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Building an end-to-end intelligent document processing solution using AWS

As organizations grow larger in size, so does the need for having better document processing. In industries such as healthcare, legal, insurance, and banking, the continuous influx of paper-based or PDF documents (like invoices, health charts, and insurance claims) have pushed businesses to consider evolving their document processing capabilities. In such scenarios, businesses and organizations […]



As organizations grow larger in size, so does the need for having better document processing. In industries such as healthcare, legal, insurance, and banking, the continuous influx of paper-based or PDF documents (like invoices, health charts, and insurance claims) have pushed businesses to consider evolving their document processing capabilities. In such scenarios, businesses and organizations find themselves in a race against time to deploy a sophisticated document analysis pipeline that can handle these documents in an automated and scalable fashion.

You can use Amazon Textract and Amazon Augmented AI (Amazon A2I) to process critical documents and for your NLP-based entity recognition models with Amazon SageMaker Ground Truth, Amazon Comprehend, and Amazon A2I. This post introduces another way to create a retrainable end-to-end document analysis solution with Amazon Textract, Amazon Comprehend, and Amazon A2I.

This solution takes scanned images of physical documents as input and extracts the text using Amazon Textract. It sends the text to be analyzed by a custom entity recognizer trained in Amazon Comprehend. Machine Learning applications such as Amazon Comprehend work really well at scale, and in order to achieve 100% accuracy, you can use human reviewers to review and validate low confidence predictions. Additionally, you can use this human input to improve your underlying machine learning models. This is done by sending the output from Amazon Comprehend to be reviewed by human reviewers using Amazon A2I so that you can feed it back to retrain the models and improve the quality for future iterations. You can also use Amazon A2I to provide human oversight to your machine learning models and randomly send some data for human review to sample the output quality of your custom entity recognizer. This automated pipeline can scale to millions of documents with the help of these services and allow businesses to do more detailed analysis of their documents.

Solution overview

The following diagram illustrates the solution architecture.

This solution takes images (scanned documents or screenshots or pictures of documents) as input. You can upload these files programmatically or through the AWS Management Console into an Amazon Simple Storage Service (Amazon S3) bucket in the input folder. This action triggers an AWS Lambda function, TextractComprehendLambda, through event notifications.

The TextractComprehendLambda function sends the image to Amazon Textract to extract the text from the image. When it acquires the results, it collates the results and sends the text to the Amazon Comprehend custom entity recognizer. The custom entity recognizer is a pre-trained model that identifies entities in the text that are valuable to your business. This post demonstrates how to do this, in detail, in the following sections.

The custom entity recognizer stores the results in a separate bucket, which acts as a temporary storage for this data. This bucket has another event notification, which triggers the ComprehendA2ILambda function. This Lambda function takes the output from the custom entity recognizer, processes it, and send the results to Amazon A2I by creating a human loop for review and verification.

Amazon A2I starts the human loop, providing reviewers an interface to double-check and correct the results that may not have been identified in the custom entity recognition process. These reviewers submit their responses through the Amazon A2I worker console. When the human loop is complete, Amazon A2I sends an Amazon CloudWatch event, which triggers the HumanReviewCompleted Lambda.

The HumanReviewCompleted function checks if the human reviewers have added any more annotations (because they found more custom entities). If the human reviewers found something that the custom entity recognizer missed, the function creates a new file called updated_entity_list.txt. This file contains all the entities that weren’t present in the previous training dataset.

At the end of each day, a CloudWatch alarm triggers the NewEntityCheck function. This function compares the entity_list.txt file and the updated_entity_list.txt file to check if any new entities were added in the last day. If so, it starts a new Amazon Comprehend custom entity recognizer training job and enables the CloudWatch time-based event trigger that triggers the CERTrainingCompleteCheck function every 15 minutes.

The CERTrainingCompleteCheck function checks if the Amazon Comprehend custom entity recognizer has finished training. If so, the function adds the entries from updated_entity_list.txt to entity_list.txt so it doesn’t train the model again, unless even more entities are found by the human reviewers. It also disables its own CloudWatch time-based event trigger, because it doesn’t need to check the training process until it starts again. The next invocation of the TextractComprehend function uses the new custom entity recognizer, which has learned from the previous reviews of the humans.

All these Lambda functions use AWS Systems Manager Parameter Store for sharing, retaining, and updating the various variables, like which custom entity recognizer is the current one and where all the data is stored.

