As per a Statista report, the food delivery app sector will see massive growth of  $2013,142 Mn by the year 2024. Yes, the growth prospect is immense but it is not easy to create, develop a brand new app and then launch it in the market and eventually succeed! You need to consult an agency, a mobile app development company to guide you from the scratch.

Lets’ delve deep and find insightful points to keep in mind while going for food delivery app development for your business.

Standard Business Models

Certain business models are followed in the process of food delivery apps. The very app created needs to ally with monetization strategies set by your business, you should know how to develop the app from scratch, market it, and reach your target audience with hopes and bright prospects.

As said a while back, it is possible to do this all by collaborating with a genuine mobile app development agency that can guide you from the scratch!

Let’s see those standard business models that are adopted towards developing a quality food delivery app for business!

P2C/ Aggregator Model

One of the major ways that a typical food delivery app operates is via the Peer-to-Consumer (P2C) model. Also called an aggregator model, it incorporates a system where customers/consumers can place their orders through some 3^rd party service provider(just like Swiggy, Zomato).

So, this model involves an on-demand marketplace that acts as an enabler for online orders and home delivery of ordered food by customers. The latter can place orders using a mobile app or web app platform. Some popular names following this model are Grubhub, Amazon Restaurants, Eat24, and so on.

 R2C/ Restaurant-To-Consumer Model

This kind of business model allows restaurants, food providers, or businesses to directly cater to their end consumers. They reach out to customers/consumers and deliver their orders directly to them. Food providers are usually full-fledged restaurants, and food trucks, that directly sell their ordered food to consumers.

Notable examples who have adopted the R2C model are Dominos, Pizza Hut, varied restaurant chains, and local food chains that send their delivery agents/delivery boys for home delivery of online orders. The very standard model is conducive for businesses that have limited resources and sitting space, and infrastructure to expand operations.

The pandemic has greatly boosted this model facilitating restaurant businesses to create specific food delivery apps for their business. Indeed businesses are flourishing thanks to the mobile app development idea duly imbibed in their core operations. This model is apt for smaller geographic locations catering to individuals directly.

How Artificial Intelligence Reshaping the Food Industry

So have you ever waited at the counter of a fast-food restaurant in doubt about what to order? Well, which could become an issue in days gone by – with the development of AI, many Food and Beverage Companies Leveraging AI, which is like preparing something special for us.

Food Delivery App Features

Once you have selected the business model most suitable to your business type, you get started with the custom food delivery app development process.

Normally, there are three features created in an app:

• Features for Customers
• Features for Delivery Agents
• Features for Admins/owners

Once you are clear with what business model you want to go for your food delivery app! You need to focus on the functional features of the app. A food delivery app has three prominent users as follows:

Features for Customers

• Easy  SignUp feature
• Advanced Search with Filters feature
• Automated/ Real-Time Location Detection feature
• Access to In-depth Restaurant  Information feature
• Smooth & Easy Order Placement feature
• Secured Online Payment feature
• Online Review/Comment/Rating feature

Features for Delivery Boys/Agents

• Creating User Profile feature 
• Notifications/Alerts feature
• Multiple Deliveries Shedulement feature
• Integration of Google Maps feature 
• Call & Chat feature 

Features for Admin/Owner Dashboard

• Managing Order- Request feature
• Real-time Menu Update feature
• Payment/Commission Administration feature
• Offers/Coupons Code Generation feature

Additional Features

• Integrating app features for Android Wear & Apple Watch 
• Geotargeting feature
• Contactless Delivery feature (Post Covid Measure)

Wrapping Words

Five basic steps to follow while starting the food delivery app development process for your specific business – thorough research & analysis of the market, choosing your most suitable business model, selecting app features, choosing a technology stack(cross-platform suitable), and hiring a dedicated web & mobile app development company to kickstart your project.

From now on, there’s no looking back, as you board your unique app journey making you competitive in the market, fetching desired business goals!

Are you an entrepreneur, Start-UP, SMB, or an enterprise in the F&B sector and want to create a unique, state-of-the-art Food Delivery App suitable for your business? Contact Us and get started!

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Smart building technology adopts multiple IoT-led advanced home automation applications that allow access management using smart locks, and energy conservation using smart thermostats or utility meters.

In the era of digital technology, smart buildings are the solution to multiple complex problems in the real-estate sector or residential/commercial building space. Just providing living space keeping occupants warm or cool, are no longer basic criteria, but providing the digital ecosystem integrating digital technologies like AI-IoT with buildings and commercial complexes is the current mandatory requirement.

IoT companies and varied other web and mobile app development companies are increasingly providing digital applications & advanced complex solutions such as smart buildings to fill the gap and stay updated in the sector.

What is Smart Building?

Smart Buildings are structures and architectures that are created using digital technologies mainly the IoT(Internet-of-Things) technology. This technology uses specific sets of hardware/physical devices like desktops, mobile phones/smartphones, etc., software like digital applications & digital solutions, and networking medium using internet technology that provides connectivity platforms to all, viz., managing HVAC (Heating, Ventilation, Air Conditioning), security, lighting, home gardening management, and so on.

IoT-enabled this very network aims to create a comfortable, safe, and secure environment for occupants, and residents of the buildings concerned. Smart Building Solutions collect, and analyze data in real-time related to varied operations conducted within building premises for efficient, improved maintenance and building upkeep thereby creating enhanced people’s (occupants) experience ultimately.

Current Top Trends in IoT : Benefits and Challenges

The Internet of Things (IoT) can be characterized as an extension of the Internet and other network connections to various sensors and devices, or “things,” giving even basic objects, like lightbulbs, locks, and vents, a higher level of computing and analytical capabilities.

Components of Smart Building

IoT-enabled Sensors

Why IoT is called Internet-of-Things? IoT sensors are the physical devices that are responsible to capture data of varied physical objects/devices connected in the network and then transmitting those data to the Cloud. This creates the concept of  “Internet-of-Things” or the IoT.

Smart building technology incorporates IoT sensors as a major component. There exist varied kinds of sensors in the market, and they come in different sizes, and shapes, and are used for monitoring multiple forms of outputs (as per requirement).

Typically sensors are classified into:

• Passive & Active Sensors
• Analog & Digital Sensors
• Scalar & Vector Sensors

And a sensor is either Static or Dynamic in characteristic.

AI-ML Analytics Software

This happens to be a key component of a smart building system. AI-ML predictive analytics translates the data that sensors collect. The analytics software analyzes data with supreme accuracy without human intervention.

Sensors provide exponential data, both structured and unstructured, that predictive analytics uses as an input to make predictions, determine the future course of actions, take proactive measures, and corrective actions for challenges or issues such as poor maintenance, equipment degradation, inappropriate & inefficient schedules, etc.

These AI-ML-IoT enabled analytics software facilitates a  smooth decision-making process, allowing maximum accuracy, efficiency, and productivity in the system.

User Interface (UI)

In any smart building technology, UI or user interface plays a significant role because it determines varied ways of delivering information to end users. For example, IoT-connected mobile applications allow users to control varied smart operations in their buildings, can control equipment, machines, etc. on a remote basis, and likewise.

Whether you happen to be an on-site operator or an asset manager, the user interface component of the IoT-led smart building technology allows you access to important data and information needed as per your job role.

UI component leverages the huge amount of data collected via sensors and provides information to end-users like you. The very user interface is easy to understand, easy to use, and flexible.

Internet Connectivity

Internet is a mandatory requirement to connect all the components and allow the core functions to automatically carry on. IoT sensors can perform data analysis (core function)  only when they are connected to the internet.

Varied connectivity solutions like cellular networking options and WiFi are good examples of this component, i.e. internet. Mobile or cellular networking options are a popular choice because they don’t interfere with routine operations.

Moreover, smart buildings adopt the wireless protocol LoRaWAN which provides a long-range connectivity solution. LoRaWAN is a low-power wireless protocol that allows the deployment of wireless sensors across a building and maintains its battery life, allowing data transmission through thick solid walls, and floors.

Smart Building – Why it’s a Good Idea

1.Comfort for residents, and occupants, as controlling equipment and smart machines that are deployed, is possible on a remote basis. Personalized comfort settings are easily adopted. Smart building solution allows you to control your household machines like television, washing machine, and other electronic gadgets even if you are away from your home (remote basis). You can control lighting, humidity, temperature, and other parameters facilitating smooth personalized, comfortable settings for all occupants respectively.

2. You can easily control the building’s HVAC (Heating, Ventilation & Air Conditioning) like the lighting system, shading, electrical set-up, security systems, CCTV, etc. All of them are automatically controlled by varied hardware and software systems incorporated as a part of a smart building system. It works based on varied data input like those of occupants’ behavior, environmental conditions, and so on, that are constantly collected, and analyzed with maximum accuracy and efficiency.

3. A smart building allows huge cost savings as automation does not need human intervention hence less manpower requirement. Cost optimization is possible owing to the analytical capabilities of AI-ML predictive analytics/data analytics that work on data patterns or building usage patterns and then accordingly adjust and improve functions and operations.

4. A smart building facilitates improved building upkeep, perfect optimization of HVAC operation, and efficient energy usage matching occupancy patterns promoting enhanced efficiency towards space utilization.

