25 research outputs found
A robust hybrid approach with product context-aware learning and explainable AI for sentiment analysis in Amazon user reviews
No full textpublishedVersio
Multi-class hate speech detection in the Norwegian language using FAST-RNN and multilingual fine-tuned transformers
No full textThe growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.publishedVersio
Augmenting sentiment prediction capabilities for code-mixed tweets with multilingual transformers
No full textPeople in the modern digital era are increasingly embracing social media platforms to express their concerns and emotions in the form of reviews or comments. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. Currently, individuals tend to express their thoughts in their native languages on these platforms, which is quite challenging due to potential syntactic ambiguity in these languages. Most of the research has been conducted for resource-aware languages like English. However, low-resource languages such as Urdu, Arabic, and Hindi present challenges due to limited linguistic resources, making information extraction labor-intensive. This study concentrates on code-mixed languages, including three types of text: English, Roman Urdu, and their combination. This study introduces robust transformer-based algorithms to enhance sentiment prediction in code-mixed text, which is a combination of Roman Urdu and English in the same context. Unlike conventional deep learning-based models, transformers are adept at handling syntactic ambiguity, facilitating the interpretation of semantics across various languages. We used state-of-the-art transformer-based models like Electra, code-mixed BERT (cm-BERT), and Multilingual Bidirectional and Auto-Regressive Transformers (mBART) to address sentiment prediction challenges in code-mixed tweets. Furthermore, results reveal that mBART outperformed the Electra and cm-BERT models for sentiment prediction in code-mixed text with an overall F1-score of 0.73. In addition to this, we also perform topic modeling to uncover shared characteristics within the corpus and reveal patterns and commonalities across different classes.publishedVersio
Metalinguist: enhancing hate speech detection with cross-lingual meta-learning
No full textAbstract The rise of social media has led to an increase in hate speech. Hate speech is generally described as a deliberate act of aggression aimed at a particular group, intended to harm or marginalize them based on specific attributes of their identity. While positive interactions in diverse communities can greatly enhance confidence, it is important to acknowledge that negative remarks such as hate speech can weaken community unity and present a significant impact on people’s well-being. This highlights the need for improved monitoring and guidelines on social media platforms to protect individuals from discriminatory and harmful actions. Despite extensive research on resource-rich languages, such as English and German, the detection and analysis of hate speech in less-resourced languages, such as Norwegian, remains underexplored. Addressing this gap, our study leverages a metalinguistic approach that uses advanced meta-learning techniques to enhance the detection capabilities across bilingual texts, effectively linking technical advancements directly to the pressing social issue of hate speech. In this study, we introduce techniques that adapt models that deal with hate speech detection within the same languages (intra-lingual), across different languages (cross-lingual), and techniques that adapt models to new languages with minimal extra training, independent of the model type (cross-lingual model-agnostic meta-learning-based approaches) for bilingual text analysis in Norwegian and English. Our methodology incorporates attention mechanisms (components that help the model focus on relevant parts of the text) and adaptive learning rate schedulers (tools that adjust the learning speed based on performance). Our methodology incorporates components that help the model focus on relevant parts of the text (attention mechanisms) and tools that adjust the learning speed based on performance (adaptive learning rate schedulers). We conducted various experiments using language-specific and multilingual transformers. Among these, the combination of Nor-BERT and LSTM with zero-shot and few-shot model-agnostic meta-learning achieved remarkable F1 scores of 79% and 90%, highlighting the effectiveness of our proposed framework
Analyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based Techniques
No full textIn recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality
Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security
No full textThis survey paper explores the transformative role of Artificial Intelligence (AI) in information security. Traditional methods, especially rule-based approaches, faced significant challenges in protecting sensitive data from ever-changing cyber threats, particularly with the rapid increase in data volume. This study thoroughly evaluates AI’s application in information security, discussing its strengths and weaknesses. It provides a detailed review of AI’s impact on information security, examining various AI algorithms used in this field, such as supervised, unsupervised, and reinforcement learning, and highlighting their respective strengths and limitations. The study identifies key areas for future AI research in information security, focusing on improving algorithms, strengthening information security, addressing ethical issues, and exploring safety and security-related concerns. It emphasizes significant security risks, including vulnerability to adversarial attacks, and aims to enhance the robustness and reliability of AI systems in protecting sensitive information by proposing solutions for potential threats. The findings aim to benefit cybersecurity professionals and researchers by offering insights into the intricate relationship between AI, information security, and emerging technologies.publishedVersio
Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers
No full textAbstract Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process
Role of levetiracetam in refractory seizures due to a rare progressive myoclonic epilepsy: Lafora body disease
No full textLafora disease is one of the rare, most fatal progressive myoclonic epilepsies reported. We present a case of a teenager with intractable seizures and progressive mental decline, diagnosed as Lafora body disease on axillary skin biopsy. He was admitted with status epilepticus with refractory myoclonic and generalised tonic clonic seizures. Despite on maximum doses of multiple antiepileptic drugs and infusions of propofol and midazolam, his seizures were refractory to all forms of medical therapy tried. Levetiracetam (LEV), a pyrrolidine derivative, was introduced; he showed a prompt response and was weaned off successfully from infusions of anticonvulsants and mechanical ventilation within 48 h of introduction of LEV, followed by an almost seizure-free status
Stable Isotopic Profiling of New Zealand Milk Powder
No full textTo contact the author please email: [email protected] thesis aims to develop a method to investigate the ability of bulk stable isotope and compound specific stable isotope tools to verify whether dietary feed fatty acids, bovine milk water and major bovine milk fatty acids are conveying biogeochemical attribution of their production region. This technique opens new insights into milk regional authenticity identification.
