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Found 22 papers, 3 papers with code
Security and Privacy Challenges of Large Language Models: A Survey
We assess the extent of LLM vulnerabilities, investigate emerging security and privacy attacks for LLMs, and review the potential defense mechanisms.
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations.
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats.
3D-model ShapeNet Core Classification using Meta-Semantic Learning
Understanding 3D point cloud models for learning purposes has become an imperative challenge for real-world identification such as autonomous driving systems.
The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics
In this study, we present our discovery of evolutionary and nature-inspired algorithms applications in Data Science and Data Analytics in three main topics of pre-processing, supervised algorithms, and unsupervised algorithms.
OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms
OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models.
Data Analytics for Smart cities: Challenges and Promises
Then, we offer a framework of these solutions, called universal smart cities decision making, with three main sections of data capturing, data analysis, and decision making to optimize the smart mobility within smart cities.
HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization
In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.
Embodied AI-Driven Operation of Smart Cities: A Concise Review
Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time.
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM
Medical Imaging is one of the growing fields in the world of computer vision.
A Survey on Optimal Transport for Machine Learning: Theory and Applications
Then, we will follow up with a mathematical formulation and the prerequisites to understand OT.
Search Algorithms for Automated Hyper-Parameter Tuning
In this paper, we examine the effectiveness of automated hyper-parameter tuning techniques to the realm of students' success.
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots
Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network.
Malware Detection using Artificial Bee Colony Algorithm
However, the more universal an algorithm is, the higher number of feature dimensions it needs to work with, and that inevitably causes the emerging problem of Curse of Dimensionality (CoD).
DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning
DRDr II is a hybrid of machine learning and deep learning worlds.
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities.
On Parameter Tuning in Meta-learning for Computer Vision
We also explore the effect of parameter tuning on performance of semantic auto-encoder (SAE).
Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks
This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires.
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises
In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations.
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics
Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently.
Evolutionary Computation, Optimization and Learning Algorithms for Data Science
We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data.
Evolutionary Algorithms and Efficient Data Analytics for Image Processing
However, the increase in features leads to the problem of the curse of dimensionality (CoD), which is considered to be an NP-hard problem.
