Small businesses thrive on in-store customers. When they reopen post-lockdown, a major challenge would be ensuring the safety of their staff and customers. Sanitizing and limiting shop occupancy are important safety measures but so is social distancing. How can small shops, with their limited resources, monitor their customers and enforce social distancing?
Object detection in real-time is a potential solution.
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A unified view of the customer journey is a must for launching powerful targeted marketing campaigns. The customer profiles feature in Adobe Campaign gives marketers this critical data to create persuasive personal campaigns. Data residing in multiple applications and databases are merged in Adobe Campaign to create comprehensive profiles for each customer.
Two methods are used to import data in Adobe Campaign: API and batch file processing. In the former, a SOAP client is used to call in-built APIs within Adobe Campaign. The latter method is, however, more common and convenient, as this allows marketers to collect a considerable amount of data from different sources and process them before feeding them to the marketing automation system. This also saves them the trouble of setting up API calls to the system, especially if they do not have a technical background.
There is a tiny hitch though. Adobe Campaign only allows import of data in text, CSV, Tab, or XML formats. But that is not always the case with the data received from upstream data sources.
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Let’s start with the obvious question, what is a tokenizer? A tokenizer in Natural Language Processing (NLP) is a text preprocessing step where the text is split into tokens. Tokens can be sentences, words, or any other unit that makes up a text.
Every NLP package has a word tokenizer implemented in it. But there is a certain challenge associated with Malayalam tokenization.
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In my previous post, I had discussed the basic architecture and features of Elastic Stack-based log monitoring system. Here, I delve into three use cases of Elastic Stack, namely, response time calculations, system metrics monitoring, and alerting.
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Adobe Campaign is one of the most robust marketing automation tools available today. From designing, building, testing, and automating your marketing campaigns, you can get a lot accomplished on a single platform. Think centralized access to up-to-date customer profiles, cross-channel marketing, mass mailing, ability to track each email, push notifications, and content customization, Adobe Campaign ticks every box.
While the simple drag-and-drop interface makes workflow creation quite easy, what interests me more as a developer is that I can make use of a JavaScript or a shell script when I’m dealing with a complex requirement.
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It is very hard to monitor logs of large environments using manual log monitoring. In such situations, we need to use centralized and near real-time log monitoring systems. This will help in detecting and resolving anomalies as soon as they occur. Among log monitoring tools, Elastic Stack is the most popular one. As an open-source solution, Elastic Stack provides some basic features. Premium features such as enhanced security, authentication mechanism, alerting, reporting, and machine learning come with Elastic Stack Features (formerly X-Pack) license.
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Natural Language Processing (NLP) is a field within Artificial Intelligence (AI) that allows machines to parse, understand, and generate human language. This branch of AI can be applied to multiple languages and across many different formats (for example, unstructured documents, audio, etc.).
Considering that the NLP market is anticipated to be worth $13.4 billion in 2020, it is worth delving deeper into this field of AI.
This article seeks to explain first how NLP works, followed by how it is used, and what the future looks like for this exciting area of AI.
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One of our clients in the banking sector recently came up with a request (or challenge, rather).
While it is true that digitalization has brought a world of difference to banking, we are still nowhere near paperless banking. Regulations require banks to collect different types of documents from customers at the time of onboarding and for various other services.
On an average, a single branch has to process at least hundreds of these documents on a daily basis. Automating this workflow would save the bank plenty of time and labor. The client wanted to build an Optical Character Recognition (OCR) solution that could be seamlessly integrated into the existing banking software.
Digital marketing is such a powerful means to connect with and influence potential customers that businesses can ignore it only at their own peril. But when is the right time to start planning your digital marketing activities?
Right when you start planning your business!
Really? What could go wrong if digital marketing is not considered at the outset?
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The field of image processing has progressed so much in the recent past that most of our customer inquiries for camera-based mobile applications now include some kind of image processing. One of them was to build a food image detection module to detect multiple food items from a mobile camera image and mark their positions. The following post details the various stages of our research and how we eventually built a highly accurate food image detector.
