emiley varuni

GB-RVFL: Revolutionizing Neural Networks GB-RVFL (Gradient Boosted Random Vector Functional Link) is a cutting-edge neural network model that combines the efficiency of Random Vector Functional Link (RVFL) networks with the power of gradient boosting. This hybrid approach enhances learning speed, accuracy, and generalization, making it ideal for tackling complex, data-intensive tasks across diverse domains. GB-RVFL (Gradient Boosted Random Vector Functional Link) is an innovative machine learning framework that merges the capabilities of Random Vector Functional Link (RVFL) networks with gradient boosting techniques to enhance performance on complex tasks. Key Components of GB-RVFL: RVFL Networks : RVFL is a single-layer feedforward neural network that incorporates random weights in its hidden layer. Unlike traditional neural networks, RVFL doesn't adjust these weights during training. Instead, only the output weights are optimized, making it computationally efficient and fast. Gr...

Hierarchical Network Structure: A New Approach to ICD-11 Complex PTSD The hierarchical network structure approach to ICD-11 Complex PTSD emphasizes interconnected symptom clusters, providing a nuanced understanding of the disorder. This model reveals how core symptoms (e.g., trauma-related distress) interact with self-organization deficits (e.g., negative self-concept), offering insights for tailored interventions and advancing research on comorbidity and treatment effectiveness. The hierarchical network structure approach to ICD-11 Complex PTSD (CPTSD) provides a comprehensive framework to better understand the disorder's complexity by analyzing the interplay between its symptoms and their relationships. CPTSD, as defined in the ICD-11, consists of two primary dimensions: Core PTSD Symptoms : These include re-experiencing traumatic events, avoidance behaviors, and a persistent sense of threat. These symptoms form the core of post-traumatic stress reactions. Disturbances in Self-O...

Advanced Network Design for 6G: Graph Theory & Slicing Advanced Network Design for 6G: Graph Theory & Slicing  explores the integration of cutting-edge methodologies to meet the demands of 6G networks, emphasizing efficiency, scalability, and tailored performance. Here's a detailed breakdown: 1. Advanced Network Design for 6G 6G networks aim to deliver: Ultra-high data rates (terabits per second speeds). Ultra-low latency (in milliseconds or less). Massive device connectivity (billions of devices globally). Seamless integration of AI, IoT, and immersive technologies like AR/VR. Designing such networks requires: Efficient Resource Utilization : Maximizing bandwidth and spectrum usage. Flexibility and Scalability : Adapting to diverse use cases like autonomous vehicles, industrial automation, and remote healthcare. Resilience : Ensuring robust performance under varying network conditions. 2. Graph Theory in 6G Graph Theory provides a mathematical framework to model...

 How Social Media Connects Us: The Science of Networks Social networks are intricate systems that reflect how individuals and groups interact, share information, and influence each other. Here's a breakdown of key concepts that help explain how social media connects us: 1. Clusters What are they? Clusters are small groups of tightly connected individuals within a larger network. They often form around shared interests, locations, or relationships. Example: Think of your Instagram feed. You likely see posts from a "cluster" of close friends, family, or people in the same hobby group. Why it matters: Clusters create a sense of community and facilitate deep connections but can also act as "echo chambers" where similar ideas are reinforced. 2. Hubs What are they? Hubs are individuals or accounts with a large number of connections, serving as critical points in the network. Example: On Twitter, a celebrity or a news organization with millions of followers acts a...

 Temporal Graphs: Modeling time-dependent network data Temporal Graphs are dynamic models that represent time-dependent network data. Unlike static graphs, they capture changes in nodes, edges, and their attributes over time, enabling analysis of evolving relationships. Applications include tracking information flow in social networks, modeling traffic systems, and understanding dynamic interactions in biological or financial networks. Key Features of Temporal Graphs Time-Stamped Edges Each edge in a temporal graph has a timestamp, indicating when the relationship existed. For example, in a social network, a timestamp could represent the exact time a message was sent. Dynamic Nodes and Attributes Nodes and their attributes can change over time, such as users joining or leaving a network, or attributes like traffic density fluctuating during the day. Directed and Weighted Temporal Graphs Temporal graphs can also incorporate direction (e.g., sender to receiver) and weights (e.g., ca...

ISRO's Last 2024 Launch To Test Technology For Chandrayaan-4; Farmers Call Bandh Today As Talks Make No Headway ISRO’s final 2024 launch focuses on testing technology for Chandrayaan-4, marking a key step in India’s lunar exploration. Meanwhile, Akhilesh Yadav faces criticism for remarks perceived as mocking Hindus, sparking controversy. Separately, farmers announce a bandh today, protesting unresolved demands after negotiations failed to reach a consensus. ISRO’s Last 2024 Launch: Testing Technology for Chandrayaan-4 The Indian Space Research Organisation (ISRO) is set to conclude 2024 with a significant mission aimed at advancing lunar exploration technology. This launch will test critical technologies for Chandrayaan-4, India’s next ambitious lunar mission. The focus is on enhancing landing, rover deployment, and scientific payload capabilities to improve upon Chandrayaan-3's success. This mission represents India’s commitment to sustaining its leadership in space explorati...