How Artificial Intelligence can Improve the software development process?

As we know that Artificial Intelligence helps Industries in many ways and makes our industrial tasks easier and more efficient. This is only because we adopting AI technologies. Nowadays many Engineers want to start a career in these technologies and that is the main reason Artificial Intelligence and machine learning becoming trending technologies these days. Only because of these demands, it becoming the biggest boom in the market. By 2021, AI-enabled tools alone are expected to produce $2.9 trillion in business value. 80% of companies spend smartly on AI. 

 Artificial Intelligence has completely transformed the way of software industry operations. As per software development experts, the software development life cycle (SDLC) has tremendous benefits with Artificial Intelligence. It bought accuracy, speed, and efficiency to the entire SDLC (Software Development Life Cycle).  Instead of correcting errors in the code, AI enables developers to concentrate on design and feature construction.  In delivering large-scale and error-free applications without delay, this approach has greatly benefited companies providing custom software development services. 

Traditionally, creating a computer program requires you to specify exactly what you want the system to do in advance and then manually engineer all the features of your technology. It is possible to encode many tasks in an explicit way since computers were still quite powerful before the advent of AI. 

 Here we discuss how artificial intelligence can improve the software development process:

Automated Code Generation

Taking a business idea and writing the code for an enormous project is time-consuming and labor-intensive. Developers are approaching to opt for a solution that helps write code before beginning production in order to save time and dollars. With the complexity of what the aim of the target objective is to collect this information that, if you write the code from scratch, can be very time-consuming. 

By automating code generation and detecting bugs from the code, AI-based assistance reduces these loads to some extent. Take an example of a project where it can be interpreted by translating the concept into executable code in your natural language and framework.

Generate Unique Software Design

Designers need to apply their advanced learning and expertise to create alternative ideas in software engineering, preparing a project and developing it from scratch, before deciding on a definitive solution.

A designer begins with a solution vision, and then retracts and forwards the investigation of plan changes until the desired solution is reached. A tedious and mistake-prone action for designers is to decide on the correct plan decisions for each step.

A few AI advances have shown the benefits of developing conventional approaches with intelligent specialists along this line. The catch here is that the operator conducts the customer as an individual partner. This partner should have the opportunity to provide sufficient input on the most professional method of designing projects.

Take the AI Design Assistant (AIDA) example, most designers are able to understand the client's needs and preferences as well as use the same to design a similar project.  AIDA serves as a website building platform that helps to explore various software design combinations by providing the required personalized design according to customer requirements.

Software Testing

The central part of every software development cycle is testing. A big problem for development teams in the identification and avoidance of errors or bugs. Fixing bugs and errors consists of a large amount of software development costs. Throughout the development life cycle, early error detection requires continuous monitoring. Current software testing activities, however, are expensive, costly, and time-consuming since errors are discovered in the code in several instances after the product has been developed and distributed to the mass market.

In less time than manual testing, AI and Machine Learning algorithms will ensure that the test conducted is error-free, allowing code testers to concentrate on more critical tasks such as maintaining code. Prototypes of AI-enabled code testing allow development teams to conduct mass tests on thousands or millions of codes. Development teams can deal with case-specific experiments, whereas routine and time-consuming tests can be done by AI-assisted automation instruments. Ultimately, this results in a decrease in errors because AI-assisted tests scope and correct errors with sheer accuracy, leading to an increase in overall software quality improvement.

For instance, with AI-enabled cloud testing, DeepCode assists developers to quickly test and release working code. Software testers use a parameter template test plan and add the code to an AI tool, which transforms the code into a working test case automatically and fixes specific bugs. Engineers are sent requests for approval for bug fixes immediately, and subsequent deployment to production. Not only does this save time, money, and energy, but the organization also generates a high ROI.

GUI Testing

In communicating with today's applications, Graphical User Interfaces (GUI) have become important. They are increasingly used in sensitive systems and it is important to test them in order to avoid failures. Testing GUIs is difficult with very few tools and techniques available to help in the testing process.

Currently, GUI testing methods are ad hoc. 

The methods of GUI testing currently used are ad hoc. They require the test designer to perform humongous tasks, such as manually designing test cases, identifying the conditions to be verified during the test execution, determining when to check these conditions, and finally assessing whether the GUI software is properly tested. Phew! Phew! That's a great deal of work now. 

