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Natural Language Processing in Healthcare: Enhancing Patient Care and Clinical Operations INTRO

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. It has numerous applications in different industries, including healthcare. The healthcare industry generates a vast amount of data that can be challenging to process and analyze without the assistance of technology. NLP has the potential to revolutionize the healthcare industry by improving the quality of care, reducing costs, and increasing efficiency. In this article, we will explore how NLP is used in healthcare.

Improving Patient Care

One of the primary benefits of NLP in healthcare is its ability to improve patient care. Healthcare professionals can use NLP to extract relevant information from patient records, such as medical history, medication allergies, and previous diagnoses. This information can be used to develop personalized treatment plans for patients. NLP can also help identify patients who are at high risk of developing certain conditions, allowing healthcare professionals to intervene early and prevent the development of the disease.

Enhancing Medical Research

NLP can also be used to analyze vast amounts of medical data to identify patterns and trends. This can help researchers develop new treatments and therapies. For example, NLP can be used to analyze patient data to determine which treatments are most effective for certain conditions. It can also help identify the side effects of different medications, allowing researchers to develop safer and more effective treatments.

Improving Clinical Trials

NLP can also be used to improve clinical trials by making the recruitment process more efficient. Clinical trials require a large number of participants and finding suitable candidates can be time-consuming and expensive. NLP can be used to analyze patient data to identify suitable candidates for clinical trials, reducing the time and cost required to recruit participants.

Improving the recruitment process is just one of the ways NLP can benefit clinical trials. By analyzing patient data, NLP can help identify patients who meet the specific inclusion criteria for a clinical trial. This process can be time-consuming and labor-intensive if done manually, but NLP can speed up the process significantly.

NLP algorithms can sift through a large amount of data and extract information relevant to the clinical trial. This information can include medical history, previous diagnoses, medication usage, and other factors that might make a patient suitable for a particular trial. By automating this process, researchers can save time and money while increasing the likelihood of finding suitable participants.

Enhancing Electronic Health Records (EHRs)

NLP can also be used to improve the accuracy and completeness of electronic health records (EHRs). EHRs are digital versions of patient medical records that contain information about a patient’s medical history, diagnosis, and treatment plan. NLP can help healthcare professionals extract relevant information from these records, ensuring that they are accurate and up-to-date. This can help improve patient care by providing healthcare professionals with the information they need to make informed decisions about a patient’s treatment plan.

Assisting Healthcare Professionals

NLP can also be used to assist healthcare professionals in their day-to-day tasks. For example, it can be used to transcribe physician notes, allowing them to focus on patient care instead of documentation. It can also be used to identify potential drug interactions and side effects, allowing healthcare professionals to adjust a patient’s treatment plan accordingly.

NLP has the potential to assist healthcare professionals in a wide range of day-to-day tasks. Here are some of the most significant examples:

Transcribing physician notes:

NLP can be used to transcribe physician notes, which is a time-consuming and often error-prone task. By using NLP to transcribe notes, healthcare professionals can save time and reduce errors, allowing them to focus on providing patient care instead of documentation.

Extracting information from medical literature:

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Build A Natural Language Generation (Nlg) System Using Pytorch


Introduction to Natural Language Generation (NLG) and related things-

Data Preparation

Training Neural Language Models

Build a Natural Language Generation System using PyTorch


Hence, to capture the sequential information present in the text, recurrent neural networks are used in NLP. In this article, we will see how we can use a recurrent neural network (LSTM), using PyTorch for Natural Language Generation.

If you need a quick refresher on PyTorch then you can go through the article below:

And if you are new to NLP and wish to learn it from scratch, then check out our course:

Table of Contents

A Brief Overview of Natural Language Generation (NLG)

Text Generation using Neural Language Modeling

– Text Generation

Natural Language Generation using PyTorch

A Brief Overview of Natural Language Generation

Natural Language Generation (NLG) is a subfield of Natural Language Processing (NLP) that is concerned with the automatic generation of human-readable text by a computer. NLG is used across a wide range of NLP tasks such as Machine Translation, Speech-to-text, chatbots, text auto-correct, or text auto-completion.