A multiple linear regression (MLR) model based on New Zealand climatic parameters was employed to verify its ability to predict δ2H values of rainfall from milk production regions. The MLR model showed promise to predict δ2H values of regional rain, yielding a high correlation to actual rain δ2H values (R2 = 0.73, P<0.05). Subsequently the model’s generated estimates of δ2H values for precipitation of the milk production regions, revealed strong geographical correlation between bulk δ2H values and δ2H values of some bovine milk short chain fatty acids (SCFA) to long chain fatty acid (LCFA).
In order to understand which of the major fatty acids in milk powder have the potential discrimination capability to allow the provenancing of milk powder, the δ2H value of C4:0 (butyric), C14:0 (myristic), C16:0 (palmitic) and the δ2H value of bulk milk which showed the highest correlation to regional rainfall was selected and analysed by employing multivariate statistics. The δ2H values of these compounds were found to be capable of explaining 91% of the isotopic variation. The hydrogen isotopic compositions of these milk compounds were able to separate milk production regions across New Zealand.
Subsequent exploration into finding the isotopic link between farm drinking water, grass/feed and milk, revealed that bovine milk bulk and fatty acid hydrogen isotope composition carries isotopic attributions both from feed and local water. However the influence of regional water on the 2H composition of the milk seems to be more pronounced.
The influence of seasonal variability on milk was examined on milk powder samples sourced from Norway. Results indicate that the milk powder fatty acid δ2H values and fatty acid concentrations were influenced by the time of sampling throughout the year, while the bulk milk δ2H and δ13C values remained relatively consistent across the sampling period. This may suggest a presence of a region specific isotope variability that may further be explored by comparing it with variability patterns of other regions.
A preliminary assessment on the potential of δ2H from milk fatty acids and bulk milk for determination of an adulterant (an unknown milk powder) in milk powder was investigated. Multivariate statistics were used to quantify the level of adulteration, which was able to resolve differences at a 5% level of adulterant in an authentic sample.
This thesis explores the potential application of stable isotope analysis to the authentication of the provenance of New Zealand milk. The approach utilized in this study could be adopted in other milk producing regions, or applied to other milk products such as infant formula for authentication purposes
Per-cutaneous dilatation tracheostomy (PCTD) in COVID-19 patients and peri-tracheostomy care: a case series and guidelines
No full textBackground & Objective: COVID 19 patients with severe respiratory failure may require prolonged mechanical ventilation. Placement of a tracheostomy tube often becomes necessary for such patients. The steps of tracheostomy procedure and post tracheostomy care of these patients can be classified as aerosol generating. We wish to highlight our modified technique to address these issues.Methods: We performed percutaneous dilation tracheostomy in three clinically challenging COVID-19 patients in our ICU and developed guidelines aiming to minimise aerosolisation during and after the tracheostomy procedure to safeguard healthcare workers.Results: Percutaneous tracheostomy was performed by a team of three experienced anaesthetists and an ICU nurse.Conclusion: The decision of surgical or percutaneous tracheostomy should be dependent on the experience of the tracheostomy performer, health-care worker safety, resource availability, and patient-centred care. We believe our modified strategic approach of brief bronchoscopy, minimum PEEP and gas flows and step-wise planned approach for PCDT offers an extra level of safety to healthcare workers.</div