Applitools is an AI-empowered GUI tester tool. The Applitools Eyes SDK automatically checks whether or not the visual code is running correctly. Applitools allows users to test their visual code as extensively as their usable UI code to guarantee that the application's visual look is as you intend it to be. To ensure that they all suit the template, users can test how their application looks in different screen formats.

Deployment Control

Machine learning and AI technologies have also had some effects on the deployment of software, such as an improvement in the efficacy of deployment control activities. The deployment process in the software development paradigm is the stage where developers often update programs or applications to newer versions. By analyzing details such as statistics from prior code releases and application logs, AI-powered tools help predict deployment failure ahead of time. In the case of a failure, this can speed up root cause analysis and recovery. In one instance, automated deployment and rollback verification based on machine learning helped an e-commerce company achieve faster application delivery and a 75 percent decrease in mean-time-to-restore from a development environment failure. In manufacturing, AI can also help applications run optimally. A machine learning-based tool has been deployed by another online company that analyzes various possible application runtime settings and automatically deploys optimal environment settings. This allowed them to halve the cost of clouds and more than double the efficiency of applications.

Requirement Gathering

As a conceptual process of SDLC, the gathering of requirements involves full human interaction. A wide variety of techniques/tools such as Google ML Kit and Infosys Nia are provided by artificial intelligence to automate certain procedures to eliminate human interference to some degree. This stage requires a lot of attention on the early identification of loopholes before moving to design. An AI method called Natural Language Processing can allow machines to understand the specifications of the user in natural language and extract high-level software models automatically. There are, of course, some problems with this approach, including difficulties in balancing the systems created.

Conclusion

Artificial intelligence tends to have a huge influence on software design and development. Software development companies are expected to take on the possible advantages of AI, which is a game-changer in the development of software.

Also Read: Uses of Artificial Intelligence and Machine Learning in Airline Industry

Apart from this if you want to start your career in Machine Learning and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Artificial Intelligence Training Institute in Bangalore and also the best Machine Learning Training institute. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 For more information contact us:

Visit: www.nearlearn.com 

Uses of Artificial Intelligence and Machine Learning in Airline Industry

Today, AI and Machine Learning are Implementing in every Industry and one of them is Aviation Industry. AI and Machine Learning work as a backbone in Aviation Industry. Artificial Intelligence is more and more rolling out across the aviation industry. The way businesses approach their data, operations, and revenue stream is disrupted by AI in the aviation industry. Artificial intelligence is already being used by the world's leading airlines to improve operational efficiency, avoid costly errors, and increase customer satisfaction. There are many different areas where the aviation industry can be empowered by machine learning.

The aviation industry leverages Artificial Intelligence with machine learning, computer vision, robotics, and NLP. In order to maximize overall customer experience, primary advantages include Predictive Maintenance, Pattern Detection, Auto-Scheduling, Targeted Ads, and Customer Feedback analysis.

A recent study shows that aviation professionals are considering using artificial intelligence to monitor pilot voices for passengers to have a hassle-free flying experience. This technology is intended to bring about tremendous changes in the aviation world.

Airlines are looking at how technology can help minimize the impact on the experience of passengers and their company of disruption. Over the next three years, 80% of them plan to invest in major programs or R&D in prediction and warning systems that rely heavily on AI, according to the SITA report. 

They came together in October 2017 to launch the New Experience in Travel and Technology (NEXTT) initiative to maximize the use of digital technologies in the face of increasing numbers of passengers. AI is highlighted as a priority, especially in relation to its ability to enhance decision-making in real-time and, thus, effectiveness.

It is now expected that AI in the aviation market will grow from $152.4 million in 2018 to $2.2 billion by 2025.

So here we discuss how Artificial Intelligence and machine learning uses in Airlines:

Identifying Passenger information while checking-In

Security is a primary concern for airports and, therefore, it is imperative that the authorities properly check the documents and identify the passengers who are travelling. AI-enabled facial recognition systems and software can help airport authorities recognise passengers by using the data and comparing it with their passport pictures. For example, Delta Airlines, one of the American airlines, has installed cameras and deployed facial recognition technology to identify their passengers while checking in.