We demonstrate this solution in the us-east-1 Region but, you can run it in any compatible Region. For more information about availability of services in your Region, see the AWS Region Table.


This post requires that you have an AWS account with appropriate AWS Identity and Access Management (IAM) permissions to launch the AWS CloudFormation template.

Deploying your solution

To deploy your solution, you complete the following high-level steps:

  1. Create an S3 bucket.
  2. Create a custom entity recognizer.
  3. Create a human review workflow.
  4. Deploy the CloudFormation stack.

Creating an S3 bucket

You first create the main bucket for this post. You use it to receive the input (the original scans of documents), and store the outputs for each step of the analysis. The Lambda functions pick up the results at the end of each state and collate them for further use and record-keeping. For instructions on creating a bucket, see Create a Bucket.

Capture the name of the S3 bucket and save it to use later in this walkthrough. We refer this bucket as <primary_bucket> in this post. Replace this with the name of your actual bucket as you follow along.

Creating a custom entity recognizer

Amazon Comprehend allows you to bring your own training data, and train custom entity recognition models to customize the entity recognition process to your business-specific use cases. You can do this without having to write any code or have any in-house machine learning (ML) expertise. For this post, we provide a training dataset and document image, but you can use your own datasets when customizing Amazon Comprehend to suit your use case.

  1. Download the training dataset.
  2. Locate the bucket you created on the Amazon S3 console.

For this post, we use the bucket textract-comprehend-a2i-data, but you should use the name that you used for <primary_bucket>.

  1. Open the bucket and choose Create folder.
  2. For name, enter comprehend_data.

  1. Uncompress the file you downloaded earlier and upload the files to the comprehend_data folder.

  1. On the Amazon Comprehend console, click on Launch Amazon Comprehend.

  1. Under Customization, choose Custom entity recognition.

  1. Choose Train Recognizer to open the entity recognizer training page.

  1. For Recognizer name, enter a name.

The name that you choose appears in the console hereafter, so something human readable and easily identifiable is ideal.

  1. For Custom entity types, enter your custom entity type (for this post, we enter DEVICE).

At the time of this writing, you can have up to 25 entity types per custom entity recognizer in Amazon Comprehend.

  1. In the Training data section, select Using entity list and training docs.
  2. Add the paths to entity_list.csv and raw_txt.csv for your <primary_bucket>.

  1. In the IAM role section, select Create a new role.
  2. For Name suffix, enter a suffix you can identify later (for this post, we enter TDA).
  3. Leave the remaining settings as default and choose Train.

  1. When the training is complete, choose your recognizer and copy the ARN for your custom entity recognizer for future use.

Creating a human review workflow

To create a human review workflow, you need to have three things ready:

  • Reviewing workforce – A work team is a group of people that you select to review your documents. You can create a work team from a workforce, which is made up of Amazon Mechanical Turk workers, vendor-managed workers, or your own private workers that you invite to work on your tasks. Whichever workforce type you choose, Amazon A2I takes care of sending tasks to workers. For this post, you create a work team using a private workforce and add yourself to the team to preview the Amazon A2I workflow.
  • Worker task template – This is a template that defines what the console looks like to the reviewers.
  • S3 bucket – This is where the output of Amazon A2I is stored. You already created a bucket earlier, so this post uses the same bucket.

Creating a workforce

To create and manage your private workforce, you can use the Labeling workforces page on the Amazon SageMaker console. When following the instructions, you can create a private workforce by entering worker emails or importing a pre-existing workforce from an Amazon Cognito user pool.

If you already have a work team, you can use the same work team with Amazon A2I and skip to the following section.

To create your private work team, complete the following steps:

  1. Navigate to the Labeling workforces page on the Amazon SageMaker console.
  2. On the Private tab, choose Create private team.

  1. Choose Invite new workers by email.
  2. For this post, enter your email address to work on your document processing tasks.

You can enter a list of up to 50 email addresses, separated by commas, into the Email addresses box.

  1. Enter an organization name and contact email.
  2. Choose Create private team.

  1. After you create a private team, choose the team to start adding reviewers to your private workforce.

  1. On the Workers tab, choose Add workers to team.

  1. Enter the email addresses you want to add and choose Invite new workers.

After you add the workers (in this case, yourself), you get an email invitation. The following screenshot shows an example email.