We can model NLG with the help of Language Modeling. Let me explain the concept of language models – A language model learns to predict the probability of a sequence of words. For example, consider the sentences below:

We can see that the first sentence, “the cat is small”, is more probable than the second sentence, “small the is cat”, because we know that the sequence of the words in the second sentence is not correct. This is the fundamental concept behind language modeling. A language model should be able to distinguish between a more probable and a less probable sequence of words (or tokens).

Types of Language Models

The following are the two types of Language Models:

Statistical Language Models: These models use traditional statistical techniques like N-grams, Hidden Markov Models (HMM), and certain linguistic rules to learn the probability distribution of words.

Neural Language Models: These models have surpassed the statistical language models in their effectiveness. They use different kinds of Neural Networks to model language.

In this article, we will focus on RNN/LSTM based neural language models. If you want a quick refresher on RNN or LSTM then please check out these articles:

Text Generation using Neural Language Modeling Text Generation using Statistical Language Models

First of all let’s see how we can generate text with the help of a statistical model, like an N-Gram model. To understand how an N-Gram language model works then do check out the first half of the below article:

Suppose we have to generate the next word for the below sentence:

However, there are certain drawbacks of using such statistical models that use the immediate previous words as context to predict the next word. Let me give you some extra context.

Now we have some more information about what’s going on. The term “sandcastle” is very likely as the next word because it has a strong dependency on the term “beach” because people build sandcastles on beaches mostly right. So, the point is that “sandcastle” does not depend on the immediate context (“she built a”) as much as it depends on “beach”.

Text Generation using Neural Language Models

To capture such unbounded dependencies among the tokens of a sequence we can use an RNN/LSTM based language model. The following is a minimalistic representation of the language model that we will use for NLG:

x1, x2, and x3 are the inputs word embeddings at timestep 1, timestep 2, and timestep 3 respectively

ŷ1, ŷ2, and ŷ3 are the probability distribution of all the distinct tokens in the training dataset

y1, y2, and y3 are the ground truth values

U, V, and W are the weight matrices

and H0, H1, H2, and H3 are the hidden states

We will cover the working of this neural language model in the next section.

Understanding the Functioning of Neural Language Models

We will try to understand the functioning of a neural language model in three phases:

Data Preparation

Model Training

Text Generation

1. Data Preparation

Let’s assume that we will use the sentences below as our training data.

  ‘what is the price difference’]

The first sentence has 4 tokens, the second has 3 and the third has 5 tokens. So, all these sentences have varying lengths in terms of tokens. An LSTM model accepts sequences of the same length only as inputs. Therefore, we have to make the sequences in the training data have the same length.

There are multiple techniques to make sequences of equal length.

One technique is padding. We can pad the sequences with padding tokens wherever required. However, if we use this technique then we will have to deal with the padding tokens during loss calculation and text generation.

So, we will use another technique that involves splitting a sequence into multiple sequences of equal length without using any padding token. This technique also increases the size of the training data. Let me apply it to our training data.

Let’s say we want our sequences to have exactly three tokens. Then the first sequence will be split into the following sequences:

‘that is perfect’ ]

The second sequence is of length three only so it will not be split. However, the third sequence of the training data has five tokens and it will be broken down into multiple sequences of tokens:

‘the price difference’ ]

Now the new dataset will look something like this:

‘the price difference’ ]

2. Model Training

Since we want to solve the next word generation problem, the target should be the next word to the input word. For example, consider the first text sequence “alright that is”.

As you can see, with respect to the first sequence of our training data, the inputs to the model are “alright” and that”, and the corresponding target tokens are “that” and “is”. Hence, before starting the training process, we will have to split all the sequences in the dataset to inputs and targets as shown below:

So, these pairs of sequences under Input and Target are the training examples that will be passed to the model, and the loss for a training example will be the mean of losses at each timestep.

