Search Results for author:
Found 18 papers, 2 papers with code
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.
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.
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.
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.
On Parameter Tuning in Meta-learning for Computer Vision
We also explore the effect of parameter tuning on performance of semantic auto-encoder (SAE).
DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection
The goal of DeepMSRF is to identify the gender of the speaker first, and further to recognize his or her name for any given video stream.
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.
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).
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.
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.
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.
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.
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.
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.
Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review
In recent years, smart healthcare IoT devices have become ubiquitous, but they work in isolated networks due to their policy.
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.
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.
Deep Learning in Healthcare: An In-Depth Analysis
Deep learning (DL) along with never-ending advancements in computational processing and cloud technologies have bestowed us powerful analyzing tools and techniques in the past decade and enabled us to use and apply them in various fields of study.