After you choose the link and change your password, you’re registered as a verified worker for this team. Your one-person team is now ready to review.

  1. Choose the link for Labeling Portal Sign-in URL and log in using the credentials generated in the previous step.

You should see a page similar to the following screenshot.

This is the Amazon A2I worker portal.

Creating a worker task template

You can use a worker template to customize the interface and instructions that your workers see when working on your tasks. To create a worker task template, complete the following steps:

  1. Navigate to the Worker task templates page on the Amazon SageMaker console.

For this post, we use Region us-east-1. For availability details for Amazon A2I and Amazon Translate in your preferred Region, see the AWS Region Table.

  1. Choose Create template.

  1. For Template name, enter translate-a2i-template.

  1. In the Template editor field, enter the code from the following file:
<!-- Copyright, Inc. and its affiliates. All Rights Reserved.
SPDX-License-Identifier: MIT Licensed under the MIT License. See the LICENSE accompanying this file
for the specific language governing permissions and limitations under
the License. --> <script src=""></script> <crowd-entity-annotation name="crowd-entity-annotation" header="Highlight parts of the text below" labels="{{ task.input.labels | to_json | escape }}" initial-value="{{ task.input.initialValue }}" text="{{ task.input.originalText }}"
> <full-instructions header="Named entity recognition instructions"> <ol> <li><strong>Read</strong> the text carefully.</li> <li><strong>Highlight</strong> words, phrases, or sections of the text.</li> <li><strong>Choose</strong> the label that best matches what you have highlighted.</li> <li>To <strong>change</strong> a label, choose highlighted text and select a new label.</li> <li>To <strong>remove</strong> a label from highlighted text, choose the X next to the abbreviated label name on the highlighted text.</li> <li>You can select all of a previously highlighted text, but not a portion of it.</li> </ol> </full-instructions> <short-instructions> Highlight the custom entities that went missing. </short-instructions> </crowd-entity-annotation> <script> document.addEventListener('all-crowd-elements-ready', () => { document .querySelector('crowd-entity-annotation') .shadowRoot .querySelector('crowd-form') .form; });

  1. Choose Create

Creating a human review workflow

Human review workflows allow human reviewers to audit the custom entities that are detected using Amazon Comprehend on an ongoing basis. To create a human review workflow, complete the following steps:

  1. Navigate to the Human review workflow page the Amazon SageMaker console.
  2. Choose Create human review workflow.

  1. In the Workflow settings section, for Name, enter a unique workflow name.
  2. For S3 bucket, enter the S3 bucket where you want to store the human review results.

For this post, we use the same bucket that we created earlier, but add the suffix /a2i-raw-output. For example, if you created a bucket called textract-comprehend-a2i-data, enter the path s3://textract-comprehend-a2i-data/a2i-raw-output. This subfolder contains the edits that the reviewers make in all the human review workflow jobs that are created for Amazon Comprehend custom entity recognition. (Replace the bucket name with the value of <primary_bucket>.)

  1. For IAM role, choose Create a new role from the drop-down menu.

Amazon A2I can create a role automatically for you.

  1. For S3 buckets you specify, select Specific S3 buckets.
  2. Enter the name of the S3 bucket you created earlier (<primary_bucket>).
  3. Choose Create.

You see a confirmation when role creation is complete and your role is now pre-populated in the IAM role drop-down menu.

  1. For Task type, select Custom.

  1. In the Worker task template section, for Template, choose custom-entity-review-template.
  2. For Task description, add a description that briefly describes the task for your workers.

  1. In the Workers section, select
  2. For Private teams, choose textract-comprehend-a2i-review-team.
  3. Choose Create.

You see a confirmation when human review workflow creation is complete.

Copy the workflow ARN and save it somewhere. You need this in the upcoming steps. You also need to keep the Amazon A2I Worker Portal (created earlier) open and ready after this step.