In addition, in their security scanners, airport authorities may also use advanced technologies to identify possible threats at large and popular airports in the world. In their mobile applications, several airlines have also implemented this technology and streamlined the entire boarding process to provide their clients with a better travel experience in the middle of their crisis. Technology like artificial intelligence and machine learning will also aid in speeding up the process of attending customers, which in turn would help the officials in a longer run.

Baggage Screening

It is also imperative for airport authorities, in addition to identifying travellers and checking their documents, to review and screen the luggage of travellers in order to detect any potential threats. The luggage screening process could be tedious using traditional methods. Security officials can, however, quickly identify dangerous and illegal items in the luggage of travellers in a much simpler way with AI-based systems. These systems assist in automated screening, which through X-rays and computed tomography can detect potential threats in the baggage.

Japan's Osaka Airport plans to install Syntech ONE 200, an AI platform for baggage screening with multiple conveyor belts. Automated baggage scanning can help security officers identify suspicious objects easily and efficiently. The compatibility of the Syntech One 200 with the X-ray protection device increases its likelihood of detecting possible hazards.

Assisting Customers

AI can be used to help customers at the airport and, at the same time, it can help a business reduce its operating costs and labour costs. Airlines companies are now using AI technologies to help their customers quickly solve problems by obtaining precise information on future flight journeys on their internet-enabled devices. In the next five years, more than 52 percent of airline companies worldwide are planning to install AI-based tools to enhance their customer service functions. 

Artificial Intelligence may address numerous common customer queries, helping them with check-in requests, flight status, and more. Nowadays artificial intelligence is also used in air cargo for different purposes such as revenue management, protection, and maintenance and it has shown amazing results till date.

Air Traffic Control

Changing the weather and the height of the control tower, which can cause air traffic delays, is one of the main challenges at the airport. Air traffic control (ATC) must rely on radars during bad weather to keep the airport operating smoothly. Some airports have installed ultra-high definition cameras with AI technology on top of the towers in an attempt to solve this issue. To provide direct views of the airport to traffic controllers, AI can be used. Searidge Technologies' AI systems, such as AIMEE, use machine learning to interpret photos, record aircraft and alert controllers so that they can signal the next aircraft to arrive on the cleared runway.

Ticket Prices and Crew Management

AI algorithms could also assist airlines to optimise ticket prices based on different factors, such as seasonality, fuel prices, rivalry, etc. Faculty, a British company specialising in AI solutions, has developed an AI model that was able to provide predictions that were accurate between 70% and 80% up to 90 days before each flight.

Then crew management is there. All aspects need to be taken into account, such as certification, availability and qualification of pilots, flight attendants and engineers. It will improve HR productivity to schedule and re-schedule workers using an AI-based roster system and ideally optimise crew layovers.

Fuel Consumption Optimization

Airlines use AI systems to collect and analyse flight data on flight distance, altitudes, actual passenger count, aircraft weight, weather, and so on to reduce the environmental effects of aircraft and reduce flight costs. Neural network models, for example, can be used to predict the fuel usage of an aircraft. Systems will apply it after pre-processing the data and training the model and then estimate the amount of fuel that is required for one flight. This helps eliminate fuel waste and decrease the excessive weight and fuel consumption of aircraft.

Maintenance Predictions

In order to enhance the efficiency of their aircraft maintenance operation, Airbus, the leading aircraft manufacturer, is introducing AI applications. A cloud-based programme, Skywise, assists in the efficient storing of data. A huge amount of data is collected and registered in real-time by the fleet, processed and stored in the Cloud server. For the airline business to determine an appropriate method for aircraft maintenance, Predictive Analytics and AI establish a systematic solution.

Route Planning

Carriers need to consider hundreds of factors when deciding route and frequency demand for specific city pairs, particularly with the rise in point-to-point travel. Demographics, industry links, week and day time, season, holidays, activities, fuel prices, etc., all determine whether or not a route will be lucrative and when. ML can manage far more data than traditional analytical methods in order to decide optimum routes and schedules. To assess both leisure and business travel demands, it can analyse search engine data, booking agent data, social media posts and comments, along with recruitment and technical pages. 

