Trending December 2023 # Applications Of Natural Language Processing In Different Sectors # Suggested January 2024 # Top 13 Popular

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NLP in Education: NLP in Marketing Technology:

Applications of NLP in MarTech are the new evolution for artificial intelligence. Tools of marketing technology include chatbots, voice search, sentiment analysis, automated summarization, machine transcription to change the face of marketing departments and roles drastically.

NLP in Healthcare

From Data mining to sorting out documentation, Natural language Processing assists the healthcare workers to simplify the complex task which was earlier not possible. With the help of its speech recognition technique, the clinicians can easily transcribe notes for useful EHR data. Moreover, when utilized with Machine learning, NLP assists in efficient clinical trials, and clinical decision support, thus mitigating the risks involved with the diagnosis and treatment of the disease. Integration of NLP in genetic coding also helps in determining the phenotypes, speech patterns and neurocognitive conditions such as Alzheimers disease.

NLP in Banking

In Banking, natural language processing helps in analyzing the plethora of documentation, so that insights can be drawn out for commercial loan agreements. By

NLP in Manufacturing

NLP in manufacturing sector helps break down the barriers between humans and technology to improve communication and productivity. The AI system can then produce real time reports from the collected data in natural language. Instead of relying on a data analyst, managers can see these reports as soon as the data is generated, detecting patterns and trends to make important business decisions in near real-time.

NLP in Business

Natural language processing, frequently known as NLP, alludes to the ability of a computer to comprehend human speech as it is spoken. NLP is a key segment of artificial intelligence (AI) and depends on machine learning, a particular type of AI that analyzes and utilizes patterns in information to improve a program’s comprehension of speech. Analytics Insight has forecasted the Market Revenue of NLP to at US$8,319 million , with a CAGR of 18.10% between 2023 and 2024. Natural language processing (NLP) can be effectively used in education for promoting language learning and improving the academic performance of the students. It assists in developing an effective process of learning in the educational setting by developing scientific approaches which can process of using computer and internet for enhancing the learning.Applications of NLP in MarTech are the new evolution for artificial intelligence. Tools of marketing technology include chatbots, voice search, sentiment analysis, automated summarization, machine transcription to change the face of marketing departments and roles chúng tôi Data mining to sorting out documentation, Natural language Processing assists the healthcare workers to simplify the complex task which was earlier not possible. With the help of its speech recognition technique, the clinicians can easily transcribe notes for useful EHR data. Moreover, when utilized with Machine learning, NLP assists in efficient clinical trials, and clinical decision support, thus mitigating the risks involved with the diagnosis and treatment of the disease. Integration of NLP in genetic coding also helps in determining the phenotypes, speech patterns and neurocognitive conditions such as Alzheimers chúng tôi Banking, natural language processing helps in analyzing the plethora of documentation, so that insights can be drawn out for commercial loan agreements. By integrating NLP in the system, banks can predict the customer needs and behaviour, With the help of Optical Character Recognition technique, and machine learning algorithm associated with NLP, the portfolio of the customer is made, so that the risks of frauds and scams can be mitigated. Moreover, its speeds up Know Your Customer (KYC), and Investment chúng tôi in manufacturing sector helps break down the barriers between humans and technology to improve communication and productivity. The AI system can then produce real time reports from the collected data in natural language. Instead of relying on a data analyst, managers can see these reports as soon as the data is generated, detecting patterns and trends to make important business decisions in near real-time.Businesses organisations are employing NLP technologies to understand human language and queries. Instead of trying to understand concepts based on normal human language usage patterns, the company’s platform depends on a custom knowledge graph that is created for each application and performs a much better job identifying concepts that are relevant in the customer domain.

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Top 8 Python Libraries For Natural Language Processing (Nlp) In 2023

This article was published as a part of the Data Science Blogathon.

Introduction

Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the basics – is all about teaching machines how to comprehend human dialects and extract significance from the text. This is additionally why Artificial Intelligence is regularly essential for NLP projects.

So in this article, we are going to cover the top 8 Natural Language Processing(NLP) libraries and tools that could be useful for build real-world projects. So let’s start!

Table Of Contents

Natural Language Toolkit(NLTK)

GenSim

SpaCy

CoreNLP

TextBlob

AllenNLP

polyglot

scikit-learn

Natural Language Toolkit (NLTK)

Entity Extraction

Part-of-speech tagging

Tokenization

Parsing

Semantic reasoning

Stemming

Text classification

GenSim

For more information, check official documentation: Link.

