizations rely heavily on data, choosing the right software for the same is critical. We have come up with a list of the top 10 data science software businesses should use in 2023. Have a look!


Keras, a programming interface, enables data scientists to easily access and use a machine learning platform. An interesting feature to note is that it is an open-source deep-learning API and framework that is written in Python.

How about a platform that brings all data sources together? Well, this is exactly what chúng tôi has in store for you. It is data integration, ETL, and an ELT platform that can bring all your data sources together. This is just the right software you need to build data pipelines.


Tensorflow is that one data science software that lays emphasis on deep learning. This software is launched by Google and is written in C++ and Python. What’s so special about Tensorflow? Well, its capabilities include ML model building either on-premise, on the cloud, in-browser, or on-model.


Ever since it was launched in 2023 by MIT data science researchers, Alteryx has evolved to become a proprietary software platform. What has made businesses rely on it is the fact that its most popular open-source tool, “featuretools,” allows the creation of automated feature engineering.

Data Robot

If you are looking for a platform that aims at automated machine learning, then Data Robot is all that you need. In addition to providing an easy deployment process, it allows parallel processing and model optimization. It is because of this reason that this data science software is used by data scientists, executives, software engineers, and IT professionals.

Trifacta Wrangler

This excellent data science software will help you in exploring, transforming, cleaning, and joining the desktop files together. How amazing is that?


This remarkable software enables data scientists to blend tools and data types. It is an open-source platform that allows users to use the tools of their choice. Not just that – they can expand them with additional capabilities. KNIME stands for the ability to work with many data sources and different types of platforms.

Apache Spark

Apache Spark is an open-source data processing and analytics engine that is all you need when the objective involves handling large amounts of data. Additionally, the ability of this data science software to rapidly process data has led to significant growth in the use of the platform.


Almost all the businesses rely on Python as it comes with a large standard library. This high-level programming language has the features of object-oriented, functional, procedural, dynamic type, and automatic memory management. The fact that Python is extensible makes it way more accepted.


RapidMiner is yet another open-source data science tool that has gained wide recognition in time. Its self-explanatory drag-and-drop application is one among the many remarkable features that make RapidMiner a part of the top 10 data science software businesses should use in 2023.

Top 10 Data Science Myths That You Should Ignore In 2023

Debunking the top 10 data science myths that you should ignore in the year 2023

In the world of Big Data, there are numerous job profiles available, such as Data Engineers, Data Analysts, Data Scientists, Business Analysts, and so on. Beginners need clarification on these profiles, as Data Scientist is the most popular and sought-after. They require assistance in determining whether Data Science is a good fit and identifying the best resources. There are several misconceptions about data science myths. As a data scientist, there are several data science myths to ignore for a successful career.

Transitioning into data science is difficult, not because you need to learn math, statistics, or programming. You must do so, but you must also combat the myths you hear from others and carve your path through them! In this article let us see the top 10 data science myths that you should ignore in 2023.

Myth 1 – Data Scientists Need to Be Pro-Coders

Your job as a Data Scientist would be to work extensively with data. Pro-coding entails working on the competitive programming end and having a strong understanding of data structures and algorithms. Excellent problem-solving abilities are required. Languages like Python and R in Data Science provide strong support for multiple libraries that can be used to solve complex data-related problems.

Myth 2 – Ph.D. or Master’s Degree is Necessary

This statement is only partly correct. It will be determined by the job role. A Master’s or Ph.D. is required if you want to work in research or as an applied scientist. However, if you want to solve complex data mysteries using Deep Learning/Machine Learning, you will need to use Data Science elements such as libraries and data analysis approaches. If you do not have a technical background, you can still enter the Data Science domain if you have the necessary skill sets.

Myth 3- All Data Roles are the Same

People believe that Data Analysts, Data Engineers, and Data Scientists all perform the same function. Their responsibilities, however, are completely different. The confusion arises because all of these roles fall under the Big Data umbrella. A Data Engineer’s role is to work on core parts of engineering and build scalable pipelines of data so that raw data from multiple sources can be pulled, transformed, and dumped into downstream systems.

Myth 4 – Data Science Is Only for Graduates of Technology

This is one of the most crucial myths. Many people in the Data Science domain come from non-tech backgrounds. Few people are transitioning from computer science to data science. Companies hire for data science and related positions, and many of those hired come from non-tech backgrounds with strong problem-solving abilities, aptitude, and understanding of business use cases.

Myth 5 – Data Science Requires a Background in Mathematics

As a Data Scientist, being good at math is essential, as data analysis requires mathematical concepts such as data aggregation, statistics, probability, and so on. However, these are not required to become a Data Scientist. We have some great programming languages in Data Science, such as Python and R, that provide support for libraries that we can use for mathematical computations. So, unless you need to innovate or create an algorithm, you don’t need to be a math expert.

Myth 6- Data Science Is All About Predictive Modelling

Data scientists spend 80% of their time cleaning and transforming data, and 20% of their time modeling data. There are numerous steps involved in developing a big data solution. The first step is data transformation. The raw data contains some error-prone values as well as garbage records. We need meaningful transformed data to build an accurate machine-learning model.

