Top Machine Learning Libraries In Java

 

Java is the only general purpose programming language which finds wide use by developers for building secure enterprise grade applications, desktop applications, web applications, and mobile apps. Java 9 further helps developers to build applications for both large and small devices by providing a number of new features – a new module system, a new command line tool, and several updated APIs. At the same time, Java is currently one of the most popular programming languages for machine learning.

A large percentage of data scientists and machine learning developers prefer Java to other programming languages while improving network security, protect cyber attacks, and detect frauds. The language features available in Java makes it easier for programmers to write machine learning algorithms. The developers can accelerate custom machine learning application development by taking advantage of the Machine Learning libraries In Java.

Brief Overview of 10 Robust Machine Learning Libraries In Java

1) Java Machine Learning Library (Java-ML)

Java-ML is designed as a collection of machine learning algorithms. It even provides interface for various types of machine learning algorithms. By design, the library is a clear interface instead of graphical user interface. Hence, only skilled Java programmers and developers are able to use it . They have option to learn Java-ML by referring to its well-documented source code as well as tutorials and code samples.

2) Java Statistical Analysis Tool (JSAT)

The Java library for machine learning was developed by Edward Raff for self-education. JSAT still provides implementation of standard machine learning algorithms in pure Java. The developers can even use JSAT as a lightweight Java library without external dependencies. But they cannot use the library to solve complex machine learning project. JSAT still helps machine learning developers to sole small to medium size problems quickly.

3) Waikato Environment for Knowledge Analysis (Weka)

The machine learning algorithms provided by Weka helps developers to simplify a variety of data mining tasks. Weka even provides a number of tools for data pre-processing, classification, clustering, regression, and visualization. The developers even have option to use the machine learning algorithms provided by Weka to a dataset directly or call the algorithms from Java code. At the same time, the developers can also use Weka to create new machine learning schemas without putting extra time and effort.

4)  Konstanz Information Miner (KNIME)

KNIME was originally an analytics and reporting library. But KNIME is currently one of the most popular build software for advanced data science. The tools provided by KNIME help users to discover new potential hidden in data, mine data for fresh insights, and predict new future. The data scientists can use KNIME to integrate different types of data collected from various sources to widely used tools. At the same time, the software developers can use KNIME to connect applications to data sources by creating custom connectors, implement new algorithms, and create new data visualization.

5) Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI)

The open source data mining software is written in Java programming language. But it is compiled with Python and Maven. Despite being designed as research software, ELKI is designed based on extensions. It allows developers to use algorithms, indexes, visualization, data types, and distance functions as extensions. However, ELKI allows developers to keep the data management tasks and data mining algorithms separated. The separation further makes it easier for programmers to evaluate data mining algorithms and data management tasks independently.

6) RapidMiner

The commercial data science platform is currently being used by large enterprises like Samsung, GE, SalesForce, Cisco, Hitachi and Siemens. RapidMiner comes with a set of features and tools to simplify various tasks performed by data scientists. It even uses automated machine learning to speed up and simplify various data science projects. The data scientists can use RapidMiner Studio to create visual workflow, RapidMiner Server to simplify model deployment and management, and RapidMiner Radoop to implement code free data science.

7) Massive Online Analysis (MOA)

The widely used data stream mining framework comes with a number of machine learning algorithms and tools to evaluate the algorithms. While using MOA, developers can avail a variety of machine learning algorithms – classification, clustering, regression, concept drift detection, outlier detection, and recommender system. The developers can use MOA to perform real-time big data streaming and large scale machine learning. They even have option to extend and scale the Java-based framework to meet complex project needs.

8) Eclipse Deeplearning4j

Deeplearning4j is a Java based platform. The distributed deep learning library is compatible with a number of JVM-based programming languages – Kotlin, Scala and Clojure. Also, Deeplearning4j finds extensive use as a scalable and open source distribution library in varied business environments on distributed CPUs and GPUs. It even features micro-service architecture and takes advantage of a robust distributed computing framework like Hadoop. The developers can even avail the tools provided by Deeplearning4j to perform machine learning ETL operations, evaluate machine learning algorithms, and integrate Java and native C++.