Let’s see how this model can then be used for text generation.

3. Text Generation

Once our language model is trained, we can then use it for NLG. The idea is to pass a text string as input along with a number of tokens you the model to generate after the input text string. For example, if the user passes “what is” as the input text and specifies that the model should generate 2 tokens, then the model might generate “what is going on” or “what is your name” or any other sequence.

Let me show you how it happens with the help of some illustrations:

n = 2

Step 1 – The first token (“what”) of the input text is passed to the trained LSTM model. It generates an output ŷ1 which we will ignore because we already know the second token (“is”).  The model also generates the hidden state H1 that will be passed to the next timestep.

Step 2 – Then the second token (“is”) is passed to the model at timestep 2 along with H1. The output at this timestep is a probability distribution in which the token “going” has the maximum value. So, we will consider it as the first generated or predicted token by our model. Now we have one more token left to generate.

Step 3 – In order to generate the next token we need to pass an input token to the model at timestep 3. However, we have run out of the input tokens, “is” was the last token that generated “going”. So, what do we pass next as input? In such a case we will pass the previously generated token as the input token.

The final output of the model would be “what is going on”. That is the text generation strategy that we will use to perform NLG. Next, we will train our own language model on a dataset of movie plot summaries.

Natural Language Generation using PyTorch

Now that we know how a neural language model functions and what kind of data preprocessing it requires, let’s train an LSTM language model to perform Natural Language Generation using PyTorch. I have implemented the entire code on Google Colab, so I suggest you should use it too.

Let’s quickly import the necessary libraries.

View the code on Gist.

1. Load Dataset

We will work with a sample of the CMU Movie Summary Corpus. You can download the pickle file of the sample data from this link.

View the code on Gist.

You can use the code below to print five summaries, sampled randomly.

# sample random summaries random.sample(movie_plots, 5) 2. Data Preparation

First of all, we will clean our text a bit. We will keep only the alphabets and the apostrophe punctuation mark and remove the rest of the other elements from the text.

# clean text movie_plots = [re.sub("[^a-z' ]", "", i) for i in movie_plots]

It is not mandatory to perform this step. It is just that I want my model to focus only on the alphabet and not worry about punctuation marks or numbers or other symbols.

Next, we will define a function to prepare fixed-length sequences from our dataset. I have specified the length of the sequence as five. It is a hyperparameter, you can change it if you want.

View the code on Gist.

So, we will pass the movie plot summaries to this function and it will return a list of fixed-length sequences for each input.

View the code on Gist.

Output: 152644

Once we have the same length sequences ready, we can split them further into input and target sequences.

View the code on Gist.

Now we have to convert these sequences (x and y) into integer sequences, but before that, we will have to map each distinct word in the dataset to an integer value. So, we will create a token to integer dictionary and an integer to the token dictionary as well.

View the code on Gist.

Output: (14271, ‘the’)

# set vocabulary size vocab_size = len(int2token) vocab_size

Output: 16592

The size of the vocabulary is 16,592, i.e., there are over 16,000 distinct tokens in our dataset.

Once we have the token to integer mapping in place then we can convert the text sequences to integer sequences.

View the code on Gist.

3. Model Building

We will pass batches of the input and target sequences to the model as it is better to train batch-wise rather than passing the entire data to the model at once. The following function will create batches from the input data.

View the code on Gist.

Now we will define the architecture of our language model.

View the code on Gist.

The input sequences will first pass through an embedding layer, then through an LSTM layer. The LSTM layer will give a set of outputs equal to the sequence length, and each of these outputs will be passed to a linear (dense) layer on which softmax will be applied.

View the code on Gist.



Let’s now define a function that will be used to train the model.

View the code on Gist.

