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Geospatial AI: A Data-Centric Approach for Growth & Development

GeospatialAI or GeoAI is an evolving field aiming to help organize, process, and analyze spatial big data with spatial science and AI methods like machine learning, deep learning, data mining, and high-performance computing. The increased availability of geospatial data, advancements in AI, and the availability of massive computing power have resulted in the mounting relevance …

Synthetic Data: The Future of Computer Vision

The term “synthetic data” is used in computer vision to describe images that are generated by algorithms instead of being captured by a camera. These images are usually generated for training artificial intelligence (AI) models. The use of synthetic data has several advantages over real data. First, it is easier to get a large amount …

6 Best Practices of Data Labeling for Object Detection Models

Data labeling is a process that involves adding target attributes to data and labeling them to train the machine and deep learning models. Data labeling is crucial in object detection to perform various computer vision tasks such as object segmentation, object counting, and object tracking. The potential benefits of AI-enabled technology for automating the decision-making …

Face Recognition: The Emerging Tech of Biometrics

Face recognition technology is becoming more prevalent by opening up new opportunities for businesses and enterprises in terms of biometrics. It is by far the most advanced and relevant AI technology that can perform a wide range of tasks. Although the concept of face recognition was already invented during the 1960s, its technological breakthrough occurred …

Machine Learning for OCR: Creating a Modern OCR Pipeline

Optical character recognition technology has a far way from recognizing just the text on computerized documents. The applications of OCR are transforming the future of data collection and processing. Whether it’s auto-extracting information from a scanned receipt or translating a foreign language using your phone’s camera, OCR technology has numerous applications that are continuously in …

Image Similarity Using Deep CNN: Theory to Code

Image similarity is becoming popular in recent times. The ability of an image similarity model to find identical images with the utmost accuracy in defined data sets is helping in many ways. Finding plagiarized photos, identifying fake accounts, discovering original images of people, products, and places are a few of the image similarity real-world applications. …

Image Annotation: Guide to Create Training Data for Computer Vision

Image annotation is one of the vital applications of computer vision, which allows the machines a high-level ability to deconstruct digital images or videos and interpret the visual information just like humans. Most of the remarkable applications of AI like self-driving cars, medical imaging, self-flying drones, etc., are only possible through image annotation. While image …

Sentiment Analysis: Analyze Your Social Audiences’ Opinions

Sentiment analysis is the application of text mining tools to identify the objective behind a piece of specified information and predicting the positive or negative outcomes out of it. Sentiment analysis helps businesses primarily to analyze social sentiments and get informed about the voice of the customers regarding a brand, product, or service. Sentiment analysis …

Top 10 Ways Deep Learning Is Scaling Computer Vision Applications

Computer vision is gradually becoming one of the most prominent fields in the world of data science. We are constantly leveraging this technology in our daily lives – be it the photo search feature on google lens or face unlocking feature on smartphones or photo tagging through auto search, etc. Deep learning allows machines and …

No-Code ML Platforms: The Future of Application Development

Machine Learning (ML) platforms or modern app platforms are a recent disruption transforming the evolution of no-code applications. As businesses across the globe are opting for a faster development time and lower operating costs, the low-code application platforms are becoming more than a trend. Streamlining the low code platforms over time is advancing the use …

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Geospatial AI: A Data-Centric Approach for Growth & Development

Synthetic Data: The Future of Computer Vision

6 Best Practices of Data Labeling for Object Detection Models

Face Recognition: The Emerging Tech of Biometrics

Machine Learning for OCR: Creating a Modern OCR Pipeline

Image Similarity Using Deep CNN: Theory to Code

Image Annotation: Guide to Create Training Data for Computer Vision

Sentiment Analysis: Analyze Your Social Audiences’ Opinions

Top 10 Ways Deep Learning Is Scaling Computer Vision Applications

No-Code ML Platforms: The Future of Application Development

Deep CNN for Image Classification: How it Makes a Difference

Deep convolutional neural network (CNN) based image classification plays an essential role in seamlessly performing most of the challenges from disease diagnosis to predicting consumerism behavior. Using Deep CNN reduces the time and effort required to spend on extracting and selecting classification features manually. In recent times, deep CNN has been applied to image classification … ...

Text-Based Image Retrieval: Using Deep Learning

Text-based image retrieval (TBIR) systems use language in the form of strings or concepts to search relevant images. Computer Vision and Deep Learning algorithms analyze the content in the query image and return results based on the best-matched content. With the rapid advancement in Computer Vision and Natural Language Processing(NLP), understanding the semantics of text … ...

