DEV Community: Pavel Tantsiura

The limitless potential of computer vision in healthcare

Pavel Tantsiura — Mon, 04 Apr 2022 07:26:08 +0000

Computer vision is a technology based on image processing and synthesis. It usually involves machine learning and allows AI to simulate human vision.

This technology aims to save time by automating manual image analysis and achieving a higher level of accuracy. For this purpose, an algorithm is fed with a vast amount of representative pictures and trained to detect particular parts of them. Developers currently use computer vision in a wide range of spheres: from Snapchat and Instagram masks to scientific research and medical objectives.

Main Reasons to Use Computer Vision in the Medical Field

🔴 Speed 🔴

With all the hardware, fast internet, and cloud we have nowadays, computers can process images in microseconds. This allows doctors to have AI analyze, let’s say, all the X-ray images while they can focus more on patients. Thus, doctors have a chance to find out more specific details through their soft skills and provide care to more of those who need it.

🔴 Accuracy 🔴

Computer vision excludes the possibility of human error to some degree and serves as an assistant for radiologists. AI can help doctors detect such conditions as cancer, pneumonia, osteoporosis, and many others.

🔴 Urgency 🔴

A combination of speed and accuracy provided by computer vision might be crucial for urgent situations.

Automatic postpartum hemorrhage estimation is an example of the application of computer vision in the medical field. It allows surgeons to understand how much blood a patient has lost.

🔴 Pattern Recognition 🔴

Radiologists may also receive help from computers. You only need to compose a dataset of images with particularly associated diagnoses and train a deep learning model based on that dataset. Then, the AI will start detecting patterns in the images. For example, it can find tumors, pneumonia, or potentially dangerous moles.

Examples of Computer Vision Applications in Healthcare

✔️ Skin Cancer Detection✔️
“1 in 5 people get skin cancer”, according to the website of SkinVision — an app that helps the user detect skin cancer. You need to download, install the app, and take a photo of a mole or spot that concerns you. Then, the app will tell you whether you need to see a dermatologist or not. The software sensitivity is 95% due to the Machine Learning algorithm.

✔️Surgery Simulations✔️
Computer vision for medical imaging is also used for training. CV-empowered training especially applies to surgeons: nowadays, they can master their skills with digital models. The Touch Surgery software allows doctors to go through simulations of different surgeries. Artificial Intelligence creates interactive 3D models of human bodies, allowing surgeons to operate them almost the same as in real life.

✔️Pneumonia Detection✔️
A pneumonia detection web app is based on a neural network that was trained on 500 chest X-ray images. The deep learning model achieved 86%+ accuracy. Yet, it is an open-source project which can only be used for research purposes, not clinical ones.

Developers of another pneumonia detection instrument complain that finding labeled data is difficult because only certified doctors can give a diagnosis. They are also unsure that the result will be relevant for other conditions since their database was limited to 1–5-year-old patients from a single hospital.

📕Helpful resources:

✔️Value of CV in healthcare - 2022 HIMSS European Health Conference & Exhibition;
✔️Research paper - a study on deep learning-enabled medical computer vision;
✔️Computer Vision in Healthcare Applications

🩺 Applications

✔️SKIN CANCER CLASSIFICATION NEURAL NETWORK - CASE STUDY
✔️Pneumonia detection from chest radiograph using deep learning
✔️Touch Surgery software - surgery simulator
✔️the website of SkinVision — an app that helps the user detect skin cancer

TOP AI USE CASES IN SUPPLY CHAIN OPTIMIZATION

Pavel Tantsiura — Mon, 07 Mar 2022 07:26:24 +0000

As a supply chain owner or C-level executive, you struggle to reduce inventory imbalances. At the same time, you want to achieve ultimate visibility and transparency across all departments. Unfortunately, the supply chain generates too much data, complicated to store and analyze. But there is a solution.

Fortunately, there are many AI and machine learning applications in supply chain management. The latest annual MHI Industry report shows 60% of supply chain businesses will invest in the adoption of AI. Thus, in the future, there will be more companies that use AI in supply chains.

In a previous article, we talked about AI in the logistics and transportation business. Now, we will highlight AI applications in the supply chain. At the end of this article, you'll find tips for AI implementation, understandable even for a non-tech person.

Let's take a quick look at the benefits you will get after implementing artificial intelligence in your supply chain.

Benefits of Artificial Intelligence in supply chain management

Intelligent technologies improve supply chain management. AI-powered software can analyze large amounts of data, define trends at a granular level, and react immediately. The benefits of AI in the supply chain listed below show more reasons why AI adoption matters for your supply chain business.

Increased productivity is the main benefit of leveraging intelligent technologies. Over 60% of supply chain executives said AI could automate decision-making and improve supply chain efficiencies.
The immediate reaction to changes is the main difference between humans and AI. AI software recognizes patterns and considers potential areas for improvements.
Optimized warehouse operations are impossible without data analytics and machine learning. Smart algorithms can plan and predict product demand. Thus, you can assign vehicles and direct them to locations with the most considerable demand.
Reduced operating costs are another benefit of AI integration. This technology optimizes every minute and mile in the supply chain. For instance, AI helped United Parcel Service (UPS) to optimize routes beforehand and save $50m.

AI and ML in the supply chain have created new performance standards for supply chain effectiveness. They also help businesses to run automated operations, analyze data, and serve clients. If you want to modernize your supply chain with AI, it is high time to get some ideas on how you can do that.

AI and Machine Learning in Supply Chain: Best Applications

From quality control to warehouse management, AI is applicable at all stages of product distribution. Below, we gathered the best AI use cases in supply chain and related technologies.

Data-driven demand forecasting

Most businesses use supply chain planning (SCP) or supply chain management (SCM) systems to balance supply and demand. But only a few stakeholders know that AI provides you with data-driven demand predictions. Such predictions are especially relevant for fresh food providers. These products are easily damaged and perishable and have a short shelf life.

As a result, most of the food waste occurs at the end of the supply chain during product distribution and consumption. Roughly one-third of the food produced around the globe gets lost or wasted every year, according to this United Nations report. For food supply chain owners, it means that they lost a vast amount of money.

Luckily, demand prediction is one of the most popular uses of artificial intelligence in operations and supply chain planning. For this purpose, you can use ready-made platforms like Demand Guru, by LLamasoft. The platform is forecasting demand in the supply chain using machine learning algorithms and identifying demand patterns.

[Source]

Apart from long-term predictions, Demand Guru predicts the everyday demand for particular products. Moreover, this software can recognize the causes of increased demand and even create simulations of such situations. As a result, you receive more precise predictions generated by machine learning algorithms.

Visual recognition for automated quality control

No one wants to buy damaged products. Thus, most supply chains have manual quality inspections to find damage during transit. This is where computer vision technology, one of machine learning in supply chain use cases, comes in handy. Computer vision can identify images and particular objects. As an example, Facebook uses computer vision to find existing users on photos and tag them.

