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- Deep Learning (2)
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- Black Widow Optimization algorithm (1)
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- Computer vision (1)
- Data mining (1)
- Explainable Recommendation (1)
- Feature selection (1)
- Food Planner (1)
- GPT-2 (1)
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- Text Classificaion (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Enhancing Zero-Shot And Few-Shot Text Classification Using Pre-Trained Language Models, Yanan Chen
Enhancing Zero-Shot And Few-Shot Text Classification Using Pre-Trained Language Models, Yanan Chen
Theses and Dissertations (Comprehensive)
In recent years, the community of natural language processing (NLP) has seen amazing progress in the development of pre-trained language models (PLMs). The novel paradigm of PLMs does not require labeled data, allowing us to experiment with increased training scale through employing freely available colossal online self-training corpus to push the limits. Language models (LMs), such as GPT, BERT and T5, have achieved high performance on a wide range of NLP tasks. Meanwhile, research on zero-shot and few-shot text classification has received increasing attention. As labelling can be costly and time-consuming, how to perform data augmentation (DA) and enhance the …
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Theses and Dissertations (Comprehensive)
This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. …
Health-Aware Food Planner: A Personalized Recipe Generation Approach Based On Gpt-2, Bushra Aljbawi
Health-Aware Food Planner: A Personalized Recipe Generation Approach Based On Gpt-2, Bushra Aljbawi
Theses and Dissertations (Comprehensive)
"What to eat today?" With the flourish of Internet, more and more people nowadays are inclined to find an answer to this most problematic question online. The recent explosion of food networks; however, produces large volumes of recipes, making it even harder to make an informed decision. This yields the need for advanced decision-making algorithms and efficient recommendation systems. Conventional recommender systems are not feasible anymore as food is a complicated feature that presents unique challenges and is less studied. For example, it can be one of the main reasons for obesity and many other chronic diseases. Food recommender system …
Explainable Neural Attention Recommender Systems, Omer Tal
Explainable Neural Attention Recommender Systems, Omer Tal
Theses and Dissertations (Comprehensive)
Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented …
Generative Adversarial Networks For Online Visual Object Tracking Systems, Ghsoun Zin
Generative Adversarial Networks For Online Visual Object Tracking Systems, Ghsoun Zin
Theses and Dissertations (Comprehensive)
Object Tracking is one of the essential tasks in computer vision domain as it has numerous applications in various fields, such as human-computer interaction, video surveillance, augmented reality, and robotics. Object Tracking refers to the process of detecting and locating the target object in a series of frames in a video. The state-of-the-art for tracking-by-detection framework is typically made up of two steps to track the target object. The first step is drawing multiple samples near the target region of the previous frame. The second step is classifying each sample as either the target object or the background. Visual object …