Conclusion

For airlines and airport authorities around the globe, the use of artificial intelligence in aviation has made many tasks simple. From the identification of passengers to bag screening and the provision of quick and reliable solutions for customer service.

Also Read: Example of How Machine Learning is changing modern advertising industry

Apart from this if you want to start your career in Machine Learning and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine LearningTraining Institute in Bangalore and also the best Artificial Intelligence Training Institute. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 

For more information contact us:

Visit: www.nearlearn.com

6 Example of How Machine Learning is changing Modern Advertising Industry

 As the world moving towards a digital age, Machine Learning, and Artificial Intelligence making their own place in industries, and these technologies became powerful assets for marketers. Advertisers can exploit advanced software and data analytics tools through AI and machine learning to deliver a much better return on their advertisement expenditure (ROAS). Over the past few years, Google Advertising automation has come to the fore, and more advertisers are adopting the possibilities of this innovative technology. 

Many media sellers do use traditional methods of data management to place advertisements. They are looking to change, but the amount of data collected today is so high that they fail to obtain simple insights that help them understand their audience and effectively target their advertising. Machine learning can help you better understand the customer, improve the experience of user ads, increase the scope and productivity of advertising, generate more revenue and radically change advertising. It is capable of processing large quantities of information with accuracy and speed that was previously impossible. It offers detailed and actionable observations if programmed correctly. 

Machine learning uses algorithms to derive data insights in order to automatically perform various predictive tasks through streamlined processes. In order to predict customer needs that result in smarter advertising decisions, it analyses interest, habits, purchase preferences, and demographics at high speeds. Machine learning will track the whole customer journey through multiple platforms and formats to allow advertisers to reach the right audience with the right content to deliver customized and enjoyable experiences at the right price. 

Here we discuss how Machine Learning is changing the modern Advertising Industry:

 

Capitalize on valuable insights

 

If you're an advertising specialist, you'll run commercials using targeted data. But the issue is that the way the data is found is not right! 

You might want to use and analyze the data related to your offer. Therefore, you might need the most prioritized information to run an effective ad campaign to accomplish this. But, adopting innovative promotional tools means a lot to your budget.

You may lose a few targeted audiences due to less advertising. For example, If you are promoting a video game, then you are likely to target young and middle-aged people. And, you don't take grandparents into consideration. Your income may be affected by these types of assumptions. Machine Learning plays a vital part here. New revenue opportunities are found by machine learning models. It limits the expenses of the budget and workforce. For this reason, at low cost and time, Machine Learning algorithms can process data sets easily. Machine learning technologies are best used to generate more digital advertising revenue opportunities. Various insights into the collected data are provided by ML solutions. These insights might include audience analysis, their search patterns, and also help to frame business strategies.

 

Improve Ad Creative

 

Targeted Audiences respond differently to ad creative and the audience won't waste a lot of time on your ads and turn to other media. Internal connections may draw your audience and attract more exposure to your services through call-to-action. And note that the viewer won't waste a lot of time on your ads and turn to other media. Internal connections may draw your audience and attract more exposure to your services through call-to-action. 

Many people believe that only quantitative data can be interpreted by ML algorithms, but this isn't true. Advances are still increasing in this application of AI. Using ML will dramatically impact the overall performance of the marketing campaign, according to the researchers. The techniques of predictive analytics help to make your campaign more innovative. Researchers found that ad images that matched their personalities were favored by subjects. But more importantly, the machine learning algorithm found that the correlation between the type of personality and type of image could influence the interest of a customer in a product. People liked not just images that suited their personalities. They also reported more favorable attitudes towards these brands and purchasing intentions.

 

Augment Contextual Relevance

 

It's a fact that innovative advertising is often the best way to create a huge reaction from the target audience. Before that, at the right moment, the marketing departments must find the right platforms and the right audience. Machine Learning models are used to process on-page data instead of only relying on the target audience data. It can do any tasks like we humans do. The devices are now increasingly capable of defining and recognizing user requirements with advances in ML. For example, in a text form, they may identify the user opinions/queries expressed. In addition, machines based on ML can also respond to emotions. Using deep learning approaches, this will be done. Similarly, advances in deep learning allow systems to process images and videos. A combination of AI, ML, and deep learning enables advertisers to incorporate technologies and do a few impressive things in their approaches.