For digitizing quality control, consider Fujitsu Advanced Image Recognition (F|AIR). This platform is a perfect solution for visual product inspection and defect detection. Thanks to this intelligent algorithm, the platform is more precise in object detection than other machine vision software.

Once the platform identifies damaged goods, it will send you a PDF report, complete with defect images. F|AIR will help you to improve the consistency of quality inspection across your supply chain and reduce quality control costs up to 80%.

F|AIR works both on-premise and in the cloud to suit the existing supply chain ecosystem. You also can integrate F|AIR API into your system with help from a dedicated development team.

AI for automated warehouse management

To use Artificial Intelligence in logistics and supply chain management, consider integrating automated robots. Such robots will streamline product picking, unloading pallets, and even packing items. Apart from saving operating costs, robots can provide you with data-based decision making.

Each day your warehouse produces a massive amount of data. This may include inventory stock levels, order numbers, and shipping details. To get the most from this data using data analytics, think about doctors with machine learning capabilities. Such robots will identify patterns, predict out-of-stock items, orders, and even returns.

Ocado Technology is the online grocery retailer that nailed automated warehouse management. Ocado does not own any brick and mortar stores and receives orders only from their online store.

To digitize its warehouse, Ocado developed most of its solutions with in-house development teams. Currently, the company's main tech stack includes cloud computing, robotics, AI, and IoT. Ocado also put much effort into fraud detection using machine learning technologies. The company has built its custom route optimization platforms to always deliver fresh groceries.

Let's see how Ocado's automated warehouse management system works.

Ocado's warehouse looks like a giant hive with a 3D grid structure inside where goods are stored.
Ocado's central system receives information about the products required.
The system sends requests via 4G to robots that start collecting the required products from baskets.
Artificial Intelligence controls all robots via an air traffic control system, so they can collect products without colliding.
Once all products are collected, the order goes to pick stations, where robots or human employees assemble orders. Currently, over 3500 robots operate in Ocado's warehouse, which is 36 acres. Thanks to such automation, Ocado processes 220,000 orders a week.

Real-time cargo monitoring

All international supply chain owners want to increase transparency by tracking ocean freight in real-time—especially those who transport food and need to control the temperature in the transportation unit.

To receive real-time cargo monitoring, consider integrating Arviem. This is a cargo monitoring platform, available for web and mobile, that tracks your cargo in the air, on land, and at sea. Moreover, it tracks the location, condition, and temperature of cargo during the journey of your products. Another advantage of Arviem's is the intelligent ETA calculation. This algorithm will inform you about possible delays so that you can take proactive action.

The Arviem system consists of two main components:

IoT devices that you attach to the cargo at the point of loading. These devices track humidity, temperature, light, and GPS location while the shipment is in transit. Then, IoT sensors send data to the analytics platform.
The analytics platform receives information from devices and provides you with a quick picture of the cargo in transit. You also receive an in-depth analysis and route visualization. In this way, you can uncover inefficiencies and logistics blind spots and improve them.

Now, let's find out what you need to adopt AI and ML in the supply chain and launch your project.

Tips to integrate AI in logistics and supply chain with The APP Solutions

Let's see how you can adopt AI and machine learning in your supply chain.

Tip #1 See the bigger picture

Our clients often quote us for AI algorithm development. But, most of them do not know exactly which problems they need to solve.

Before integrating Artificial Intelligence because it's hype tech, take a look around. Identify the problems you need to solve or areas to improve. If you are in doubt, consider hiring Data scientists. They will gather your business data, analyze it, and advice areas to integrate AI.

Tip #2 Choose wisely

With a clear understanding of your problems, start searching AI developers. During the search, pay attention to the developer's certifications and Clutch profile with reviews from previous clients. Remember to ask about their experience with ML platforms, such as TensorFlow, IBM, and others. You may also want to take a look at their ML-specific case studies.

Tip #3 Develop a proof of concept

Your ML developers need to determine the right data sources for your AI project. They also need to decide the data types to ensure the supply chain has enough information. Next, you and your team will start creating a project scope for proof of concept. In this way, your development team will concentrate on the most critical aspects of your supply chain. As a result, you will receive the right type of AI that drives meaningful outcomes and uncover a clear path for further improvements.

Why adopt AI and machine learning in supply chain management

The adoption of AI into the supply chain is the main priority for 55% of supply chain stakeholders. Such technology helps to increase product quality, improve transparency, and make your business more predictable. Thus, to keep up with the trends in your industry, you also need to integrate AI and machine learning into the retail supply chain.

We hope this article has inspired you to further modernize your supply chain. If you have any questions on AI and ML in the supply chain or have enough courage to develop such software from scratch, write to us, and we will help.

A gentle introduction to dApps in 2022

Pavel Tantsiura — Thu, 17 Feb 2022 05:44:00 +0000

What happens on blockchain, stays on blockchain…and so does your dApp.

In the cryptoverse, Bitcoin tends to guzzle all the limelight. But little did we know that Ethereum will not only overtake Bitcoin but also revolutionize the way we build and use applications.

dApps or decentralized apps have become that trailblazer which is accountable for a new type of software. According to the statistics, dApps are now gaining traction with over 3900 applications that account for 913.28k transactions. These positive dynamics make decentralized apps a lucrative business opportunity to tap into.

So, today, I’d like to tell you more about this hyped beast and whether you should jolt into building one for your business.

A Primer on dApps

First things first, let’s flesh out the definition of dApps in case you haven’t heard of it yet.

Well, dApps are decentralized applications that rely on a blockchain. They are similar to apps, but instead of being hosted on one centralized server like Google Play Store or the Apple App Store, they run on a decentralized network of computers.

Ethereum is considered to be the industry-standard protocol for building this type of application since Ethereum was designed to accommodate smart contracts. The latter constitutes a salient part of dApps and facilitates automatic transactions if certain conditions are met without a third-party entity.

Smart contracts — self-executing contracts that can be completed or executed when certain conditions are met. They operate independently and transparently to the outside world. It is potentially a cheaper, faster, and safer alternative to conventional contract law, lawyers, notaries, and other third parties.

What’s the big deal? Well, smart contracts remove the need for a third party to handle and secure transactions between peers. This translates into a faster run, enhanced security, lower costs, and more accuracy. And now imagine that you have it all conveniently packed into a dApp.

By David McBee: Pexels

How is dApp different from traditional apps?

All hype aside, there should be something that puts dApps on a pedestal over common mobile solutions.

Just to be clear, a dApp is just like any other software application you use — be it an iOS app or a website. However, its differentiators come from Ethereum, and that is decentralization. The latter stems from smart contracts that are hosted on multiple computer nodes all across the world.

Therefore, Ethereum smart contracts serve as the backend for dApps, which is another differentiator from traditional applications. In layman’s terms, a smart contract replaces a server-side component present in a traditional app. However, once you deploy it, there’s no way back. It can only be deleted to introduce any changes you need.

Also, mind that a large monolithic smart contract isn’t a one-size-fits-all option, since it comes with huge deployment costs. That’s why some developers have an external data source (yes, it kinda makes your dApp centralized).