9) Mallet

Mallet is designed as a Java-based package for a variety of machine learning applications to text. The sophisticated tools available in Mallet make it easier for developers to simplify document classification, sequence tagging, topic modelling, and numerical optimization. At the same time, Mallet transforms text documents into numerical representation efficiently and flexibly through a system of pipes. The user can even extend Mallet through add-on packages to meet complex project needs.

10) Encog Machine Learning Framework

In addition to supporting advanced machine learning algorithms, Encog also supports classes for data normalization and processing. The framework further provides multi-threaded training algorithms that can scale according to multicore hardware. Encog makes it easier for programmers to model and train machine learning algorithms by providing a GUI-based workbench. It supports an array of standard machine learning algorithms including neural networks, genetic programming, Bayesian networks, hidden Markov models, and support vector machine.

On the whole, the Java developers have option to choose from a wide range of Java libraries for machine learning. Some of these libraries are machine learning development platforms, whereas others provide a collection of machine learning algorithms. Hence, the developers must keep in mind the precise needs of each project while comparing these widely used machine learning libraries for Java programming language.

Blazor : Will it slay the JavaScript/SPA Frameworks?

 

It has been a long time since Microsoft allowed support of .NET directly on client-side. The last attempt was with Silverlight which got famous when it launched but due to lack of support from browsers and security concerns it stopped; like what happened to Adobe Flash.

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What is BLAZOR?

Blazor is an attempt to bring .NET core to the browser directly which allows supported language like C# to run directly on the browser. Blazor allows developing Single Page Application (SPA) very quickly and by using a single language on both server and client.

Blazor allows having features similar to any other SPA framework like Routing, Template, Binding – one or two way, Rendering, etc.

The name comes by combining Browser and  MVC Razor i.e Blazor.

How does it work?

It entirely relies on WebAssembly (Wasm). Wasm was developed in 2015, to run high-level language directly on browsers. It is supported by W3C which means it is standard and could be utilized by different platform too. There are runtimes available to run C++ codes on browsers too. Since it is from W3C all latest browsers generally have the support of Wasm.

Blazor runs via Wasm but in-between there needs to be a runtime. Mono Wasm allows required .NET assemblies downloads on browser directly which runs on Mono Wasm Virtual Machine.

What are all advantages of Blazor?

• A single roof of programming language to build client and server-side codes.
• Wasm is generally faster than JavaScript (JS) because those are binary/compiled code. No need for parsing of scripts.
• A typed system, a fewer scope of errors due to same. Razor, model binding, etc.
• All sweet features of C# and .NET core: Dependency Injection (DI), delegates/events, etc.
• Visual Studio Integrated Development Environment (IDE) to allow a rich experience to develop applications quickly with many provided inbuilt or plug-in supported features.
• A fallback approach to enable Blazor to run if Wasm is not available to any browser.
  No issue of Garbage collection like all .NET platform, Mono Wasm supports it too.

Limitations of Blazor

• Still is in the development phase, not recommended for production use.
• Limited debugging support of Blazor codes since it runs on Wasm.
• No way to access DOM elements has to rely on JavaScript for same.
• The second biggest, there is not much components/control available in Blazor to have richer User Experience (UX). Like as simple as AutoSuggestion, we have to rely on JavaScript plugin for same.

Demo of Blazor

https://www.youtube.com/watch?v=IGj49kaYPEc
The source code used in demo: https://github.com/viku85/BlazorDemo

Conclusion and Future

Blazor is easy to use with minimal effort in comparison to any JS SPA framework available in the market. Since it is on .NET, there is less learning curve for people coming from the .NET environment.

A lot of changes and development is going on with Blazor, many refinements, performance improvements, feature add-ons but still not having a final version to use on production.
I believe with the support of controls like Kendo or inbuilt controls in Blazor, application development would be much quicker and feature rich.

