Welcome to ONPASSIVE

As a social media professional, Mr. Ash Mufareh, founder of the GoFounders community and  ONPASSIVE Company , he says that you might have probably already used many social networks such as Facebook, Twitter, Instagram, Pinterest, etc. Look at any social media juggernaut, and you’ll see that people use many different types of social media to connect online for all sorts of reasons. Grouping social networks by topic quickly become overwhelming and sometimes wholly distracting. To keep one updated with the latest news, fashion, and trends in the world of body image, we have reached a point where anything is possible, even if you are male. According to Ash, a visionary behind ONPASSIVE, a business solution platform, more and more systems add features, they become more blurred and change over time, which most people do not have time to do.  While there was a time when networks could be categorized by content, the time for that is over. Mr. Ash Mufareh with his extensive knowle

ONPASSIVE - Work From Home Due to COVID-19, many businesses have stopped their regular work and businesses have been forced to adopt remote working practices.  ONPASSIVE  is one of the biggest examples of an IT company, that is adopting software solutions in this situation. There has been a massive shift from office-based work to remote working, both employers and employees have learned to adjust to new norms in society. There are many financial, medical, and social pressures with a dramatic increase in stress levels of people all across the country. In this article, we’ll show you exactly how to work effectively with seven tips on how to cope with stress while working from home in order, while helping to maintain a positive mental attitude. 1. Dressing for the part When you stick to a routine that mimics normal working habits, you are maintaining a positive work-life balance, by improving your mental wellbeing. Having a shower and getting dressed can go a long way in se

·       INTRODUCTION Data mining and machine learning technology have already achieved remarkable success in many knowledge engineering areas, including classification, regression, and clustering. However, many machine learning processes work well only under a standard assumption: the training and test data are drawn from the same feature of space and equal distribution. When the distribution changes, most models need to be rebuilt from scratch using newly collected training data. In many real-world applications, it is expensive or impossible to re-collect the required training data and restore the models. It would be nice to reconstruct the need and effort to collect the training data. In such cases,  transfer   learning  between task domains would be desirable.  ·       What is Transfer Learning? In this blog, we give a detailed overview of transfer learning for classification regression and clustering processed in machine learning areas. There has been a conside