 

Target More Defined Segment

 

The aim of any advertiser is to segment the audience based on their search. The deep analysis of their search helps an advertiser to have improved personalization and satisfaction. But, you need a significant amount of data to start with if you want to provide personalised services. The introduction of ML algorithms then helps to turn raw data into something useful. Machine Learning approaches help reduce your target customers and achieve good outcomes through digital ads. 

Truth in the form of consumer data is not easy to come by for companies. So, when gathered in abundance and leveraged by networks such as Facebook and Google, it becomes a powerful way to reduce your target audience dramatically to one more likely to claim your offer. 

Data is collected by Facebook and helps you to build audiences. More significantly, the platform uses machine learning to assess who is most likely to complete the goal you are bidding for within that audience.

 

Boost Conversion

 

Advertisers always want better applications for innovations that are trending. So, chatbots operated by Machine Learning, image processing, and voice recognition are the perfect match for sales improvement. In the future, machine learning can have a great influence on an ad campaign. Ad imagination, bidding techniques, and personalization are enhanced by Machine Learning models. Thus, invest in Machine Learning in this new environment and stay competitive.

 

Bid More Strategically

 

Not all impressions in programmatic ads are worth what you are willing to bid on them. Some are. And others are perhaps more worthwhile. Demand-side platforms do not need guesswork anymore to determine these impressions. These platforms will use machine learning technology to make bids and optimizations that once demanded professional buyers. 

For example, take Google's Smart Bidding: an automated bidding technique that uses machine learning in any auction to optimize for conversions, or conversion value. This is known as "bidding auction-time."

Conclusion

In the coming days, Machine Learning will give benefits to every industry. The job performed by humans will be carried out by computers. Machine learning would also be a game-changer for the future of the industry. 

 Also Read: Top Machine Learning Algorithms for Beginners

Apart from this if you want to start your career in Machine Learning and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine LearningTraining Institute in Bangalore and also the best Artificial Intelligence Training Institute. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 

For more information contact us:

Visit: www.nearlearn.com

Top Machine Learning algorithms for Beginners

As we all know, around the world all nearly manual tasks becoming fully automated, and all of the things possible only because of AI and Machine Learning. Machine Learning algorithms help computers to work automatically as we do. We living in the era of the technological world where we can see the enhancement of technological advancement over the years. In the coming years, Machine Learning and AI will be the most popular technology in the market that everyone wants to implement in businesses for better productivity. Govt. also started focusing on these technologies and providing investment for AI and Machine Learning. 

An ML algorithm is a method that runs on data and is used to create a machine learning model that is ready for output. Learning tasks can include learning the feature that maps the input to the output, learning the hidden structure in unlabeled data, or 'instance-based learning,' where a class label is created by comparing the new instance (row) to instances stored in memory from the training data. Instance-based learning does not produce an abstraction from particular cases. These algorithms are highly automated and self-modifying because, with the addition of an increased amount of data and with minimal human interaction needed, they continue to evolve over time.

 So here we discuss the best machine learning algorithms for beginners:

 Linear Regression

It demonstrates the connection in the midst of an independent and a dependent variable and deals with continuous values of prediction/estimates. The effect on the dependent variable is described while the independent variable is modified, as a result of which an independent variable is known as the explanatory variable, while the dependent variable is named as the interest of factor. For example, it can be used in the insurance domain for risk assessment to identify the number of applications for users of multiple ages. The predictor value in a simple linear regression is an independent value that has no underlying dependence on any variable. The x and y relationship is described as follows:

Y = mx+c

Here the slope is m and the intercept is c.

 Logistic Regression

Logistic regression is an effective statistical way of modeling one or more explanatory variables for a binomial result. By estimating probabilities using a logistic function, which is the cumulative logistic distribution, it calculates the relationship between the categorical dependent variable and one or more independent variables. 

There are two aspects of Logistic Regression, Hypothesis and Sigmoid Curve. The resulting probability of the occurrence can be derived on the basis of this hypothesis. Data obtained from the hypothesis would then fit into the log function that forms the 'sigmoid' S-shaped curve.