SpaCy

SpaCy is an open-source python Natural language processing library. It is mainly designed for production usage- to build real-world projects and it helps to handle a large number of text data. This toolkit is written in python in Cython which’s why it much faster and efficient to handle a large amount of text data. Some of the features of SpaCy are shown below:

It provides multi trained transformers like BERT

It is way faster than other libraries

Provides tokenization that is motivated linguistically In more than 49 languages

Provides functionalities such as text classification, sentence segmentation, lemmatization, part-of-speech tagging, named entity recognition and many more

has 55 trained pipelines in more than 17 languages.

For more information, check official documentation: Link.

CoreNLP

Stanford CoreNLP contains a grouping of human language innovation instruments. It means to make the use of semantic analysis tools to a piece of text simple and proficient. With CoreNLP, you can extract a wide range of text properties (like part-of-speech tagging,named-entity recognition and so forth) in a couple of lines of code.

Since CoreNLP is written in Java, it requests that Java be introduced on your device. Notwithstanding, it offers programming interfaces for some well-known programming languages, including Python. The tool consolidates various Stanford’s NLP tools like the sentiment analysis, part-of-speech (POS) tagger, bootstrapped pattern learning, parser, named entity recognizer (NER), coreference resolution system, to give some examples. Besides, CoreNLP upholds four dialects separated from English – Arabic, Chinese, German, French, and Spanish.

For more information, check official documentation: Link.

TextBlob

TextBlob is an open-source Natural Language Processing library in python (Python 2 and Python 3) powered by NLTK. It is the fastest NLP tool among all the libraries. It is beginners friendly. It is a must learning tool for data scientist enthusiasts who are starting their journey with python and NLP. It provides an easy interface to help beginners and has all the basic NLP functionalities such as sentiment analysis, phrase extraction, parsing and many more. Some of the features of TextBlob are shown below:

Sentiment analysis

Parsing

Word and phrase frequencies

Part-of-speech tagging

N-grams

Spelling correction

Tokenization

Classification( Decision tree. Naïve Bayes)

Noun phrase extraction

WordNet integration

For more information, check official documentation: Link.

AllenNLP

For more information, check official documentation: Link.

Polyglot

This marginally lesser-realized library is one of my top choices since it offers an expansive scope of analysis and great language inclusion. On account of NumPy, it likewise works super quick. Utilizing multilingual is like spaCy – it’s proficient, clear, and fundamentally a fantastic choice for projects including a language spaCy doesn’t uphold.

Following are the features of Polyglot:

Tokenization (165 Languages)

Language detection (196 Languages)

Named Entity Recognition (40 Languages)

Part of Speech Tagging (16 Languages)

Sentiment Analysis (136 Languages)

Word Embeddings (137 Languages)

Morphological analysis (135 Languages)

Transliteration (69 Languages)

For more information, check official documentation: Link.

Scikit-Learn

For more information, check official documentation: Link

Conclusion

So in this article, we have covered the top 8 Natural Language Processing libraries in python for machine learning in 2023. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

You can check my articles here: Articles

Follow me on LinkedIn: LinkedIn

Effective Applications Of Artificialintelligence In Healthcare Industry

Performing Repetitive Tasks

In the healthcare industry, professionals have to do certain tasks repeatedly on a regular basis. The continual tasks consume more time and make the workers lose their interest. In such a case, the AI application simply the repetitive process like CT scans, X-rays, test analysis and some other tasks. On the other hand, the amount of data in cardiology and radiology is more where the AI bots reduce the process involved in it.

Medical Data and Record Management

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Treatment Design

The deployment of AI system analysis in the healthcare industry enables the medical practitioners to opt out the customized and accurate treatment design for the patients. Simply, the AI system makes use of the patient medical information to provide this support for the physicians.

Health Monitoring Process Healthcare System Analysis

Digital Consultation

Nowadays, there are several healthcare mobile applications available in the app store to enable digital consultation. This means that healthcare mobile app development integrates with AI technology to provide medical consultation depending on the patient’s medical history. Merely, the application users have to input their symptoms, so that application makes use of its features and compare them to the database. Finally, the application gives the perfect medication in accordance with the medical history of the patient.

Medication Management

Medication management is one of the necessary techniques to check whether a patient following the medication at regular intervals or not. The AI features incorporated into the webcam of the mobile phone confirm the patients’ medical prescription. This specialty of AI enables the patient to manage their medical condition even if they have severe health issues. The medication management helps the doctors to make better decisions.