We need to wait for the final release (if it happens) and a wide variety of supported controls which may kill JS Frameworks at least in the Microsoft development environment.

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The views and opinions expressed in this article are those of the author. An avid techie, Vikash enjoys sharing his thoughts on computing and technology in his personal blog.
To know more about our company, please click on Mindfire Solutions. 

Virtual Fitting Room Applications Are Developed Best Using JavaScript?

 

Introduction:

The continuous innovation and development of technology have led to the creation of Virtual Fitting Room Applications. Now customers don’t have to be physically present at a store for trying out any clothing items, beauty products, and accessories. You can do it virtually through Virtual Fitting Room mobile applications.

The virtual fitting room is a technology that has revolutionized the way retailers and E-commerce businesses are offering online shopping experiences. Virtual fitting room applications are based on technologies like Artificial Intelligence (AI) and Augmented Reality (AR). With these new innovative applications, buyers don’t have to leave their homes to check the size, fitting, and style of the products that they want to purchase.

Ever since the pandemic struck, in-store shopping has taken a hit. People are hesitant about going out to retail stores and trying out things themselves. Based on a survey conducted by a leading advisory group, more than half of women and men who visit the store don’t feel safe trying out clothes in the store dressing room.

Market Trends:

As lockdowns were happening globally, clothing products saw a decline of about 43% in sales. But virtual fitting room technology is changing these numbers, and retailers are slowly getting back in the business. After the Covid-19 pandemic, the demand for virtual fitting rooms is increasing drastically.

A report predicted that by 2025, the market for virtual fitting would reach the value of 6.5 Million U.S dollars. According to an industry report by Statista, the global market size for virtual fitting rooms is expected to reach over 12 Billion U.S dollars by 2028.

Many platforms and languages can be used while adopting this technology. In the following article, we will discuss why virtual fitting rooms applications are best developed with JavaScript.

In order to understand why JavaScript (js) is the best way to develop a virtual fitting room application, we need to know how the concept of virtual fitting rooms works.

How Does Virtual Fitting Room Technology Work?

The virtual fitting room works on Artificial Intelligence (AI) or Augmented Reality (AR). In the case of Augmented Reality, a person’s body is scanned with a webcam to create a 360 degree-3D model of the body. The virtual fitting room apps that work on AI utilize machine learning algorithms to create a 360 degree-3D model of a body.

Irrespective of the technology used to scan the body, post scanning, the 3D models are combined with Radio Frequency Identification (RFID). The RFID keeps track of the products that the buyer has added to the virtual fitting room.

Finally, the virtual fitting room technology lays the product of the user’s choice on the 360-degree 3D model of their bodies. It helps shoppers check whether the item has the proper fitting, styling and how well it suits them.

Benefits Of Virtual Fitting Rooms:

Here are benefits that virtual Fitting Room Applications can offer to retailers:

● Support online sales:

Virtual fitting room application provides an enhanced shopping experience for the customers. A research paper shows that 40 % of shoppers are willing to buy more expensive products if they experience shopping through augmented reality features. Thus, virtual fitting room applications are increasing the online sales of enterprises.

● Increase customer Retention:

The virtual fitting room applications increase the target audience’s engagement with the brands and retailers. It allows shoppers to see for themselves how the product will look on them. Therefore, customers keep on trying various items, and the retention time goes on increasing. The businesses that have used virtual fitting room applications witnessed a 5 percent boost in their retention time.

● Decrease in product Return:

One of the reasons for high returns was the wrong fitting of the product. But with virtual fitting room applications, these blunders can be avoided. After opting for this technology, a retail company reported about a 36 percent decrease in the returns rate.

Why Use JavaScript To Develop Virtual Fitting Room Applications?

JavaScript is an object-oriented programming language which helps to develop dynamic web pages and applications.

As virtual fitting room applications are based on AI and AR, JavaScript is one of the best languages you can use to develop them. From the surface, it may seem like a strange choice as most enterprises use programming languages like Python or Scala for their AI/AR-based projects.