Now let’s have a look at the Ethereum-specific code. In most cases, developers would use Solidity, Serpent, and Vyper programming languages. Below is an example of a ‘Mission complete’ smart contract in Solidity.

pragma solidity ^0.8.10; contract MissionComplete { function printMissionComplete () public pure returns (string) { return ‘Mission Complete!’; } }

Here, your Dapp can execute the code in the smart contract by calling the function printMissionComplete().

As for the frontend, there is no specific language you need to master to make an interface for your dApp. You can use a surefire combo of HTML, CSS, JavaScript, etc or an equivalent stack.

Standard frontend+smart contract backend = Dapp

Why Use dApp as an alternative: Main benefits

When weighing the cons against the pros of decentralized applications, you should never confuse the starting and ending points of innovation. dApps are still at their infancy stage, hence the majority of their current shortcomings can be eliminated in the future.

As for the benefits of using dApps over traditional apps, these include:

Enhanced security — Along with its decentralized nature, a dApp is secured with cryptographic tokens to access the application. Also, thanks to blockchain, dApps are inherently able to keep hackers at bay since all of the hosts must approve of the change.
Unrivaled data privacy— Information is stored as multiple encrypted copies, while the owner of the key has exclusive access. This prevents the information from being stolen or misused.
Zero downtime— Once the smart contract is on the blockchain, your dApp is infallible (unless Ethereum goes down or something like that.)
Cost of development— although it’s an arguable advantage, dApp development doesn’t incur regular spending such as cloud server fees, or maintenance costs. The cost includes contract deployment (which is still impressive), while dApp users pay for all transactions linked with your contracts.

However, it’s not all butterflies and rainbows for decentralized applications. First of all, it’s hard to maintain dApps due to their blockchain-based nature. Also, the Ethereum platform is open about the potential performance overheads of its applications. According to Ethereum itself, there is around 1,000,000x overhead of standard computation currently.

Nevertheless, dApps are in vogue because of their capability to integrate with decentralized finance and yield services which used to be accessible to affluent investors exclusively.

How to build dApps on Ethereum

Decentralized applications have smart contracts as their backbone. The latter is powered with code written in special programming languages. For example, Ethereum has the Solidity smart contract language, while the Waves blockchain platform has RIDE. Hence, dApps can run on top of distributed computing systems such as Ethereum or Bitcoin, while also being stored and executed on a blockchain system.

I will not go into the specifics of building a dApp since it should be made as a standalone post. Instead, I’ll chart out the general recommendations for building a decentralized solution:

Create a smart contract, i.e. write your code in Solidity ( it will connect to the client-side of the application). Here, Remix is usually used as an IDE for your Ethereum-friendly and smart contract development.
Connect your IDE to Ganache. The latter is an Ethereum simulator that allows you to test your smart contract on a real-like blockchain.
Take care of the front end. You’ll need a user interface to interact with your blockchain. A popular pick for this is Web3.js, which is a JavaScript library from the Ethereum foundation.
Test the application. Along with common testing types, you can also include smart contract testing as well as transaction testing.
Deploy your dApp on IPFS. The latter is a distributed system for storing your application.

dApps as a driver of Web3 in 2022

In the 2020s, the news about the potentially disruptive internet iteration has blown up the tech community. Web 3.0 is this headline grabber that makes a slew of promises, including uninterrupted service, no central authorities, and data ownership.

It is heralded the third generation of the Internet, where blockchain technology will play a salient role. The latter will lay the ground for a more autonomous Internetverse, meaning no single entity will govern it.

Decentralized applications are also predicted to make the backbone of Web 3.0 by promoting accessible Internet for every user. Web3, in turn, will enable dApps to replace censored centralized social networks, while allowing users to maintain ownership over their data and be compensated for the value they provide.

But all hype aside, it doesn’t matter whether Web 3 will supplement the current digital generation. The core blockchain technologies that power Web3 and dApps are real and powerful and made for the new generation of development practices.

Some dApps have already demonstrated their potential and raised their profiles as a lucrative investment opportunity. Let’s see the most popular ones:

Minds: a social media platform that improves value transfer between content creators and consumers.
Raydium: Solana’s top decentralized exchange that provides next-level liquidity, and frictionless yield farming.
Golem: first decentralized marketplace for computing power combined with flexible tools to help developers securely distribute and monetize their software.
Steemit: is a blockchain-based blogging and social media platform that rewards its users for both posting and creating content.
Audius: a blockchain-based streaming platform that allows artists to create unique content with a completely unchanging date and time. As you see, dApps can come in numerous flavors — anything from P2P lending services to engaging games to social networks.

The Final Word

Just like anything else in blockchain, dApps are wrapped in myths and legends. However, as they gain more traction and rationale, the stigma around dApps is vanishing. By developing a decentralized application, you can tap into a less-competitive market that is secured for the future.
As for the viability of these solutions, it doesn’t matter whether we’ll have Web 3 as the main Internet version. Today, it’s clear that both realities can successfully coexist together as decentralization is swelling in popularity. Therefore, no matter the future of Web3, dApps will remain as the viable alternative to traditional applications.

FASHION AND TECHNOLOGY: HOW ONLINE CLOTHING RETAILERS CAN LEVERAGE AI IN 2022

Pavel Tantsiura — Wed, 09 Feb 2022 05:46:06 +0000

What do fashion and technology have in common? Retail clothing companies apply Artificial intelligence, Machine Learning, and Augmented Reality to provide outstanding customer service, not only in-store, but also online. Thus, fashion and retail businesses are predicted to spend $7.3 billion annually on the adoption of AI-powered solutions by 2022.

If you're considering developing a fashion e-commerce website or looking for technological trends to refresh your ready-made e-commerce apparel store in 2020, read this article to the end. In this blog post, you will learn how the clothing retail industry leaders leverage technology in the fashion industry, and what AI solutions to consider for your online fashion store.

How technology influences apparel online retail

The main struggles of all online retailers are how to increase sales in retail clothing, reduce shopping cart abandonment rates, and make shoppers return regularly. To achieve this, shoppers should find products more streamlined, receive highly personalized product recommendations, as well as the ability to order products avoiding long checkouts. Let's see how AI and ML technologies help fashion brands to achieve these goals.

Improved engagement because the first thing your customers interact with your fashion e-commerce website is proactive AI chatbots, who ask clients about their preferences and speed up product search. Also, fashion retailers who allow their customers to search for products by images, or discover similar products face a higher engagement rate
Increased income due to more accurate product recommendation feeds and more suitable upselling options. AI Machine Learning algorithms analyze a shoppers' behavior, gather information about a customer's preferences in style, and understand your fashion shoppers even better than human stylists.
A higher customer retention rate is the result of previous advantages. If the customer receives relevant fashion products every time they interact with your online store, they will shop at your fashion e-commerce website more often.
Greater brand loyalty. Each time shoppers receive assistance from AI-powered chatbots, and find fashion items via image Recognition-powered search, they begin to perceive your online fashion store as a close friend who understands their needs.