# train the model train(net, batch_size = 32, epochs=20, print_every=256)

I have specified the batch size of 32 and will train the model for 20 epochs. The training might take a while.

4. Text Generation

Once the model is trained, we can use it for text generation. Please note that this model can generate one word at a time along with a hidden state. So, to generate the next word we will have to use this generated word and the hidden state.

View the code on Gist.

The function sample( ) takes in an input text string (“prime”) from the user and a number (“size”) that specifies the number of tokens to generate. sample( ) uses the predict( ) function to predict the next word given an input word and a hidden state. Given below are a few text sequences generated by the model.

sample(net, 15) Output:

‘it is now responsible by the temple where they have the new gospels him and is held’

sample(net, 15, prime = "one of the") Output:

‘one of the team are waiting by his rejection and throws him into his rejection of the sannokai’

sample(net, 15, prime = "as soon as") Output:

‘as soon as he is sent not to be the normal warrior caused on his mouth he has’

sample(net, 15, prime = "they") Output:

‘they find that their way into the ship in his way to be thrown in the’

End Notes

Natural Language Generation is a rapidly maturing field and increasingly active field of research. The methods used for NLG have also come a long way from N-Gram models to RNN/LSTM models and now transformer-based models are the new state-of-the-art models in this field.

To summarize, in this tutorial, we covered a lot of things related to NLG such as dataset preparation, how a neural language model is trained, and finally Natural Language Generation process in PyTorch. I suggest you try to build a language model on a bigger dataset and see what kind of text it generates.

In case you are looking for a roadmap to becoming an expert in NLP read the following article-


Big Data In Healthcare: Where Is It Heading?

Big data is making huge strides in the healthcare sector and is transforming medical treatment

Big data continues to revolutionize the way we analyze, manage, and use data across industries. It’s no surprise that one of the most notable sectors where data is making big changes in healthcare.

In fact, the onset of a global pandemic has accelerated innovation and adoption of digital technology, particularly in big data and big data analytics. This enabled healthcare providers to reduce treatment costs, avoid preventable diseases, predict outbreaks of epidemics, and improve the overall life quality. On the flip side, the same events have also exposed many weaknesses of the healthcare sector. Here we outline the impact of big data and data analytics in healthcare as well as give a few examples of key applications of big data in the healthcare sector.

Big Data in Healthcare: Promise and Potential

A report from IDC shows that big data is expected to grow faster in healthcare than in other industries like financial services, manufacturing, or media. It’s estimated that the healthcare data will see a compound annual growth rate of 36% by 2025.

The international big data market in the healthcare sector is estimated to reach $34.27B through 2023 at a CAGR of 22.07%. Globally, it’s estimated that the big data analytics sector will reach more than $68.03B by 2024, driven massively by ongoing North American investments in practice management technologies, health records, and workforce management solutions. Recent findings from McKinsey & Co hint that big data in healthcare can save us between $300B to $450B each year.

4 Key Applications of Big Data Analytics in Healthcare

Information obtained from big data analytics provides healthcare experts with valuable insights that were not possible before. A great amount of data is applied at every step of the healthcare cycle: from medical investigation to patient experience and outcome.

1. Big Data in Diagnostic Predictions

Thanks to data analytics and big data, it’s possible to diagnose the disease quickly and accurately. Normally, medical providers need to examine patients, discuss their ailments, and compare their symptoms to diseases they already know. But, because there’s always more than can meet the eye, big data ensures a smarter way to diagnose complex cases. For example, physicians can simply collect patient data and feed it into a system that will suggest possible diagnoses. These algorithms then propose high-value tests and minimize the excess of unnecessary tests.

2. Big Data in Personal Injury Claims

Usually, when a personal injury lawsuit is filed, the injured person attaches documents, including a medical report, a police report, and medical expenses. But to sue someone and win the case, legal professionals have to appoint an expert to evaluate all the records and ensure they’re valid, process the claim, and pay it out. However, this process isn’t just unnecessarily long but also very tedious since it’s reliant on human labour.

