Percorrer por autor "Jonker, Richard A.A."
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- DyPrune: dynamic pruning rates for neural networksPublication . Jonker, Richard A.A.; Poudel, Roshan; Fajarda, Olga; Oliveira, José Luís; Lopes, Rui Pedro; Matos, SérgioNeural networks have achieved remarkable success in various applications such as image classification, speech recognition, and natural language processing. However, the growing size of neural networks poses significant challenges in terms of memory usage, computational cost, and deployment on resource-constrained devices. Pruning is a popular technique to reduce the complexity of neural networks by removing unnecessary connections, neurons, or filters. In this paper, we present novel pruning algorithms that can reduce the number of parameters in neural networks by up to 98% without sacrificing accuracy. This is done by scaling the pruning rate of the models to the size of the model and scheduling the pruning to execute throughout the training of the model. Code related to this work is openly available.
- Medication dispensing system architecturePublication . Costa, Bruno A.; Paudel, Roshan; Jonker, Richard A.A.; Afonso, Inês Santos; Lopes, Rui Pedro; Lima, José; Mesquita, L.M.R.; Pereira, Ana I.Non-adherence of prescribed medication is a global problem that a ects many people. There are many factors related to this problem that contributes to further increase the problem and impact in a patient's life. It is proposed a system to assist in the management of medicine and the communication with carers. The system has many components that include mechanical components, electrical components and management system, and servers. It is intended to o er a system that can help reduce the non-adherence factors and the consequences in the life of the patients and their carers.
- Medication dispensing system architecturePublication . Costa, Bruno A.; Paudel, Roshan; Jonker, Richard A.A.; Afonso, Inês Santos; Lopes, Rui Pedro; Lima, José; Mesquita, L.M.R.; Pereira, Ana I.Non-adherence of prescribed medication is a global problem that a ects many people. There are many factors related to this problem that contributes to further increase the problem and impact in a patient's life. It is proposed a system to assist in the management of medicine and the communication with carers. The system has many components that include mechanical components, electrical components and management system, and servers. It is intended to offer a system that can help reduce the non-adherence factors and the consequences in the life of the patients and their carers.
- Portuguese twitter dataset on COVID-19Publication . Jonker, Richard A.A.; Poudel, Roshan; Fajarda, Olga; Matos, Sérgio; Oliveira, José Luís; Lopes, Rui PedroOver the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID- 19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://github.com/bioinformaticsua/ Portuguese-Covid19-Dataset
- Using natural language processing for phishing detectionPublication . Jonker, Richard A.A.; Poudel, Roshan; Pedrosa, Tiago; Lopes, Rui PedroWe live in a world where computers are constantly changing the way we do things. People spend many hours on their phones or computers, whether it be for work or leisure purposes. The danger is that these unsuspecting users can be targeted for attacks at any time and can fall victim to many types of scams or phishing attacks. These attacks can be harmful to the user by getting valuable credentials, money or even installing malicious software on their devices, all while the user is unaware of what has just happened. In a business environment these can lead to mass data breeches which could end up costing a company millions of euros. Many users are not trained to recognize phishing texts, so an alternative solution is needed to help prevent users from falling into these traps. In this paper we will be investigating Natural Language Processing (NLP), a subsection of Machine Learning (ML) to try generate solutions to the problem of phishing. We will investigating different NLP solutions: Word2Vec, Doc2Vec and BERT, and different ML solutions: RNN, LSTM, CNN and TD-IDF. All of these different approaches provide good classification results ranging from f1-scores of 90.03–98.94.