Object Detection – A Simplified Solution In 2021

Object detection has grown significantly over the years. Classifying and finding an unknown number of individual objects within an image or video is considered as one of the challenging and impossible tasks that becomes a solution beyond what is required for image classification. Object detection is primarily powered by deep learning and convolutional neural networks … ...

Machine Learning for Transformers – Explained with Language Translation

Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. Researchers across the globe are working around the clock to achieve more progress in artificial … ...

Understanding Images from Pixel Level with Semantic Segmentation

Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for … ...

Harnessing AI for an Effective COVID Vaccine Rollout

AI (artificial intelligence) has been a vital practice since the early days of the COVID-19. Tracking active patients count, analyzing thousands of scientific research papers for definitive treatment options, and in vaccine development processes AI has been widely used. The application of next-generation technology frameworks and digital tools not only helped in fastening healthcare operational … ...

How Machine Learning Can Ace Influencer Marketing Game?

Influencer marketing has grown significantly in the past few years. Due to the pervasive use of social media channels to promote products and services, influencer marketing can be considered as a new-age digital revolution. With a digitally empowered general populace, the CPG industry is now able to authenticate and communicate brand stories across distinct socials … ...

Sentiment Analysis -Constitute Customer Emotions For Better Service

The increasing demand to channelize customers’ emotions along with their positive and negative reactions for optimizing operations and increasing revenues. Analyzing these reactions and extracting intelligence from them will help brands gauge the ROI of their PR and marketing efforts. And this era of the internet is making it far much easier to collect opinions … ...

Computer Vision-Navigating The Future Of Autonomous Vehicles

The prominence for computer vision applications in the autonomous vehicles segment has witnessed a tremendous growth in recent years. Classifying and detecting objects, signs, humans, other vehicles on the path, marking 3D maps, real-time traffic insights, etc., are a few research advancements in the field of computer vision. And these advancements are driving the growth … ...

Computer Vision in Manufacturing for Industrial Automation & Transformation

Though various forms of computer vision (A.K.A machine vision) are used in manufacturing for decades, the recent advancements in machine learning and computer vision are increasing the demand and need for computer vision applications on the shop floor. According to Grand view research, the global market for computer vision technology is expected to reach USD … ...

Disruptive Machine Learning Trends For 2022

Machine Learning (ML) is one of the most disruptive trends of all time. With an increasing number of organizations indulging it today, Machine Learning (ML) technology is becoming one of the most powerful applications. While businesses are adopting transformation, it subsequently is speeding up the growth of ML and other supporting tools of ML. As … ...

Building Smart Cities: The Role of Artificial Intelligence & Machine Learning

Smart cities are a new-age revolution to maximize the utilization of technology, optimize the consumption of natural resources, and human capital to fuel sustainable economic growth and participatory governance. Smart cities are mostly employed across highly populated urban areas in major countries. For instance, these cities adopt a combination of cameras, sensors, and artificial intelligence … ...

Computer Vision for Smart and Sustainable Agriculture

The recent advancements in Computer Vision technology are upgrading the overview of its applications in agriculture. The combination of Artificial Intelligence, Computer Vision, and Machine Vision is making the world of farming more advanced than humankind ever discovered. Employing Agri-Tech is disrupting the traditional dynamics of farming by helping farmers in better crop yielding. Using … ...

Revolutionizing Retail With Computer Vision & Deep Learning

During the 1970s when the concept of Computer Vision was first introduced, there weren’t enough human and technological resources to bring the ideas to reality. With the exponential amounts of data generated in recent years, there is a tremendous leap in technology that has put Artificial Intelligence and Computer Vision in the front seat for … ...

Extracting Business Value With Image Processing

Image processing is an art of mathematically altering digitized images to extract quantitative information by leveraging cutting-edge technologies. With advancements in technology and hardware (camera) – along with the availability of raw data from various sources in the form of text, images, and videos that needs to be processed – companies are adopting artificial intelligence … ...

OCR & Computer Vision -Creating a Modern Algorithm

Today we are accessible to a mountain of intelligent technologies. And no doubt that computer vision stores a vital space among all of them. When we talk about computer vision, the foremost application that we think of is Image Recognition. But indeed, a computer vision also encompasses OCR (Optical Character Recognition) algorithm, which allows seamless … ...

Intertwining Machine Learning and APIs

APIs (or Application Programming Interfaces) have been identified as important intermediaries between technologies like machine learning(ML) and their end-users. With big data streaming in vast data pools, organizations are turning towards machine learning APIs to leverage the technology and withdraw the complexities involved in creating and deploying machine learning models. APIs are making machine learning … ...

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