5. An environment-friendly digital set-up. It lessens environmental impact as indoor and outdoor environmental conditions can be pre-analyzed and accordingly adjustments are done. Likewise, occupants’ behavior and other relevant data are adequately analyzed to facilitate optimization of water and energy consumption patterns leading to reduced emissions.

6. Smart buildings allow smooth integration capabilities that prevent the need to construct entirely a new building to benefit from the smart technology. Modern digital technologies, AI-ML-IoT sensors-empowered smart building solutions can be seamlessly integrated, and embedded into the existing structures.

7. AI-ML-powered predictive analytics allows predictive maintenance in the smart building system. Digital technology can detect flaws and equipment challenges using real-time data and historical equipment records, and analyze them to take corrective actions/proactive decisions.

8. It facilitates an efficient HVAC operation that leads to improved indoor air quality and fresh air. This way, it supports enhanced well-being and good health of the occupants; physical distancing efforts are duly maintained through access control systems and space optimization.

Seamless & Smart Management

Did you know Americans spend 90% of their days indoors between their work and home? That’s the current scenario. Smart building technology has a major role to play in the entire ecosystem. How? Let’s check below:

Smart building technology helps owners, and operators of residential & commercial real estate properties, Viz. apartments, buildings, warehouses, offices, retail stores & shops, hotels, etc. to deliver an enhanced occupant experience and maximize the scope of their business returns.

AI-ML- IoT-Enabled Predictive Maintenance

AI-ML-enabled predictive analytics helps in predicting equipment downtimes like apartment generators/power backup machines and the like. Predictive maintenance is the major keyword in a smart building system. It improves productivity as it helps to lessen unexpected equipment/machine downtime.

Machines, and equipment monitoring sensors such as those of video surveillance cameras gather real-time data. This helps the analytics-powered predictive models to identify data patterns, trends, and equipment faulty scope that are detected in advance, i.e. before they occur! Herculean data with subtle abnormalities and patterns are perfectly identified, and analyzed by predictive algorithms, as this work is beyond human capacity.

Maximum accuracy leads to managing the tasks skillfully and efficiently. Smart building leads to cost-effective management in all.

Efficient Energy Management

Smart building technology allows smooth energy consumption. There’s always been constant pressure on building operators, builders, and real-estate owners to reduce energy costs.

By adopting a digitally capable smart building system, building authorities manage to provide efficient and improved lighting, HVAC, leading to a reduction in energy costs. This efficiency is possible using IoT-powered sensors and meters that access real-time data meant for actionable insights.

For instance, sensor data that is used for performing condition-based management determines the exact energy requirement and ensures that energy is not wasted due to unwanted running of equipment, when residents/occupants are not in the building, equipment should switch off automatically, or, when it is cold weather, cooling machines do not operate unnecessarily. This leads to efficient energy management and huge cost saving eventually.

Digital Twins for Reducing GHG Emissions

It is a common scenario when our buildings are either over-heated or over-cooled though out the year. This calls for a mechanism that facilitates an efficient way to manage energy and cost.

Reducing GHG (Greenhouse Gas) emissions in residential/commercial buildings is critical to addressing climate change concerns and the way natural disasters create havoc the world over due to this.

As per a report (source– Architecture 2030), 50% of GHG emissions/CO2 emissions originate from the building environment. Digital Twins happen to be a significant part of smart building technology. This technology helps engineers to diagnose issues remotely using 3D models and photographs of varied equipment.

Digital Twins if suitably paired with broadband help in reducing greenhouse gas emissions with greater effectiveness. Digital Twins also facilitates improved operations and greater transparency in the system. Overall, helps in cost reduction as well as emission reduction.

Upgrade Your Building with a Smart Solution

Modern buildings are not just meant to provide overhead shields/roofs. They are much more than this and have evolved into smart facilities with the IoT-powered connected infrastructure. Life has become much easier, much safer,  and very comfortable with reduced impact on the environment.

Digital capabilities including IoT-led smart building technology are increasingly boosting business revenues across domains and business realms.

Implementing a sound, state-of-the-art smart building technology would be an apt idea for your business, too.

Let’s connect and start your smart journey today itself.

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Although cannot match the exact ability of the human brain, Deep Learning allows systems to collect data in clusters and make predictions with superb accuracy.

Deep Learning facilitates various AI (Artificial intelligence) applications, AI services & solutions, and enhances automation capability without any human intervention, it incredibly performs physical and analytical tasks with the highest accuracy.

For example, digital products & services like digital chatbots/virtual assistants, credit card fraud detection systems, voice-controlled TV remotes, and emerging technologies like self-driving cars, all are seamlessly backed by Deep Learning capabilities.

Deep Learning technology is essentially a neural network that has three or more layers. These neural networks simulate human brain behavior and try to learn from large amounts of data extracted.

In neural networks (with three or even more layers), a single layer can still make predictions that are approximately correct while additional layers help to optimize as well as refine for accuracy.

Deep Learning – How Does it Work?

Several layers of neural networks are a set of algorithms that mimic human brains driving Deep Learning technology. Deep Learning is powered by these algorithms that are modeled like human brains, or the way they (human brains) work! What configures the neural network? Training with enormous data configures the neurons present in the very network.

This allows the consequent Deep Learning model to adequately train to process new data. Deep Learning models accept data/information from varied data sources and then analyze them in real-time without any human intervention.

Deep Learning allows optimization of GPUs (Graphics Processing Units) for training models and prepares them to process multiple computations at a time/simultaneously.

Many AI applications are backed by Deep Learning to improve automation tasks and various analytical tasks. When you browse the internet, when you use mobile phones and other AI-ML-enabled electronic devices, you automatically interact with Deep Learning technology.

Other myriad AI-ML-Deep Learning applications include generating captions for YouTube videos, voice commands, speech recognition on smart speakers/smartphones, self-driving cars, facial recognition, and so on.

Deep Delve into Deep Learning Neural Networks

Also called Artificial Neural Networks, Deep Learning Neural networks emulate the human brain through a fine combination of Data Inputs (X), Weights (W), and Bias (B) – the learnable parameters within neural networks.

These elements (X, W, B) collaborate to work together towards accurately recognizing, classifying, and describing objects within the present data.

Simplest Types

• Forward Propagation
• Backward Propagation

Deep Learning Neural Networks are composed of several layers of nodes that are interconnected, and each of them is built upon the previous layer to optimize and refine the categorization or the predictions. This is how the computations progress through the network and is called Forward Propagation.

The two layers of the Deep Neural Network, input, and output layers, are called Visible Layers. In the input layer, the Deep Learning model ingests data to process while in the output layer, the final classification or the final prediction is made.

Backward Propagation is another method or process that uses gradient descent kind of algorithms, for calculating errors in predictions, and then adjusting the function’s weights & biases by moving backward through the network layers, in an attempt to train the very model.

Both the processes, Forward Propagation and Backward Propagation make it possible for a neural network to make predictions, to correct errors if any. Gradually the Deep Learning algorithm adjusts, fits itself, and becomes more efficient and accurate over time.

Complex Types

• CNNs (Convolutional Neural Networks)
• RNNs (Recurrent Neural Networks)

Deep Learning Algorithms are very complex in character. Forward Progression & Backward Progression are the simple types of Deep Learning algorithms but CNN’s & RNNs are the complex types that address specific datasets or problems.

CNNs (Convolutional Neural Networks) are primarily used in computer vision, and applications related to image classification and can detect varied patterns, and features within an image, thereby enabling tasks such as object detection, object classification, object recognition, and so on.

RNNs (Recurrent Neural Networks) are primarily used for NLP (Natural Language Processing) applications and speech recognition applications. RNN leverages sequential data or time-series data.

Deep Learning Evolution – A Summary

The Deep Learning evolutionary journey started with the creation of a specific computer model in 1943. Warren McCulloch and Walter Pitts developed a computer model that was based on the neural networks of the human brain. They used  ‘threshold logic’, a fine combination of specific algorithms, and mathematics, to mimic, to copy the thought process.

From that day onward, Deep Learning has continued to evolve except for two major breaks in its development during the infamous AI (Artificial Intelligence) winters, somewhere between 1974 -1980, and 1987-1993.

Note- Artificial Winters refers to a period when AI funding and commercial research dries up. It is a quiet period for AI-related activities/funding/research, development, etc. Whereas, Artificial Summers refers to a period seeing AI innovation and investments peak, and become active.

In The 1960s

In 1960, Henry J. Kelley developed the basics of a continuous Back Propagation Model. Then, in 1962 Stuart Dreyfus developed a simpler version that was based on the chain rule. Alexey Grigoryevich Ivakhnenko developed the Group method of data handling while Valentin Grigorʹevich Lapa wrote Cybernetics & Forecasting Techniques, and they were the ones who made the earliest efforts in developing deep learning algorithms in the year 1965.

In The 1970s

The first Artificial Intelligence (AI) winter occurred during the 1970s. It hugely impacted Deep Learning research (and the whole AI). However, few individuals continued AI-ML Deep Learning research without external help/funding. Kunihiko Fukushima was the first to use CNNs (Convolutional Neural Networks). He designed neural networks with various pooling & convolutional layers.

And then in 1979, he developed ANN (Artificial Neural Network) which was termed Neocognitron and it used a multilayered design, on a hierarchical pattern. This design allowed computer system to learn and recognize visual patterns.

Though invented in 1960 by Henry J.Kelley, Back Propagation Model significantly evolved in 1970. It was made possible by Seppo Linnainmaa when he wrote his master thesis and a FORTRAN code for Back Propagation.

