Hierarchical model - PowerPoint PPT Presentation

The push towards a domain-agnostic information model from a vehicle-centric data approach is explored due to emerging industry requirements. COVESA projects like AUTOSAR Vehicle API and EV charging necessitate a shift. The proposal introduces the Hierarchical Information Model (HIM) to organize data

1 views • 10 slides

Clinical trials often use composite outcomes, but conventional analysis methods have limitations in accurately reflecting clinical reality. Hierarchical outcomes offer flexibility by defining a hierarchy of events based on importance. Analyzing trials using hierarchical outcomes involves comparing p

7 views • 20 slides

This project focuses on building a macrostructural standalone model for the economy of North Macedonia. The model layout includes a system overview, theory, functional forms, and features of the MFMSA_MKD. It covers various aspects such as the National Income Account, Fiscal Account, External Accoun

2 views • 23 slides

This content provides an insightful introduction to the NAMI family support group model, emphasizing the importance of having a structured model to guide facilitators and participants in achieving successful support group interactions. It highlights the need for a model to prevent negative group dyn

6 views • 23 slides

Explore the concepts of clustering and retrieval in machine learning, focusing on K-means and Hierarchical Clustering algorithms. Learn how clustering assigns labels to data points based on similarities, facilitates data organization without labels, and enables trend discovery and predictions throug

1 views • 48 slides

The Shannon-Weaver Model is based on the functioning of radio and telephone, with key parts being sender, channel, and receiver. It involves steps like information source, transmitter, channel, receiver, and destination. The model faces technical, semantic, and effectiveness problems. The Linear Mod

1 views • 8 slides

Regression diagnostics involve analyzing outlying observations, standardized residuals, model errors, and identifying influential cases to assess the quality of a regression model. This process helps in understanding the accuracy of the model predictions and identifying potential issues that may aff

2 views • 12 slides

This research explores the implementation of Hierarchical Rings with Deflection (HiRD) routing as a solution to the performance and energy inefficiencies found in traditional hierarchical ring designs. HiRD guarantees livelock freedom and efficient delivery while simplifying the network structure by

0 views • 52 slides

This detailed process outlines the steps involved in preparing, building, and debugging a back-end programming model known as MFMSA_BIH. It covers activities such as data preparation, model building, equation estimation, assumption making, model compilation, and front-end adjustment. The iterative p

0 views • 10 slides

To establish a working relationship for the development of a site suitable for radio-controlled model aircraft use, the proposal suggests local land ownership with oversight from a responsible agency. Collins Model Aviators is proposed as the host club, offering site owner liability insurance throug

0 views • 20 slides

Providing an overview of the UBU Logic Model within the UBU Performance Oversight Engagement Framework, this session covers topics such as what a logic model is, best practice principles, getting started, components of the logic model, evidence & monitoring components, and next steps. The framework

1 views • 33 slides

Learn how to set up a second hierarchical data table in Windchill PDMLink by selecting rows from the first table to view and interact with related objects in a structured manner. The process involves customization and user actions within the tables to manage parts and documents efficiently.

0 views • 8 slides

A regression model was built to predict the number of crew members on cruise ships using potential predictor variables such as Age, Tonnage, Passenger Density, Cabins, and Length. The model showed high correlations among predictors, with Passengers and Cabins being particularly problematic. The full

0 views • 16 slides

This research focuses on achieving exact Byzantine consensus on undirected graphs under the local broadcast model, where communication is synchronous with known underlying graphs. The model reduces the power of Byzantine nodes and imposes connectivity requirements. The algorithm involves flooding va

0 views • 7 slides

A study conducted by Zheng Li, Ying Wei, Yu Zhang, and Qiang Yang from the Hong Kong University of Science and Technology on utilizing a Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification. The research focuses on sentiment classification testing data of books, training

0 views • 28 slides

The research presented at the 33rd AAAI Conference on Artificial Intelligence focuses on developing an explainable deep model for recommendation systems. It addresses challenges in extracting explicit features from noisy data and proposes a Deep Explicit Attentive Multi-View Learning Model. This mod

0 views • 19 slides

Explore the calibration of a conceptual rainfall-runoff model in Alpine catchments, focusing on the importance of incorporating snow data. The study assesses the benefits of using multi-objective approaches and additional datasets for model performance. Various aspects such as snow cover, groundwate

0 views • 16 slides

The research discusses the use of Hierarchical DAGs in summarizing data with a focus on disease ontology and animal diseases. It explores how general concepts can summarize specific items and their relationships. The study also presents motivated examples of popular papers summarization in SIGMOD, s

0 views • 27 slides

The research discusses methods for hierarchical classification of entities into incomplete ontologies. It explores the challenges of evolving web-scale datasets and the need for classifying entities in an incomplete ontology structure. The Hierarchical Exploratory EM model is detailed, providing ins

0 views • 27 slides

This research paper presents a novel approach, HRL4Ba, for Next-basket Recommendation (NBR) by addressing the challenge of guiding recommendations based on historical baskets that may contain noise products. The proposed Hierarchical Reinforcement Learning framework incorporates dynamic context mode

