Why Should Companies Invest in IT Solutions in Australia? 

Why Should Companies Invest 


in IT Solutions in Australia?

 In today’s fast-paced digital landscape, businesses all over Australia are constantly seeking ways to stay ahead of the competition, reduce operational costs, and enhance productivity. One of the key strategies to achieve these goals is by adopting Managed IT Solutions in Australia.  

These innovative services provide a comprehensive approach to managing a company’s IT infrastructure, ensuring that it remains secure, efficient, and adaptable to evolving technology trends.  

What Are Managed IT Solutions? 

 Managed IT Solutions, often referred to as Managed IT Services, involve the outsourcing of IT development, management, and support to a specialised provider. They offer a range of services, including app development, testing services, network management, project management, business analysis cybersecurity, data backup and recovery. Managed IT Solutions in Australia can be customised to meet the specific needs of each business, whether it’s a small startup, a mid-sized company, or a large enterprise. 

The Benefits of Managed IT Solutions in Australia 

Cost Efficiency:  Managed IT Solutions allow businesses to reduce their IT operational costs. Instead of hiring an in-house IT team, companies can opt for a subscription-based model that offers predictable monthly expenses. This cost-saving approach enables businesses to allocate their financial resources more efficiently. 

Enhanced Security:  Cybersecurity is a top concern for businesses today. Managed IT Solutions in Australia provide robust cybersecurity measures to protect against data breaches, malware, and other cyber threats. With the continuous monitoring and rapid response capabilities of MSPs, companies can maintain a strong defense against cyberattacks. 

Proactive Maintenance:  MSPs take a proactive approach to IT management. They monitor systems for issues, potential vulnerabilities, and performance enhancements. This proactive maintenance minimizes downtime, prevents unexpected disruptions, and keeps operations running smoothly. 

Scalability:  Businesses in Australia often encounter fluctuations in their IT needs. Managed IT Solutions can scale up or down according to the evolving requirements of the business. This flexibility ensures that companies only pay for the services they use, making it a cost-effective solution. 

How Managed IT Solutions in Australia Can Benefit Your Business? 

Now that we’ve discussed the advantages of Managed IT Solutions, let’s consider how these services can benefit your business in Australia: 

Focus on Core Competencies  By outsourcing to experts, your business can focus on its core actions and strategic initiatives. This not only improves productivity but also allows for better resource allocation. 

Access to Expertise  Have access to a team of experienced IT professionals who stay up to date with all recent industry trends and technologies. This expertise ensures that your IT infrastructure is in capable hands. 

 Reduced Downtime: Downtime can be costly for any business. With MSPs constantly monitoring your systems, they can quickly address issues before they escalate, minimising downtime and potential revenue loss. 

Compliance and Regulation Managed IT Solutions in Australia are well-versed in local regulations and compliance standards. This expertise ensures that your IT infrastructure adheres to industry-specific requirements, lowering risk of legal issues. 

In conclusion, Managed IT Solutions in Australia offer a strategic advantage for businesses looking to optimize their IT operations, reduce costs, and enhance security.  

 If you’re seeking to streamline your IT management and stay competitive in the Australian market, consider integrating Managed IT Solutions into your business strategy. Don’t miss out on the opportunities that this powerful technology solution can provide for your organisation’s success.

Press Release

Press Release- iCare and 


Adactin Parternership

Our strategic partnership with iCare marks a significant milestone as the media spotlight has been drawn to our achievements through their press release. We are one of the five companies selected for application development work for iCare NSW, under an application development panel contract worth an estimated $305 million by June 2026.

Adactin Group included in iCare’s press release serves as a testament to the recognition of our commitment to delivering excellence and innovation to our clients.

We are proud of our achievements and extend a humble gratitude to all our clients and employees who added value to our growth.

https://www.itnews.com.au/news/icare-signs-off-60-million-for-application-development-599777

Introducing QTechEd

🚀Presenting QTechED as our new name for Training Academy. The new name implies Quality Education in Information and Technology. Keeping pace with the ever-changing technology and as part of the growth and evolution, we have renamed our Adactin Academy with this new brand.