Virtual Nurses

The app developers make use of machine learning to help patients who suffer from chronic disorders. The virtual nurse applications can able to clear the queries regarding a medical condition, detect symptoms and necessity of doctor recommendation.

Precision Medicine

Genomics and genetics take the support of DNA information to know the mutation and the connection of a disease. So, AI is implemented to detect cancer and vascular diseases in the early stages to cure it as soon as possible. In simple words, AI can predict the health disorders that may occur later by analyzing the genetics of an individual. Precision medicine is a boon for people to save their lives.

Creating Effective Drugs

Also read: Top 10 Programming Languages for Kids to learn

Final Thoughts Ritesh Patil

Ritesh Patil is the co-founder of Mobisoft Infotech that helps startups and enterprises in mobile technology. He loves technology, especially mobile technology. He’s an avid blogger and writes on the mobile application. He works in a leading Android & iOS application development company with skilled iOS and Android app developers that have developed innovative mobile applications across various fields such as Finance, Insurance, Health, Entertainment, Productivity, Social Causes, Education and many more and has bagged numerous awards for the same.

Different Alternatives Of Lucidchart In Detail

Introduction to Lucidchart

Lucidchart is a web-based platform to design diagrams, which helps us understand the requirement better and easily. This tool allows us to easily share our ideas and information within the team and effectively process this information. This Lucidchart or Lucidchart alternative helps us visualize our ideas and information that others can understand. With the help of these, we can increase productivity because they help us implement things very fast and with all clarity we need.

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We have so many alternatives for Lucidchart at present. Some are paid, and some are free, which can be used according to the need. Here we will see each alternative we have in detail with some guidance to use it effectively; also, we can design UML diagrams, flowcharts, database representation and much more quickly.

List of Lucidchart Alternative

Here we will see different alternatives we have for Lucidchart at present; we will see each in detail to know how to use it when needed.

1. SmartDraw

As we can see, it provides us with support and compatibility with various tools available.

So it can be the best choice to be the alternative for Lucidchart.

Usage of SmartDraw:

Process Management

Technical Drawing

Visual Communication

Data Flow Diagram

Mind Mapping

List of UML Tools

List of Concept- And Mind-Mapping Software

Flowchart

List of Concept Mapping Software

Operating System:

Mac

Windows 10

Windows 7

Vista

Use:

1. Go to their official website by typing the below link.

2. They will ask for a sign up which you can create or use the existing one if you have one.

3. After this, we can create the diagram according to our needs.

2. Draw.io

Use:

1. First type chúng tôi in the browser, then you will navigate to the below URL:

3. Creately

This is also one of the alternatives for Lucidchart we have. This tool comes up with two editions; one is offline, and another one is cloud-based. Creately is a Saas-based tool that helps us easily visualize ideas, information, and business requirements as part of the process.

Below is the list of areas where we can use this tool to increase productivity and stabilize things.

Usage:

Infographic

Technical Drawing

Data Flow Diagram

Mind Mapping

Data Visualization

Flow Charts

Business Charts

Projects Charts

Organizational Charts

Mind Maps

UMLS

Operation System

Linux

Windows

Mac

Use:

1. To use it, go to their official website by typing the following link in the browser.

2. After signing up, you can start using this tool; it has all the options available to create the desired diagram.

You will see the below page once you start with it.

4. Gliffy

This is also one of the alternatives we have for Lucidchart, which is easily accessible also. This tool is based on HTML5 and is a cloud-based app. It is also used to serve the same purpose that we are discussing to create the diagrams. It helps us to create flowcharts, UML, and many more things. It also helps us to share ideas and information easily and clearly with the organization.

Usage:

Floor Plans

Flow Charts

UML

Venn Diagrams

Also, we can design some other kinds of diagrams online only.

Browsers List:

Internet Explorer 9+

Google Chrome

Safari

Firefox

Use:

1. To use this tool, we can easily type in the URL in the browser to start with it.

2. After this, we need to sign up to use it. Then only, we can start using the tool.

You will see the below page on the browser for Gliffy.

Conclusion – Lucidchart Alternative

We have many options for Lucidchart alternatives; some are free, and some are paid. We can go ahead with any tools that suit our requirements better. But these tools are very helpful for the organization to understand the need and increase the team’s productivity.

