Search Results for author:
Found 17 papers, 2 papers with code
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities.
Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining
of postsecondary degree awards that have been ambiguously expressed in the student tracking reports of the National Student Clearinghouse (NSC).
Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction
In this work, we trained a variety of machine learning models, using cross validation for hyperparameter optimization, to predict biomass yields, and we showed better accuracy than similar work that employed more complex techniques.
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.
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.
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.
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
+3
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.
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.
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.
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.
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.
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.
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score.
GELATO and SAGE: An Integrated Framework for MS Annotation
The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO).
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