However, this concept was ultimately applied to neural networks only in 1985 when Williams, Hinton, and Rumelhart demonstrated this DL model (Back Propagation DL Model) in a neural network that could provide some interesting distribution representations.

In The 1980s & 1990s

Yann LeCun was the first to provide any practical demonstration of the Back Propagation Model at Bell Labs in the year 1989. Then the second Artificial Intelligence winter kicked in during this tenure, i.e. during 1985-90s. This hurt DL research and neural networks.

It was during this period that the situations pushed  AI to a pseudoscience status. Then it bounced back in 1995 with the development of SVM (Support Vector Machine) and in 1997, LSTM (Long Short-Term Memory) was developed for recurrent neural networks. In 1999, GPUs (Graphics Processing Units) were developed.

From 2000-2010

The Vanishing Gradient Problem appeared somewhere around the year 2000. It was exposed that those lessons or features that formed in lower layers were not being taken/learned by upper layers as well. Learning signals could not reach the upper layers, so this gap existed.

However, it was also found that this problem was not meant for all neural networks, just those with gradient-based learning models. In the year 2001, Gartner (then META Group) revealed a research report explaining data growth opportunities and challenges as three-dimensional.

This also led to the onset of Big Data during that period. In 2009, Professor Fei-Fei Li at Stanford launched ImageNet. He assembled one free database that consisted of over 14 million labeled images. These labeled mages were required to train neural nets.

2011-2020

The speed and efficiency of GPUs had significantly increased by 2011. This enabled them to train CNNs without pre-training on the layer-by-layer pattern. The very increased speed made Deep Learning create a significant impact in the ecosystem, such as the creation of AlexNet.

ALexNet was a CNN whose architecture won many international technology awards in 2011 & 2012. Rectified Linear Units helped in enhancing speed & dropout. Then in 2012, Google Brain released The CAT Experiment. It explored the challenges of ‘unsupervised learning’. And Deep Learning uses ‘Supervised Learning’. Many experiments and projects followed during this tenure.

In 2014, GAN (Generative Adversarial Neural Network) was introduced by Ian Good fellow in the DL segment. Using GAN, two NN (Neural Networks) can play against each other in a game. GAN facilitates the perfection of a product.

Importance Of Deep Learning

Deep Learning facilitates maximum accuracy and superiority in terms of data handling and management. Technology companies worldwide are increasingly investing in AI-ML Deep Learning technology as it allows maximum trust via supreme accuracy. This further leads to better decision-making abilities across industries. Deep Learning technology makes machines smarter.

For instance, Google ALphaGO defeated Lee Sedol, one of the world’s legendary professional  Go players, and it had become the news headline.

Google Search engine immensely uses Deep Learning technology, and in other applications like speech recognition systems, self-driving cars, drones, etc. the very digital technology is impacting across industries with immense digital capabilities leading to garnering maximum business returns.

Also Read:

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However, it is compatible with other OS, too. It is a set of development tools that are used to create/develop Android applications on the Android platform.

Android SDK (Android Software Development Kit) contains the following tools/objects:

• Libraries
• A debugger
• An emulator
• Specific relevant documentation for APIs (Android Application Program Interfaces)
• Tutorials that explain Android OS
• Sample Source Code

About Android Software Development Kit (Android SDK)

Android SDK (Android Software Development Kit)  is a collection of software development tools and varied libraries. These tools & libraries are mandatory for Android applications development/web & mobile app development on the Android platform.

Whenever Google releases a new version or has a new update of Android software, simultaneously the corresponding SDK also gets released with that software.

In the new version/updated version of SDK, certain new features are incorporated that have not been available in its previous version.

Some tools in the Android SDK are very essential tools required for Android app development and without them, you cannot think of starting an app development process. These tools facilitate a smooth development process from the development to debugging part.

Android SDK is compatible with other OS (Operating Systems) like Linux, Windows, macOS, and so on.

For Developers 

Thus, as mentioned above, you get a new corresponding SDK with every new version of Android released by Google. Android developers must download the latest version and install its SDK for writing programs that will include the latest features introduced in the new release.

The very SDK represents Android’s delivered toolkit for a particular version as well as the technology of its OS (Operating System).

The development platforms which are compatible with Android SDK are Windows(XP/later version), Linux(recent distribution), and Mac OS X(10.4.9 or later version).

You can download the Android SDK components separately. Even third-party add-ons are available to download.

Command Prompt/IDE

You can write your Android programs using SDK in the Command Prompt, another method, IDE (Integrated Development Environment) is also there which is more commonly used by developers across the world.

Several IDEs available are NetBeans, Intellij, and others but the most recommended IDE is Eclipse together with the ADT(Android Development Tools)plug-in.

Almost every IDE provides a GI (graphic interface) that helps developers in performing their development tasks smoothly and efficiently.

Android apps/applications are written using Java Codes, you need to install a JDK (Java Development Kit), too.

Android SDK – Components 

The most important role in the development cycle of all Android applications is those of the integrated components of Android SDK. What are those components? Let’s see below:

Component#1 Android SDK Tools

An important component of Android SDK, Android SDK tools contain a complete set of development tools and debugging tools. Varied  SDK developer tools are as follows:

• Android SDK Build- tools
• Android Emulator
• Android SDK Platform-tools
• Android SDK Tools

1.  Android SDK Build-Tools

Android SDK Build-Tools are used to build actual binaries of an Android app. Their main functions involved are build, debug, run, as well as test an Android application. If you update your Android system make sure that its corresponding updated version of SDK is also downloaded.

2.  Android Emulator

An Android emulator is actually a device that does the simulation of an Android device on your system. On your Android emulator, you can see the virtual Android device on your system where you code, and run your Android application.

An Android emulator comes with specific configurations for multiple devices like different models of Android phones, Wearable OS, tablets, Android TV devices, etc.

3. Android SDK Platform- Tools

This tool is beneficial when you work on a project and then errors occur, and error messages flash on your screen. Android SDK platform-tools are particularly used for testing purposes. It consists of:

 ADB (Android Debug Bridge) –  It is a command-line tool. It helps to communicate with the system/device on which the application is built. It allows certain actions to perform, viz., to install an app, debug an app, etc.

Fastboot – It permits you to flash a device using a new system image.

Systrace tools  – These tools help in collecting & inspecting timing information. It is indeed a significant tool needed for app debugging.

4. Android SDK Tools

Android SDK tool is also another component of the SDK tool. It has a complete set of tools, and along with other utilities plays a major role in an Android app development process.

It consists of a complete set of debugging tools as well as development tools for android applications.

Component#2 Android SDK Platforms

For every Android software one SDK platform is available for sure. It looks like this:

For instance, in the given image, an Android 11.0(R) is shown installed.

All these Android software are numbered as per their respective versions. SDK for a new version will have more new features and show better compatibility with other OS, however, the previous, older versions lack these feature updates and look obsolete, and are far less compatible than the latest counterparts (versions).

Component#3 SDK Update Sites

Some sites are embedded in the SDK Update sites, and they check for Android SDK updates tools. One thing to ensure is that you do not ‘unclick’ the button below as they conduct default checks and if you ‘unclick’ they won’t check updates for them.

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Machine Learning (ML) is a subset of Artificial Intelligence (AI), the king of digital technology. A significant area of computational science, ML (Machine Learning), allows decision-making outside the realm of human interaction.

The very digital technology facilitates smooth analysis, and interpretation of patterns, and structures in data. That enables the process of learning, reasoning & decision-making without human intervention.

Thus, Machine learning(ML) enables users to feed algorithms(computer algorithms) with massive volumes of data so that those computer algorithms can analyze, and make data-driven recommendations, leading to decisions solely based on the input data.

If the ML algorithm identifies any corrections, it can incorporate that information for improving its capability for future decision–making!

Definition – ML is the abbreviated form of Machine Learning, the most powerful digital technology existing today. A branch of AI (Artificial Intelligence), ML (Machine Learning) is based on the idea that computers or systems can learn or gain from data and then identify patterns leading to decision-making without human intervention.

The technology purely focuses on the concrete use of data, and algorithms that would allow imitating the way humans learn and behave, and improving its accuracy, gradually.

Real-Life Example

When you type your question on the Google search engine, what do you get in return?

Multiple replies to your one-question command!

Similarly, when you speak over Alexa and ask your questions, Alexa will reply to you with probable answers.

The language you speak, the language you type, Google, Alexa, and the likes, will reply in your language only.

Which technology is behind this activity?

It is the digital technology Intelligence, your favorite Artificial Intelligence, and Machine Learning techniques. Whether you know it or not, AI-ML lives with you, around you intangibly on your smartphones, smart speakers, healthcare devices, vehicles, gadgets, and so on.

Thus, AI-ML allows seamless conversation between humans & machines!

In the given example, data, ML (Machine Learning) provide the foundation for those magical powers that  Alexa wields on you (that you are so fond of!)

Machine Learning: How Does it Work?

Simply put, in ML (Machine Learning), computers/systems apply certain statistical learning techniques that automatically lead to identifying patterns in data. And these techniques are ultimately used to make predictions accurately.

Thus, the basic concept of ML (Machine Learning) incorporates the use of statistical learning, and optimization methods, thereby allowing computers/systems to analyze datasets, and then identify patterns!

