Globose Technology Solutions

The technology of facial recognition can improve the customer experience What do you think would happen if at the mirror and saw another person? However, even if we alter aspects of our external appearance, such as the shade or the cut of our hair and facial expression when wearing glasses, we still identify ourselves when looking at the mirror. It is because our brains have an inbuilt facial recognition technology that is able to recognize that we're looking at the identical person. We identify ourselves, not by taking a look at our fingerprints, or taking a picture of our iris, but by looking ourselves at the mirror. Different 'signatures' for the human body are in existence and facial recognition continues to be the most popular biometric method for identification for a variety of reasons, including its convenience and ease of application. It is now incorporated into many areas of life, from the tag of photographs (Google as well as Facebook) and even unlocking your pho

AI Image Annotation For Machine Model Training Quick Start Image annotation is important in computer vision, which is the technology that allows computers to gain a high-level understanding of digital images or videos and to see and interpret visual information in the same way that humans do. Computer vision technology enables incredible AI applications such as self-driving cars, tumour detection, and unnamed aerial vehicles. However, most of these remarkable computer vision applications would not be possible without image annotation. Annotation, also known as image tagging or labelling, is a crucial step in the development of most computer vision models. Datasets must be useful components of machine learning and deep learning for computer vision. We need a large number of high-quality image datasets to build successful image annotation models. What is image annotation? The process of labelling images in a given dataset in order to train machine learning models is known as an image an

AI/ML Algorithms In Self Driving Vehicles Introduction The development of self-driving cars is one of the most popular and trendy areas of AI and Machine Learning. In 2020, we saw advancements from companies like Waymo, which allows customers to hail self-driving taxis, a service known as Waymo One. In Shenzhen, Alibaba’s AutoX debuted a fleet of fully automated cars with no accompanying safety drivers. Automotive Artificial Intelligence is rapidly displacing human drivers by enabling self-driving cars that collect data about their surroundings using sensors. Machine Learning algorithms are now widely used to solve problems from financial market forecasting to self-driving cars. It is critical to increasing the use of machine learning to perform new tasks with the integration of sensor data processing in a centralized electronic control unit (ECU) in a car. Driving scenario classification or driver condition evaluation via data fusion from various internal and external sensors- such as

Applications Of AI In National Defense And Government Industry Glance The use of artificial intelligence is growing. 90% of top companies are investing in AI on an ongoing basis. Customers who are pleased with the personalized experiences they receive from brands as a result of AI begin to expect the same experience from every brand and institute with which they interact. According to Accenture, 92% of US citizens believe that improved digital services would improve their perception of government. As a result, governments are eager to invest in AI.  By incorporating AI into every aspect of their work, the government and public sector stand to gain significantly. AI in government must consider privacy and security, compatibility with legacy systems, and evolving workloads. There are numerous benefits to implementing AI in defense and government. Here, we discuss what is AI in government, the benefits of AI, its use cases, and more, in-depth.  What is AI in government? Artificial Intell

How image data collection is used for Facial Recognition? Introduction Imagine a world in which there is no need for ID cards, passports, or any physical identification at all. This world would be possible once we could recognize humans using facial recognition on our phones, computers and tablets. With the rise of social media and the capture of image data, it’s easier for facial recognition software to find your photos. Even if you’re not using a facial recognition app, there is a very good chance that your face has been captured in a photo on Facebook or Instagram at some point in time. Facial recognition is nothing new, in fact, it's been here since the 1960s, but it sparked conversations after the 2010s when Facebook started recognizing people in images. It was used to unlock our smartphones from the beginning and recently moved to more serious issues in law enforcement. Imagine what it could do as the technology progresses! But in order to develop a model that recognizes face

Face Recognition algorithms for machine Learning Applications and Security Attribute In the year 2019 the AI market estimated at 18.3 billion dollars. Based on Statista Digital Economy Compass, it is expected to expand even more over the years to come. One of the fastest growing AI technologies, as well as being one of the most controversial are facial recognition technologies. There are places where people are able to use their faces to signify purchases of food items or enter their home, whereas in some places, using face recognition technologies is prohibited completely. What can technology do to benefit as well as detrimental? The face recognition technology, it's use to accuracy, as well as safety application are some of the issues we'll discuss in this article.   WHAT IS FACIAL RECOGNITION? According to definition facial recognition can be described as a technique that can recognize an individual based on their appearance. It is based on complex mathematic AI as well as m

Importance Of Medical Dataset To Improve Healthcare Records Introduction How would you go about creating an AI model that can tell you whether a patient has an infection or not based on images and videos? We need a large amount of data to build a successful machine learning model. Data acquisition has always been a top priority for companies to build machine learning algorithms. This is especially true when the data sets in question are used to train autonomous self-learning systems. Intelligent models, especially AI-powered models, require a different approach than standard business data preparation. Furthermore, because healthcare is the focus vertical, it’s critical to focus on datasets that serve a purpose, rather than being used for record-keeping. But why should we focus on training data when massive amounts of organized patient data already exist in medical databases and servers at retirement homes, hospitals, medical clinics, and other healthcare facilities? The reason for this

Medical Datasets Applications In Healthcare Industry Introduction It is believed that the time before the introduction of the stethoscope, there was an extremely funny incident. In the past when doctors used to apply his ear to an individual's chest in order to listen for heart sounds. in the event that a woman went to the clinic to have a check-up and his male counterpart was uncomfortable to make the move. They began to use tubes to hear the sounds of the heart and eventually led to the development of the stethoscope, developed by the doctor Dr. Rene Laennec. Technology's advancement and the advancement of healthcare also went together. Modern medicine could not be considered modern without the contributions of technology. It could be the use of ECG that makes use of the principles of electrical conductivity to learn about heart functions or Ultrasonography that makes use of piezoelectric crystals that convert electrical energy into sound energy to produce images of organs wi

Image Data Collection For Machine Learning Glance This article offers an overview of data collection to aid in AI modeling training within computer vision. Preparing data to prepare for the machine-learning (ML) is a crucial element in training the most efficient ML model which can be utilized by computers to analyze the image or video data. This article will focus on the preparation of machine learning data and the process of creating an array of data using images or video from cameras to build a custom algorithm for machine learning. Based on the purpose you may re-use Video Dataset or photos from databases that are private or public datasets , or capture footage to create data to be used in machine learning. Particularly, we deal with the following things: Gathering data to build machine learning models How do you prepare data and build an image dataset to aid in computer vision Image Datasets - capturing images, image data and other data Data Collection To Train AI Models AI model