6th World Machine Learning and Deep Learning Congress

Artificial Intelligence (AI): The discipline of making analytical machines intelligent, enabling an entity to function appropriately and with foresight in its environment. Machine Learning : Techniques for learning and performing cognitive functions (examples include algorithms for supervised and unsupervised learnings) Deep Learning : A statistical machine-learning approach based on deep neural networks that attempt to mimic brain architecture for learning. Potential Applications of Deep Learning: Banking – A bank wants to analyze large volumes of video footage from the ATM Machines to detect abnormal activity. Deep Learning techniques can be used to extract images from video footage and identify normal and suspicious or fraudulent activities. Insurance – An insurance company wants to be able to compute claims costs directly from images. A deep learning system can be trained to take all accident images to classify the accidents by severity of damage and the pa...

No one can even imagine the future of healthcare without encountering Artificial Intelligence (AI ), though the field of AI been around since 1956, it has made unforgettable contributions to medical practices. Only recently has the hype of machine-based learning begun to merge with reality. Many industries have been disrupted by the influx of new technologies in the Information Age. Healthcare is no different. Particularly in the case of automation, artificial intelligence (AI) and Machine Learning, doctors, hospitals, insurance companies, retail, e-commerce, and industries with ties to healthcare, all have been impacted. And the impact is more positive and sustainable than other industries. AI, Machine Learning and Deep Learning in HealthCare represents a collection of various technologies enabling machines to sense, comprehend, act and learn so that they can perform clinical and administrative healthcare functions. Unlike legacy technologies, that are only algorithms/ to...

In the last blog, we saw that how RPA Originated and emerged with the support of Artificial Intelligence and Machine Learning. But the big question is: Where is RPA heading? Where will it go in its upcoming future? The development of RPA technologies has already come a long way from the days of simple screen scraping, and RPA continues to transform how many companies approach their business activities, especially when it comes to scaling and streamlining processes. It’s a superior technology that has made it’s way to the forefront for the benefits it provides and the ease at which these benefits can be obtained. However, the market is required to keep on evolving significantly further and more inventive RPA solutions are solutions. Companies analysts expect that the combination of RPA solutions with considerably more intelligent technologies like Artificial Intelligence has awesome potential for widespread adoption across all Sectors. Artificial Intelligence, Machine ...

While most of the technologies were developing, the emergence of term “ RPA - robotic process automation ” can be dated to early 2000. RPA is a developing technology, in any case, despite everything it depends on the technologies like artificial intelligence, screen scraping, and workflow automation and raises these technologies to another level, propelling their abilities in a significantly improved manner. RPA is highly dependent on two factors and those are screen scraping and workflow automation, but in ways that give more benefits for the business clients. RPA provides a special automation that can only have drag and drop feature in a visual way that does not involve any coding knowledge and altogether independent from that. On the other hand, just like numerous web scraping tools, some RPA (Robotic Process Automation) software also facilitate utilization of optical character recognition (OCR) technology to mold to changing websites without the involvement of human emp...