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Cycling Weekly set out on the hunt for the best gravel riding we could find and found ourselves in Aberfoyle, just north of Glasgow and we weren't disappointed. With lochs, mountain views and mile after mile of traffic free gravel roads, they thought they found heaven!<br/><br/>In association with Komoot.

An Epic Scottish Gravel Biking Adventure

⏲ 19:43 👁 1M

HDR

Our Safety Culture: Working Near Live Traffic

⏲ 54 seconds 👁 231

Sharx Demo

Sharx Security Demo Live Cam: rotary traffic circle Derry NH USA

⏲

<p>A mum from Deal has hit out at a London hospital after receiving a letter booking her son in for an appointment - more than a year after he died.</p><p>Imogen Holliday was sent an email inviting three-year-old Raffy for a visit to Great Ormond Street</a> exactly 14 months after he'd passed away there.</p><p>Also in today's podcast, a leading Kent charity is calling on whoever forms the next government to ban so-called no-fault evictions.</p><p>It was part of the renters reform bill that was going through parliament before the election was called. We've been speaking to Canterbury based Porchlight</a>.</p><p>A business owner in Rainham says a new red route along the high street is turning customers away.</p><p>The introduction of the double red lines along the A2 was meant to help enforce existing traffic restrictions</a> and tackle congestion. Find out what one trader's had to say and the response from the local council.</p><p>A Kent veteran who landed on the beaches of Normandy on D Day is returning there this week to mark the 80th anniversary.</p><p>Peter Smoothy from Herne Bay was one of 156,000 allied servicemen who took part in the invasion. He'll travel to Normandy thanks to the Taxi Charity which is run by London black cab drivers. Hear from Micky Harris from Longfield who's one of them.</p><p>And in sport</a>, it was a mixed weekend for Kent after two games in the T20 Blast.</p><p>The Spitfires beat Middlesex away from home on Friday night, but lost to Hampshire Hawks yesterday.</p>

Podcast: Deal mum hits out at London's Great Ormond Street Hospital after receiving an appointment letter for her dead son

⏲ 23:6 👁 105K

Gotthard Tunnel Traffic

Gotthard Tunnel & San Bernardino & ChiassoLive Traffic Stream June 8

⏲ 11 hours 54 minutes 58 seconds 👁 8

AnantaArtBD

Incredible traffic jam in Dhaka ,Bangladesh। Dhanmondi-27.

⏲ 5 minutes 10 seconds 👁 548.2K

Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \

Session 17: Deployment of Machine Learning Models in Open RAN Networks

⏲ 4:9 👁 75K

The high-speed passenger train, which hit another carrying freight in the centre of the country, belonged to the private RegioJet company.

Trains collide in Czech Republic, killing at least 4 and injuring 23

⏲ 0:35 👁 775K

Nagdulot ng baha at traffic ang malakas na buhos ng ulan sa Metro Manila at karatig-lugar kaninang hapon.<br/><br/><br/>State of the Nation is a nightly newscast anchored by Atom Araullo and Maki Pulido. It airs Mondays to Fridays at 11:05 PM (PHL Time) on GTV. For more videos from State of the Nation, visit http://www.gmanews.tv/stateofthenation.<br/><br/>#GMAIntegratedNews #KapusoStream #BreakingNews<br/><br/>Breaking news and stories from the Philippines and abroad:<br/>GMA Integrated News Portal: http://www.gmanews.tv<br/>Facebook: http://www.facebook.com/gmanews<br/>TikTok: https://www.tiktok.com/@gmanews<br/>Twitter: http://www.twitter.com/gmanews<br/>Instagram: http://www.instagram.com/gmanews<br/><br/>GMA Network Kapuso programs on GMA Pinoy TV: https://gmapinoytv.com/subscribe

Localized thunderstorm, nagpaulan sa malaking bahagi ng Metro Manila at karatig-probinsya | SONA

⏲ 1:53 👁 60K

If you have travel plans anywhere in the U.S. on June 8, AccuWeather's Justin Povick has just the forecast for you.

Thunderstorms to cause travel delays this weekend

⏲ 1:12 👁 55K

KARACHI: Heavy traffic pressure from Nipa bridge

KARACHI: Heavy traffic pressure from Nipa bridge

⏲ 1:43 👁 50K

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

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