But JavaScript has some advantages that can add more value than Python or Scala. Here are some reasons why JavaScript is the best language for developing virtual fitting rooms.

● Excellent Performance:

Millions of dollars are invested in JavaScript so that it can run fast. Modern JavaScript is more advanced and translates machine code as same as Java. This allows AI-based applications to perform better in JavaScript.  Hence virtual fitting rooms can offer a more superior user experience than Python and Scala.

● Less Development Time:

We all already know that Python has less development. The same is the case with JavaScript. But the development time for JavaScript is a little quicker. Synaptic, the neural network library of JavaScript, has various features, which gives js a bit of an edge in AI-based application development.

That doesn’t mean that Python is not good. When it comes to adopting deep learning technology, there is no competition to Python. Both languages have something unique to offer. As development times go down, the production costs also go down. Hence enterprises can save a lot by using JavaScript for developing virtual fitting rooms.

● Improved Security:

JavaScript comes with built-in security. Therefore, JavaScript prevents any attacks to the application by malicious codes.

● JS has separate library for AR Features:

JavaScript offers a library named ‘AR.js’ that can provide Augmented Reality features to any web-based application using a few lines of HTML. It is a free, open-source platform and has been used by many developers. In the GitHub repository, AR.js has more than 13,000 stars.

One of the benefits of using the AR.js framework is its cross-platform and browser compatibility. This means that the web app developed on the platform is compatible with iOS as well as Android.

The AR.js comes with different AR frameworks like A-frame, ARToolKit, and three.js, making this framework very easy for developing AR-based web applications. It offers a high performance of 60fps, even older devices, so there is no need to spend money on external hardware.

That being said, AR falls short in some instances as compared to AI. There are some rendering limitations to AR. The tracking accuracy of the Augmented Reality technology is too low, which can compromise the application’s user experience. Therefore, AI in collaboration with AR helps to avoid these glitches and make the technology more effective. The data rendering done in AI is more accurate than AR technology. It also provides better identification of body parts of the 3D model.

Final Thoughts:

From the benefits listed in the above section, you can see why JavaScript is the best choice for developing AR or AI-based web applications such as virtual fitting rooms. The language is easy to code, offers better performance, provides a framework and library for adding AR features, and is even cost-effective in many aspects

The virtual fitting rooms are not only limited to the clothing industry; they are also relevant for shoes, watches, sunglasses/ spectacles, cosmetics, and jewelry. Anything wearable and used as a styling option can be paired with virtual fitting rooms applications.

Instead of hiring developers to create a virtual fitting room, retailers and E-commerce platforms can outsource this job to a service provider with relevant industry experience and reduce the workload.

Like other businesses, if you too are looking to develop Virtual Fitting Room Applications, Mindfire Solutions can be your partner of choice. We have deep expertise in JavaScript . We have a team of highly skilled and certified software professionals, who have developed many custom solutions for our global clients over the years.

There is an interesting project we have done to develop a virtual fitting room application for an advertising and content management company with JavaScript. Click here to know more: Case study on fashion imagery solution.

Getting started with AWS Lambda

 

AWS Lambda is an ingredient in amazon serverless computing. Lambda allows us to run server-side code without thinking about the server. It abstracts all of the other components i.e. servers, platforms, virtual machines, etc. that are needed to run server-side code. So we can just focus on the code and not the server. That way the time to production or deployment becomes very less. We can write a lambda function, configure it and run it in minutes.

Another great benefit of lambda is that we just pay for the compute time we consume. That means it will charge only for the time that our code is actually executed. Also, the first one million requests are free. We have to pay for request thereafter. This a very cost-effective way to run the server-side code. To get started first we need an AWS account. After creating the account we need to go to the AWS management console.

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Create a Lambda function with Node.js

Let’s create a lambda function that picks a random number between 2 given number. First of all, login into the AWS console and then click the “Lambda”  button under the compute section. Then you can come to the “Select blueprint” section. Then, under the select runtime combo box, select the latest node.js version. Amazon gives you some basic blueprints there. We will just select the simple hello world function to start with.