Deploying the CloudFormation stack

Launch the following CloudFormation stack to deploy the stack required for running the entire flow:

This creates the remaining elements for running your human review workflow for the custom entity recognizer. When creating the stack, enter the following values:

  • CustomEntityRecognizerARN – The ARN for the custom entity recognizer.
  • CustomEntityTrainingDatasetS3URI – The path to the training dataset that you used for creating the custom entity recognizer.
  • CustomEntityTrainingListS3URI – The path to the entity list that you used for training the custom entity recognizer.
  • FlowDefinitionARN – The ARN of the human review workflow.
  • S3BucketName – The name of the bucket you created.
  • S3ComprehendBucketName – A random name that must be unique so the template can create an empty S3 bucket to store temporary output from Amazon Comprehend in. You don’t need to create this bucket—the Cloudformation template does that for you, just provide a unique name here.

Choose the defaults of the stack deployment wizard. On the Review page, in the Capabilities and transforms section, select the three check-boxes and choose Create stack.

You need to confirm that the stack was deployed successfully on your account. You can do so by navigating to the AWS CloudFormation console and looking for the stack name TDA.

When the status of the stack changes to CREATE_COMPLETE, you have successfully deployed the document analysis solution to your account.

Testing the solution

You can now test the end-to-end flow of this solution. To test each component, you complete the following high-level steps:

  1. Upload a file.
  2. Verify the Amazon Comprehend job status.
  3. Review the worker portal.
  4. Verify the changes were recorded.

Uploading a file

In real-world situations, when businesses receive a physical document, they scan, photocopy, email, or upload it to some form of an image-based format for safe-keeping as a backup mechanism. The following is the sample document we use in this post.

To upload the file, complete the following steps:

  1. Download the image.
  2. On the Amazon S3 console, navigate to your <primary_bucket>.
  3. Choose Create folder.
  4. For Name, enter input.
  5. Choose Save.

  1. Upload the image you downloaded into this folder.

This upload triggers the TextractComprehendA2ILambda function, which sends the uploaded image to Amazon Textract and sends the response received from Amazon Comprehend.

Verifying Amazon Comprehend job status

You can now verify that the Amazon Comprehend job is working.

  1. On the Amazon Comprehend console, choose Analysis jobs.
  2. Verify that your job is in status In progress.

When the status switches to Completed, you can proceed to the next step.

Reviewing the worker portal

You can now test out the human review worker portal.

  1. Navigate to the Amazon A2I worker portal that you created.

You should have a new job waiting to be processed.

  1. Select the job and choose Start working.

You’re redirected to the review screen.

  1. Tag any new entities that the algorithm missed.
  2. When you’re finished, choose Submit.

Verify that the changes were recorded

Now that you have added your inputs in the A2I console, the HumanWorkflowCompleted Lambda function adds the identified entities to the already existing file and stores it in a separate entity list in the S3 bucket. You can verify that this has happened by navigating to <primary_bucket> on the Amazon S3 console.

In the folder comprehend_data, you should see a new file called updated_entity_list.csv.

The NewEntityCheck Lambda function uses this file at the end of each day to compare against the original entity_list.csv file. If new entities are in the updated_entity_list.csv file, the model is retrained and replaces the older custom entity recognition model.

This allows the Amazon Comprehend custom entity recognition model to improve continuously by incorporating the feedback received from human reviewers through Amazon A2I. Over time, this can reduce the need for reviewers and manual intervention by analyzing documents in a more intelligent and sophisticated manner.


With this solution, you can now process scanned and physical documents at scale and do ML-powered analysis on them. The cost to run this example is less than $5.00. For more information about exact costs, see Amazon Textract pricing, Amazon Comprehend pricing, and Amazon A2I pricing.

Cleaning up

To avoid incurring future charges, delete the resources when not in use.


This post demonstrated how you can build an end-to-end document analysis solution for analyzing scanned images of documents using Amazon Textract, Amazon Comprehend, and Amazon A2I. This allows you to create review workflows for the critical documents you need to analyze using your own private workforce, and provides increased accuracy and context.

This solution also demonstrated how you can improve your Amazon Comprehend custom entity recognizer over time by retraining the models on the newer entities that the reviewers identify.

For the code used in this walkthrough, see the GitHub repo. For information about adding another review layer for Amazon Textract using Amazon A2I, see Using Amazon Textract with Amazon Augmented AI for processing critical documents.

About the Author

Purnesh Tripathi is a Solutions Architect at Amazon Web Services. He has been a data scientist in his previous life, and is passionate about the benefits that Machine Learning and Artificial Intelligence bring to a business. He works with small and medium businesses, and startups in New Zealand to help them innovate faster using AWS.



