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Esferasoft Solutions is the leading offshore web development company, providing full-stack services - web development, web design & mobile (iOS & Android) app development.
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Given an image, how do we automatically describe what is init? Automatically generating a language description for any image has pulled in interests recently. The main reasons of this was it connects two major artificial intelligence fields: computer vision and natural language processing. The Convolutional Neural Network(CNN) are used to extract feature and last hidden layer decodes the language modelling up to the word level. The training is done with the COCO dataset of over 2 lakh images and their captions.
1. The useful application for image captioning is to retrieve information directly from any particular image in a textual format automatically.
2. Other field would be to serve as an intermediate for visually impaired people, where AI can help them look beyond images and let them know what inside an image is.
3. Image captioning would be to explain what is happening in a video, frame by frame.
A chatbot is an artificial intelligence (AI) software which transcribe a conversation with a user in natural language. Chatbot applications efficient interactions between customer services and people, smoothing customer experience. Chatbot helps customer engagement processes and operational efficiency by reducing the man power and cost of customer service. We have created a simple lexicon model based on Naive Bayes algorithm.
1. Prevent churn - Churn prediction is one of the significant use cases for membership based enterprises. To know the reasons for churn and understand churn is best for client lead conversation
2. Chatbots can include another layer of intuitiveness to online business, enabling clients to cooperate past menus and catches
3. Automate Bookings and Reservations - Text based reservation frameworks can be simpler to utilize and can automate the hussle for your manually filling long forms.
Automatic Text Summarization is one of the intersting and complex problem in AI. It is the process of filtering the most important information from the root source to produce a short and concise version of that article,conversation or a blog . Automatic Text summarization follows two approaches
1. Abstractive (where the model forms its own phrases and sentences to offer a more coherent summary),
2. Extractive (where important sentences are selected from the input text to form a summary) .
1. Automatic summarization presents a chance to gather the nonstop downpour of data into little snippets of data.
2. SEO and Marketing - When assessing search inquiry for SEO, it is basic to have a balanced comprehension of what your rivals are discussing in their content. Long document summarization can be a useful asset to rapidly dissect hunderds of searched results
3. Question Answering and Bot- Huge scale outline could turn into an amazing question answer nothing procedure.By gathering the most pertinent reports for a specific inquiry, a summarizer could mass a durable answer as a multi-archive outline.
Emotions plays a crucial aspect in our life. They are represented in our day to day conversations.We have created our Emotion Analysis Classifier which is trained over different emotions on a larger dataset with the help of LSTM Neural Networks. Applications of emotional analysis are being used heavily in following fields: Competitor monitoring, Employee feedback, Computing customer satisfaction metrics, Social Selling, Reputation management.
1. Intent Analysis - Intent investigation fastens up the process by breaking down the client's expectation behind a message and recognizing whether it relates a sentiment, news, showcasing, protest, proposal, thankfulness or inquiry. 2. Social Media Monitoring 3. Track trends over time 4. Keep a finger on the competition
Real time object detection is a computer technology which is related to computer vision and image processing which detects the frames of objects and classify them into one of hundreds of pretrained classes. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance. To check out how this works, allow camera access and it will classify real time the objects right into the browser window..
1. Optical Character Recognition - commonly known as OCR can be used to decode the texts(printed) or handwritten into machine-encoded text within a scanned document, a photo of a record, a scene-image
2. Objects Tracking - Object tracking has a selection of makes use of, some of that are surveillance and safety, visitors monitoring, video communique, robot imaginative and animation.
Its an astonishing site when we see an AI creating a new fake human face. An algorithm which has been used on over 13 lakh american people images and fed to GANs (Generative Adversarial Networks). IN short the neural networks can fabricate the new faces which are not existed on the Earth based on a large dataset accumulated from different sources. The GAN network will intercept existing visual data and manipulate it into the new data.
1. Face Detection
2. Social Profile Verification
3. Advance Security
4. Attendence Monitoring Systems and User Authentications
5. Lip Reading