We will skip to the configure function section to create a new function. We will name our function random-number-generator. Then specify the description. Then the run time that is node 4.3. Our function is a small function so we will select to Edit code inline. The blueprint of amazon gives a very basic function.

We will change this default code to generate our random number between two given numbers.

In the beginning, just add console.log(‘Loading function’). This will help in debugging the code.’ In the default amazon function, there are some event values that are logged and in the end, it returns the first value in the callback function. Then we will add a handler function to the exports variable. And this function receives 3 variables. I.e. event, context, and callback.

 exports.handler = (event, context, callback) => {
            console.log(‘value 1 =’ , event.key1);
            console.log(‘value 2 =’ , event.key2);
            console.log(‘value 3 =’ , event.key3);
            callback(null, event.key1)
}

The callback is something we will call when our result is ready and we want to send some result back to the user. It takes 2 parameters. 1st one is the error and the second one is the success message. The variables could be string or JSON object.

We will delete all these default codes and write our own code. So, first of all, we will define and set the minimum and maximum number.

 exports.handler = (event, context, callback) => {
           let min = 0;
           let max = 10;
}

Now we will define another variable for the random number.

exports.handler = (event, context, callback) => {
            let min = 0;
            let max = 10;
            let generatedNumber = Math.floor(Math.random() *  max) + min;
}

Mmath.random() generates a random number between 0 and 1. And it’s a floating-point number, so we multiplying it by max and the round it and add the minimum. That gives us a random number between the minimum and maximum number.

Now we are done and want to return the random number. So we will call the callback function.

callback(null, generatedNumber);

 Here there is no error handler implemented so we will just return null in place of the error parameter. And the  generatedNumber.

That’s it, the code part is done.

Now scroll down. And let’s define our handler. The default is index.handler. Index refers to the filename and handler is the name of the variable that is attached to the exports. We will leave this by default.

Now, we will create a new Role and give the role name as ‘basic-lambda-execute-role’. Then under the policy template, we will select ‘Simple Microservice Permissions’.

Next is the advanced settings.

Each lambda function will run in a container & that container will have some memory allocated to it. So here we can pick how much memory should be allocated to our function. Our function is a basic function. So will select 128MB. That is more than enough for our function.

This does not only defines the memory allocated to the function but also the amount of processing power amazon uses to execute our function. If we have a more resource-intensive function then we can increase the memory usage and we will get a faster performing function. Then for the timeout, we will leave it to 3 secs that is enough. If our function does not finish within this timeout then Amazon will return an error message. We will leave the VPC to no VPS and move next. In the next page, amazon lets us review our configuration for our function. And then click the create function. There we will get the msg that our function is created. And we can see the dashboard for our function.

On the dashboard we can see our code, configuration, triggers, and also we can monitor our function as well

Let’s test it by clicking the Test button there. If we scroll down we can find that the function has executed successfully and also we can see the result random number.

So that’s it. We have our random number generator lambda function is running now.

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The views and opinions expressed in this article are those of the author. To know more about our company, please click on Mindfire Solutions. 

Overlooking Web Accessibility

 

The Internet is an ever-increasing storehouse of knowledge. The web and the internet as a whole serve as an important resource in many aspects of our lives: education, employment, recreation, commerce and more. Web Accessibility simply means that the web is to be made accessible to everyone. And that includes people with special abilities too – an aspect generally overlooked in haste. 

The concept of Web Accessibility has been around for a decade, but it is unfortunate that true meaning in its entirety has been lost on many of us, the web developers. It’s time that we built ramps to our sites so that it benefits not only people with disabilities but also enhances the experience of all types of users as a whole.

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Let’s see some examples:  When you are watching a video in a noisy environment and cannot perceive the audio correctly. Without being able to hear the audio, you have to guess what the whole video is about. It can be frustrating, right?