Things to Know about Free Form Templates

A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to […]

The post Things to Know about Free Form Templates appeared first on 1redDrop.



A single file that includes numerous supporting files is commonly known as a form template. Some files will define or show the controls to appear on the free form templates or design. The collections of these supporting files or templates are also called form files. While designing free form templates, users should be able to view and also work with the form files. 

It will create a new free form template by copying and storing those files within a folder. A form template (.XSN) file designing or creation of a single file will include various supporting files. Users may fill out the online form by accessing the .XML form file, which is a form template.

Designing Free Form Templates

There are numerous processes that define free form template design, and are as follows:

  • Designing the form’s appearance – the instructional text, labels, and controls
  • Controls will assist with user interaction behavior on the form template. You can design a specific section to appear or disappear when the user chooses a particular option
  • Whether the form template may include some additional views. For a permit application form design, for example, you have to provide different views for each person. One view especially for the electrical contractor, next for the receiving agent, and finally, the investigator. He or she will deny or approve the permit application
  • Next, you need to know how & where to store the form data. Designing free from templates will allow users to submit their data within the database either online or direct access. If not, they can also store the same in any specific shared folder
  • It is essential to design the other elements, colors, and fonts within the form template
  • Users must be able to personalize the form. Allowing users to include various rows within the optional section, repeating section, or a repeating table
  • Users should receive a notification when they forget to input a mandatory field or make mistakes within the form
  • After completing the free form templates design, you can publish the same online using a .XSN file format

Club Signup Form

A simple registration form can help your Club Signup Form creation process go smoother. This signup form could be an ideal solution for a new club membership registration for any organization or club.

Application Form

Application form templates are much easier to use & set-up to streamline your application process. You can customize this online form and utilize the same for numerous applications. Make use of this application form as a job application form, volunteer applications, contest entries, or high school scholarship applications. It is an ideal solution for scholarship programs, nonprofit organizations, business owners, and many such users and use cases.

Scheduling Form

Scheduling form templates are handy and can be used for numerous appointment booking requirements. A scheduling form is also utilized for various appointment scheduling or online reservations and booking purposes. Regardless of your business requirement, it is easy to customize the form template.

Concept Testing Survey

While testing a new design or concept, it is essential to gather the responses quickly. Freeform templates for a concept testing survey make it much easier to gather product feedback and reach the target audience. It is essential to conduct market research while planning to release a new product. A mobile-friendly form will allow you to utilize the survey questions for collecting the product’s consumer input quickly.

Credit Card Order Form

It is not always a complex process to provide an online credit card payment form for the customers. This form template will allow you to access numerous services or products for collecting card payment information. You can utilize this yet-another endless and simple payment form.

Employment Application Form

The employment application form for recruitment will assist the HR team to gather the required information from candidates. During the interview or application process, you can easily remove any expensive follow-ups. Some of the fields are contact information, employment history, useful information, etc. as well as an outline of the job description, consent for background checks, military service record, anticipated start date, any special skills, and many more. It is optional to enable notifications for the form owners to receive an alert or email when a new employment application is submitted.


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Are Chatbots Vulnerable? Best Practices to Ensure Chatbots Security



Rebecca James
credit IT Security Guru

A simple answer is a Yes! Chatbots are vulnerable. Some specific threats and vulnerabilities risk chatbots security and prove them a wrong choice for usage. With the advancement in technology, hackers can now easily target the hidden infrastructure of a chatbot.

The chatbot’s framework has an opportunity for the attackers ready to inject the malicious codes or commands that might unlock the secured data of the customers and your business. However, the extent of the attack’s complexity and success might depend on the messaging platform’s security.

Are you thinking about how chatbots are being exposed to attacks? Well! Hackers are now highly advanced. They attack the chatbots in two ways, i.e., either by social engineering attack or by technical attacks.

  • An evil bot can impersonate a legal user by using backup data of the possibly targeted victims by social engineering attack. All such data is collected from various sources like the dark web and social media platforms. Sometimes they use both sources to gain access to some other user’s data by a bot providing such services.
  • The second attack is technical. Here also attackers can turn themself into evil bots who exchange messages with the other bots. The purpose is to look for some vulnerabilities in the target’s profile that can be later exploited. It can eventually lead to the compromise of the entire framework that protects the data and can ultimately lead to data theft.