Varied ML techniques based on data input, and leveraging data mining are then used to identify historic trends, and then inform future models!

Typically, a supervised ML algorithm roughly consists of three components

A Decision-Process

It is a recipe of certain steps and calculations which takes in the data and makes guesses about the kind of pattern an algorithm is searching for.

An Error-Function

It involves a method that measures the efficacy of ‘guesses’ made, it measures how good those guesses have been by performing comparisons with known examples available. It measures if the decision process got it right. And, if it did not get it right, then it helps to find out the misses and their intensity.

An Optimization-Process/Updating-Process

It involves a method that allows an algorithm to find out/locate the misses and related updates, like, how did the decision-process come to this very conclusion/final decision that has led to so many misses; and, it ensures next time this kind of misses won’t happen(intensity of misses will be less).

Can you relate to the following representation now?

Thus, Machine Learning (ML), as a subset of AI (Artificial Intelligence), is the magnificent field of computer science consisting of learning algorithms to improve P (performance), executing given T (tasks), over time with related E (experience).

Machine Learning Models – Types/Categories

Defined by the presence/absence of human influence on raw data sets (like, a reward is offered, labels used or not, specific feedback is provided, etc.), there exist various types of ML(Machine Learning) models:

Supervised-Learning Model

Datasets that are being taken/used are already classified and pre-labeled by users. This facilitates the algorithm to see the accuracy of its performance.

Unsupervised-learning Model

Raw datasets that are being taken/used are not labeled (unlabeled). An algorithm does the identification of patterns as well as relationships within the present data and no help from users is involved in this model.

Semi-Supervised- Learning Model

This model involves structured, unstructured datasets, which allow an algorithm to make independent conclusions. The two data types(structured, and unstructured) are combined into one training dataset that allows the ML algorithm to learn how to label the unlabeled data.

Reinforcement-Learning Model

This model allows an algorithm to learn from its own experiences. It learns from its varied trials and tested methods, leading to misses and errors.

To be lucid, under this ML model, the dataset adopts a system called the ‘rewards & punishment’ system, and it offers feedback to the algorithm allowing it to learn from personal experiences. In short, the Reinforcement Learning model is a behavioral ML model.

Deliver Content Personalization through Machine Learning

Let’s see how Machine Learning  AI Capabilities are enabling content personalization at length, leading to organizations’ ability to scale and enhance their customer experience!

What are a few commonly used Machine Learning Algorithms?

The main purpose of ML (Machine Learning) is to make data analysis by using various ML algorithms. Developers across the globe can leverage Machine Learning capabilities to improve the efficiency, accuracy, and productivity of various tasks undertaken without any manual help.

Professionals in the field of data science, computer science, digital technology, and other areas can use strong ML algorithms for their specific goals.

Let us see a few ML algorithms that companies are adopting in their business settings the world over:

Linear Regression ML Algorithm

Linear regression ML algorithm analyzes the relationship between independent input variables and target variables (at least one variable has to be present). Then it is used to predict continuous outcomes, viz., variables that can take any numerical outcome.

For instance, based on available data regarding property and its neighborhood, can an ML model predict a house’s sale value? If the data relationship under observance tends to follow a straight line, it leads to creating a linear relationship in the process. It allows to observe data points whether they are increasing, decreasing, or remaining at the same level relative to any independent variable(like, position or time elapsed).

ML models are employed to analyze data and map linear regressions. In short, the linear regression ML algorithm allows an ML model to map out a linear relationship/ map out a straight line via the datasets being used.

Logistic Regression ML Algorithm

Logistic Regression ML algorithm falls under a supervised learning model. It is used for classifying problems, and here it does not follow the method of linear regression of continuous output, but a logistic model does the prediction work here.

The very logistic model predicts the probability or chances of a binary event occurring. For instance, based on a given email in a folder, can an ML model predict if the content inside this email is spam or not? Thus, ML models can use a logistic regression algorithm for determining varied categorical outcomes. 

Neural Networks AI-ML Algorithm

Neural networks are AI (Artificial Intelligence)- ML algorithms that try to replicate the manner in which human brains process multiple information to understand, interpret and apply intelligence towards classifying data.

These AI-ML algorithms are widely used in areas like data and speech for recognizing patterns, language translation, predicting financial conditions/market situations, and have many more applications.

All these patterns they recognize through millions of interconnected processing nodes. It is through layers, that data is fed forward, and these layers are given the task to process, and assign weights to them before sending them to the next layer of nodes, and the chain continues. Neural Networks algorithms constitute powerful ML models to serve desired purposes and tasks.

Decision – Trees ML Algorithms

Decision trees  ML algorithms are data structures that have nodes and are used for testing against the given input data. All these input data are tested against the leaf nodes down the tree, and the motive is to produce the desired and correct output.

Due to their tree-like structures, they can easily be understood just by visual representations of them. The decision tree ML algorithm is designed for categorizing data based on certain categorization schemes.

Decision trees are a supervised learning method; it is that field of ML (Machine Learning) that refers to how a predictive ML model is created via the training of a learning algorithm! 

Random-Forest ML Algorithms

Using various Decision Tree models, Random Forest models can classify data at once (in one go). Just like the Decision-Tree method, Random Forest algorithms are used for determining varied classifications of categorical variables/regression of continuous variables.

Based on a user’s specification, these Random Forest models can generate many Decision Trees leading to the formation of ensembles. And then based on input data, each tree makes its specific prediction.

After that, the Random Forest ML algorithm predicts by combining all predictions done by all the Decision Trees within the ensembles.

What is TensorFlow? The Machine Learning Library Explained

TensorFlow is a hands-on machine learning platform that is free and open source.

Why Machine Learning (ML) is so Important?

Data remains the core, the base of all businesses across industries the world over. It helps you to stay ahead of your competitors if you follow data-driven decisions for your business rather than a legacy approach.

Machine Learning (ML) is the key to unlocking valuable customer data, and corporate data, and based on those data, the decision-making process occurs thereby keeping an organization ahead of the competition!

That’s why organizations/ companies across the world are adopting AI-ML digital tools & technologies leading to enhanced efficiency and productivity in their business landscape.

Two main reasons behind the importance of ML are:

Data Scaling Capability

Organizations across industries face numerous data volumes every day, and these data exist in structured/unstructured forms. These datasets need thorough processing for varied end-users.

ML models are programmed in such a manner that facilitates these data to process on their own and come out with an accurate conclusion. ML provides data processing power to companies for improved and efficient outcomes.

The manner ML models identify patterns, make predictions and help in decision making, is unmatched making it an important digital technology.

Uncovering Hidden Insights

ML allows for unexpected findings. ML algorithms can suitably find out insights that are so hidden and buried inside herculean data sources. Owing to its capability to update autonomously, an ML algorithm can provide more enhanced, improved, and accurate data analysis with every run it undertakes.

It means, it improves itself, on its own by teaching itself out of every analysis it does for datasets. Without human intervention, ML algorithms’ iterative nature of learning is valuable and unique, and in the process identify patterns, and uncover insights that amaze the world.

Evolution Of Machine Learning

Machine Learning (ML) is getting popular with its inherent capabilities befitting industries today, however, the concept is not new. As per Forbes, ML (Machine Learning) originated in the 1950s.

Arthur Samuel wrote the first program that involved a  game of checkers for IBM in the year 1952. Soon followed ML works from other pioneers, like in 1957 Frank Rosenblatt designed the first neural network, and in 1981, Gerald DeJong introduced explanation-based learning.

A major shift occurred in the realm of Machine Learning during the 1990s. The focus was now aligned towards a data-driven approach while shifting from a knowledge-based approach. This decade was a critical period for ML evolution. Scientists had started developing computer programs that would be able to analyze large volumes of data as well as learn in the process.

In the following decade, during the 2000s, Unsupervised Learning made forays into the ML world. Eventually, the decade witnessed the advent/invention of Deep Learning, and also Machine Learning became ubiquitous as a practice.

Another remarkable instance in the ML evolution was marked by IBM supercomputer Deep Blue. In 1997, Deep Blue, equipped with an ML algorithm beat Russian chess grandmaster Gary Kasparov. Similarly, in 2016, Google’s DeepMind AI-ML program AlphaGo gave a big boost to the ML fraternity.

In the present scenario, AI-ML researchers, and scientists, are working hard to expand their applications across industries. AI-ML applications are becoming popular today because they have moved from server-based systems to the seamless environment created by Cloud technology.

Google introduced ML capabilities alongside Deep learning in its Google Next product. Companies like Microsoft, Amazon, IBM, and Baidu have been creating innovative ML platforms through their varied enterprise cloud services and open-source projects.

Undoubtedly, Machine Learning is gaining a massive adoption on a global scale owing to its inherent capabilities in helping companies achieve goals and business outcomes.

Pros and Cons of Machine Learning

Who Can Use Machine Learning?