Let’s take another one:  If you have broken your arm in an accident and can’t use the mouse to explore the web. You have to remain cut off from the internet until you recover. Or find ways to manage to access it with difficulty, mostly through pain, or be at the mercy of people who would spare some time to assist you.

There are people out there who face these challenges at every instance of their attempts to access the Web. The true essence of Web Accessibility lies in addressing such concerns and ensuring that the Web is accessible by all, without any discretion. 

The World Wide Web Consortium(W3C) published a set of guidelines, Web Content Accessibility Guidelines or WCAG 1.0 in 1999, as an initiative to the Web Accessibility Initiative(WAI) project. The revised version, WCAG 2.0 was published in 2008, which is more technology-neutral, and therefore, is widely accepted by the developers to make their site more accessible.

It may seem like a huge task to accomplish at first, but in reality, it takes only small steps to make your website accessible to all. Steps that should be undertaken are 

• Using alternative texts, and descriptions for the images.
• Adding subtitles and transcripts for videos.
• Ensuring that your site is fully and equally accessible by the keyboard.
  Making use of the Accessible Rich Internet Tags (ARIA) tags.
• Having a good color contrast.

These are some tools which can help to make your website more accessible:

• WAVE by WebAIM
• Accessibility Developer Tools by Google Accessibility
• Some plugins like: WP Accessibility and Accessibility by Userway
• accessScan

So, let’s look at the bigger picture and start taking the necessary steps towards building a platform that is more accessible and more usable, and fulfill our responsibilities as web developers. It’s high time we focused on the masses who might be unable to access the internet just like normal people can. The onus lies with us to take individual responsibility of the same and spread the awareness to others. The realization that mere oversight or negligence on our part can be the source of much trouble for others should guard us against it. 

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The views and opinions expressed in this article are those of the author. To know more about our company, please click on Mindfire Solutions. 

Best Practices for Designing and Developing Fintech Apps Using Blockchain Technology

Financial technology or fintech is a burgeoning industry that seeks to make financial systems more efficient and accessible for all users. With the increased demand for faster transactions and greater security, leading-edge technologies like blockchain are gaining fast adoption. Through the use of decentralized networks, blockchain-based fintech apps, also known as decentralized finance or (DeFi) apps, are providing a secure and efficient platform for users to transact.

At present, the fintech blockchain market is valued at $6.2 billion and is projected to reach a value of $36 billion by 2028.

In this blog post, we will look into different types of fintech apps that can be developed with blockchain technology and some best practices to follow while designing and developing them.

Types of Fintech Apps Developed with Blockchain Technology

• DeFi Banking Apps

Decentralized finance (DeFi) banking apps offer users the ability to access a variety of financial services without the need of traditional banking institutions. These apps enable users to store their money in digital wallets, transfer funds between accounts, purchase insurance, and much more.

• Lending/Borrowing Apps

Blockchain-based lending and borrowing apps allow users to access credit by providing collateral in the form of cryptocurrencies. They also offer a secure platform for peer-to-peer lending, enabling people to borrow money from other individuals without the need for a middleman or credit agency.

• NFTs Marketplace

Non-Fungible Tokens (NFTs) are unique digital assets that represent ownership of a particular item. NFTs can be used in fintech apps for everything– from trading crypto-collectibles to managing online portfolios. NFT marketplaces enable users to buy and sell these tokens in a secure and transparent manner.

• Crowdfunding Platforms

Decentralized finance (DeFi) crowdfunding platforms provide a secure way for people to invest in projects they believe in. These apps make it easy for users to participate in fundraising activities without the need for a middleman or expensive transaction fees.

• Decentralized Crypto Exchange Platform

Decentralized crypto exchange platforms allow users to buy and sell cryptocurrencies without the need for a third-party intermediary. These platforms offer secure transaction processing, low fees, and fast settlement times, making them ideal for fintech applications.