To ensure chatbots security, the bot creators must ensure that all the security processes are in place and are responsible for restoring the architecture. The data flow via the chatbot system should also be encrypted both in transit and rest.

To further aid you in chatbot security, this article discusses five best practices to ensure chatbots security. So, let’s read on.

The following mentioned below are some of the best practices to ensure the security of chatbots.

It’s always feared that data in transit can be spoofed or tampered with the sophistication of cybercriminals’ technology and smartness. It’s essential to implement end-to-end encryption to ensure that your entire conversation remains secured. It means that by encryption, you can prevent any third person other than the sender and the receiver from peeping into your messages.

Encryption importance can’t be neglected in the cyber world, and undoubtedly the chatbot designers are adapting this method to make sure that their chatbot security is right on the point. For more robust encryption, consider using business VPNs that encrypt your internet traffic and messages. With a VPN, you can also prevent the threats and vulnerabilities associated with chatbots.

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2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

Moreover, it’s a crucial feature of other chat services like WhatsApp and other giant tech developers. They are anxious to guarantee security via encryption even when there’s strict surveillance by the government. Such encryption is to fulfill the legal principles of the GDPR that says that companies should adopt measures to encrypt the users’ data.

User identity authentication is a process that verifies if the user is having secure and valid credentials like the username and password. The login credentials are exchanged for having a secure authentication token used during the complete user session. If you haven’t, then you should try out this method for boosting user security.

Authentication timeouts are another way to ensure your chatbots security. This method is more common in banks as the token can be used for the predetermined time.

Moreover, two-factor authentication is yet another method to prove user identity. Users are asked to verify identity either by a text message or email, depending on the way they’ve chosen. It also helps in the authorization process as it permits access to the right person and ensures that information isn’t mishandled or breached.

The self-destructive message features open another way for enhancing chatbot security. This option comes in handy when the user provides their personally identifiable information. Such information can pose a serious threat to user privacy and should be destroyed or deleted within a set period. This method is handier when you’re associated with backing or any other financial chatbots.

By using secure protocols, you can also ensure chatbots security. Every security system, by default, has the HTTPS protocol installed in it. If you aren’t an IT specialist, you can also identify it when you view the search bar’s URL. As long as your data is being transferred via HTTPS protocol and encrypted connections, TLS and SSL, your data is secured from vulnerabilities and different types of cyber-attacks.

Thus, make sure to use secure protocols for enhanced security. Remember that when Chatbots are new, the coding and system used to protect it is the same as the existing HIMs. They interconnect with their security systems and have more than one encryption layer to protect their users’ security.

Do you know what the most significant security vulnerability that’s challenging to combat is? Wondering? Well! It’s none other than human error. User behavior must be resolved using commercial applications because they might continue to believe that the systems are flawed.

No doubt that an unprecedented number of users label the significance of digital security, but still, humans are the most vulnerable in the system. Chatbot security continues to be a real big problem until the problem of user errors comes to an end. And this needs education on various forms of digital technology, including chatbots.

Here the customers aren’t the ones who are to be blamed. Like customers, employees can make a mistake, and they do make most of the time. To prevent this, the chatbot developers should form a defined strategy, including the IT experts, and train them on the system’s safe use. Doing so enhances the team’s skillset and allows them to engage with the chatbot system confidently.

However, clients can’t be educated like the employees. But at least you can provide them a detailed road map of securely interacting with the system. It might involve other professionals who can successfully engage customers and educate them on the right way to interact with the chatbots.

Several emerging technologies are keen to play a vital role in protecting the chatbots against threats and vulnerabilities in the upcoming time, among all the most potent method behavior analytics and Artificial Intelligence developments.

  • User Behavioral Analytics: It’s a process that uses applications to study the patterns of user behavior. It enables them to implement complex algorithms and statistical analysis to detect any abnormal behavior that possibly represents a security threat. Analytical tools are quite common and powerful; thus, this methodology can become a fundamental component of the chatbot system.
  • Developments in AI: Artificial technology is a two-end sword that offers benefits and threats simultaneously. But, as AI is predicted to fulfill its potential, it will provide an extra security level to the systems. It is mainly because of its ability to wipe a large amount of data for abnormalities that recognizes security breaches and threats.

The Bottom Line

Security concerns have always been there with new technologies and bring new threats and vulnerabilities with them. Although chatbots are an emerging technology, the security practices that stand behind them are present for a long time and are effective. Chatbots are the innovative development of the current era, and emerging technologies like AI will transform the way businesses might interact with the customers and ensure their security.