Predictive analytics reduces the amount of time needed to process the information, making it more time-efficient and saving on salaries. AI-powered systems use the generated data to predict the outcome of personal injury cases that are ordinary and simple to handle.

This process involves feeding AI systems with data on past cases that are similar in order to analyze and identify patterns in how the past personal injury claims were solved.

3. Big Data Improves Patient Engagement

Increasingly more consumers– and hence, potential patients – are interested in wearables that record every step they take, sleeping quality, their rates, etc., on a daily basis. All this critical data can be coupled with other trackable data to uncover potential health risks lurking. Tachycardia and chronic insomnia can signal the risk of heart diseases, for instance.

Today, a number of patients are directly involved in monitoring their own health, and incentives from health insurers can encourage them to lead a healthier lifestyle (such as giving money back to people using wearables).

The application of IoT devices and smart wearables, which healthcare providers now recommend, is among key healthcare technology trends. These technologies automatically collect health metrics and offer valuable indications, removing the need for patients to travel to the nearest medical facility or for patients to collect it themselves. It’s clear that the latest tech helped generate tons of valuable data that can help doctors better diagnose and treat common and complex health problems.

4. Big Data in Telemedicine

We can’t talk about telemedicine without mentioning big data and its role. With the application of high-speed real-time data, medical providers can perform operations while physically being miles away from the patient. While this might sound strange, it’s as real and possible as it could be. Big data has made possible not only robot-assisted surgeries but also accurate diagnosis, virtual nursing assistance, and remote patient monitoring.

Big data and telemedicine have made it possible for patients and doctors to :

Avoid waiting for lines

Reduce unnecessary consultations and paperwork

For patients to be consulted and monitored anywhere and anytime

Prevent hospitalization

Improve the quality of service and reduce costs

How Is The Cloud Revolutionizing Fintech And Healthcare Industry?

Cloud computing is revolutionizing the fintech and healthcare industries in enormous ways

During the early phase of the epidemic, several businesses were compelled to substantially alter their operations. Rapid digital transformation is vital to thrive financially, meet changing customer requirements, and keep staff engaged. In this article, we will see how the cloud revolutionized the fintech and healthcare industry. Read to know more about the role of cloud computing in industries like FinTech and the healthcare industry.

Using Cloud Computing to Overcome the Office and Lab Work Model

Cloud computing platforms have enabled businesses, schools, and government agencies to overcome pandemic-related obstacles and significantly increase innovation and market agility.

The cloud computing business is predicted to reach almost $500 billion in 2023, up from US$243 billion in 2023. Amazon Web Services alone is increasing at a 33% annual rate. In the previous year, this accounted for 75% of the company’s operational profits.

Rather than retreating to the status quo, corporate leaders must continue to use emerging technologies to challenge industry stagnation. Here’s how the cloud is transforming the health and finance industries.

Cloud-Based Services are Ripe for Disruption

“On-premise” storage, or in-house systems that can restrict scalability and storage, has historically been a problem for business leaders in the dental and healthcare industries. On-premise servers and an aging infrastructure severely limit providers’ ability to implement new tools and make use of the data they already have as diagnostic systems become more sophisticated.

Additionally, the limitations present difficulties on the patient’s side. Accessing health records, making online appointments, and connecting with various healthcare providers for multi-system health needs are among these difficulties.

Even though these problems have been around for a long time, the healthcare crisis brought on by the pandemic overwhelms them and makes them worse, making it even harder for many patients to get the care they need.

Upgrading EHR to Better Cloud Systems

Upgrading to better systems that can work faster, save money, and adapt to consumers’ and patients’ needs is necessary to solve these issues. In a recent case study, MIT Sloan looked at how Intermountain Medical Center in Utah updated its out-of-date electronic health record (EHR) system to deal with common issues.