Multiple industries are benefitting from ML capabilities. Some of the use cases as per industry are:

Manufacturing Industry

ML helps in  Predictive maintenance & condition monitoring

Retail

In the retail sector, ML helps in upselling & cross-channel marketing

Healthcare & Life Sciences

ML helps to identify, recognize diseases, and leads to risk-mitigation

Financial Services

ML helps towards risk analytics & regulation

Travel & Hospitality

ML helps towards dynamic pricing

Energy

ML helps in this sector in optimizing energy demand and supply 

Top  ML Applications in 2022 and beyond

• Virtual Personal Assistants
• Example – Google Assistant, Alexa, Cortana, Siri
• Traffic Predictions
• Social Media Personalization
• Email Spam Filtering
• Online Fraud Detection
• Stock Market Trading
• Assistive Medical Technology
• Automatic Language Translation (AI-ML-NLP)
• Personalized Healthcare Treatments
• Product Recommendations
• Sentiment Analysis
• Banking Domain Functions

What Next?

Be proactive! That’s the need of the hour. Companies around the world are grappling with challenges related to data sharing and access, viz., there are various types of endpoints being used to access information. Data stays in the cloud, on desktops, laptops, smartphones, tablets, pen drives, and others.

Moreover, in most cases, these devices belong to the end-users, and customers and not directly to the companies involved. This poses risks to data safety and security.

AI-ML-predictive analytics along with IoT technology can help companies to mitigate the risks involved. Varied applications like fraud detection, image recognition, demand forecasts, predicting consumer buying behavior, and predicting healthcare diseases (like cancer) at an early stage, make the ML (Machine Learning) technology a significant booster for your overall growth.

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Android operating system is the open-source OS that Google has designed for varied devices but the primary devices are mobile phones/smartphones.

Android operating system (OS) has been created/developed using Linux Kernel. Being open-source software, Android OS allows easy modification benefits for Android developers who can use different GUIs on the same basic operating system. The open-source code that is used for Android Application development is termed AOSP (Android Open Source Project).

Google Play is the mechanism used for official Android app distribution to end-users. Also, it allows the release of an app in a gradual, staged manner, and distributes pre-release versions of the app to testers.

What is Android Studio?

Android Studio provides the fastest tools for building apps on every type of Android device.

For Android App Development, Android Studio happens to be the official IDE (Integrated Development Environment) that is based on IntelliJ IDEA. Android Studio provides the fastest tools required to build apps/mobile apps/mobile applications on just any type of Android device.

Owing to IntelliJ’s powerful code editor, and developer tools, it (Android Studio) offers myriad features that allow enhanced productivity when you go for building Android applications.

Some of these features are:

• Built-system is gradle-based & flexible.
• An emulator is feature-rich as well as fast.
• You can now develop for all Android devices under a unified environment.
• Without restarting your app, you can apply changes to push code to your running app.
• You can build common app features, and import sample code thanks to code templates availability and GitHub integration.
• Massive testing frameworks & tools.
• Lint tools availability to for catching/checking performances, version compatibility, usability, and varied other challenges.
• C++ & NDK Support.
• Integrated, built-in support for the Google Cloud platform; this makes an easy integration between Google Cloud Messaging and App Engine.

Thus, the above-listed features are an introduction to basic Android Studio features. If you want to see the latest updates on features, we will show you soon the latest Android Studio release notes that will specify advanced features you can seamlessly avail of by going for android app development for your organization!

Top Trends That Will Rule Android App Development

A lot new is happening in the Android universe. we will discuss the Top Android App Development Trends for the year 2022. So, is your business well equipped in the competitive app market? Read on, and find yourself!

SDK is freely available on the Android system, if you want to develop an application over Androids, you can easily install SDK without paying any amount. These Android apps support those devices that run on the platform Android Operating System and are written in Java.

These applications run on JVM (Java Virtual Machines). Ever since its inception in 2011, Android ranks as the best- seller under the OS category with a stupendous 2 million + active users on monthly basis.

Why is the Android OS popular among Developers?

Android OS is an open-source operating system that caters to the largest number of communities, and developers and that makes it a probable reason for having smooth inter-app integrations & increased marketing in the business world. A striking reason for its popularity is the reduced cost & lesser resources and greater success ratio built on varied development scenarios & environments.

Superb features like massive storage, attractive appearance viz beautiful user interface, media support & messaging, highest connectivity rates, etc. make Android OS a popular and one of the biggest markets in the technology world. Multilingual platform, resizable widgets, web browsing, Android beam, multi-touch & multi-tasking abilities, and GCM (Google Cloud Messaging) are major attractions of the very platform/operating system.

Application Programming Interface (API) Level

Android has multiple versions in the market. Each version has distinct features. API level (Application Programming interface) level is the main asset behind it. API level happens to be a specific API framework identifier. Varied Android versions have varied features as well as advancements!

Architecture for Android Development

The architecture for an Android app starts with the top UI (User-Interface)layer. UI of an Android app supports its front-end features such as an email facility, calculator, alarm clock, world clock, phone locking system, in-built camera, and many others.

The very GUI features account for the first appearance of the app; attractive features, and utilities capture users’ attention who eventually get motivated to download and install the very app boosting business goals.

The next layer (2^nd layer) is the Android framework layer in the Android architecture. This layer contains service providers, windows firewalls, and varied managers like activity manager, packages manager, and so on.

The third layer of the Android development contains Android runtime and libraries and they consist of SSL, SQL, LIB-C, SQLite, etc.

The bottom later (lowest) consists of the kernel and it contains all the drivers, viz., Bluetooth driver, Wi-Fi driver, etc.

Android Software Stack

The following diagram represents the major components of the Android platform.

Android Development Basics

Libraries

For Android app development, libraries are the basic tools. 

Some of these libraries used in Android app development are as follows:

• Android.App: This library happens to be the basic one for all the android applications. Android.App is the key to providing access to the app model/application model.

• Android.Content: Another significant library is Android.Content. It happens to be the second most important library. It enables functions such as the support of content access and creates a smooth relationship between varied application components, and the messages being transferred between them.

• Android.Opengl: This library makes way for graphics 3D rendering through the JAVA interface by adopting/using JDK (Java Development Kit). 

• Android.Database: The primary database that Android supports is SQLite.The very library connects the application/app to the database while accessing its data via the content providers.

• Android.Text: Another library that allows the app to render text graphics on an Android device is Android.Text. The library supports rich text manipulation and thus making them text-ready.

• Android.Os: This library provides an Android app with varied operating system services. These services include significant functions like inter-process communication, messages, memory management, and allocation of tasks.

• Android.View:  An important feature in the Android app development process that supports UI (user-Interface) alteration, if any.

• Android.Webkit: This very library integrates web browsing capabilities to the very Android app, by inheriting specific web applications.

• Android.Widget: This library (Android. Widget), is a significant GUI (Graphic User Interface) tool/ library that includes significant components like buttons, lists, scroll bars, radio buttons, layout managers, and so on.

What Is the Android SDK?

Android SDK means Android Software Development Kit. Google has developed Android SDK exclusively for the Android platform.

Android Application Framework

In the earlier paragraphs, we have mentioned IDE Android Studio. Let’s elaborate a bit!

Dedicated IDE (Integrated Development Environment) Android Studio is used for creating Android applications required by companies. Using IDE Android Studio, specific, custom resolutions can be set for multiple Android versions. The application framework provides high-level services at the front end of the app concerned.

Android Application framework involves activities that include as follows:

• Activity Manager – Activity Manager is actually the Java class that is incorporated within an Android application and that interacts with all the activities that are running on the program overhead currently. It is used to track them all.

• Resource Manager – It manages all the resources that have been embedded with that application in the development cycle. It is a significant tool window that allows a smooth import of varied draw-able to the project via the route of a resource provider. Resource Manager tool also manages a type of resource called mipmap (application logo) in the settings of the very app being created.

• Content Provider – In an Android application, the content provider class facilitates the smooth provision of massive data to varied other classes within the application. It works as an intermediary in the whole process and also acts as a data centralizer allowing for data collection in one place from where the rest of the apps can seamlessly access these data. It is through the input/data provided by the content provider that developers can smoothly conduct CRUD (Create, Read, Update, Delete) activities.

•  Layouts (View System) – The appearance of the app impacts users’ decision to install it onto their mobile phones. It is the layout of the very app that allows users to interact with its screen and can see varied functions getting displayed over there. Varied GUI widgets like image view, text view, buttons, etc. are objects that are available in this view-system/layout part. For instance, Constraint Layout, Linear layout are a few layout types present. These layouts can be declared at runtime, or while creating the UI (User0Interface).

• Notifications Manager – The notification Manager resides in the title bar of the Android app that is being created, and is accountable for modifying/altering notifications therein. Via notification class, notifications are created in the app. The notification class uses the notifications manager class from the context for creating notifications concerned.

Why Companies should go for Android Development?

Android is a popular computing platform that is increasingly enabling various Android devices to scale in the global marketplace. Ever since its commercial version release in 2008, it is becoming a major business source for companies across sectors, globally.

Android development has found its way into varied devices, viz. mobile phones, desktops, laptops, tablets, television, automobiles, projectors, recreational vehicles, healthcare wearable, fitness gadgets, etc.

Android-based touchscreen interface can manipulate controls, smartphone with Bluetooth communicating with Android-powered control system, etc. are the varied interfaces companies can start thinking leveraging the power of Android development and its immense capabilities.

Companies can adopt Android apps for business for mobile applications as well as non-mobile applications depending on their specific requirements.

AI-ML-IoT enabled custom android development can take companies to heights while opening a plethora of new business avenues on their platter.

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Blockchain incorporates existing digital technologies & techniques that include asset tokenization, distributed digital ledgers, immutable records management, encryption, and decentralized governance for capturing/ recording information that participants or users need for interactions and transactions over a network. 