In a nutshell, Artificial Intelligence solutions, and machine learning algorithms, transform the way shoppers interact with e-commerce fashion retail while increasing the number of loyal customers and income.

Top 5 use cases of technology in fashion online retail

Now, let's take a look at e-commerce technology trends and how key fashion brands utilize them.

Fashion and AI-powered computer vision for similar product suggestions

Each time shoppers see that their desired item is out of stock or in the wrong size, they abandon your online fashion store, and you lose money. The solution is an advanced product suggestion algorithm, powered by Computer Vision. Unlike other AI algorithms trained with customer data and behavior patterns, this artificial system obtains information from product images, analyses its attributes such as color, size, style, etc., classifies them, and suggests other items based on visual similarities. RIS "29th Annual Retail Technology Study" says only 3% of retailers use Computer Vision in their online store, while 40% of fashion brands will implement this tech within the next two years.

One example of an online fashion retailer that implemented Computer Vision for a more precise recommendation is Macy's, an American department store chain. To integrate intelligent suggestions with cognitive learning capabilities, a fashion retailer teamed up with IBM Watson, the most powerful AI platform for business. Once you start to discover products, you receive suggestions of visually similar products in the "More Like This" section. You can apply filters, including color, patterns, style, and price, to receive different suggestion results.

AR/VR in product merchandise

The main reason why some fashion shoppers avoid buying online is that they need some in-store experience where they can touch and try items. The absence of a try-on option concerns fashion jewelry retailers, who receive the most income from offline sales. So, how to fill in the gap between fashion online shopping and buyers' desire to try jewelry before buying?

The solution is a virtual try-on feature powered by Augmented Reality, integrated into an online store or e-commerce mobile application. AR try-on uses algorithms and sensors to detect the position of a buyers' camera on mobile devices, and then overlays 3D objects atop real-world surroundings, making the online shopping experience more personal, unique, and engaging. Thus, virtual AR try-on helps fashion shoppers to try any product without leaving their homes, which significantly impacts a buyers' decision to make a purchase. Around 72% of fashion shoppers bought a piece they didn't intend to buy after using AR virtual try on.

Pandora, a fashion jewelry retailer, applied this technology for its shopping mobile application. We participated in this project as developers of 3D models of jewelry.

To try on any jewelry from Pandora's product catalog, users need to complete the face-scanning process so the app's algorithm can see the borders of the face and then place 3D models of jewelry, developed by us. Pandora app users can also get all the information about the jewelry that caught their attention.

[Our 3D jewelry pieces created for Pandora AR store app]

Chatbots with Natural Language Processing

Shoppers who have questions concerning your fashion products will be disappointed and leave your online store if customer support answers too slowly. For 44% of online shoppers, a live chat where they can have their questions answered is a decision-making factor to shop at your store.

At the same time, maintaining a contact center with hundreds of online managers might be too costly. If you want to provide customers with high-quality services while keeping costs low, consider integrating a chatbot with Natural Language Processing. Servion Global Solutions' research predicts AI will power 95% of customer interactions by 2025.

Apart from online chatting and answering FAQ, chatbots can show order status updates, compare products, search for products, and perform as virtual assistants for online shoppers. Chatbots ask customers to type the item needed. Thanks to Natural Language Processing, a chatbot understands the user's input and comes up with relevant search results.

Louis Vuitton is one of the fashion brands that have leveraged AI-powered chatbots for their Facebook page and increased customer engagement. LV chatbot is powered by mode.ia platform and utilizes Natural Language Processing to help clients to find the exact item they are searching for.

Fashion shoppers can put the request (e.g., red dress in size small), and then browse products by color, price, patterns, etc. After the shopper has found the perfect item, LV chatbot redirects the customer to the Louis Vuitton website to complete a purchase.

Image recognition for Visual Product search

Fashion online stores that have a long journey to finding desired products no longer satisfy the needs of modern customers. Also, the vast popularity of fashion influencers, such as Selena Gomes, Kylie Jenner, and Kim Kardashian, push shoppers to search for similar bags such as those their idols have.

The best way you can optimize on-site search is to integrate image recognition technology. Visual product search works like Shazam for clothes. To allow customers to find items using pictures, developers apply Deep learning technology and teach the Image recognition algorithm using a massive amount of training data to identify different products, colors, shapes, etc. Then, the AI algorithm identifies items from the image uploaded, finds similar or identical items in your product catalog, and shows the search results to customers. Thus, shoppers save time on typing words to keyword-powered search and receive desired fashion items immediately. Visual product search integration results in twice as quick checkout, compared to keyword search and a 20 – 30% increase in conversion for product categories.

Farfetch, an online luxury fashion retail platform, integrated Visual Search powered with image recognition to their mobile shopping app. To make visual search possible, Farfetch co-operated with Syte.ai, an AI startup, that specializes in developing Image Recognition algorithms for online retailers and marketplaces across all industries.

Now, shoppers can easily find the same clothes as their favorite singer or Instagram influencer wears by taking photos and uploading them to the app.

[Farfetch AI clothing search via image, source]

AI fashion stylists

As you may know, personalization is key to fashion shoppers' hearts. To make customized recommendations even more relevant to customers, consider investing in AI-powered stylists that analyze information from uploaded images, detect patterns in photos and suggest similar items from your product catalog that meet a customer's preferences.

Thread, the online fashion company, nailed fashion AI outfit personalization in their online virtual stylist. The mission of Thread is to apply Artificial Intelligence to help men to dress well, more comfortably, with efficient accessibility. Thus, this service is targeted at men who value their time and money and prefer using this service than surfing the Internet for relevant products.

After you upload your photographs, measurements, info about your current wardrobe, and budget, an online service assigns you an AI stylist who will suggest clothes for you to buy from partners' catalogs. Currently, the platform is partnering with over 500 brands with different pricing and styles, including high street retailers such as Marks & Spencer and River Island, to luxury clothing brands like Burberry.

Technological future of AI and fashion online retail

The online fashion retail industry is the most competitive yet profitable niche. To stay afloat with your online fashion brand, you need to empower your online store with streamlined in-site search, immediate customer support, and more personalized suggestions. Now that you are aware of AI and ML-powered fashion retail trends, the last thing remaining is to hire developers, experienced in AI and ML algorithms.

Top Impacts of Artificial Intelligence in 2022

Pavel Tantsiura — Tue, 25 Jan 2022 08:36:57 +0000

AI-based technologies have been the focus of attention over the last few years. The numbers attest to the sudden spike of intelligence software. Thus, by 2028, the revenue of the global AI market is projected to reach over $997 billion.

Artificial intelligence has proven to be the disruptive element of the digital data-driven era. Backed by tech giants like Amazon and Google, its dominance is now future-proof.

Today, we’ll have a look at the most significant use cases of automation in 2021. You’ll also learn about the game-changing impact of COVID-19 on the AI market.

Artificial Intelligence VS COVID-19: What’s the link?