0 views • 16 slides

This research explores the challenges and solutions in semi-supervised entity classification within incomplete class hierarchies. It addresses issues related to food, animals, vegetables, mammals, reptiles, and fruits, presenting an optimized divide-and-conquer strategy. The goal is to achieve semi-

1 views • 18 slides

This content provides an in-depth look into the IEC 61850 standard, focusing on logical devices, logical nodes, and their implementation in substation automation systems. It covers use cases, system specifications, model mapping, measurements examples, and hierarchical data modeling, offering valuab

0 views • 20 slides

This project focuses on establishing a leading center for promoting innovation and social entrepreneurship within higher education institutions. It aims to encourage students and staff to develop creative solutions for community challenges, expand social involvement, and foster sustainable positive

1 views • 13 slides

Explore the key concepts of the Multiple Regression Model, including model specification, parameter estimation, hypothesis testing, and goodness-of-fit measurements. Assumptions and properties of the model are discussed, highlighting the relationship between variables and the econometric model. Vari

2 views • 31 slides

Learn to build a hierarchical ratings model with 4 criteria, including subcriteria for Comfort, using a step-by-step approach. Explore how to select covering criteria, create performance scales, add and rate alternatives, and prioritize rating intensities for effective evaluation. Streamline your ra

0 views • 12 slides

The Cognitive Model of Stereotype Change, as researched by Hewstone & Johnston, delves into three key models for altering stereotypical beliefs: the bookkeeping model, the conversion model, and the subtyping model. These models suggest strategies such as adding or removing features to shift stereoty

0 views • 58 slides

This study explores the efficient and effective detection of duplicates in hierarchical data, focusing on fuzzy duplicates and hierarchical relationships in XML. It discusses the current and proposed systems, including the use of Bayesian networks for similarity computations. The methods involve vec

0 views • 25 slides

RegCM4 is a community model developed since the 1980s, with over 800 scientists contributing to its advancements. It features a fully compressible, rotating frame of reference and a limited area dynamical core based on the Penn State/NCAR Mesoscale Model 5 (MM5). The model uses hydrostatic and nonhy

0 views • 14 slides

Explore top-k graph summarization techniques on Hierarchical Directed Acyclic Graphs (DAGs) like Disease Ontology, ImageNet, and Wikipedia Categories. Understand motivations for summarization, related works, and the kDAG-Problem. Discover algorithms, experiments, and conclusions for efficient graph

0 views • 38 slides

This analysis involves building a regression model to predict the number of crew members on cruise ships. The dataset includes information on 158 cruise ships with potential predictor variables such as age, tonnage, passengers, length, cabins, and passenger density. The full model with 6 predictors

0 views • 15 slides

In December 2014, a multi-wall penetration loss model for HEW system-level simulation was proposed by Kejun Zhao, Yunxiang Xu, and Xiaoyuan Lu from the National Engineering Research Center for Broadband Networks & Applications. The model provides more accurate calculations of penetration loss in ind

0 views • 11 slides

Dive into the concepts of hierarchical and non-hierarchical modeling in computer graphics. Explore how hierarchical models represent complex objects with explicit sub-part dependencies, while non-hierarchical models treat objects independently. Understand the benefits and challenges of each approach

0 views • 30 slides

Explore how a Bayesian Hierarchical Model can be leveraged to design quality into clinical trials and ensure compliance with ICH E6 R2 Quality Tolerance Limits. Learn about the Risk-Based approach, Quality Tolerance Limits methodology, and the application of Bayesian modeling for early phase studies

0 views • 14 slides

Explore the concepts of hierarchical body modeling in OpenGL Homework 3 by creating body part hierarchies, recording transformation matrices, and understanding the hierarchy structures. The homework focuses on building hierarchies for body parts like thighs and shanks, applying transformations, and

0 views • 23 slides

This study presents a machine learning approach for the hierarchical classification of transposable elements (TEs) based on pre-annotated DNA sequences. The research includes data collection, feature extraction using k-mers, and classification approaches. Proper categorization of TEs is crucial for

0 views • 18 slides

The Centers for Medicare and Medicaid Services (CMS) introduced the Hierarchical Condition Categories (HCC) coding payment model in 2004 for Medicare Advantage and Prescription Drug Plans. This model adjusts payments based on enrollees' demographics and health status, using 70 HCC categories correla

0 views • 19 slides

This research focuses on developing a parallel linear solver using hierarchical matrix techniques to solve particle-laden turbulence problems in radiation environments. Motivations include tackling large linear systems from various applications and leveraging heterogeneous machine architectures. The

0 views • 22 slides

This research presents an enhanced hierarchical word sequence language model leveraging word association techniques. It explores the motivation behind the model, smoothing techniques for data sparsity, and the basic idea of the proposed approach, focusing on patterns and word generation. The propose

0 views • 21 slides

Sensitivity studies analyze how the priorities of alternatives change as criteria priorities vary. Explore graphical sensitivity techniques and interpretations to make informed decisions in hierarchical structures.

0 views • 10 slides

- Hierarchical clustering is a versatile technique in data mining that creates a hierarchical decomposition of objects based on similarity or distance measures. This clustering method offers insights into data relationships through dendrograms, allowing for the identification of outliers and the exp

0 views • 36 slides