Learn The Different Versions Of Kotlin In Detail

Introduction to Kotlin version

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Version of Kotlin

In July 2011, JetBrains performed some research and explored a new language that blends well with the JVM nicely.

Jetbrains being the initiator, decided and checked some of the features that it had was with an exception in Scala. The exception was that the scale was slow due to the slow compilation; it was not recommended because kotlin needs to compile faster.

In 2012 kotlin came with the next version where it made the entire project open source under Apache 2 license.

Kotlin released its first stable version, 1.0, on February 15 2023, which Jet Brains have committed to having long-term backward compatibility within it.

With the same released, supported by Google I/O 2023 for kotlin on android.

Kotlin released its next version, which is 1.2, which was released on November 28 2023, which has the ability to share code between JVM and JavaScript platform feature that was newly aggregated as part of a release. In fact, an entire Java full-stack demo was made with Kotlin/JS Gradle plugin.

Kotlin 1.3 was released on October 29, 2023, making asynchronous programming more evolved.

Kotlin, with its all major changes, was released in August 2023 with some slight changes for apple’s platform in c or Swift programming language.

These were overview on the broad level now; let’s see some of the versions with their characteristics with each release :

# Kotlin 1.1 Beta 2: This was the first beta release based on the previous discussion on features and extractions. Then kotlin 1.1 released with the candidate stage; in a sense, almost all the development work was done, and on March 1, 2023, it got released with JavaScript support and coroutines, etc., which was a big step to move further.

# Kotlin 1.1.1: This version release was made on March 14 2023, with a focus on address regression and was basically a bug fixing update for 1.1.

#Kotlin 1.0.7: This version of kotlin is the latest update used to update the back portion of the program.

# Kotlin 1.1.2: This version of the kotlin release was made on April 25 2023, which is used for bug fixing and tool update for kotlin to improve the performance.

# Kotlin/Native 0.2: Again, this version was released on May 12 2023, with feature and bug fixing to update the technology review.

# Kotlin/Native 0.3: This version was released on June 22 2023, with some of the new releases for modification and working with some windows related issues.

# Kotlin 1.1.3: This version was released on une 23, 2023, with some of the new tooling updates for kotlin 1.1.

# Kotlin 1.2 M2: This version was released on August 9 2023, which primarily focuses on the stability of the language and internal compilation.

# Kotlin 1.1.4: This version release is related to new bug fixing and tool updates for some of the deprecations.

# Kotlin 1.1.50: Another version was released on September 22 2023, with a bug fix and update for 1.1.

# Kotlin 1.1.60: This is a version released on November 13 2023, a bug fix version for updating the kotlin 1.1 version released earlier.

# Kotlin 1.2: The actual release of version 1.2 was made on November 28 2023, which is used to share platforms for version release.

# Kotlin 1.2.20: This version was released for a new bugfix and tooling update for which had added support for Gradle build cache and improved compilation for android apps.

# kotlin/Native version 0.7: This version of release was made on April 27 2023, which emphasized making the entire language smoother interop, frozen objects, optimizations, and many more features for working.

# Kotlin 1.2.50: This version of the release was made on June 14 2023, for Kotlin update of the previous version of bug fixing.

# Kotlin/Native v0.8: This version of the Kotlin release is a news release that is used for safer concurrent programming extending the other features and enhancing it.

# Kotlin 1.3 M2: This was the release made on August 19 2023, which was a breakthrough release because of the second milestone, which has set up the release of some new features like improves smart-casting and other compile-time analyses, Standard library functions for unsigned types and collections.

# Kotlin 1.3 RC: Next great release with a lot of updated features was made on September 20, 2023, with a lot of critical bug fixing available on previous versions of the release made earlier.

# Kotlin 1.3.70: The last flagged release of Kotlin with this range of release was made on March 3 2023, with all the updates and bug fixing in this kotlin.

# Kotlin 1.4 M1: This release was made on March 23 2023, with key improvisation and new features like enabled the type inference algorithm, contracts are, and evolutionary changes in the standard library.

# Kotlin 1.4.30: This Kotlin is released on February 4 2023, which got released with a new JVM backend and multiplatform language changes.

# Kotlin 1.5.0 M2 released: The last release for Kotlin 1.5.0-M2 was made on March 29, 2023, which ensured smooth migration from all the previous releases made so far with a lot of feature enhancement and improvisation.

Conclusion

Kotlin is a programming language that is nowadays quite a preferable language over android language for front-end development and programming. It is quite an adaptable language because of easy syntax and flexibility for quick compilation with Java in the environment. It has a lot of features that make it quite versatile.