The global pandemic has crippled a great number of businesses and laid bare tech gaps in multiple industries. However, soon after the Covid-19 pandemic was declared, the World Health Organization suggested that artificial intelligence (AI) could be a crucial asset for fighting back the virus. Companies have also realized the positive impact of replacing error-prone human enforcement with advanced automated systems.

And just like that, AI came into the fray as a powerful healthcare and general business tool that allows companies to detect and diagnose virus strains fast, as well as battle pandemics by using personal data.

Thus, based on typical computed tomography pictures, an artificial intelligence system recognized 17 out of 25 COVID-positive patients in a 2020 NCBI study, while specialists diagnosed all patients as COVID-negative.

With that said, let’s have a recap of the most important AI application areas that never ceased to amaze us in 2021 and will continue to do so in 2022.

Artificial intelligence and healthcare

While on this note, we’ll have a closer look at how automation and algorithms bring us closer to precise medicine and better patient outcomes.

According to a study, one in 10 people with a major vascular event, infection, or cancer is misdiagnosed. However, intelligent algorithms are uniquely positioned to minimize the error rate. With its ability to comb through oceans of data, AI holds the potential to make medical examinations more accurate and faster.

Let’s take the high-speed diagnosis of COVID-19 as an example. The whole diagnostic process usually needs a careful examination by an experienced professional that takes hours. If we pit human abilities against AI, the latter exchanges data with hospital CT scanners and analyses images of the lungs taken by a CT scanner. Then it discerns the signs of coronavirus and assesses the lesions - all these in mere seconds.

Moreover, trailblazing applications in AI in healthcare also include:

More accurate medical imaging
AI-assisted surgeries
Remote patient monitoring (e.g telemedicine)
Automated gene editing
Virtual nursing assistants
Chatbot-assisted triage, and others.

Overall, the complexity and advent of data in healthcare bode well for the proliferation of advanced AI algorithms for more efficient and accessible medical services.

Automated marketing

Granular personalization used to be a far-fetched dream of marketers all over the world. Today, it’s a dream come true thanks to Artificial Intelligence. AI-driven customer segmentation allows brands to divide the audience into meaningful groups based on their preferences. This, in turn, leads to more contextual marketing efforts that rely on the specific customer persona.

According to Forbes, AI-enabled marketing efforts boost sales by 52% and help retain more customers. In the case of new products, it increases its chances for success by almost 50%. Also, automation goes a long way in the industry - from user data collection to content generation.

Faster data analysis, streamlined marketing efforts, and cost savings have secured intelligent marketing into the future. From email marketing to dynamic pricing, we’ll see more AI adding to brand sales and getting actionable marketing insights.

Manufacturing and business processes

According to Forbes, the pandemic-induced crisis demonstrated manufacturers an opportunity to strengthen production and make it more sustainable by adopting new resources. Hence, a growing number of manufacturers are doubling investments in artificial intelligence-based analytics and intelligent systems.

Thus, the global AI in the manufacturing market size stood at $1.82 billion in 2019 and is expected to grow to $9.89 billion by 2027. Heralded as a driving force behind Industry 4.0, artificial intelligence is widely used in quality control, inventory management, and monitoring.

Predictive maintenance has also proved to be an important application of AI. According to Capgemini, 29% of AI implementations in manufacturing are for maintaining machinery and production assets. Thanks to data analysis tools and computer vision, smart systems can detect anomalies and possible defects well before they lead to costly downtimes.

Thus, General Motors analyzes images from cameras mounted on assembly robots, to identify signs of faulty robotic components. Shell has also been spearheading the adoption of predictive maintenance. The latter helps the petrochemical manufacturer to boost equipment reliability and extend the overall operational life of its assets.

Logistics and supply chain management

Intelligent algorithms are hitting new adoption levels each year, with logistics being another focus in the AI race. Supply chain disruptions to the sector caused by the pandemic solidified the need for more automation and data-driven management. Besides crisis management, the benefits of AI for transportation are manifold.

Smart tools and solutions help analyze huge data sets in real-time, thus balancing supply and demand gaps and assisting companies in planning production activities. AI solutions allow carriers to analyze existing routes, identify bottlenecks and focus on the best route. This, in turn, reduces both time and the overall cost of warehousing and delivery. AI- and ML-based data processing tools help capture details related to the shipment of the goods in real-time and estimate delivery windows.

Moreover, around 40% of respondents state that artificial intelligence can help improve inventory management. The foretelling power of AI can help suppliers forecast the demand, optimize stock levels, and reduce lead times for customers. Warehouse robots have also become popular for performing essential warehouse tasks, such as picking, sorting, and transportation.

Banking and financial sector

The banking and financial services industry is one of those brick-and-mortar fields that are opposed to digital transformation. Nevertheless, artificial intelligence has managed to permeate this industry and disrupt legacy processes. According to McKinsey, the potential of AI for banking is the greatest across industries. Thus, artificial intelligence can potentially unlock $1 trillion of value for banks, annually.

Besides improved customer experience and reduced costs, smart systems can avert frauds, improve loan decisioning, and automate the investment process.

Today, AI takes the form of document automation software and banking chatbots. Also, most financial services companies have implemented the technology in areas like risk management (56%) and revenue generation through new products and processes (52%).

However, as decentralization comes into the limelight, we’ll certainly see more of this duo revolutionizing the banking industry.

The Future of Artificial Intelligence

Artificial intelligence has stepped in as the driving force behind the majority of emerging technologies. Big data, analytics, robotics, and IoT are all relying on intelligence to change our world for the better.

In the foreseeable future, global industries are set to witness more AI adoption as the acute need for automation is surfacing due to the pandemic crisis. Runner-up companies will be those that have managed to adapt, become more flexible, and offer a better customer experience. Artificial intelligence can address those needs by providing more automated applications and taking the form of business intelligence and robotic automation.

Web 3.0 vs Web 2.0: What are the differences?

Pavel Tantsiura — Thu, 20 Jan 2022 07:37:38 +0000

Web 3.0 seems to become the overriding trend that is gaining momentum each day. Everyone - from developers to investors - are engrossed in bickering over its cons and pros.

By what makes it THAT special? And why isn't Web 2.0 good enough? Let's find out: https://theappsolutions.com/blog/development/the-difference-between-web2-vs-web3/

Best Apps For Emergency Medicine In 2022 (Free and Paid)

Pavel Tantsiura — Tue, 14 Dec 2021 06:25:41 +0000

The job of EM clinicians can be demanding, nerve-racking, and even isolating. Not only do they handle multiple patients at a time, but they also have to determine the next best step in mere seconds.

Severe pains, breathing difficulties, or a heart attack — healthcare workers have to be prepared for any emergency medicine situation. And the only thing that will help them stabilize patients is sufficient medical knowledge.

Do they have the time to comb through dozens of medical references? No.

But they have their smartphones that can become the ultimate medical encyclopedia.

In particular, mobile devices can provide instant access to emergency medicine expertise, whether you’re a healthcare professional or just a medical enthusiast.

