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Data Labelling Services – 3 Step

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  Introduction: DataLabeling   training data acts as the first step in the machine learning development cycle under Computer Vision. To train a machine learning model to identify a specified category of objects from the collection of data, we would need to collect representation data samples that have to be classified and analyzed along with a Machine Learning algorithm for handling each sample. The key aspect to make  Artificial Intelligence Services / Machine Learning Company  models work is to have properly organized and precisely labeled data. DLS is used to generate accurate and high-quality labels using AI and ML models based on data collection. Data Annotation  – Different Types The 4 different types of annotation are, Text Labeling Image Labeling Audio Labeling Video Labeling Data Annotation – Market Trends Data Annotation Tools Market   size is set to surpass USD 7 billion by 2027, according to a  new research report   by Global Market In...
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Do You Know How Computer Vision Analytics helps to improve Health and Workplace safety

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  credits:   https://www.optisolbusiness.com/insight/using-computer-vision-to-improve-health-and-workplace-safety Recent developments in the field of training  Neural Networks  (Deep Learning) and advanced algorith m  training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. This has led to a profusion of initiatives to use such trained models in the domain of Health and Safety (HSE) at the workplace. Here is a summary of initiatives that Optisol Datalabs has been working on in the past year to improve safety and avoid accidents.  Computer Vision Analytics  helps to improve Health and Workplace safety Computer Vision Process Flow Computer vision analytics  model typically follows this process flow. The first step is to generate or collect training data....
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Top 5 reasons to build a Digital workplace for manufacturing enterprises

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credits: https://www.optisolbusiness.com/insight/top-5-reasons-to-build-a-digital-workplace-for-manufacturing-enterprises  The digital workplace is all about the employees’ ability to do their job by collaborating, communicating, and connecting with others. The goal is to forge productive business relationships within and beyond natural work groups and to enable knowledge sharing across the organization. A digital workspace is an integrated technology framework designed to deliver and manage app, data, and desktop delivery. For a digital workspace solution to be successful, it must provide a unified, contextual, and secure experience for IT and end-users. The digital workplace allows easier access to virtual meetings and removes the barriers of time, location, devices, and network connections, it provides employees greater work-life balance while increasing productivity and agility for the organization. Improve services and processes. By 2021, only one-quarter of midsize and l...
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Personality Traits Analysis among Social Media Influencers

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  credits:   https://www.optisolbusiness.com/insight/personality-traits-analysis-among-social-media-influencers The idea of this project is to build a  sentiment analysis model  t hat detects the emotions that underlie a tweet. It makes associations between words and emotions and the aim is to classify the tweets into sentiments like anger, happiness, sadness, enthusiasm etc. rather than the usual sentiment classification that only involves truly contrasting sentiments of Positive and Negative. We have considered 3 different approaches to build this Social IQ analysis pipeline. They are Universal Sentence Encoder Doc2Vec LSTM The performance of each of these approaches are explained below. The Data: The data can be downloaded from GitHub —  https://raw.githubusercontent.com/tlkh/text-emotion-classification/master/dataset/original/text_emotion.csv The d a ta is in tabular form and each row is divided into 4 columns namely the tweet_id, sentiment, author and conte...
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