BluePes Blog: Insights & Trends

Future Cybersecurity Threats and How Businesses Can Prepare

Cyberattacks are no longer a question of "if" but "when". From AI-powered phishing to insider threats, businesses in every industry are grappling with increasingly complex challenges. In 2025, staying ahead means understanding the evolving threat landscape and preparing for the unexpected. In this article, we’ll explore the most pressing cybersecurity risks, real-world examples, and actionable steps your business can take to protect itself.

Nov 25, 2024
5 min

Deep Learning Platforms

Artificial neural networks (ANN) have become very popular among data scientists in recent years. Despite the fact that ANNs have existed since the 1940s, their current popularity is due to the emergence of algorithms with modern architecture, such as CNNs (Convolutional deep neural networks) and RNNs (Recurrent neural networks). CNNs and RNNs have shown their exceptional superiority over other Machine Learning algorithms in computer vision, speech recognition, acoustic modeling, language modeling, and natural language processing (NLP). Machine Learning algorithms based on ANNs are attributed to Deep Learning.

Mykola Lavrskyi
May 11, 2020
7 min

Data Science Usage in Natural Disasters Predictions

Millions of people are affected by natural disasters each year. Wildfires, floods, tornadoes, volcanic eruptions, are just the beginning of a long list of potential disasters. Some can last a few seconds, while others can last for weeks. However, their effects can be felt for decades or even longer, and impact the global economy, infrastructure, agriculture, and human health. The worst part is that the future impact of disasters will grow dramatically due to climate change. Some regions, which previously rarely suffered floods or wildfires, now regularly experience the effects of these natural disasters. Researchers have collected a large amount of data and developed models that predict disasters, but most of these models are far from perfect. For instance, the amount of data that is monitored by satellites and various ground sensors all over the world each minute is incredibly large, and therefore presents a major challenge for researchers. Having lots of information can be an asset, but data requires computational resources. As more data is collected, computational models become increasingly complex and slow. Furthermore, since just a few minutes’ notice in advance of a flood or wildfire can save people’s lives, predictive models must be able to work and do corrections in real time. Artificial Intelligence (AI) techniques and approaches, like data mining, machine learning, and deep learning, can assist in disaster prediction. It is possible for AI to find hidden dependencies in data, which can be a basis for better understanding the mechanism of disasters, and, as a result, making better predictions. Good predictions and warnings reduce economic losses and save lives. We can’t stop most disasters, like floods, hurricanes, volcano eruptions, but we can be prepared for them. In this article, we will illustrate how data science can help in predicting different natural disasters.

Mykola Lavrskyi
Apr 20, 2020
9 min

Chatbots in NLP

Chatbots or conversational agents are so widespread that the average person is no longer surprised to encounter them in their daily life. What is remarkable is how quickly chatbots are getting smarter, more responsive, and more useful. Sometimes, you don’t even realize immediately that you are having a conversation with a robot. So, what is a chatbot? Simply put, it is a communication interface which can interpret users’ questions and respond to them. Consequently, it simulates a conversation or interaction with a real person. This technology provides a low-friction, low-barrier method of accessing computational resources.

Mykola Lavrskyi
Apr 13, 2020
5 min

Sentiment Analysis in NLP

Sentiment analysis has become a new trend in social media monitoring, brand monitoring, product analytics, and market research. Like most areas that use artificial intelligence, sentiment analysis (also known as Opinion Mining) is an interdisciplinary field spanning computer science, psychology, social sciences, linguistics, and cognitive science. The goal of sentiment analysis is to identify and extract attitudes, emotions, and opinions from a text. In other words, sentiment analysis is a mining of subjective impressions, but not facts, from users’ tweets, reviews, posts, etc.

Mykola Lavrskyi
Apr 06, 2020
6 min

Why Businesses Choose Self-Hosted Jitsi for Secure Video Communication

With more and more people spending time at home in recent years, finding ways to organize work well and be in touch with work teams is a top priority. There are many specialized services like Skype, Google Hangout, or Microsoft teams here to help us. But there is an interesting alternative: Jitsi, a set of open-source projects that allows you to quickly build and deploy secure video conferencing solutions for your company.

Mykola Lavrskyi
Mar 16, 2020
4 min

How to Choose a Software Development Partner in Ukraine?

Nowadays, Ukraine is considered one of the best countries to work from if you are in the IT sector. While the current level of general economic growth is modest, the software industry has been blooming for the past decade, attracting more talent and creating a stable network of professionals.

Mykola Lavrskyi
Mar 02, 2020
6 min

Reinforced Learning

Artificial Intelligence uses three basic methods for machine learning: supervised learning, unsupervised learning, and reinforcement learning. In general, these methods are called learning paradigms. The learning paradigm chosen is determined by the specific task at hand. We choose supervised learning for classification and regression tasks. Cluster identification or anomaly detection are typical tasks that can be solved within the unsupervised learning paradigm. The primary goal of reinforced learning is to create software agents that can automatically interact with an environment, learn from it, and determine the optimal behavior in order to optimize its performance. In this article, we will discuss reinforced learning paradigms in detail.

Mykola Lavrskyi
Dec 09, 2019
7 min

How Can Data Science Help My Organization?

Nowadays, there is a tendency to hire data scientists or even form data science groups in companies. This does not only apply to specific activity sectors or large organizations. Small and midsize businesses are more frequently involving data scientists, in order to get actionable insights from collected information. So, how does data help to run and grow everyday businesses? There are several areas where collected data and the insights drawn from that data can have a significant impact on business.

Mykola Lavrskyi
Nov 11, 2019
6 min