Today, we’ll have a look at the top 10 emergency medicine apps. Most of these emergency medicine apps are well-known in emergency departments and among physicians. Some of the mentioned references are free, but most of them are subscription-based.

The State Of Market For Emergency Medicine Apps

The advent of mobile devices in the healthcare sector has transformed many aspects of clinical practice. In healthcare settings, telemedicine solutions and emergency medical apps are becoming more widespread to aid practitioners in a variety of activities, including critical care and emergency medicine.

According to a report, the global emergency medicine apps market size stood at $588.9 million in 2019. During the research period of 2020 to 2027, it is projected to climb to a CAGR of 39.4%.

Among the biggest market drivers is the increasing disease frequency such as trauma, stroke, cardiovascular diseases, and other urgent care emergency medicine conditions. Thus, CVD is the leading global cause of death and accounted for approximately 18.6 million deaths in 2019.

That is why EM clinicians should strictly follow instructions while providing immediate treatments.

Overall, emergency medicine apps fall into the three categories according to their type:

Pre-hospital medic and triaging mobile apps, including emergency medicine;
Hospital-based mobile apps for professional communication;
Post-hospital mobile apps for medication management, rehabilitation mobile apps, and care providing.

Each category of medical mobile apps includes a different set of functionality based on their target users. However, the majority of healthcare apps must have data integration with Epic to enable quick exchanging of medical data and access comprehensive medical info on any patient.

Now that we’ve gone over the basics, let’s have a look at the 10 best apps for emergency medicine according to their rating and popularity.

WikEM

Availability — Android and iOS
Price — Free (+in-app purchases)
Rating — 4.8 (Android), 4.8 (iOS)

WikiEM, which is also known as the Global Emergency Medicine Wiki, is one of the largest online point-of-care references to assist physicians with their daily practice. This emergency medicine app is like a Wikipedia version for emergency medicine. Global Emergency Medicine Wiki is open to the public and boasts over 100,000 downloads.

This online emergency medicine knowledge library contains thousands of medical notes to aid professionals in patient management. The point-of-care smartphone app is designed for a variety of medical professionals, primarily those working in emergency medicine.

Heart Pathway

Availability — iOS
Price — Free
Rating — 4.5 (iOS)

Heart Pathway was built as a clinical protocol and decision tool for better risk stratification of patients with chest pain. This emergency medical app uses an evidence-based algorithm that considers a patient’s age, symptoms, cardiac risk factors, and ECG changes. This data is then used to estimate a HEART value.

The HEART score is a validated medical tool developed in the Netherlands in 2008. A score of 3 or less carries a less than 1% risk of major adverse cardiac events in the next 30 days. These patients can be safely discharged from the ER without further workup.

Thanks to its analytical functionality, this application proved to reduce patient length of stay by 10+ hours and cut down medical costs. Therefore, this free emergency medicine application helps discern low-risk chest pain patients who can be safely sent home without hospitalization.

From a technical side, the Heart Pathway emergency medicine tool relies on Impathiq’s IQ Engine, a scalable technology platform that helps medical providers implement new clinical pathways into EMR-integrated web applications.
Currently, the app is available only on iOS free of charge.

MDCalc

Availability — Android, iOS
Price — free
Rating — 4.9 (iOS), 4.9 (Android)

MDCalc is a mature medical calculator that offers a variety of clinical decision tools including risk scores, algorithms, formulas, dosing calculators, and more at one click. According to EB Medicine, over 50% of U.S. medical workers use MDCalc’s 370+ tools on a day-to-day basis.

Created by board-certified physicians, this free medical calculator assists 35+ specialties and 200+ patient problems. Although MDCalc is mainly used by clinicians in North America, it is establishing a global presence, with users in over 100 countries.

Another boon of MDCalc is that it allows medical providers to get AMA PRA Categories 1TM CME credits by reading the clinical information for 150+ calculators.

Emra PressorDex

Availability — iOS
Price — $16.99
Rating — 3.1 (iOS)

This medical app is based on a pocket guide to critical medications that provides comprehensive information on pressors, vasoactive drugs, continuous infusions, and other medications. The application provides you with needed information for choosing the right medication and dosages for acutely ill patients at an emergency department.

Emra PressorDex is one of the digital products of The Emergency Medicine Residents’ Association. The latter is the resident organization that is involved in emergency medicine physicians-in-training.

Emra PressorDex is currently available only on iOS.

PalmEM

Availability — iOS
Price — Paid ($23,99)
Rating — 4.6 (iOS)

PalmEM is a comprehensive point-of-care reference that covers everything from pediatrics to ultrasound and other most commonly encountered conditions in emergency medicine. This emergency medicine app includes a whole range of knowledge, including medication references, clinical decision rules, and other info on various conditions.

However, the summaries are by no means in-depth examinations. Instead, references are meant to be a quick overview of the subject for those who already have a medical background at emergency medicine departments.

Although the app is paid, users don’t have to buy annual subscription fees, since the application is available for a one-time download fee.

Amion Physician Calendar

Availability — Android, iOS
Price — free
Rating — 4.5 (iOS), 3.1 (Android)

Amion is a free open access physician scheduling app that helps healthcare institutions keep track of shifts, schedules, vacation resorts, and others. Along with scheduling, this application includes a great number of features for emergency physicians and healthcare providers.

These includes:

Receiving schedule change alerts;
Customizing calendars with color-coding;
Exchanging HIPAA-secure text messages;
Group text messaging;
Accessing colleagues’ Doximity profile for information on fax numbers, back lines, and emails.

Amion Physician Calendar is available on Android and iOS for free.

Pedi-STAT

Availability — Android, iOS
Price — $5.49 (Android), $4.99 (iOS)
Rating — 4.4 (iOS), 4.6 (Android)

Pedi-STAT is another point of care emergency medicine app that aids nurses, physicians, and other professionals in the emergency or urgent care environment.

Pedi-STAT was created as a drug dosage application that offers medical references in a color-coded mobile format to minimize discrepancies in an emergency. For each type of pediatric emergency, EM specialists can look up the drug dosage, ranges when relevant, and any other pertinent information. Users can adjust the patient’s age, weight, or height.

This emergency medicine app assists emergency physicians in a rapid calculation of the accurate medication dosage for a patient according to their parameters such as weight and age. Peti-stat also offers resources like resuscitation guidelines for young patients.

Among other emergency medicine features are the following:

Information on airway interventions including endotracheal tube sizes, depth, and others;
Age and weight-specific pediatric equipment;
Seizure medication dosages;
Management of hypoglycemia;
Reference of age-specific normal vital signs;
Calculated pain management medications;
Medical management of allergic reactions and anaphylaxis, and others. Healthcare professionals can download Pedi-STAT on Android and iOS for $5.49 and $4.99 respectively.

UpToDate

Availability — Android, iOS
Price — Individual or institutional subscription required
Rating — 4.0 (iOS), 4.5 (Android)

UpToDate is an emergency medicine app that is among the most popular clinical decision support references with hands-on healthcare expertise.

