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More now of the government's decision to allow foreign citizens to serve in Australia’s armed forces. Defence Personnel Minister Matt Keogh says the scheme would target people who are already permanent residents and loyal to Australia, but the Shadow Defence Minister is not clear on the program's parameters.

Defence force to recruit foreigners to curb shortage

⏲ 1:47 👁 15.8M

BeardedDev

SQL Tutorial - Stored Procedures Optional Parameters

⏲ 9 minutes 41 seconds 👁 1.1K

Forrest McDaniel

SQL Server Optional Parameters

⏲ 12 minutes 45 seconds 👁 147

Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \

Session 14: Introduction to Supervised Machine Learning in Open RAN

⏲ 4:28 👁 40K

dataprojecthub

SQL | Null Function in SQL | ISNULL | COALESCE | IFNULL | NVL

⏲ 7 minutes 1 second 👁 3.7K

Hey Delphi

SQL : How To Include An Optional Null Parameter In SQL Server

⏲ 1 minute 13 seconds 👁 1

Thegroup Atmospheres was created in 1972 in New York, and releasedwith different line-up two albums, both released in 1974. The first line up released \

Atmospheres - album Featuring Clive Stevens & friends 1974

⏲ 40:6 👁 20K

Essential SQL

SQL NULL is Crazy!Lets see how NULL affects Expressions and Comparisons within SQLServer

⏲ 7 minutes 30 seconds 👁 1K

Hey Delphi

SQL : Stored Procedure NULL Parameter

⏲ 1 minute 31 seconds 👁 6

Hello and welcome to Session 18 of our Open RAN series! In this session, we'll explore the exciting world of machine learning and its diverse applications in optimizing Open RAN networks. We'll dive into various use cases where machine learning models play a pivotal role in enhancing network performance, improving customer satisfaction, and ensuring network security. Let's delve into the details of how machine learning is transforming Open RAN.<br/><br/><br/>Network Optimization:<br/>Machine learning models can analyse network performance data and optimize resource allocation, improving overall network efficiency and quality of service. These models can dynamically adjust parameters such as bandwidth allocation, frequency allocation, and power control to ensure optimal network performance.<br/><br/>Predictive Decisions:<br/>By analysing historical data, machine learning models can make predictive decisions about network traffic patterns, allowing for proactive management and optimization. This capability enables networks to anticipate and adapt to changing traffic demands, improving user experience and network efficiency.<br/><br/>Network Design:<br/>Machine learning can assist in network design by analysing terrain data, population density, and other factors to optimize the placement of network components for maximum coverage and efficiency. This approach ensures that network resources are deployed in the most effective manner, minimizing costs and maximizing performance.<br/><br/>Customer Satisfaction:<br/>Machine learning models can analyse customer behaviour and feedback to predict and address potential issues, leading to improved customer satisfaction. By understanding customer needs and preferences, networks can tailor their services to meet user expectations, enhancing overall satisfaction and loyalty.<br/><br/>Fraud Detection:<br/>Machine learning can help detect unusual patterns in network usage that may indicate fraudulent activity, enhancing network security. These models can identify anomalies in user behaviour, signalling potential security threats and allowing for timely intervention to mitigate risks.<br/><br/>Traffic Steering:<br/>Machine learning models can analyse network traffic patterns and dynamically steer traffic to optimize resource usage and improve user experience. By intelligently routing traffic based on real-time conditions, networks can reduce congestion and improve overall network performance.<br/><br/>Subscribe to \

Session 18: Machine Learning Use Cases in Open RAN

⏲ 6:32 👁 5K

ClickHouse

ClickHouse v24.5 Release Webinar

⏲ 1 hour 1 minute 46 seconds 👁 477

Prime Coding

Top 70 SQL Interview Questions One Shot | DIGITAL | NINJA | PRIME | Many More

⏲ 1 hour 19 minutes 45 seconds 👁 2K

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