Myth 7- Learning Just a Tool Is Enough to Become a Data Scientist

The Data Science profile requires a diverse set of technical and non-technical skills. You must rely on something other than programming or any particular tool that you believe is used in Data Science. We need to interact with stakeholders and work directly with the business to get all of the requirements and understand the data domain as we work on complex data problems.

Myth 8- Companies Aren’t Hiring Freshers

This statement made sense a few years ago. However, today’s freshmen are self-aware and self-motivated. They are interested in learning more about data science and data engineering and are making efforts to do so. Freshers actively participate in competitions, hackathons, open-source contributions, and building projects, which aid in their acquisition of the necessary skill set for the Data Science profile, allowing companies to hire freshers.

Myth 9 – Data Science competitions will make you an expert

Data Science competitions are ideal for learning the necessary skills, gaining an understanding of the Data Science environment, and developing developer skills. However, competition will not help you become a Data Scientist. It will improve the value of your resume. However, to become an expert, you must work on real-world use cases or production-level applications. It is preferable to obtain internships.

Myth 10 – Transitioning cannot be possible in the Data Science domain

How Should Businesses Manage Ai?

Instead, the key questions should be, ‘how can AI enable our strategy?’

Why is AI getting all the attention now?

You probably know the answer to this already: ChatGPT. 

The system launched late last year and has surged in popularity, even though it’s still in “teething issue” territory. 

Businesspeople praise its ability to produce human-like content based on vast amounts of data. It even writes code. So many consider these things game-changers. 

And, regarding its success, business leaders are widening their horizons towards other AIs with different functionalities. 

Ultimately, no matter what kind of assistance you need in business, there’s an AI that can do it for you, Farrell told a Corporate Governance Institute Webinar this week.

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When it comes to AI, why should leaders consider strategy above all else?

Two main reasons:

Strategy is your top priority

Company strategy is the highway that connects ideas and results. Without a good one – your business has little hope of getting anywhere. 

For that reason, it’s common – and, to many analysts, healthy – to frame new business developments with a strategic context. Significant strides like the dawn of mainstream AI functionality become far more relevant when you use this tactic. 

In this way, your company isn’t just accepting new trends; it’s embracing them to benefit the firm’s specific needs and goals.

Leaders are the source of strategy

Boards and executives have a huge role in shaping company strategy. 

Because of this, leaders should think of AI and strategy as two essential parts of the same machine. Both need the proper consideration for that machine to work.

How should businesses manage AI? – let’s get specific: 

Farrell had several tips:

Take a look at both your company and your industry. Where does AI fit in both, and how can it make life easier for workers?

Think of AI as an “emerging ecosystem”. There are future skills that people will need to learn as it becomes part of their daily life. 

Decide AI’s boundaries in your company

. “We cannot outsource our creativity and critical thinking to AI,” he said, “but we can use it to help expand our own skills.”

Shop around. The functionalities and clever marketing around AI might make it seem more capable than reality. Your company should explore its options to find the best AI solution.

Will AI replace my workers?

It’s the elephant in the room for many: will there be mass layoffs in the face of growing AI capabilities? 

This question generates a lot of debate in business, and since we’re only at the beginning of a new AI era, it’s impossible to tell for definitely. 

Farrell, however, believes the vast majority of workers shouldn’t worry. 

“Some people will lose their jobs,” he said. “Some people will be allocated to different jobs, but it won’t lead to mass global layoffs.”

He put this down to AI’s true functionality as an assistant rather than a replacement. It is meant to help people do their work. Rarely is it supposed to do someone’s work for them, he said.

5 Cybersecurity Trends Organizations Must Be Aware Of In 2023

Business is based on trade offs. Digital solutions provide ease of doing business to corporations since they accelerate the flow of information. On the other hand, they provide new opportunities for criminals to steal value from businesses.

Knowing current cybersecurity trends can help businesses utilize the benefits of digital platforms while limiting cyber threats. This article discusses five major cybersecurity trends and their implications for businesses. 

1. Cyberattacks are on the rise

The number of cyberattacks has exploded in the last decade (see Figure 1). This pattern appears to be continuing and will likely continue for some time. For example, Gartner predicts that in 2025, more than half of companies will check the cybersecurity posture of candidate business partners to make business with.

Figure 1: Number of malware attacks in millions

In comparison to 2023, the total number of cyberattacks climbed by 30% in 2023. When compared to the previous year, ransomware was the most common type of attack. Ransomware attacks surged by more than 100% in total.

Phishing assaults, according to Deloitte, are another form of cyber danger that harms businesses as much as ransomware. ENISA, on the other hand, warned about the potential of brute force assaults due to the new algorithms and models used by criminals.

Despite the fact that we are only a few months into 2023, the cyber risk landscape does not appear to be bright. Following Russia’s invasion of Ukraine, Fitch Ratings gave a notice to businesses about the increased cyber threats.  