Best Practices for Developing Blockchain-Based Fintech App

• Choose the Type of DeFi App

Before beginning development on a fintech app using blockchain technology, it is important to identify the type and functions of the application required for your business. Conduct diligent market research to understand your competitors, and features trending among DeFi users, and identify features that align with your business. This will help you determine the most suitable blockchain technology stack for your project and minimize future technical debt.

• Decide Tokenomics

It’s important to consider the tokenomics model of your app before you start developing, as they are an essential part of DeFi applications. Tokenomics is the design principle behind token-based economies. It includes considerations such as incentivizing early adopters of your product, maintaining liquidity in the market, and aligning user motivations with growth.

To offer features such as staking, decentralized exchanges, and liquidity pooling, you will have to introduce crypto tokens along with the mechanism to apply them through your fintech app. For creating an effective token economy, you can leverage tools like OpenZeppelin to ensure the implementation of standard tokens such as ERC721 and ERC20

• Implement Optimized Development Processes

Developing a successful fintech app requires following a set of best practices for the development process. This includes adapting agile methodologies, test-driven development, continuous integration/delivery, and automated testing. These processes will help ensure that your app is bug-free, meets quality standards, and is ready to deploy.

• Prioritize Security

While developing a fintech application, security must be the top priority. This means incorporating robust authentication with multi-factor authentication (MFA) and access control measures to protect user data from unauthorized access.

Additionally, developers should utilize encryption for any sensitive information and implement smart contracts for secure transactions. Your developers must be well-versed with different cyber threats such as cross-site scripting, SQL injections, etc, and implement appropriate solutions to mitigate them.

• Focus on Enriching User Experience

When designing the UX/UI of a fintech app, it is important to keep usability principles in mind in order to create an intuitive and enjoyable experience for users. These principles include things such as simplicity, consistency, visibility, feedback, task orientation, and accessibility. Keeping these principles in mind will help ensure that your app is easy to use while also providing all the features necessary for effective financial management.

• Develop Scalability Strategies

Building an application with scalability in mind from the outset is key to ensuring its long-term success. Utilizing the right technology stack, understanding capacity planning needs, and implementing performance optimization techniques can all help ensure your app has the capacity to grow over time with minimal downtime.

• Follow Regulatory Compliance

The financial sector is highly regulated and fintech apps must adhere to the prevailing laws and regulations to run operations smoothly. Developers should always research legal requirements before launching an app and ensure all features comply with necessary standards. This includes considering licensing needs, consumer protection obligations, anti-money laundering and counter-terrorism financing measures, privacy policies, etc.

Conclusion

Developing a successful fintech application requires careful consideration of various aspects such as project scope, technology stack, user experience, scalability needs, and regulatory compliance. By following the right development processes, businesses can create an app that enables users to manage their finances in an intuitive and secure manner.

It’s important to keep in mind that blockchain-based fintech apps require specialized development expertise. At Mindfire Solutions, we have a team of experienced blockchain developers who are well-versed in cutting-edge technologies like blockchain. From creating smart contracts to testing your blockchain-based applications, our end-to-end solution can help you design and develop a top-notch fintech application from the ground up.

Visit Mindfire Solutions to learn more about our services.

Role of AI in Chronic Care Management

Effectively managing chronic diseases such as asthma, diabetes, cancer, and several others have been one of the biggest challenges for healthcare providers worldwide. According to a study, chronic diseases are responsible for 70% of deaths and about 50% of the disease burden globally. Owing to this, we will discuss how AI in chronic care management can change these numbers.

As the pandemic unleashed a global health crisis, many countries faced a shortage of healthcare professionals and medical resources. This caused several hurdles for patients with chronic conditions to get treatment at the right time. Such conditions forced chronic patients to adapt to remote and digital treatment, which is the new normal in the current world.

For medical professionals to deliver quality remote healthcare, it has become essential to leverage fast-emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML).

Both AI and ML have already shown how they can significantly improve the efficiency of operations in various other industries like e-commerce, manufacturing, automotive, etc. In this article, we will take a look at the role of AI in Chronic Care Management (CCM).

