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_marta-y-fina-su-historia-completa-sueos-de-libertad-sub-mafin.webp "PASSWORD: sapphicstory<br/><br/>01 - https://www.dailymotion.com/video/x8yqp6e<br/>02 - https://www.dailymotion.com/video/x8whhb8<br/>03 - https://www.dailymotion.com/video/x8yqpje<br/>04 - https://www.dailymotion.com/video/x8yqq2a<br/>05 - https://www.dailymotion.com/video/x8yqrmw<br/>06 - https://www.dailymotion.com/video/x8yqs0a<br/>07 - https://www.dailymotion.com/video/x8yqsls<br/>08 - https://www.dailymotion.com/video/x8zb11m<br/>09 - https://www.dailymotion.com/video/x8zl54i<br/>10 - https://www.dailymotion.com/video/x8zl5v0<br/><br/>More episodes coming soon, we're working on it. <br/>Thank you for your support and patience.<br/><br/>Universe #mafin")
_session-14-introduction-to-supervised-machine-learning-in-open-ran.webp "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-21-nfv-network-function-virtualization-concept-in-open-ran.webp "In this session, we'll explore the fundamental concepts of NFV (Network Function Virtualization) in the context of Open RAN. We'll delve into the orchestration of virtualized network functions, the role of NFV Management and Virtualization, and how these elements work together to transform traditional network architectures.<br/><br/>Understanding NFV in Open RAN:<br/><br/>NFV Fundamentals: Delve into the core principles of NFV, where traditional hardware-based network functions are replaced with software-based virtual instances, driving agility and scalability.<br/>Essential Components: Learn about the critical components of NFV architecture, including Virtual Network Functions (VNFs), NFV Infrastructure (NFVI), and the NFV Management and Orchestration (MANO) layer.<br/>Benefits of NFV: Explore how NFV optimizes resource utilization, accelerates service deployment, and reduces operational costs, fostering a more adaptable and responsive network ecosystem.<br/>NFV Applications in Open RAN: Understand the pivotal role of NFV in Open RAN, enabling the virtualization of RAN functions and facilitating the seamless deployment of new services.<br/><br/>Understanding NFV and Orchestration:<br/>NFV is a technology that virtualizes network functions traditionally performed by dedicated hardware. Orchestration is the automated arrangement, coordination, and management of these virtualized network functions to enable efficient network operation.<br/><br/>NFV Management and Virtualization (NFVM):<br/>NFVM is a key component of NFV architecture that manages the lifecycle of virtualized network functions. It handles tasks such as instantiation, monitoring, scaling, and termination of virtualized functions.<br/><br/>Orchestration Function:<br/>Orchestration in NFV involves coordinating the deployment and interconnection of virtualized network functions according to service requirements. It ensures that network resources are allocated efficiently and dynamically based on demand.<br/><br/>Conclusion:<br/>NFV and orchestration play a crucial role in the evolution of Open RAN, enabling operators to build agile, scalable, and cost-effective networks. Understanding these concepts is essential for anyone involved in the design, deployment, or management of modern telecom networks.<br/><br/><br/>Subscribe to \")
_casa-italia-celebrating-the-euros-one-dish-at-a-time.webp "Since 1976, Casa Italia have been serving up hearty Italian food in the heart of Liverpool.")
_session-16-optimizing-open-ran-with-machine-learning-124-concept-overview.webp "Hello and welcome to Session 16 of our Open RAN series! Today, we're diving into the fascinating world of machine learning and its impact on Open RAN networks. We'll be focusing on how machine learning can boost Open RAN performance, specifically in predicting throughput based on MCS coding schemes. This is a crucial aspect for optimizing network performance and resource allocation in Open RAN environments.<br/><br/>1. Introduction to Machine Learning in Open RAN:<br/>Machine learning plays a pivotal role in enhancing Open RAN networks by enabling predictive capabilities, particularly in throughput optimization. By leveraging machine learning models, Open RAN can predict throughput based on the Modulation and Coding Scheme (MCS) coding scheme. Throughput prediction is critical for optimizing network performance and efficiently allocating resources, ensuring a seamless user experience.<br/><br/>2. Developing Machine Learning Models for Throughput Prediction:<br/>Developing a machine learning model for throughput prediction in Open RAN requires several key considerations. Firstly, the model needs to be trained on a dataset that includes throughput data and corresponding MCS values. The model should be designed to handle the complex relationships between these variables and predict throughput accurately. Mathematical functions and algorithms such as regression and neural networks are commonly used for this purpose, as they can effectively capture the underlying patterns in the data.<br/><br/>3. Deployment of Machine Learning Models in Open RAN:<br/>The deployment of machine learning models in Open RAN involves several steps. Once the model is trained and validated, it is deployed to the network where it operates in real-time. The model continuously monitors network conditions and predicts throughput based on incoming data. This information is then used to dynamically allocate network resources, optimizing performance and ensuring efficient operation.<br/><br/>4. Training Data Acquisition Process:<br/>Acquiring training data for the machine learning model involves collecting throughput data and corresponding MCS values from the network. This data is then cleaned and formatted to remove any inconsistencies or errors. The cleaned data is used to train the model, ensuring that it can accurately predict throughput in various network conditions. The training data acquisition process is crucial as it directly impacts the accuracy and reliability of the machine learning model.<br/><br/>Subscribe to \")
_effective-wazaif-for-everyday-problems-business-food-health-amp-more.webp "ChatGPT<br/><br/>Write a description for my wazaif video in different problems like business, food etc<br/>ChatGPT<br/>Sure! Here's a description for your Wazaif (Islamic supplications) video tailored to different problems such as business, food, and more:<br/><br/>Title: Effective Wazaif for Everyday Problems: Business, Food, Health & More<br/><br/>Description:<br/><br/>Welcome to our Wazaif video, where we provide you with powerful Islamic supplications to help you overcome various life challenges. Whether you’re facing issues in your business, struggling with food and sustenance, dealing with health problems, or seeking solutions for other everyday troubles, these Wazaif are here to guide you.<br/><br/>In this video, you will find Wazaif for:<br/><br/>1. Business Success:<br/>Enhance your business prospects and overcome financial hurdles with these supplications. Learn the specific Wazaif that can attract prosperity and stability to your business ventures.<br/><br/>2. Food and Sustenance:<br/>Ensure abundance and blessings in your food and sustenance. These Wazaif are aimed at removing scarcity and inviting Allah's bountiful provisions into your life.")
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