2. Building a fortress is not a cybersecurity solution anymore

Until a few years ago, IT professionals structured an organization’s cybersecurity model as a fortress, in which everything within the office networks was assumed to be fundamentally trustworthy. To shield business infrastructure from the outside world, they build/buy hardware-based cloaking and web filtering solutions like VPNs and on-premise firewalls.

When employees operate in an office environment with secure Wi-Fi, laptops, and on-premise platforms, such a cybersecurity plan works well. However, after the Covid-19 pandemic people started to work from anywhere with any devices and corporations migrated to the public cloud platforms as an outcome of hybrid/remote working. As a result, ensuring Wi-Fi, laptop, or network security is no longer viable.

New-normal of working dictates that users, gadgets and networks should not be trusted inherently and always verified before providing access to any document or tool. Furthermore, because users and gadgets may be dangerous, staff should have access to as little data as possible. This new cybersecurity paradigm is called zero trust architecture.     

According to Microsoft’s study, 35% of companies fully adopted zero trust cybersecurity architecture and another 42% started implementing zero-trust architecture in 2023. Significant transformation is apparent on VPNs where around 60% of companies switched them with zero trust mentality implemented software defined perimeters (SDP).  

According to AIMultiple, the following cybersecurity solutions will be popular in 2023 and beyond due to their ability to secure businesses with hybrid/remote workers and public cloud platforms:

Secure web gateway (SWG): A web security tool that ensures least access principle thanks to URL filtering capabilities 

SDP: A zero trust driven web cloaking tool that allows micro segmentation. Thus, recently preferred over VPNs. 

Zero trust network access (ZTNA): Ensures safe remote login to firms’ data and tools.

Secure access service edge (SASE): Cobines networking and security tools such as firewalls, ZTNA and SWGs.

3. Digital supply chains are on the risk

In today’s world, supply chains are monitored and coordinated via supply chain software and telematics (Internet of Things devices). Therefore, an attack on supply chain tools or gadgets might entirely disrupt operations.

The hacking of SolarWind in 2023 demonstrated how a strike on an supply chain IT infrastructure company might disrupt the movement of commodities. According to Gartner, cyberattacks on supply chain infrastructure and software will increase in the coming years, with nearly half of all organizations being impacted by such assaults by 2025.  

As global political tensions rise, digital supply chain attacks may become more common as a means of causing damage to infrastructure and the well-being of opponents. The 2023 SolarWind hacking case, for example, is being blamed on Russians by US authorities. 

4. AI models improve both cyber risks and security

Improvements in AI capabilities introduce better defense capabilities for firms against cyberattacks. According to Capgemini more than 70% of organizations use AI models at least to some degree to cope with cyber risks. 

AI models are effective in:

Detection: AI/ML models are taught to detect abnormalities in regular patterns which might indicate cyberattacks.

Prediction: AI models are used to forecast the emergence of different types of cyberattacks.

Response: When a cyberattack occurs, AI models automate the response that should be taken. They can, for example, remove patient zero from the network to prevent infection.

Professional hackers have enhanced tools for research and development of malicious software, including AI models. Hacking is now a multi-billion dollar industry so they have capital. Criminals can quickly adapt their attack methods thanks to AI models. In 2023, AI capabilities were used to improve the finance sector targeting the malicious code Emotet. 

5. State actors are significantly involved in cybersecurity issues

Cybersecurity is a national security issue. Digital platforms are used by the supply chain, the finance sector, and government entities. As a result, state actors intervene in cybersecurity to improve national cybersecurity postures. They also encourage or orchestrate cyberattacks on their opponents’ infrastructure to harm it.

On the bright side, cyber-related regulations and control mechanisms have been improved. Biden’s Executive Order on Cybersecurity, for example, requires federal agencies to improve their cybersecurity posture. The Executive Order also affects commercial enterprises that cooperate with federal agencies. Forbes views Biden’s choice as a blueprint for a safer internet for everyone, emphasizing the importance of cybersecurity and techniques like the zero-trust cybersecurity paradigm.

In addition there are some upgrades at cyberattack reporting. On March 10th, the US House and Senate passed legislation requiring companies in the financial, transportation, and energy sectors to notify any cyberattack to the government within three days. If a company makes the payment for a ransomware assault, the government must be notified within 24 hours. Senator Rob Portman, has expressed the rationale behind the decision with his concerns about a possible increase in Russian-based cyber attacks on US companies.

Senator’s concern highlights the darker side of government involvement in cybersecurity. According to ENISA, state-related entities was active cyber threats in 2023. 2023 can be even worse. For example, before the invasion of Ukraine, Russian intelligence services launched a series of cyberattacks to weaken Ukraine’s infrastructure. Political tensions and war-related cyberattacks, according to the Guardian, will continue to escalate.        

You can check our sortable/filtrable cybersecurity software, cybersecurity companies and secure web gateway vendors lists to improve your cybersecurity posture.

If you have further questions regarding cybersecurity trends you can contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





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