Blockchain does not involve any intermediary such as a bank, to validate and protect the transactions being done. Blockchain development is the new-age mantra for companies globally to meet business goals.

With its decentralized nature, Blockchain is becoming the mainstream technology empowering varied applications and possessing massive use-cases across industries. The only technology that can guarantee ‘ trust’ in the ecosystem.

Businesses are increasingly adopting Blockchain technology worldwide, integrating it into the existing model, or completely recreating a new one focusing on Blockchain capabilities. However, there’s been a huge demand-supply gap. Blockchain development is the need of the hour.

Let’s go bit-by-bit and understand the basic terminology of Blockchain technology, its massive capabilities, and how it impacts businesses across industries the world over. A complete guide to Blockchain development!

In Short – 

• Blockchain digital technology enables businesses, people, and machines, who may or may not know each other, to conduct varied monetary transactions, exchanging information, and assets with each other without the involvement of any intermediary. In short, the technology enables all of them to exchange value in a digital environment and there’s no need for third-party involvement or negotiator, just the two or more entities, on a direct basis

• Blockchain is a kind of digital ledger or distributed ledger, and today, varied Blockchain solutions are continuing to mature unabated, for example, in terms of scalability, and are expected to underpin, and support entirely new business models, social and economic models that will ultimately redefine the society we live in.

• Complete Blockchain technology combines 5 design elements for authenticating users, validating varied transactions while recording information to the ledger, and all of these are done in a way that cannot be changed and corrupted at any time, later. Once done, cannot be undone, to be clear in terms of Blockchain’s secured transaction model that is corruption-proof, and no mediator is needed.

Why BlockChain Development for Companies

If you are an organization from just any industry, Blockchain-inspired record management, and secured transactions without involving a third-party mediator that can lead to huge cost-saving potential, are a few reasons you should think about Blockchain development to propel business growth. Be it the banking sector,  healthcare industry, retail management, fintech, real estate, and others, blockchain such as cryptocurrency is increasingly revolutionizing respective eco spaces. 

Mobile payment solutions, healthcare apps, retail apps, mobile banking solutions, etc. are highly incorporating the Blockchain capabilities, globally. You must consult a dedicated web  & mobile app development company that can guide you regarding blockchain development and your project requirement, viz., Ethereum development, cryptocurrency wallets, hyper ledger development, smart contract, your internal/private Blockchain like Quorum development that allows private transactions, NEO, EOS, Qtum, TEzos, and so on.

Startups, SMBs, and enterprises across the globe are taking a keen interest in adopting blockchain for their businesses, thanks to the technology that can record, store, and move any sorts of assets, and transactions in a decentralized manner with great ease and perfect automation. Multiple use-cases and applications of Blockchain technology in multiple areas across the business framework are catapulting industries to invest more and more in the technology concerned!

Why the Name ‘Blockchain’

Blockchain allows varied transactions, like transactions of money, information, assets, etc., and these transactions are grouped in blocks. As they are grouped in blocks, they get recorded one after the other, in a chain of blocks. That’s why the name Blockchain is kept. 

The links that connect blocks with their content are secured, protected purely by cryptography, and this prevents previous transactions from being forged or destroyed or changed. Secured authorization access is guaranteed! Without any middleman or the mediator or the central authority, the ledger, or the transaction network is trusted because of the authenticity that the Blockchain development provides to entities involved.

Types of Blockchain

There are two types of Blockchain and the very types determine who can participate within a Blockchain network.

These two types are – Public & Private blockchains.

Public Blockchain

In the Public Blockchain network, all participants have access to the network that can take part in the consensus. Anyone who has internet access can join this type of Blockchain network. There’s no onboarding requirement and as a user, you just don’t have to ask anyone for joining permission.

In fact, there’s no single authority on this network type and is decentralized in nature. All transactions done over a public blockchain are visible to every node present on the very network. For example, Bitcoin is a public blockchain network, and transactions done over it can be viewed by anyone. Using a block explorer, you can view all the latest bitcoin blocks and transactions done so far.

Private Blockchain

The private Blockchain network assures one thing  – your privacy. There is inbuilt trust amongst participants in the very blockchain network. The participants who are invited to this blockchain network can only access the information stored therein. Private Blockchain Networks are considered semi-trusted networks.

In this type of blockchain, all participants mutually agree upon using blocks, and how to leverage the blockchain capabilities. For example, Consortium Blockchain is a private blockchain network that allows users to leverage its capabilities by evenly distributing authority to participants and that can act in the best way possible benefiting those in the network.

There exists transaction privacy from the public glare. Consortium Blockchain (a private blockchain) can even restrict the authority to participate in consensus. This blockchain type can enforce trust by restricting participants, allowing them just to involve invalidation. These groups of participants are known as consortiums. Then, data privacy is greatly maintained under this blockchain type.

Blockchain Protocols for your Custom Solution

There exist varied Blockchain protocols but the most well-known cryptocurrency protocol is Bitcoin. The network of Bitcoin blockchain was developed for Bitcoin cryptocurrency. The main function that the Bitcoin blockchain network does is to store Bitcoin value. And Bitcoin values can be transferred from one to another and in a trustless manner!

The Ethereum protocol is a general-use blockchain protocol. Ethereum takes forward functionalities/functions that had been created by the  Bitcoin cryptocurrency towards the provision of a protocol that would facilitate the writing of smaller programs, as well as simple value transfers! The core of Ethereum is its effects and the ability to add logic as well as codes instead of mere simple fixed value transfers!

As a technology guide, we’d recommend you adopt a general use blockchain protocol such as Ethereum or Hyperledger fabric. These two protocols can help you leverage the best blockchain capabilities for your custom blockchain solution. They are programmable blockchains, have multiple use cases, and can be used for several scenarios. Ethereum protocol and other types of general use protocols, use, and apply smart contracts, that allow them to encode business logic and state.

Blockchain – How Does it Work

So, how does blockchain work behind the scenes? We will see in-depth detail:

• Data Distribution – How Data is Distributed?

In a scenario where multiple companies are involved, it’d be so easy to build a centralized database system. But what if no company wants to be the central authority and take accountability for database management? In that case, we use a quality blockchain distributed ledger. Why so? Because using a blockchain ledger does not require any central authority.

It removes this requirement from the scenario. Moreover, a distributed ledger system allows all participants who are having blockchain nodes, to have a copy of that ledger so that they can perform custom auditing matching their unique requirements, and integrating them with their respective systems. Usually, it is not necessary that each company/participant should have a separate node of its own. They can share nodes between themselves as per mutual understanding and get the work done appropriately.

It works the way that using a blockchain network, each node gets connected to other nodes. One-to-one relationships of these nodes with participants or companies are not necessary.

• Changing the State of Data in Blockchain

Data in a blockchain network represents its state. This is the reason why digital tokens such as cryptocurrency fit in a blockchain environment. For example, when we talk about physical coins/currency, its ownership changes with every owner it has. A person having a coin in his pocket is considered the owner of that coin. And as that coin moves to other pockets, its state of ownership changes accordingly. Thus, to change the data state from one value to another, Blockchain uses certain transactions to do it.

The very digital technology sends a particular transaction across the blockchain network and each node gets one copy of that very transaction.

The blockchain that sends the transaction throughout a network is copied to all node peers of the blockchain participants. Thus, despite each node now processing the transaction, it needs a validation process by adopting a consensus mechanism. And why the consensus mechanism? It enables a certain amount of consistency, as well as trust for the very distributed ledger managing database.

• How to Trust the  Consistency Aspect of Ledger Data

It is not easy to determine what is true when data changes so frequently over a distributed network. These changes should occur throughout all the nodes.

Blockchain technology adopts a consensus mechanism for validation, to agree on the data across all these blockchain nodes. A consensus mechanism used by blockchain provides a platform over which all decentralized nodes are converted to the same state.

During the transfer of responsibility, any value, it is the order that matters. The consensus mechanism ensures the correctness of the order of multiple transactions while maintaining the integrity of the blockchain network. During this process of consensus mechanism, a transaction group is validated in the form of a block, and participants in the blockchain network must agree to allow that block to be included in the very network.

As of today, there exist varied blockchain consensus algorithms. That includes proof of work, proof of authority as well as proof of stake. Each of these algorithms has a different approach to solving consistency matters. In short, the very consensus mechanism facilitates a situation where the distributed ledger arrives at a common state, getting consistent with each other.

• Block – What is its Meaning?

A block in the blockchain network is a cluster of data that is responsible for storing transaction information across the network. The number of transactions happening in a block is purely time-based. It tells you how many transactions occurred, say, in the last  10 minutes,20 minutes, within a block.

Based on the consensus mechanism, blocks that are validated are automatically added to the blockchain at every node. And since all the nodes in the network have the same blocks in the chain, the ledger maintains a complete consistency. Consequently, all the nodes consist of the same and common validated data now. Data consistency is accurately maintained.

• How to Trust if Ledger is Immutable?

So, if you think that you can change data if it is in your node, you are wrong. Because data is immutable here. How?

To create a link between blocks, blockchain technology uses a cryptographic hash. This way, the order of all transactions occurring can be agreed upon using the consensus mechanism(its algorithm).