🌟We are excited to share the new portfolio and add-ons in QTechED to suit everyone’s needs in technology. The new division will remain a part of Adactin and continue to perform and grow, affiliated with the group.

.Net, Machine Learning & AI

.Net, Machine Learning & AI

Introduction:

The fields of machine learning and artificial intelligence (AI) have seen remarkable growth and innovation in recent years. Industries are leveraging these technologies to enhance their applications and provide smarter, more intuitive user experiences. One such powerful toolset for developing AI and ML-driven applications is .NET. In this blog, we will explore the synergy between .NET, machine learning, and AI, along with a practical example that demonstrates their capabilities.

Artificial Intelligence:

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. In other words, artificial intelligence (AI), is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience.

 AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), automated decision-making, and competing at the highest level in strategic game systems (such as chess and Go).

Machine Learning:

Machine learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions. Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and based on training, they build the model & perform a specific task.

Understanding .NET:

.NET is a versatile framework developed by Microsoft, designed to build a wide range of applications for various platforms such as Windows, web, mobile, cloud, and IoT. It provides a rich set of libraries, tools, and languages that simplify the development process, making it easier for developers to create robust and scalable applications.

Empowering Applications with Machine Learning and AI:

Machine learning and AI empower applications to learn from data, detect patterns, and make intelligent decisions. By incorporating machine learning algorithms and AI models into .NET applications, developers can create intelligent systems that can analyse vast amounts of data, gain insights, and automate tasks that were once manual and time-consuming.

ML.NET:

//Step 1. Create an ML Context var ctx = new MLContext();   //Step 2. Read in the input data from a text file for model training IDataView trainingData = ctx.Data     .LoadFromTextFile<ModelInput>(dataPath, hasHeader: true);   //Step 3. Build your data processing and training pipeline var pipeline = ctx.Transforms.Text     .FeaturizeText(“Features”, nameof(SentimentIssue.Text))     .Append(ctx.BinaryClassification.Trainers         .LbfgsLogisticRegression(“Label”, “Features”));   //Step 4. Train your model ITransformer trainedModel = pipeline.Fit(trainingData);   //Step 5. Make predictions using your trained model var predictionEngine = ctx.Model     .CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel);   var sampleStatement = new ModelInput() { Text = “This is a horrible movie” };   var prediction = predictionEngine.Predict(sampleStatement);

ML.NET is a free, open-source, and cross-platform machine learning framework for the .NET developer platform. With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.

Practical Example: Sentiment Analysis with .NET, Machine Learning, and AI:

Let’s delve into a practical example to showcase the integration of .NET, machine learning, and AI. We will build a sentiment analysis application using these technologies, which can automatically analyse the sentiment (positive, negative, or neutral) of a given text.

Step 1: Dataset Collection:

To train our sentiment analysis model, we need a dataset that contains text samples labelled with their corresponding sentiment. We can utilize existing sentiment analysis datasets available online or create our dataset by manually labelling texts.

Step 2: Data Preprocessing:

Before training a machine learning model, it is essential to preprocess the data. This step involves cleaning the text, removing stop words, tokenizing, and converting it into a suitable format for model training.

Step 3: Model Training:

In this step, we employ a machine learning algorithm, such as Naive Bayes, Support Vector Machines (SVM), or Recurrent Neural Networks (RNNs), to train our sentiment analysis model. The .NET framework provides libraries such as ML.NET, which simplify the training and evaluation process.

Step 4: Model Integration:

Once the model is trained, we integrate it into a .NET application. Using the ML.NET library, we can load the trained model and make predictions on new text samples. This enables our application to automatically determine the sentiment of any given text.

Step 5: User Interface Development:

To provide a user-friendly experience, we develop a graphical user interface (GUI) using .NET technologies like Windows Presentation Foundation (WPF) or ASP.NET. The interface allows users to input text and receive real-time sentiment analysis results.