It’s an evidence-based clinical decision support resource that covers everything from drug topics to recommendations for the point of care. The application comprises over 25 specialties.
UpToDate is a fantastic way to remain up to date on medical breakthroughs and news, whether you’re studying medicine or simply enjoy learning new things.

The mobile app features are quite standard and include:

Login;
Search with auto-completion;
Earn and track free CME/CE/CPD credit;
Favorites;
Print and email topics or graphics to patients and colleagues;
Options for desktop access and mobile complete for remote use;
Clinical calculators;
Detailed medical graphics and others. However, this emergency medicine application is quite pricey since you need to buy an annual subscription of $500 or have an institutional account. You can also get 30 days of a free trial.

Emergency Central

Availability — Android, iOS
Price — $159.95
Rating — 4.7 (iOS), 2.8 (Android)

Built for emergency medicine, Emergency Central shares comprehensive information on various specialties, diseases, drugs, and test information for clinicians at the point of care. This emergency medicine app also offers treatment resources with instant answers which are especially helpful for emergency care.

Its core features include:

In-depth information on treatment strategies, follow-up, and others;
Differential diagnoses;
1-year free drug updates;
Crosslinks for faster access;
Full PubMed database;
Text search.

5-Minute Emergency Medicine Consult

Availability — Android, iOS
Price — $99.95
Rating — 4.7 (iOS), 2.8 (Android)

Rosen & Barkin’s 5-Minute Emergency Medicine Consult is a smartphone and tablet app that provides useful, quick-read information on over 600 medical issues seen in emergency medicine.

This mobile resource, which was written and edited by practicing doctors, is great for verifying accurate diagnoses and initiating treatment in the fast-paced context of urgent and emergent care.
Each entry is laid out in a tried-and-true rapid-access manner, enabling users to search for medical conditions, confirm a diagnosis, and start treatment.

The app is available on Android and iOS.

The Bottom Line

The proliferation of urgent care centers, as well as initiatives to reduce hospital admissions, has led to shifts in the healthcare environment. Digitalization is one of the most prominent shifts that allow physicians and critical care nurses to improve emergency department traffic as well as streamline patient care.

The list of mobile apps mentioned above includes general-purpose mobile solutions that can be helpful to healthcare professionals or provide instant reading at emergency medicine departments.

Top 5 Predictive Analytics Tools In 2021

Pavel Tantsiura — Thu, 21 Oct 2021 06:29:49 +0000

Data is the new oil.

How many times have you heard this cliched catchphrase? Our guess is - over 100 times.

While we’re all tired of reminding that data is extremely important for future business intelligence, there’s a double meaning behind this phrase.

First of all, just like oil, raw data isn't valuable in and of itself. Secondly, the value of oil has decreased dramatically in 2021, while data is in more demand than ever before.

The true value of data crystallizes when it is gathered completely and accurately, gets intertwined with other relevant analytics data, and is used to make forecasts.

The latter is when predictive analytics tools step on the scene.
Today, we’ll unravel the mission of predictive analytics and data mining by looking into the most popular predictive analytics tools in 2021.

A Short Primer On Predictive Analytics

Predictive Analytics Market Size is expected to register a record value of $5.7 billion by 2026. A myriad of factors drives the adoption of the predictive analytics market. These include digital transformation, innovations in business intelligence, data science, Artificial Intelligence, Machine Learning, and neural networks as well as remote practices due to the COVID-19 pandemic.

Essentially, predictive analytics relies on data science, Artificial Intelligence, statistical algorithms, and Machine Learning algorithms to predict possible outcomes. The assumptions predictive analytics makes are based on historical data.

But the biggest challenge for data science and predictive analytics in 2021 is that all historical data has been rendered useless.

Guess, who’s to blame? (clue: it starts with C and ends with 19). Luckily, innovative predictive analytics tools are here to ensure business resilience. The new future needs new data patterns.

What Are Predictive Analytics Tools?

Predictive analytics software refers to smart tools that process the existing data and use it to look into the future. But it’s not like you’re reading tea leaves to forecast your business future. The powerful combo of Machine Learning and Data Science ensures predictive analytics fetches an accurate prediction of your business's future.

In 2021, data scientists don’t leverage the traditional approach of relying on historical data. The new real-time forecasting approach feeds on the current data that is relevant given the current state. In particular, predictive analytics now relies on any data gathered since March.

How Your Company Can Benefit From Predictive Analytics Software

Most business owners follow their gut feeling when they need to make an important decision. This flair is usually based on their own experience, professional advice, as well as their partners, friends, or relatives. And even if the idea pans out, it’s no more than a fluke.

On the contrary, predictive analytics gives you a significant competitive advantage. It uses data mining, Machine Learning, and statistical techniques to squeeze the juice out of available data.

Now, imagine that you always have a heads up on your business, know upcoming business trends, and the best solution in any situation. That’s right, you become invincible with 100% business resilience and predictive analytics.

Here’s how predictive analytics solutions can improve your bottom line:

Find new product/service opportunities - by analyzing customer’s preferences, buying behavior, and responses, companies can tap into new areas of business or improve existing services;
Optimize product and performance - predictive analytics tools also provide a deep look into the commercial offerings and supply chains as well as point out the improvement opportunities;
Gain a deeper understanding of customers;
Reduce costs and risks - by studying your past mistakes, you can prevent similar mistakes as well as select adequate precautionary actions to minimize the losses incurred.
Meet consumer expectations - the advanced predictive analytics software will help you fine-tune your marketing tactics to cover the varying requirements of your current customers.

Real-World Use Cases of Predictive Analytics

If you’re still unmotivated to know more about predictive analytics, I’ll give you an inspo board. This is how businesses of all sizes use predictive analytics solutions.

Churn Prevention

A churn prediction system uses big data to identify customers who may stop using your service or buying your product regularly. Such systems are used by most large SAAS companies to identify those most at risk of customer churn.

Fraud Detection

Predictive analytics tools and data mining may be some of the most effective ways an e-commerce merchant, bank, and other businesses can detect fraud. The predictive analytics system analyzes past data, trends, and variables, and then provides smarter fraud score algorithms.

Predictive maintenance

To keep their assets in peak operating condition, manufacturers pay due diligence to the well-being of their machines. Predictive analytics paves the way for predictive maintenance and monitors the current state of machinery for any irregularities or potential downtimes.

Rolls-Royce, for example, monitors the aging of safety-critical I&C components with the help of Artificial Intelligence and predictive analytics software.

Healthcare diagnosis

Predictive analytics aims to inform clinicians of the possible events and outcomes, thus allowing them to prevent and treat health issues. It can be anything from detecting early signs of patient deterioration to delivering predictive care for at-risk patients.

Virtual assistants

Coupled with deep learning, predictive analytics empowers the accurate work of virtual assistants. Solutions like Siri, OK Google, and Alexa are the most prominent examples of predictive analytics tools.

They collect data, and through deep learning and predictive analytics, model the response and predict user behavior.

Now let’s have a look at what data analytics tools can fast-forward your data-centered transformation.

What Data Analytics Platforms And Tools Are In Vogue Right Now?

