This research focuses on a system, known as ATPP, based on deep marked temporal point processes, designed for predicting mobile app usage patterns. By leveraging deep reinforcement learning frameworks and context-aware modules, the system aims to predict the next app a user will open, along with its

0 views • 24 slides

Explore the concepts of address prediction, recovery, and interrupt recovery in EECS 470 lecture featuring slides developed by prominent professors. Topics include branch predictors, limitations of Tomasulo's Algorithm, various prediction schemes, branch history tables, and more. Dive into bimodal,

0 views • 42 slides

Exploring the fundamentals of MPEG-4 Part 10, also known as H.264/AVC, this overview delves into the codec flow, macroblocks, slices, profiles, reference picture management, inter prediction techniques, motion vector compensation, and intra prediction methods used in this advanced video compression

2 views • 32 slides

In recent years, the agriculture industry has witnessed a significant transformation fueled by technological advancements. Among these innovations, computer vision has emerged as a game-changer, offering unparalleled opportunities to optimize crop management and enhance yield prediction. Leveraging

7 views • 19 slides

This presentation discusses implementing AI-based enhanced FDIR and prognostics on-board solutions for constellations to improve fault detection, root cause analysis, and failure prediction, aiming to enhance service availability and reduce operational costs.

1 views • 19 slides

Explore the world of machine learning in stock price prediction, covering algorithms, neural networks, LSTM techniques, decision trees, ensemble learning, gradient boosting, and insightful results. Discover how machine learning minimizes cost functions and supports various learning paradigms for cla

2 views • 8 slides

Efficient air pollution monitoring and prediction models are essential due to the increasing urbanization trend. This research aims to develop novel attention-based long-short term memory models for accurate air pollution prediction. By leveraging machine learning and deep learning approaches, the s

0 views • 17 slides

Explore the significance of State of Charge (SOC) prediction in lithium-ion batteries, focusing on battery degradation models, voltage characteristics, accurate SOC estimation, SOC prediction methodologies, and testing equipment like Digatron Lithium Cell Tester. The content delves into SOC manageme

0 views • 32 slides

KFRE, a validated risk prediction tool, aids in predicting the need for kidney replacement therapy in adults with chronic kidney disease. Developed in Canada in 2011, KFRE has undergone validation in over 30 countries, showing superior clinical accuracy in KRT prediction. Caution is advised when usi

0 views • 9 slides

This PowerPoint presentation provides real-time experimental lightning flash prediction based on initial conditions data from GFS and WRF models. The forecast covers Day 1 and Day 2 with detailed insights on 24-hour accumulated total lightning flash counts and 3-hourly accumulated total lightning fl

0 views • 8 slides

Cutting-edge real-time lightning flash prediction model output for Day1 with 24-hour accumulated total lightning flash counts and 3-hourly accumulated total lightning flash counts overlaid with max reflectivity data. Stay tuned for Day2 forecast updates. Prepared by experts at the Indian Institute o

0 views • 8 slides

This presentation contains real-time lightning flash prediction data based on various initial conditions and model observations. It showcases forecasts for lightning activity on specific dates, including accumulated total lightning flash counts and lightning threat assessments. The study also includ

0 views • 10 slides

This Real-time Experimental Lightning Flash Prediction Report presents a detailed analysis of lightning flash forecasts based on initial conditions. Prepared by a team at the Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, India, the report includes data on accumulated total li

0 views • 6 slides

This presentation provides real-time experimental lightning flash prediction based on initial conditions for a specific period. The forecast includes accumulated total lightning flash counts and hourly variations along with maximum reflectivity overlaid. Prepared by a team of researchers, this data

0 views • 8 slides

Experimental lightning flash prediction based on initial conditions for Day 1, including total lightning flash counts and hourly accumulated data with maximum reflectivity overlays. Day 2 forecast updates coming soon. Prepared by the Indian Institute of Tropical Meteorology.

0 views • 8 slides

Explore the concepts of privacy-preserving prediction and learning in machine learning research, including differential privacy, trade-offs, prediction APIs, membership inference attacks, label aggregation, classification via aggregation, and prediction stability. The content delves into the challen

0 views • 11 slides

Real-time lightning flash prediction report based on initial conditions. Prepared by a team at the Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, India. Includes 24-hour accumulated total lightning flash counts data for Day1, along with 3-hourly accumulated total lightning fla

0 views • 6 slides

Wetland Prediction Model Assessment was conducted in a GIS pilot study for the Kinston Bypass project in Lenoir County. The goal was to streamline project delivery through GIS resources. The study focused on Corridor 36, assessing various wetland types over a vast area using statistical and spatial

0 views • 16 slides

Machine learning often requires real-time, accurate, and robust predictions under heavy query loads. However, many existing frameworks are more focused on model training than deployment. Clipper is an online prediction system with a modular architecture that addresses concerns such as latency, throu

0 views • 17 slides

StreamDFP introduces a general stream mining framework for disk failure prediction, addressing challenges in modern data centers. By predicting imminent disk failures using machine learning on SMART disk logs, it aims to enhance fault tolerance and reliability. The framework adapts to concept drift