By upgrading the technology that powers its 22 hospitals and 185 clinics, Intermountain was able to save millions on procurement and internal IT costs while also significantly improving patient outcomes. What we already know is confirmed by the MIT analysis: Cloud-based systems can simplify patient management, which can lower attrition rates, recoup revenue lost, and build stronger, more long-lasting relationships with patients.

How Updated Version of EHR Work for the Dental Industry?

One of the highest rates of attrition in the healthcare industry is experienced by dental practices, where the average practice loses 20% of its patients. Attrition could be reduced by as little as 3%, which could add US$73,000 to annual production. Cloud-based services help patients remember their appointments, streamline communications, and replace outdated booking systems. Long wait times are avoided when outdated systems are replaced, resulting in tangible improvements in dental providers’ retention rates.

Finance & the Cloud

Cloud-based technologies are helping banks scale in the financial sector to better track fraud, expedite loan applications, and respond to market fluctuations-based surges in customer activity. New mobile banking features, money laundering patterns, and AI-automated underwriting decision analyses are all made possible by cloud-based tools.

Sadly, many banks lack cloud adoption because they rely on internal servers that have inherent limitations. Only 12% of tasks performed by North American banks are currently handled in the cloud. 90% of U.S. banks have digital transformation plans in place but haven’t switched over to them. Bank of America built its cloud, whereas giants like Wells Fargo and Capital One are either already using cloud technologies or in the process of migrating over. The refreshed and further developed cloud-based innovation has saved Bank of America billions of dollars.

Highly Regulated Systems are Slow to Adapt

Data has historically been reluctantly moved from on-premise servers and data centers by businesses in highly regulated industries, which are notoriously slow-moving sectors.

Which Directive Is Used To Detect The Errors In Sass?

In SASS, the directive is a special symbol that starts with the ‘@’ character. There are various kinds of directives that are used in the SCSS code, instructing the compiler to process the code in a particular way.

In this tutorial, we will learn about the @error and @debug directives used to throw an error or debug the code, respectively.

@error directive in SASS

The error directive is represented like ‘@error’, and we can use it whenever we require to throw an error. For example, if some condition doesn’t meet, we require to throw an error.


Users can follow the syntax below to use the ‘@error’ directive to detect the errors in SASS.

@error "error message";

In the above syntax, an error message is replaced by a real error, which we need to show in the output.


In the example below, we have created the ‘colors’ object in the SASS, which contains the different colors and their hexcode.

Also, we have created the changeStyle() function, which takes the color as an argument. It checks whether the map contains the color passed in the argument as a key. If yes, it returns the hexcode of color. Otherwise, it returns an error.

We have invoked the changeStyle() function by passing the ‘blue’ as an argument, and users can see the error in the terminal while compiling the SCSS.

$colors: ( green: #00ff00, white: #ffffff, ); @function changeStyle($color) { @if map-has-key($colors, $color) { @return map-get($colors, $style); } @error "Color is not included in the style: '#{$style}'."; } .container { style: changeStyle(blue); } Output

On execution, it will produce the following output −

{ “status”: 1, “file”: “C:/Data E/web devlopment/nodedemo/scss/style.scss”, “line”: 11, “column”: 60, “message”: “Undefined variable: “$style”.”, 11 of scss/style.scss, }


In the example below, the divide() function takes two values as a parameter. If the second argument is equal to zero, we throw an error. Otherwise, we return the division of the number.

@function divide($a, $b) { @if $b == 0 { @error "Division by zero is not allowed."; } @return $a / $b; } .one { width: divide(10, 2); } .two { width: divide(10, 1); } .three { width: divide(10, 0); }

{ “status”: 1, “file”: “C:/Data E/web devlopment/nodedemo/scss/style.scss”, “line”: 4, “column”: 12, “message”: “Division by zero is not allowed.”, }

@debug directive in SASS

The ‘@debug’ directive is used for debugging the SASS code. By debugging the code, developers can know where is the exact error in the code. For example, we can print values of the variables by debugging code and can catch the errors manually.


