INTRACOM TELECOM is a global telecommunication systems and solutions vendor operating for over 40 years in the market. The company innovates in the wireless access and transmission field, offers a competitive telco software solutions portfolio and combines its offerings with a complete range of professional services. Our mission is to shape future through technology and we recognize that human capital is the key factor to achieve this in today's business environment. Our company's highly specialized and experienced personnel are pivotal to achieving demanding objectives and advancing the capabilities of the company to better serve its customers. Within this framework, we are looking for an agile and highly-motivated "ML Engineer / Data Scientist" to join a team of future shapers.

Artificial intelligence is helping telecoms operators to boost the RAN capacity of their 4G networks by 15 percent. More people than ever are relying on telecoms networks to work, play, and stay connected during the pandemic. Operators are doing all they can to ensure their existing networks have enough capacity to cope with demand. "Video streaming continues to experience high year on year growth and that has been exacerbated by the pandemic and resulting lock-downs, Yes, 5G grabs the spotlight, but 4G is carrying the brunt of this traffic. So, while investment in 5G infrastructure continues, operators need intelligent ways to maximize and extend existing 4G network capabilities in the short to medium term – keeping their CAPEX to a minimum."

Spire is all about helping our customers know what is next, so they can make better decisions. This month we are moving this forward by launching a true super-computer into orbit – 1-2 teraflops!

By Ravi Saraogi Telecommunications might not be the first sector that comes to mind when we think of industries that are considered the pioneers in customer experience. During COVID-19, the expectations for customer experience are even more profound and the stress on telecom providers couldn't be higher. While we would not have been able to maintain our ability to work, stay socially connected, learn, shop, get healthcare, and more during the pandemic without the connectivity that telecom companies deliver, the current customer experience has tremendous potential for transformation. Despite the growing global dependency on reliable and fast connectivity, downward trends that have impacted telecom companies for years continue to affect their present performance and hinder their ability to emerge stronger after the crisis. Slow growth due to declining ARPU (average revenue per user), industry saturation, customer churn, competitive disruptions, and the need to achieve return on investment into 5G and other technologies all continue to impact the industry.

Humans are not perfect drivers; we are vulnerable to many physical and emotional factors influencing our driving behavior. A study suggests that many road accidents occur due to a lack of response time for drivers. In order to make informed judgments, drivers need a smart assistance system that can predict a possible event beforehand and prevent a fatal crash or serious injuries. V2X is an intelligent transport system comprising of Vehicle-to-vehicle (V2V), Vehicle-to-infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) communications. Biometric seat technology; autonomously managed municipality; and highway system are also part of advanced IoT technologies.

It is no secret that artificial intelligence (AI) is a technical marketing whitewash. Many companies claim that its algorithms and data scientists enable a differentiated approach in the networking infrastructure space. However, what are the practical applications of AI for connectivity and, in particular, 5G? Here I will provide my insights into each and highlight what I believe is the practical functionality for operators, subscribers and equipment providers. Automation is all about reducing human error and improving network performance and uptime through activities such as low to no-touch device configuration, provisioning, orchestration, monitoring, assurance and reactive issue resolution.

With a more complex network comes more complex problems -- in the sense that tracking down the cause of a service outage or other issue will be more difficult because of the system's complexity. More importantly, the plethora of possibilities makes planning and managing a complex ecosystem more difficult, from determining how best to supply services based on current and future demand to where and how to deploy equipment and services in the most efficient manner possible. Such a complex network requires a simplified solution, and deploying artificial intelligence (AI) and machine learning (ML) to solve these problems could be the solution carriers need to ensure they remain in control of their networks. AI/ML solutions would enable carriers to bring the benefits of 5G to customers, as well as aligning to a CTO's KPIs, overcoming the current situation in which monolithic, centralized networks strain themselves to deliver the advanced services customers are increasingly demanding. AI solutions, of course, use machine learning to discover patterns in large datasets, providing a clear picture of cause and effect in resource usage to enable data enrichment for better prediction and decision-making timing, demand and a thousand other factors that determine the quality of connections, whether voice or data.

With a more complex network comes more complex problems -- in the sense that tracking down the cause of a service outage or other issue will be more difficult because of the system's complexity. More importantly, the plethora of possibilities makes planning and managing a complex ecosystem more difficult, from determining how best to supply services based on current and future demand to where and how to deploy equipment and services in the most efficient manner possible. Such a complex network requires a simplified solution, and deploying artificial intelligence (AI) and machine learning (ML) to solve these problems could be the solution carriers need to ensure they remain in control of their networks. AI/ML solutions would enable carriers to bring the benefits of 5G to customers, as well as aligning to a CTO's KPIs, overcoming the current situation in which monolithic, centralized networks strain themselves to deliver the advanced services customers are increasingly demanding. AI solutions, of course, use machine learning to discover patterns in large datasets, providing a clear picture of cause and effect in resource usage to enable data enrichment for better prediction and decision-making timing, demand and a thousand other factors that determine the quality of connections, whether voice or data.

With a more complex network comes more complex problems -- in the sense that tracking down the cause of a service outage or other issue will be more difficult because of the system's complexity. More importantly, the plethora of possibilities makes planning and managing a complex ecosystem more difficult, from determining how best to supply services based on current and future demand to where and how to deploy equipment and services in the most efficient manner possible. Such a complex network requires a simplified solution, and deploying artificial intelligence (AI) and machine learning (ML) to solve these problems could be the solution carriers need to ensure they remain in control of their networks. AI/ML solutions would enable carriers to bring the benefits of 5G to customers, as well as aligning to a CTO's KPIs, overcoming the current situation in which monolithic, centralized networks strain themselves to deliver the advanced services customers are increasingly demanding. AI solutions, of course, use machine learning to discover patterns in large datasets, providing a clear picture of cause and effect in resource usage to enable data enrichment for better prediction and decision-making timing, demand and a thousand other factors that determine the quality of connections, whether voice or data.

With a growth in advertisements and cold-messaging we are now receiving a nonstop coherent threads of commercial messages and emails. A user, like you and I, sometimes find it difficult to find a text/email which is actually useful to us or the one which we seek. Detection systems such as Spam detection system are becoming increasingly useful to classify the important data amongst the bundle of raw and undesired data. In this post we'll look at one such detection model, a spam detection model using NLP (natural language processing) and also learn about classification using Naïve Bayes. You can see that we are interested in calculating the posterior probability of P(h d) from the prior probability p(h) with P(D) and P(d h). UCI have an available data set of more than 5000 mixed text messages, click here.