If you haven’t unearthed the potential of predictive modeling data and predictive analytics, you can use the list below to get acquainted with top suppliers in this field.

SAS Advanced Analytics - Best business intelligence tool for enterprise

SAS Advanced Analytics software is coupled with cutting-edge algorithms that assist businesses in solving even the most intractable problems and embracing predictive analytics.

SAS predictive analytics software has a full suite of analytics tools that guide your decision-making process and enable process automation at each data stage. Thus, you can rely on SAS advanced analytics for data and text mining, forecasts, data visualization, and machine learning.

Besides, key features of SAS data management solution include:

Data Mining;
Statistical Analysis;
Forecasting;
Text Analytics;
Predictive Analytics;
Optimization and Simulation.

In particular, SAS Data Mining capabilities underpin predictive modeling to uncover juicy opportunities and look into possible outcomes. Text Analytics helps sift through massive volumes of unstructured data to detect emerging trends and empowers the process of predictive analytics. Optimization and Simulation functionality models scenarios and offers recommendations to real-life challenges.

Among other pros of SAS are the easy-to-navigate drag and drop display and great visual dashboards. However, to use predictive analytics, you’ll need comprehensive training.

SAS Advanced Analytics Pricing Plans include a free trial and quote-based plans per user. In the second case, you’ll need to contact a vendor.

IBM SPSS Analytics Platform - Powerful analytics solution for researchers

No list of data and predictive analytics giants would be complete without IBM products. IBM SPSS Statistics is a cloud-based analytical software that empowers companies with granular statistical analysis of business data.

IBM SPSS Statistics is a go-to data predictive platform for lots of companies, research centers, and independent analytical agencies that solve unique business tasks.

Advanced statistical procedures and visualization in the SPSS system provide a reliable and user-friendly platform for processing subject data and solving complex business and research tasks. So whether it’s beating competitors or conducting research, you can count on this modeling tool.

The software supports the following statistical research methods:

regression analysis,
decision trees,
forecasting,
neural networks,
categorization,
combinatorial analysis,
complex sampling and others.

Among other key features of the SPSS Statistics Analytics:

The system provides customized functions and user interfaces that cater to different skill levels and assigned responsibilities.
SPSS Statistics covers all major parts of the complex analytical process - from data preparation and management to predictive analytics and reporting.
SPSS offers you sophisticated statistical capabilities for analyzing the exact outcome.
SPSS provides business users with design, plotting, reporting, and presentation features for more clarity, making it easy to put your results in a digestible form for other stakeholders.

Pricing plans vary depending on the version. IBM SPSS Statistics is available for a free trial. It’s the main element of the SPSS Statistics suite that equips users with data access, data management, data preparation for analysis, data analysis, and report creation.

You can also extend your Based edition for $99.00 per user per month with three optional add-ons or buy perpetual and term licenses for Base, Standard, Professional and Premium packages, including options to customize your configuration.

SAP Predictive Analytics Platform - Ultimate software for Big Data

This predictive analytics software enables AI-powered analytics and aims to make it as accessible as possible to the general audience. With augmented analytics features, business owners can tap into predictive analytics products with no intervention from data scientists.

SAP Predictive analytics offers both on-premise implementations and a cloud-based version.

SAP Predictive Analytics opens up a wide range of possibilities:

Conversational analytics - Natural Language Processing functionality allows business users to ask questions just like they would to their colleagues and get answers explained in natural language.
Automated Machine Learning - self-service models that uncover relationships in your data and produce new insights with no manual input.
Smart Insights - allow users to drill into their data visualizations without manual effort. Thus, they can look at the top contributors behind values or variances with just a click.
Multifaceted set of features - allow you to automate data preparation, predictive modeling, and deployment, and easily monitor and re-train models.
Uses integration with the R programming language to drive a variety of additional algorithms and custom scripts.

SAP is compatible with Microsoft Azure and provides users with multiple Microsoft Azure services with one Advantco adapter and with no coding needed.

You can either try SAP Predictive Analytics for free or request a demo.

Knime Analytics Platform - Analytics platform With intuitive Node-based workflow GUI

Knime Analytics Platform is an open-source framework for predictive analytics for data analysis. This framework allows you to implement the full cycle of data analysis including data reading from various data sources, transformation, and filtering, analysis, visualization, and export.

It’s considered the top platform among predictive analytics tools for business intelligence due to its statistics and specific calculations, automatic AI learning, and integrated predictive modeling.

Knime Analytics is an enterprise-grade tool for predictive analytics that relies on the might of the Eclipse platform. A powerful combination of other add-ons like Machine Learning algorithms and data mining assist business users in unraveling hidden data and gleaning business intelligence.

The main differentiator of KNIME is its modular workflow approach. The latter documents and stores the analysis process, while leaving results always available for the user.

Core KNIME features include:

Streaming Analytics,
Big Data Analytics,
Statistical analysis,
Reporting and analytics,
Intelligent data analysis (IDA),
Data Visualization,
Machine Learning,
Import/export of workflows (for exchanging with other KNIME users),
API availability,
Forecasting and predictive modeling.

Moreover, Knime Analytics offers over 2000 modules, a wide range of integrated tools, sophisticated algorithms, and ready-to-deploy examples. This predictive analytics software supports only desktops and is a great fit for both SMEs and enterprises. It works on Microsoft Azure and enables data science cloud deployment.

The drag and drop node function enables users to automate a huge number of database processes such as transactions, reading/writing, and others.

The service is available on an annual subscription that is based on 5 users and 4 cores for execution.

Rapidminer Studio - Top free predictive analytics software

RapidMiner is a powerful and multi-user analytics platform with an extensive library of 1500+ algorithms and tools that provide the best model for every use case.

RapidMiner Studio is an end-to-end data analytics tool. It is empowered by data modeling and machine learning that enable robust predictive modeling. The software is mounted on a fast drag and drop interface and offers active community support.

Users can access ready-made templates for common use cases, including customer churn, preventive maintenance, supply chain management, and others. Here, data scientists repurpose existing code and embed new functionality via pre-built extensions.

This analytics platform provides data mining and Machine Learning procedures for a spectrum of procedures, including:

Predictive and statistical modeling,
Data set loading and transformation,
Analytics Data visualization,
Scoring,
Data sets transformation,
NLP extension, and others.

Designed for data science teams, RapidMiner software amplifies the entire life cycle of data science, including data preparation to machine learning, visualization of results, model validation, predictive model deployment, and model optimization.

New users start with a 30-day trial of RapidMiner Studio Enterprise and then request pricing for their enterprise.

The Bottom Line

Data analytics has quickly made it to the front and center for businesses of all sizes. To follow the right path and meet the right future, you need to be able to gather, analyze data, and juice new insights. To do that, companies leverage business Intelligence services that bring easy data mining to the table.

In this post, we had a quick look at the most popular data analytics platforms and predictive analytics tools in 2021. We can tell that there are no clear champions since all of them target different pain points of data analysis. You should make a decision based on your business needs and budget.