The sigmoid/logistic function is given by the equation below. 

1 / (1 + e^-x)

We write the logistic regression equation as follows— 

y = e^(b0 + b1*x) / {1 + e^(b0 + b1*x)}

The two coefficients of the x input are b0 and b1. Using the maximum probability function, we estimate these coefficients.

 Decision Tree

The decision-tree algorithm is a much more complicated algorithm. A unique advantage of using this algorithm is that it can work not only with problems of classification but also with regression. It is also important to remember that both supervised and non-parametric are (meaning that there are no assumptions with probability distributions of the data). They are easier to understand and easier to visually explain in terms of how they work behind the scenes than other models (thinking of a tree and how its branches split). Decision trees can handle both categorical and numerical data, which is often what is needed for machine learning algorithms in real-world applications.

Naive Bayes

Naive Bayes is a classification based on the Bayes Theorem of conditional probability classifiers. A Naive Bayes classifier assumes that the appearance in a class of a selective feature is unrelated to any other feature's appearance. 

The Bayes Theorem provides a standard technique for the posterior probability estimation of P(c|x), P(c), P(x), and P(x|c). There is an assumption in a Naive Bayes classifier that the effect on a given class(c) of the values of the predictor is independent of other predictor values. The Bayes Theorem has a lot of benefits. It can be implemented easily. In addition, Naive Bayes needs a small amount of data from training and the results are normally correct.

K-means

K-means is an unsupervised algorithm that solves problems in clustering. It measures the centroids of k clusters and assigns the least distance between its centroid and the data point to a data point for that cluster.

How K-means form a cluster:

• For each cluster, the K-means algorithm picks k points, called centroids. 
• A cluster with the closest centroids, i.e. k clusters, is formed by each data point.
• Now, based on the existing cluster members, it produces new centroids.
• With these latest centroids, the closest distance is determined for each data point. Until the centroids do not change, this process is repeated.

How Determine the value of K:

In K-means, there are clusters and each cluster has a centroid of its own. For that cluster, the sum of the square difference between the centroid and the data points within a cluster is the sum of the square value. Often, if the sum of square values is added for all clusters,  it becomes total within the sum of the square value for the cluster solution.

SVM (Support Vector Machine)

For classification or regression problems, the Support Vector Machine Algorithm is used. In this, by finding a particular line (hyperplane) that separates the data set into multiple classes, the data is divided into different classes. The Support Vector Machine Algorithm tries to find the hyperplane that maximizes the class distance (known as margin maximization) as this increases the probability of more accurately classifying the data.

An SVM algorithm can be created when it comes to trading, which categorises the equity data as a favourable buy, sell or neutral class, and then classifies the test data according to the rules.

 KNN (K Nearest Neighbour)

The KNN algorithm divides the data points into different classes on a basis of similar measure such as the distance function. Then, by searching through the entire data set for the most related instances (the neighbours) of K and summarizing the output variable for these K instances, a prediction is made for a new data point. This could be the mean of the results for regression problems and, for classification problems, this could be the mode (most frequent class). 

The K Nearest Neighbours Algorithm may require a lot of memory or space to store all the data, but it only performs (or learns) a calculation when, just in time, a prediction is required.

 ANN (Artificial Neural Network)

Artificial Neural Networks share the same underlying concept of our nervous system as neurons. It consists of neurons that serve as layer-stacked units that transmit data from the input layer to the final output layer. There is an input layer, a hidden layer and a final output layer on these neural networks. There might be a single layered neural network (Perceptron) or a neural network with several layers.

Human facial recognition is an example of Artificial Neural Networks. Pictures with human faces can be recognised and categorised from "non-facial" images. However, depending on the amount of images in the database, this may take several hours, while the human mind can do this immediately.

Conclusion

From the above discussion, it can be inferred that Machine learning algorithms are models that learn from data and enhance experience regardless of the involvement of human being.

Also Read: Top Best  Machine Learning DataSets For Practicing

So these are the top ML Algorithms that you should know. Apart from this if you want to start your career in Machine Learning and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine Learning Training Institute in Bangalore and also the best Artificial Intelligence Training Institute. They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

For more information contact us:

Visit: www.nearlearn.com