Key Benefits of Chronic Care Management

Before we understand the importance of AI in Chronic Care Management, let us look at some key benefits of Chronic Care Management: 

• Chronic Care Management or CCM offers an organized approach to dealing with chronic conditions, making the process more coordinated for patients as well as the healthcare provider.
• Healthcare providers can build long-term relationships with patients through their CCM services, which can result in increased revenue.
• It can prevent unnecessary visits to partitioners.
• Researchers have proved that CCM can offer quality healthcare to patients at a lower cost.

What is the Role of AI in Critical Care Management?

Here is how AI in chronic care management can turn around the situation:

●     Medical Data Analysis

An extensive medical data set will be required to utilize AI in chronic care management at its full potential. In today’s digital world, it can be easily gathered throughout the patient life cycle via mobile applications, IoT devices, and patient portal software. An Al-based algorithm can analyze this pool of data and generate new insights and opportunities for both patients and medical professionals in CCM. This can streamline the overall chronic care management processes.

●     Prognosis and Prevention of the Disease

One aspect of CCM is to prevent diseases from happening. With the help of AI, practitioners can identify the choric disease to which the patient is susceptible. This allows the doctors to take the correct preventive measures and circumvent the chronic condition.

In a study published by Yannis Paschalidis in Harvard Business Review, healthcare professionals were able to forecast hospitalizations due to diabetes and heart disease a year in advance using machine learning and Electronic Health Records (EHRs) with an accuracy of 82%. 

●     Diagnosis of Diseases

Many recent studies have proved that AI can also be indispensable when it comes to the diagnosis of diseases. Al-algorithms can easily detect diseases based on data points like medical imagery, ECG data, patients’ demography, and their medical history. Such developments have led to time and cost-effective CCM service. It also has significantly improved remote diagnosis.

●     Treatment

The healthcare data can be used to create AI/ML models that can assist physicians in medication modeling and treatment suggestions. These models can be further applied to suggest appropriate dosage and treatment plans for the patient.

A personalized plan and treatment for individual patients can also be created by AI. The personalized treatment can enable clinicians to intervene before a patient’s condition becomes critical, making CCM more effective.

Recently, a company named IBM Watson has achieved some remarkable results by utilizing AI in the field of oncology. The organization performed genetic data analysis and was successfully able to identify the rare secondary leukemia caused by myelodysplastic syndromes.

●     Remote Patient Monitoring (RPM)

With an AI-powered CCM, healthcare professionals can constantly monitor a patient’s vitals like blood pressure, pulse rate, temperatures, etc., from the comfort of their home. The AI can also send an alert to health professionals if there is any sudden or critical change in the vitals of a patient.

●     Virtual Assistant

Chronic diseases generally last for a lifetime. As the patient gets older, it gets difficult for them to keep track of medication and appointments, which causes disturbance in their chronic care management. In such cases, an AI-powered virtual assistant can come in handy for the patient. It can assist them by ensuring drug adherence and also monitor their vital data.

For example, a Meditech company created AI-embed nurse avatars that send notifications to the patient each morning for a check-in routine, record their vitals, and sends alerts for timely intake of medicine.

Another aspect of chronic care management is measuring and managing the patient’s chronic pain. Here again, AI can assist in detecting chronic pain by monitoring the facial muscle movements of patients who are unable to self-report the pain to their physicians.

Conclusion

Artificial Intelligence can offer actionable insights to guide clinical decisions and allow physicians to diagnose, treat and handle chronic conditions remotely, thus making chronic care management more efficient, accessible, and affordable.

That’s why many healthcare organizations have begun to utilize AI in their chronic care management services. If you are also looking to leverage AI to improve your chronic care management, you will need to hire or work with people who have relevant expertise and skill set.

Mindfire Solutions is an IT-service provider that has worked with several healthcare organizations over the years to provide customized healthcare tech solutions that are highly cost-effective, secure, and scalable. Visit Mindfire Solutions to learn more about us.