Working And Different List Of Plugins In Ansible

Introduction to Ansible Plugins

Ansible plugins are separately available functions which are used to work with Ansible modules. These are the codes developed to provide additional support to Ansible modules while working on remote target hosts. Though the Ansible package comes with many plugins, we can also write our custom plugins. In Ansible, plugins are prepared in an architecture that adds more flexibility and expandability to the feature set. Most importantly, plugins are different from modules and run only locally on the Ansible controller node within the Ansible process. In addition, plugins provide additional options and extensions to the core functionality of Ansible, like output logging, inventory connection, data transformation.

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Working of Ansible Plugins

Ansible plugins work locally on the Ansible controller node, which means some data is given to a plugin, and the plugin processes that data. Modules use this processed data to perform some tasks on remote target machines. This might look easy, but one can easily get puzzled while choosing or using plugins, as the syntax is not in YAML of JSON. Plugins follow a unique syntax which is different for different plugins.

To confirm your plugin is working or loading. You can use ansible-doc to check the plugin, run the command like below:

Ansible package is shipped with a range of plugins, including Action, Callback, Cache, Become, Connection, Inventory, Lookup, Shell, Vars, Filters, Test. Also, you can add your customized plugins by writing the incompatible version of Python, and code should have error reporting, return strings in Unicode, and line with Ansible documentation and configuration standards. In the next section, we will learn about some of the default plugins types with examples.

List of Ansible Plugins

Below is a list of plugins types provided by Ansible packages, but you must note that this list if not exhaustive and can be extended in future releases or updated in the current release. In addition, these plugin types further have plugins that can use in our playbooks. For the latest list of plugin types, you can refer below the official Ansible community page.

To get a list of available plugin’s list of a plugin type, you can use the below command:

Also, for practical purposes, here we have an Ansible control server named ansible-controller and two remotes hosts named host-one and host-two. We will create playbooks, run Ansible commands on the ansible-controller node, and see the results on remote hosts. The way of use in these examples is completely to show you how to use these, but your real-life usage/requirements may be different from these.

1. Action

These plugins work on the front end and are executed in the local controller node before calling any module. These plugins are associated with some modules and are executed by default when such modules are used. Therefore, you cannot list action plugins.

2. Become

These plugins are enabled by default, and these are used to ensure that Ansible can use certain privileges on remote machines while running such commands, which need higher privileges. Plugin list includes sudo, su, doas, runas. These plugins are used along with become_methodkeyword in a playbook. For example, we have a playbook with content like below, here we are running this playbook with the root user, but as we mentioned becomes parameters in the playbook. We are trying to run the command as ec2-user. In the output, we will see that command ran as ec2-user.

Please note, this user must exist on the target machine, else the playbook will fail.

var: id_var[‘stdout_lines’]

ansible-playbook ansible_sudo.yaml

We get the output like below:

3. Lookup

These plugins are used to fetch data from an external source. This uses Jinja2. One of the important plugins is the file which is used to read the contents of a file. For example, we will take contents from a local file on the Ansible control machine and display its output using a plugin file which is a lookup plugin type. For this, we create a playbook, like below:

msg: These are the contents of /tmp/samplefile – {{lookup(‘file’, ‘/tmp/samplefile’)}}

Code:

ansible-playbook ansible_lookup_file.yaml

We get output like below:

4. Vars

These plugins provided additional data to Ansible plays which inventory, playbook, and parameters provided on the command line did not provide. These plugins are automatically enabled and used with keywords like host_vars and group_vars.

5. Cache

These plugins are used to store a cache of Ansible facts so that we do not have to gather those, again and again; this can be helpful in cost-saving where I/O is measured and incur costs.

6. Filters

These allow you to filter data within the controller node and provide it to playbooks and templates. This uses Jinja2. In this type, the variety of plugins is vast also; there are some mathematics-related plugins like abs, int, pow, root. To check some of these, we can make a playbook with content like below: –

msg: Fifth power of -2.5 is “{{ var_pow }}”

Code:

ansible-playbook filter_math.yaml

The output will be like below:

Conclusion

As we saw in this article, Ansible plugins can perform a vital part in Ansible playbooks, especially when we have needs like data transformation, filtering, etc. You must have good knowledge of using the plugins if you have multiple environments and your needs are to process data locally on the controller node. So learn it first and then use it.