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Found 14 papers, 1 papers with code
Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments
This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems.
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.
Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin
We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users.
CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models
Online news and information sources are convenient and accessible ways to learn about current issues.
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
The remaining cluster with surviving clients is then used for training the best FL model (i. e., remaining FL model).
Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort.
Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset
In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews.
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services
Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices.
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important.
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings
In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time.
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm.
