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Found 29 papers, 3 papers with code
Students Success Modeling: Most Important Factors
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students.
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.
Word Embedding Neural Networks to Advance Knee Osteoarthritis Research
Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media.
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.
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning
Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge.
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.
Generative Adversarial Network Applications in Creating a Meta-Universe
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications.
Generative Adversarial Network
Image-to-Image Translation
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An Integrated Approach for Video Captioning and Applications
There is a trade-off between the computation of many frames and the speed of the captioning process.
A Review of Deep Transfer Learning and Recent Advancements
Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs by reusing an obtained knowledge from a source data/task in training on a target data/task.
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.
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.
Sarcasm Detection: A Comparative Study
Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text.
Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning
The proposed system functions and operates as followed: it reads a video; representative image frames are identified and selected; the image frames are captioned; NLP is applied to all generated captions together with text summarization; and finally, a title and an abstract are generated for the video.
The Use of Video Captioning for Fostering Physical Activity
With the above in mind, this paper proposes a video captioning framework that aims to describe the activities in a video and estimate a person's daily physical activity level.
A Concise Review of Transfer Learning
The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data.
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.
A Brief Review of Domain Adaptation
However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e. g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time.
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: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning
This paper addresses the problem of identifying two main types of lesions - Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients.
On Parameter Tuning in Meta-learning for Computer Vision
We also explore the effect of parameter tuning on performance of semantic auto-encoder (SAE).
Deep Learning at the Edge
The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers.
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.
The SWAG Algorithm; a Mathematical Approach that Outperforms Traditional Deep Learning. Theory and Implementation
A widespread practice is to use the same type of activation function in all neurons in a given layer.