What is a cryptographic hash? It is also an algorithm. It is assigned to map data of arbitrary sizes and make them a bit representation of the fixed sizes. It is just like a digital fingerprint. For instance, the Bitcoin cryptocurrency uses an algorithm – SHA-256 hash. If you have performed any function using an SHA-256 hash algorithm on a 100-page document, a 256-bit hash value will be the function output.

And when you change even just one character in that document and then regenerate the hash, a different 256-bit hash value will be the output. So, what would it be like when you use a block as an input to the hash function? The output that you get will be an entirely unique hash value for the data in the block.

To detect changes made to the blocks, if any, Blockchain uses hashes only. It includes the hash value of the previous block while generating the hash value for the next block. This way, through hashes, all the blocks are chained together!

Thus, using blockchain technology, trust is guaranteed. The very digital technology assures 100% trust just by using hashes. This way, it is sure that data records and history have not changed at all. That data has not been altered by any means. An immutable chain of transactions is created in order by including the previous block’s hashes while creating a new block.

Data is immutable and is guaranteed based on hashes. In the chain, if any modification/change is being done with any block, the upcoming blocks/later blocks will show a different hash. And discrepancy will be discovered via the validation process.

• Trusted Logic at Each Node

Storing data over a blockchain network is a trusted practice that you should know now for your business returns. The major reason behind your decision for blockchain development could be trusted data storage and data security. Blockchain technology allows you to store data in a consistent and trusted manner.

And you can apply this at each of your nodes, you can add logic that is executable at each node in the same form. There exists a logic that facilitates a seamless transfer of responsibilities of the product amongst participants. Application on a distributed computing system is called DApp (decentralized application).

Ethereum Dapps are known as Smart Contracts. A smart contract is composed of logic that is run and executed as a mandatory part of any transaction happening. Over Ethereum, you adopt a programming language called Solidity, and this language is used to create your program logic over the Ethereum network.

So, how does it work?

When you use a smart contract, you are creating an instance. Smart contracts are referenced using an address when they are being deployed to the blockchain network.  A smart contract instance carries program logic and state data.

A transaction is executed when a product responsibility is transferred to another party. Worth noting is that the smart contract logic is executed at each & every node! Also, Smart Contract is immutable just like data in the blockchain. No changes can be done once data enters the network. It means that once deployed, you can not change the logic of your smart contract. That’s why, smart contract logic is so trustworthy across the network, as it always executes the same on all nodes. If any code change is required, you need to deploy an entirely new smart contract at a new address!!

Why Choose Blockchain Development

Blockchain technology assures immense security for your data across industries. In the present surge of cyber threats and hacking software, blockchain comes as a big relief to businesses on a global platter. B2b,b2c, Start-UP, SMB, or an enterprise, blockchain development is the need of the hour. The blockchain development solutions & services lead to the creation of decentralized blockchain networks and this provides massive security and traceability of innumerable data and transactions from time to time.

A reliable web & mobile development company can help you develop a quality blockchain solution matching your business needs. The company can even guide you on whether you need a custom blockchain solution or need end-to-end blockchain application development services for desired outcomes. Blockchain development for your organization will lead you to leverage the best use cases offered by the very digital technology.

A typical blockchain solution supports:

• Supply Chain & Logistic Management
• Health Record Management(EHR)
• Financial Transaction Management
• Insurance Claim Management
• Asset Management
• Safe Document Management
• Identity & Access Management
• Electronic Voting
• E-Commerce & Marketplace Management

Blockchain development solutions can range from :

• Private Blockchain networks

It allows you for a secured and scalable ecosystem that is permission-based and involves a very less downtime(if any), or zero downtime

• Smart Contracts

Blockchain protocols allow integrity and  security, enforcing strict multi-party agreements

• DApps (Decentralized Apps)

Peer-to-peer apps with thorough encryption, zero downtime, and not any single point of failure assured

• DAOs (Decentralized Autonomous Organizations)

Blockchain model that boosts collaborative decision-making, and leads to a high level of productivity and business outcomes

• Crypto Wallets

Digital wallets that store, handle, and manipulate cryptocurrencies like Bitcoin, Ripple, Etherium, etc. in an efficient and effective manner 

• Blockchain-enabled Market Platforms

Facilitating fraud-free,safe and secured,transparent peer-to-peer transaction platforms.For example, for lending platforms, b2b,b2c online marketplaces,etc.

• Platforms for Asset Tokenization

Just like NFT platforms. It creates decentralized platforms and allows trades amongst users using their tokens.

What Value Does Blockchain Add to Your platform?

Blockchain technology has multiple use cases across industries in varied functional areas.

You can think of implementing a value-based blockchain solution across your financial domains, government channels, healthcare or retail domains, and varied industries that you want to bring about safety, security, transparency, and scalability to the ecosystem. A unique blockchain development solution can transform the business model that can lead you to an elevated position in the market giving you a competitive edge over others.

The  Blockchain technology can add value to your businesses by securing:

• Traceability
• Transparency
• Improved speed
• Huge cost reduction and time-saving opportunities
• Competitive edge

Continue Reading about more Blockchain-related topics:

What is Ripple Blockchain App & how does it work?

Prediction of Blockchain and its Future Operations

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Well, that’s the pressure you undergo while aligning your company’s goals, making competitors’ analyses, and having that edge over them are all a part & parcel of your sales journey.

AI-ML digital technology helps you to understand your prospective customers, target audience, and their specific needs. Machine Learning(ML) is the subset of AI (Artificial Intelligence).

What is Demand Forecasting

AI/ML-enabled predictive analytics help you to predict your consumers’ demand, their buying behavior, and pattern helps to provide actionable insights facilitating a smooth, improved decision-making process.

Although no technology can forecast demand with 100% accuracy, AI-machine learning models can surely help you with the precise demand forecasting that can take you closer to your business goals. Let’s discuss!

The process of predicting demand for certain products/services that are likely to be purchased in the future is termed demand forecasting. It helps manufacturers in deciding what to produce/manufacture and whatnot, retailers what to keep in stock and what not!

Demand Forecasting helps to improve:

• Managing Supply-Chain, Order-fulfillment & Logistics
• Managing Customer Relations
• Managing Marketing Campaigns
• Manufacturing Flow Management

Traditional demand assessment/forecasting methods in sales  that have been practiced for decades are:

• Qualitative  demand forecasting method
• Quantitative demand forecasting method

Why Demand Forecasting Methods using Machine Learning

In contrast to those traditional methods mentioned above, demand forecasting methods in sales are now adopting modern tools and techniques using Machine Learning digital technology.

The very ML approach helps in:

1. Massive Data Analysis (structured/unstructured)
2. Accelerating the speed for processing data
3. Providing maximum accuracy in the forecast
4. Based on the latest data input, automating forecast updates
5. Identifying varied hidden patterns in data
6. Enhancing adaptability to changes that occur
7. Creating a robust mechanism/system

ML (Machine learning) takes demand forecasting to a higher level. Enhanced forecasts with maximum accuracy, and reliability increase as they are based on real-time data, those data that are pulled from varied internal/external sources, viz. social media, demographic details, weather information, online reviews, etc. Supply chain networks leverage ML algorithms and external data and adapt as per external changes and demands.

Moreover, as all new products lack historical data, Machine Learning algorithms/forecasting tools help in identifying historical data, similar products (having similar characteristics & features), and their lifecycle curves, and then using those datasets as a substitute for making precise predictions/forecasting for sales-demand.

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Parameters: Choosing a Perfect Demand Forecasting ML Software

• Functionality Aspects: When you are choosing demand forecasting software for your sales, you need to see if it matches your company’s requirements. AI-ML-powered tools enable you to forecast the demands of your customers in the future, and the software needs to be suitable in the following aspects:

1. Whether you are looking for short-term, long-term prediction models, or, both
2. Can forecast demand new products
3. Estimating price accurately/precisely
4. Can do multitiered planning for  multiple product groups, channels, and regions
5. Capability for an accurate comparison of ‘What-If’ scenarios such as price changes, market fluctuating, changes in assortments, promotions, etc.
6. Multidimensional modeling functionality
7. Adequate dashboards, reports in granular forms
8. Creating Halo effects, avoiding cannibalization of products (when demand fluctuates for one product, it impacts another product that is complementary or competitive in nature).

• Compatibility with Internal Systems: You need to check if the software is compatible with your internal business tools, and whether can connect well with your sales management solution, ERP, etc.
  
  The compatibility of the demand forecasting solution with your internal system is important because that enables a seamless process of data sharing and collecting related information, historical data, etc., and then coming out with demand trends/demand forecasting for sales of products.
  
  It also enables you to streamline procurement as well as capacity management by integrating smoothly with your internal systems, Warehouse Management System (WMS) or Inventory Management System (IMS)

• External Factors & Data Sources: You need to consult a reliable mobile app development company that can guide you to build your software from the scratch. According to your industry type, business type(b2b,b2c), external factors amount to the accuracy of your forecasts, and predictions for future demand in sales.
  
  For instance, macroeconomic trends, weather conditions, third-party syndicated data, customer POS information, online reviews, social media platform data, etc. are significant data sources that can allow for smooth AI-ML-powered demand forecasting methods.
  
  The more data you have at your disposal, the effectiveness, and accuracy increase for demand forecasts and predictions! A successful AI- Machine Learning-based demand forecasting method requires sensible investments on your part, viz. hire qualified, capable ML engineers, specialists, and data engineers who can create some outstanding demand forecasting software for your business!