Users can follow the syntax below to use the ‘@debug’ directive of the SASS.

@debug $var-name;

In the above syntax, ‘var-name’ is replaced by the actual variable name to print its value while debugging the code.


In the example below, we have used the @debug directive to debug the code of the SASS. We have defined the height and border variables and stored the respective values.

After that, we have used the @debug directive to print the values of the height and border variable, which users can observe in the output.

$height: 45rem; $border: 2px, solid, blue; div { @debug $height; @debug $border; } Output

On execution, it will produce the following output −

C:/Data E/web devlopment/nodedemo/scss/style.scss:5 DEBUG: 45rem C:/Data E/web devlopment/nodedemo/scss/style.scss:6 DEBUG: 2px, solid, blue Rendering Complete, saving .css file…

Users learned to use the @error and @debug directives to catch an error while compiling the SASS code. We can throw an error using the @error directive and catch the error using the @debug directive by debugging the code.

Programming Language Vs Scripting Language: Key Differences

Here is the major difference between programming language vs scripting language

Many individuals are unaware of the distinctions between scripting languages and programming languages, and they frequently use the phrases interchangeably. They may sound similar, yet they are extremely different. Anyone interested in entering the realm of software development must understand the distinctions between scripting language and programming language. Recent innovations in the programming world, however, have blurred the boundary between them.

Both languages are utilised in the development of software. All scripting languages may be used as programming languages, but not the other way around. The main distinction is that scripting languages are interpreted rather than compiled. Before the introduction of scripting languages, programming languages were used to create software such as Microsoft PowerPoint, Microsoft Excel, Internet Explorer, and so on. However, there was a need for languages to have new functions, which led to the development of scripting languages. Let us now examine the distinctions between scripting languages and programming languages in further depth. Here we will explore the difference between programming language vs scripting language.

Programming Language

A programming language is used to communicate with computers to create desktop software, internet, and mobile apps. It is a set of instructions intended to achieve a certain aim. Programming languages include C, C++, Java, and Python, to name a few. Programming languages typically include two components: syntax (form) and semantics (meaning).

Key Features of Programming Language

Simplicity: Most current languages, such as Python, have a straightforward learning curve. There is generally a compromise between a language’s simplicity and its speed and abstraction.

Structure: Every programming language has a present structure, such as syntax, semantics, a set of rules, and so on.

Abstraction: It refers to the programming language’s ability to conceal intricate features that may be superfluous for consumers. It is one of the most significant and necessary characteristics of object-oriented programming languages.

Efficiency: Programming languages are translated and executed effectively to prevent wasting too much memory or taking too long.

Portability: Because programming languages are portable, they should be easy to transfer from one machine to another.

Scripting Language

A scripting language is a programming language that is specially designed for use in runtime settings. It automates work completion. They are employed in system administration, web development, gaming, and the creation of plugins and extensions. These are interpretive languages. Scripting languages are generally open-source languages that are supported by practically every platform, which implies that no special software is necessary to run them because they are a series of instructions that are executed without the aid of a compiler.

Key Features of Scripting Language

Easy to learn and use: They are simple to learn and apply. JavaScript and PHP are two of the most user-friendly scripting languages.

Open-source and free: All they have to do now is research them and incorporate them into their current system. They’re all open-source, which means that anybody on the planet can help shape them.

Powerful and extensible: enough so that the relevant tasks may be completed using the scripts. Scripting languages are also quite adaptable.

Lighter memory requirements: They are interpreted rather than compiled, unlike programming languages. As a result, they demand less memory from the computers that operate them.

Runtime Execution: A system that allows code to be run during runtime allows an application to be configured and adjusted while it is running. In reality, this capability is the most crucial characteristic that makes scripting languages so useful in most applications.

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