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Found 10 papers, 1 papers with code
Vulnerability Detection in Smart Contracts: A Comprehensive Survey
While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures.
Optimisation of federated learning settings under statistical heterogeneity variations
On the basis of these, we present recommended guidelines for FL parameters and aggregators to optimise model performance under different levels of IID and with different datasets
A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health
In this paper, we propose a novel framework for evaluating the nuanced conversation abilities of LLMs.
ResNLS: An Improved Model for Stock Price Forecasting
Stock prices forecasting has always been a challenging task.
Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach
Recommender systems have been successfully used in many domains with the help of machine learning algorithms.
A Fast Parallel Tensor Decomposition with Optimal Stochastic Gradient Descent: an Application in Structural Damage Identification
Our approach is based on stochastic gradient descent (SGD) algorithm which allows us to parallelize the learning process and it is very useful in online setting since it updates $\mathcal{X}^{t+1}$ in one single step.
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
Federated Learning (FL) has recently emerged as a promising method that employs a distributed learning model structure to overcome data privacy and transmission issues paused by central machine learning models.
Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring
The proposed image-hashing-based system can detect the student's excessive face and body movement (i. e., anomalies) that is resulted when the student tries to cheat in the exam.
NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent
Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $.
Online Tensor-Based Learning for Multi-Way Data
The online analysis of multi-way data stored in a tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $ has become an essential tool for capturing the underlying structures and extracting the sensitive features which can be used to learn a predictive model.