Wrapping Notes: Machine Learning enabled-demand forecasting methods involve a very low volume of manual work. Owing to a high level of automation capability, they can smoothly incorporate multiple data sources, and variables, and smoothly manage a large volume of data leading to great business outcomes.

In contrast to traditional forecasting methods that can manage established products, Machine Learning methods are the best fit for new products for short-term/mid-term planning amidst volatile demand scenarios. That’s why you are advised to adopt ML (Machine Learning) capabilities to boost your sales in the future using modern demand forecasting methods!

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Why IoT? Because a digitalization process needs an adequate amalgamation of hardware, software, and networking system with strong internet connectivity, and IoT works that way only. Connecting objects like industrial sensors, and wearables to the internet and facilitating seamless interaction between these objects is what an IoT automation does.

Let’s discuss this today, and see how IoT solutions, can benefit your business and the varied challenges surfacing while implementing the technology.

Benefits

Improved Productivity

IoT solutions integrated into your business help to bring maximum productivity, increasing output. For instance, in a smart factory in the manufacturing industry, machines interact with each other, and their productivity increases thanks to the AI-IoT-sensor-based robots, who allow seamless operations, and smooth functioning without human intervention/ human labor. IoT solutions help to efficiently manage all the production activities in varied stages of manufacturing. Besides monitoring production, the IoT technology also helps employees in real-time skill-development training making them more productive in their work areas.

Better Asset Utilization & Maintenance

Asset maintenance and maximum utilization are the priority for any business. It facilitates efficiency and cost-effectiveness in an organization. Assets such as tools, machinery, equipment, coffee machines, etc. need to be regularly monitored and maintained. IoT solutions allow real-time insights about machines, assets, and help in the decision-making process. These assets are connected via sensors and using IoT technology seamlessly interact with each other for smooth functioning. On account of an emergency, sensor alarming helps by getting proactive, switching off machines automatically, and preventing overheating or any faulty situations.

Cost-Effectiveness

As a business, your foremost aim is to develop a great product and then promote it thoroughly to reach the specific target audience. Getting the right customers by branding your product aptly and then gaining profits, all these phases need money involvement and huge investment costs. IoT solutions help a great deal in this scenario, by predicting the downtime of machines, and you can take proactive measures thereby escaping massive costs for rectifying them. In the same way, IoT technology improves energy efficiency leading to cost reduction and significant cost savings. Worth mentioning here, that if you manage an efficient cost reduction, you can establish a competitive advantage over your other companies/businesses developing similar products as yours!

Real-Time Analytics & Understanding Customer Behavior

AI-ML-IoT-powered digital technology greatly helps in uncovering the real-time insights and analysis of the market, customers, potential targets, etc. Real-time analytics is one of the major benefits that IoT services offer. It helps gather valuable data insights, understand consumer behavior, customer buying pattern, purchasing history, customer preferences, etc. These aspects are crucial for your business marketing activities, building brand loyalty as well as sustaining your present customers and acquiring new ones. Digital Twins is another booming segment of IoT technology where real-time analytics play a significant role for valid reasons. IoT solutions using real-time analytics help you detect problems in advance thereby allowing you to take preventive measures on time. It facilitates an enhanced decision-making process. Gathering, and analyzing interpreting data from varied sources, customers’ activities such as Internet & mobile usage, social media activities, and those captured by video surveillance, CCTV, etc., is the way IoT led analytics work.

Top IoT Trends and Its Beneficial Impact on Businesses

What’s the IoT you think? The complete full form is “Internet of Things,” and this refers to the process where electronic devices or other virtual objects are interrelated and can transfer data between themselves over a network where human-to-computer or human-to-human communication is not necessary.

Challenges/Obstacles

Internet-Security Issues

As per the Kaspersky Laboratory survey, 65% of the respondents surveyed globally have the opinion that IoT technology can create  Internet security issues. On the other hand, 77% of industrial organizations consider cyber threats/attacks a major challenge. Data safety and security are a prime concern for companies and adopting IoT applications to their ecosystem means unique, quality, and trustworthy ones! A perfect IoT solution can mitigate this risk if implemented carefully and planned manner, and this has reduced cyber attacks in the industrial sector from 36% to 16%.

Employees’ Lack of Trust

The digital transformation of an organization involves people, technology, and process. It demands the fine blending and tuning of each stakeholder. Employees (mainly elderly ones) do not trust the effectiveness of  IoT applications in their daily working systems. Privacy and security concerns are worrying factors among those who think IoT devices pose threat to these concerns. They are apprehensive about the way IoT-enabled connected devices collect their data and doubt their ways of handling their information responsibly. Employers need to devise a strategy to dispel employees’ doubts and fear, winning their trust towards adopting innovative technology like IoT. Transparency of communication between top management and subordinates is the right approach to address this grave issue of employees’ mistrust.

Connectivity Restrictions

Connectivity problem is a big challenge when adopting IoT automation/technology in an organization. The underlying reason happens to be the inappropriate internet connection model. Mostly, internet providers still depend on the centralized client-server type connection that allows connecting multiple devices over a network. Since the number of devices is only growing, certain technical restrictions crop up. The definite solution recommended here is the wider adoption of Cloud technology in the ecosystem. Microsoft Azure IoT Hub is an example. Cloud technology allows connecting millions of IoT devices and maintaining security aspects, too.

Bottom-line

The IoT technology is not a choice but a necessity for your organization. Adopting digital technologies like AI-ML-IoT is only boosting growth bringing efficiency & productivity to the core. The IoT technology is booming despite challenges erupting in the process. As the number of connected devices is only increasing day by day, this creates an opportunity for you to tap the immense potential of IoT technology, and move with the flow. Innovative IoT solutions are an answer to your revolutionary growth!

If you want to stay competitive in the market, implementing an adequate IoT solution for your business is the right choice. Connect with our experts today.

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AIET (Artificial Intelligence & Emerging Technologies) in Medicine is helping the industry in identifying diseases, diagnoses, remote patient monitoring, clinical research & drug discovery, monitoring epidemics like the COVID-19 crisis, and many more.

AI/ML algorithms, IoT sensor-based technology, simulation technologies in medicine like AR/VR/MR, and Robotics, all are incessantly taking care of varied healthcare aspects, globally.

Let’s explore how AI along with multiple new-gen technologies is helping the healthcare sector actively and massively!

Artificial Intelligence/Machine Learning

AI/ML business leaders have recognized the massive capabilities of technologies in medicine, and harnessing their fullest potential is their ultimate goal. As per one survey (March-April 2021) conducted by HIMSS,1/3^rd of the total 2,743 US healthcare respondents emphasized that AI/ML were their topmost priority for the next 5 years.

This shows the massive impact of technology in the healthcare industry. AI-ML-powered predictive analytics help in disease detection & diagnosis, providing personalized treatment, AI-enabled artificial neural networks help in detecting cancer and other vital signs leading to timely treatment. In medical imaging, clinical trials, drug development, and related fields, AI-Ml technology is helping healthcare immensely!

Telemedicine

Telemedicine is a significant new medical technology, and especially during the pandemic, it has proved its mettle emphatically. Post-2020, there’s been a major shift in the healthcare practices that enable restricting the spread of corona infections, and other diseases. Telemedicine is of three types, viz. real-time interactive telemedicine, store-and-forward telemedicine, and remote patient monitoring telemedicine. The very digital technology provides healthcare the benefits of remote patient care, real-time virtual consultation, telePharmacy, teleradiology, teleNeurology, and so on. The telemedicine sector is expected to reach a massive figure of US $460 billion by 2030.

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AR/VR/MR

New medical technology means new-gen digital technology that is capable of transforming the ways modern medicine is being practiced. These practices include computer-generated 3D graphics and digital imaging tools that give more power to the healthcare providers and medical team of doctors/surgeons for deep and accurate analysis of patients’ conditions.

Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR) technologies along with AI-ML digital imaging tools are transforming the sector in novel ways.

For example, healthcare teams can plan surgeries, therapies, and myriad treatment processes quite in advance, by using AR/VR/MR simulation tools. Not only this, they can even see the procedures and respective results much ahead in advance.

MR (Mixed Reality) tools can manage and control medical procedures & equipment remotely, and efficiently on time. These modern technologies in healthcare are picking trends gradually but on a consistent note.

Internet of Medical Things (IoMT)

IoMT (Internet-of-Medical-Things) is the combination of IoT(Internet-of-Things) and telehealth technologies and has massive use-cases. IoMT is the network of medical devices, healthcare infrastructures, and digital/web/mobile applications, connected over internet platforms. The market for IoMT (Internet-of-Medical-Things) is set to reach a massive US$6.2trillion by 2025. Globally, the healthcare industry is leveraging IoMT capabilities on a large scale. Telemedicine, the newest trend in the sector is greatly supported by IoT(internet-of-Things) and IoMT technologies.

Conclusion

The aforementioned digital technologies are becoming synonymous with healthcare trends and medical practices around the world. Without them, the healthcare industry cannot survive. AI/ML, IoT-enabled sensors, Blockchain, Robotics, and Analytics are varied other digital tools and technologies that are creating waves bringing revolutionary changes in the healthcare space.

Are you a healthcare provider, hospital, or privately owned clinic, looking to adopt Artificial Intelligence and related digital technologies in your business? Contact Us

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