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Best Technology Stacks For Mobile App Development



What’s the Best Tech Stack for Mobile App Development? Read To Know

Which is the Best Tech Stack for Mobile Application Development? Kotlin, React Native, Ionic, Xamarin, Objective-C, Swift, JAVA… Which One?

Image Source: Google

Technology Stack for smartphones is like what blood is for the human body. Without a technology stack, it is hard even to imagine smartphones. Having a smartphone in uncountable hands is rising exponentially. For tech pundits, this is one unmissable aspect of our digital experience wherein tech stack is as critical as ROI.

The riveting experience for a successful mobile app predominantly depends on technology stacks.

The unbiased selection of mobile apps development language facilitates developers to build smooth, functional, efficient apps. They help businesses tone down the costs, focus on revenue-generation opportunities. Most importantly, it provides customers with jaw-dropping amazement, giving a reason to have it installed on the indispensable gadget in present times.

In today’s time, when there are over 5 million apps globally, and by all conscience, these are whopping no.s and going to push the smartphone industry further. But now you could see mobile app development every ‘nook and corner.’ But the fact is not who provides what but understanding the behavioural pattern of users.

So the pertinent question is, which is the ideal tech stack to use for mobile app development?

In native mobile app development, all toolkits, mobile apps development language, and the SDK are supported and provided by operating system vendors. Native app development thus allows developers to build apps compatible with specific OS environments; it is suitable for device-specific hardware and software. Hence it renders optimized performance using the latest technology. However, since Android & iOS imparts — — a unique platform for development, businesses have to develop multiple mobile apps for each platform.

1. Waz

2. Pokemon Go

3. Lyft

1.Java: The popularity of JAVA still makes it one of the official programming languages for android app development until the introduction of Kotlin. Java itself is at the core of the Android OS. Many of us even see the logo of Java when the device reboots. However, contradictions with Oracle (which owns the license to Java) made Google shift to open-source Java SDK for versions starting from Android 7.0 Nougat

2.Kotlin: According to Google I/O conference in 2019- Kotlin is the officially supported language for Android app development. It is entirely based on Java but has a few additions which make it simpler and easier to work.

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2. How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

3. 5 Top Tips For Human-Centred Chatbot Design

4. Chatbot Conference Online

It’s my gut feeling like other developers to say that Kotlin is simply better. It has a leaner, more straightforward and concise code than open-cell Java, and several other advantages about handling null-pointer exceptions and more productive coding.

HERE’S A Programming Illustration Defining the CONCISENESS OF KOTLIN CODE

public class Address {

private String street;

private int streetNumber;

private String postCode;

private String city;

private Country country;

public Address(String street, int streetNumber, String postCode, String city, Country country) {

this.street = street;

this.streetNumber = streetNumber;

this.postCode = postCode; = city; = country;



public boolean equals(Object o) {

if (this == o) return true;

if (o == null || getClass() != o.getClass()) return false;

Address address = (Address) o;

if (streetNumber != address.streetNumber) return false;

if (!street.equals(address.street)) return false;

if (!postCode.equals(address.postCode)) return false;

if (!city.equals( return false;

return country ==;



public int hashCode() {

int result = street.hashCode();

result = 31 * result + streetNumber;

result = 31 * result + postCode.hashCode();

result = 31 * result + city.hashCode();

result = 31 * result + (country != null ? country.hashCode() : 0);

return result;



public String toString() {

return “Address{“ +

“street=’” + street + ‘\’’ +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’ +

“, city=’” + city + ‘\’’ +

“, country=” + country +



public String getStreet() {

return street;


public void setStreet(String street) {

this.street = street;


public int getStreetNumber() {

return streetNumber;


public void setStreetNumber(int streetNumber) {

this.streetNumber = streetNumber;


public String getPostCode() {

return postCode;


public void setPostCode(String postCode) {

this.postCode = postCode;


public String getCity() {

return city;


public void setCity(String city) { = city;


public Country getCountry() {

return country;


public void setCountry(Country country) { = country;



class Address(street:String, streetNumber:Int, postCode:String, city:String, country:Country) {