Conclusion:

The integration of .NET, machine learning, and AI opens up endless possibilities for developers to create intelligent and data-driven applications. In this blog, we explored a practical example of sentiment analysis, where we leveraged the power of .NET to train a machine learning model and built an application that can analyse sentiment in real time. This is just one example of how these technologies can be combined to create innovative solutions. As technology continues to advance, the collaboration between .NET, machine learning, and AI will play an increasingly pivotal role in shaping the future of software development.

References:

Microsoft Documentation: https://docs.microsoft.com/dotnet/

ML.NET: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet

Satinder Singh

Software Dev Blog

A Complete Guide to Using Clover – A Code Coverage Tool

Quite often we come across situations in the software development industry where we ensure that every single feature is tested before a release, but we still find something broken once the application is released into production. It can be either due to a couple of testers not creating all the test cases required, or missing the execution of a couple of existing tests during the regression cycle. This can be either a manually tested regression suite or an automated suite, we still run into the same problem, wherein the so-called test coverage is 100%, but we still find something broken once released.

To ensure the entire application code has been covered as part of the testing process we can use the Clover code coverage tool. In this blog, I will be focusing on using Maven as the build tool and will be using a sample java spring application. So this is how the tool works.

Basically, when the project is built, all the classes in the project get instrumented and these instrumented classes get packaged as part of the jar/war file. To keep it simple, you can think of instrumentation as the Java classes being slightly modified, to track which lines of the class are being traversed when the application is used. Along with the instrumentation, the clover.db registry is created, where the project structure and the details of all the classes are stored.

Now when you deploy this jar/war file, the instrumented classes are deployed, and the clover coverage recorder files are created. These .db files store the coverage information of which lines of code have been traversed when the application is in use.

Now let us try it out on a sample application.

You can download any public sample application available on Github to try it out. I have downloaded this publicly available application from Github: https://github.com/joakim666/spring-boot-spring-loaded-java8-example

Steps:

1. Add the below profiles section in the pom.xml of this project.

2. Build the application using the below command:

Once the build is done, you will find that an ‘instrument’ folder is created under your project and a ‘clover.db’ registry has been created. If you navigate into the classes folder (‘target\classes\hello’) you will also find that there are additional .class files created with _CLR4 appended. These are the instrumented classes.

3.Run the application using the below command from the project directory:

Once the application is started, you can see that there are additional clover .db files created having names such as ‘clover.db.liverec’, ‘clover.db6ivvjj_lkzicfyg’ etc. These files are used to store the details of which lines of code have been traversed in each of the classes when the user is using the application.

4. Now let us generate a Clover report before we use the application and see what the coverage looks like and what lines of code are covered. To generate a clover report we will first need the clover jar file. You can copy the clover jar from the maven repository ({your home directory}\.m2\repository\org\openclover\clover\4.4.1\clover-4.4.1.jar) and place it in your project directory. Once done you can run the below command.

5. You can now open ‘dashboard.html’ under the ‘CloverReports’ folder to view the coverage details. Here you can see that currently there would be only about 30–35% coverage, which would mainly be the code that was used to bring up the application. You can also see that the methods responsible for displaying the home page, login page etc have not been used yet (Highlighted in red).

6. Now navigate to the home page of the application using the URL http://localhost:8080/home. On the home screen click on the ‘here’ link that is displayed. This will take you to the login screen. Once done, generate the report once again using the command in step 4 and view the coverage information in the Clover report.

You can now see that the coverage percentage has increased from 30% to around 65%. Also, the ‘home’ method responsible for displaying the home screen, and the ‘login’ method responsible for displaying the login screen are shown as covered in your Clover report.

And this is how you can ensure that 100% of all your code has been tested, either manually or through automated capabilities, before releasing an application to the production environment without any margin for human error.

Clover also provides advanced capabilities for excluding specific packages, classes and sections of code such as the catch blocks. It also provides capabilities for capturing coverage in a distributed infrastructure as well.