0 views • 26 slides

This content discusses the theoretical justification of popular link prediction heuristics such as predicting connections between nodes based on common neighbors, shortest paths, and weights assigned to low-degree common neighbors. It also explores link prediction generative models and previous empi

0 views • 39 slides

This presentation discusses the use of decision trees to enhance program-based static branch prediction, focusing on improving the Ball and Larus heuristics. It covers the importance of static branch prediction, motivation behind the research, goals of the study, and background on Ball and Larus heu

0 views • 36 slides

Categorical data outcomes often involve binary decisions, such as re-election of a president or customer satisfaction. Prediction models like logistic regression and Bayes classifier are used to make accurate predictions based on categorical and numerical features. Regression models, both discrimina

0 views • 67 slides

This project focuses on developing Crash Prediction Models (CPMs) for U.S. roundabouts to enhance planning and design decisions. Geometric and operational features, as well as driver learning curves, are analyzed to understand their impact on crash severity. Data collected from various states forms

0 views • 40 slides

Learning about the significance of predictions in news events and the process of causality mining for accurate forecasting. The research delves into problem definition, solution representation, algorithms, and evaluation in event prediction. Emphasis is placed on events, time representation, predict

0 views • 36 slides

This content showcases various synthetic models for analyzing transcriptome data, including integrative models, trait prediction, and deep Boltzmann machines. It explores the generation of synthetic transcriptome data and the training processes involved in these models. The use of Restricted Boltzma

0 views • 14 slides

This paper presents the WSP framework, a network coordinate-based approach for predicting response times in web services. It explores the motivation behind web service composition, quality-of-service evaluation, and the challenges of QoS prediction. The WSP framework enables the selection of web ser

0 views • 30 slides

Peer prediction mechanisms play a crucial role in soliciting high-quality information from human agents. This study explores the importance of peer prediction, the mechanisms involved in incentivizing truthful reporting, and the convergence of learning agents to truthful strategies. The Correlated A

0 views • 7 slides

This study discusses the inclusion of feed intake data from U.S. Holsteins in genomic prediction, focusing on residual feed intake (RFI) as a new trait. The research involves data from research herds and genotypes of cows, with genetic evaluation models and genomic evaluation for predicting feed int

0 views • 17 slides

This PhD study focuses on traffic prediction using Big Data technologies within Software-Defined Networking (SDN). It explores the separation of data and control planes in SDN architectures, emphasizing the benefits of centralized control for network operations. Additionally, the study delves into t

0 views • 33 slides

CloudScale is an automatic resource scaling system designed to meet Service Level Objective (SLO) requirements with minimal resource and energy cost. The architecture involves resource demand prediction, host prediction, error correction, virtual machine scaling, and conflict handling. Module 1 focu

0 views • 37 slides

The document discusses amendments to the Manual on WIPPS for climate prediction, including new recommendations for weather, climate, water, and environmental prediction activities. It introduces concepts such as Global Climate Reanalysis and the coordination of multi-model ensembles for sub-seasonal

0 views • 10 slides

Accurate prediction of protein secondary structure is crucial for understanding tertiary structure, predicting protein function, and classification. This prediction involves identifying key elements like alpha helices, beta sheets, turns, and loops. Various methods such as manual assignment by cryst

0 views • 30 slides

The Prediction-Focused Approach (PFA) offers a unique Bayesian method for integrating and interpreting hydrogeophysical data. Unlike traditional methods, PFA focuses on forecasting target variables rather than model parameters, utilizing an ensemble of prior models to establish a direct relationship

0 views • 23 slides

The ACE RAM Workshop conducted by George Pruteanu in Barcelona focused on topics such as RAM prediction, FMEA, maintenance concepts, preventive and predictive maintenance, condition monitoring systems, corrective maintenance, and design for maintenance. The workshop delved into reliability predictio

0 views • 16 slides

The Community Hydrologic Prediction System (CHPS) is a revolutionary initiative replacing the outdated NWS River Forecast System. It aims to improve hydrologic modeling infrastructure by incorporating modern forecasting concepts and models, enhancing data sharing, and fostering collaboration within

0 views • 15 slides

CoPTA introduces a novel TLB architecture with contiguous pattern speculating capabilities to optimize address translation, especially for big-data workloads. By modifying TLB and LSQ to support TLB speculation, performance improvements in memory contiguity and prediction accuracy were achieved. The

0 views • 6 slides

Radiomics involves extracting quantitative features from medical images to help in diagnosis, treatment response prediction, and prognosis. However, a recurrent problem in radiomics is the lack of satisfying classification or prediction models due to insufficient data or irrelevant information. To a

0 views • 16 slides

This presentation discusses the importance of link prediction in social networks, including applications like recommending new friends, predicting actor participation in events, suggesting interactions between organizations, and overcoming data sparsity in recommender systems. It covers motivations,

0 views • 99 slides

This text provides valuable insights into blockchain technology, Bitcoin history, core concepts of blockchain, Bitcoin network structure, inherent problems in Bitcoin, and the model for Bitcoin price prediction using graph chainlets. It covers essential aspects such as the distributed ledger, chain

0 views • 29 slides