var street: String

var streetNumber:Int = 0

var postCode:String

var city: String

var country:Country


this.street = street

this.streetNumber = streetNumber

this.postCode = postCode = city = country


public override fun equals(o:Any):Boolean {

if (this === o) return true

if (o == null || javaClass != o.javaClass) return false

Val address = o as Address

if (streetNumber != address.streetNumber) return false

if (street != address.street) return false

if (postCode != address.postCode) return false

if (city != return false

return country ===


public override fun hashCode():Int {

val result = street.hashCode()

result = 31 * result + streetNumber

result = 31 * result + postCode.hashCode()

result = 31 * result + city.hashCode()

result = 31 * result + (if (country != null) country.hashCode() else 0)

return result


public override fun toString():String {

return (“Address{“ +

“street=’” + street + ‘\’’.toString() +

“, streetNumber=” + streetNumber +

“, postCode=’” + postCode + ‘\’’.toString() +

“, city=’” + city + ‘\’’.toString() +

“, country=” + country +




I’d say KOTLIN IS THE BEST FIND FOR ANDROID APP DEVELOPMENT.Google has dug deeper with some plans ahead since announcing it as an official language. Moreover, it signals Google’s first steps in moving away from the Java ecosystem, which is imminent, considering its recent adventures with Flutter and the upcoming Fuchsia OS.

Objective C is the same for iOS what Java is for Android. Objective-C, a superset of the C programming language( with objective -oriented capabilities and dynamic run time) initially used to build the core of iOS operating system across the Apple devices. However, Apple soon started using swift, which diminishes the importance of Objective -C in comparison to previous compilations.

Apple introduced Swift as an alternative to Objective-C in late 2015, and it has since been continued to be the primary language for iOS app development.Swift is more functional than Objective-C, less prone to errors, dynamic libraries help reduce the size and app without ever compromising performance.

Now, you would remember the comparison we’ve done with Java and kotlin. In iOS, objective-C is much older than swift with much more complicated syntax. Giving cringeworthy feel to beginners to get started with Objective-C.

Image Source: Google


NSMutableArray * array =[[NSMutableArray alloc] init];


var array =[Int]()


In cross-platform app development, developers build a single mobile app that can be used on multiple OS platforms. It is made possible by creating an app with a shared common codebase, adapted to various platforms.

Image Source: Google

Popular Cross-platform apps:

  1. Instagram
  2. Skype
  3. LinkedIN

React Native is a mobile app development framework based on JavaScript. It is used and supported by one of the biggest social media platforms- Facebook. In cross-platform apps built using React Native, the application logic is coded in JavaScript, whereas its UI is entirely native. This blog about building a React Native app is worth reading if you want to know why its stakes are higher.

Xamarin is a Microsoft-supported cross-platform mobile app development tool that uses the C# programming language. Using Xamarin, developers can build mobile apps for multiple platforms, sharing over 90% of the same code.

TypeScript is a superset of JavaScript, and is a statically-typed programming language supported by Microsoft. TypeScript can be used along with the React Native framework to make full use of its error detection features when writing code for react components.

In Hybrid mobile app development, developers build web apps using HTML, CSS & JavaScript and then wrap the code in a native shell. It allows the app to be deployed as a regular app, with functionality at a level between a fully native app and a website rendered(web browser).

Image Source: Google
  1. Untappd
  2. Amazon App Store
  3. Evernote

Apache Cordova is an open-source hybrid mobile app development framework that uses JavaScript for logic operations and while HTML5 & CSS3 for rendering. PhoneGap is a commercialized, free, and open-source distribution of Apache Cordova owned by Adobe. The PhoneGap platform was developed to deliver non-proprietary, free, and open-source app development solutions powered by the web.

Ionic is a hybrid app development framework based on AngularJS. Similar to other hybrid platforms, it uses HTML, CSS & JavaScript to build mobile apps. Ionic is primarily focused on the front-end UI experience and integrates well with frameworks such as Angular, Vue, and ReactJS.

To summarize, there are 3 types of mobile apps- Native mobile apps, Cross-platform mobile apps, and Hybrid mobile apps; each offers unique technologies, frameworks, and tools of their own. I have enlisted here the best mobile app technology stacks you could use for mobile app development.

The technologies, tools, and frameworks mentioned here are used in some of the most successful apps. With support from an expert, a well-established mobile app development company, that may give much-needed impetus in the dynamic mobile app development world.


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