A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning
Abstract In Machine Learning, the datasets used to build models are one of the main factors
limiting what these models can achieve and how good their predictive performance is …
limiting what these models can achieve and how good their predictive performance is …
Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction
Background Data drift can negatively impact the performance of machine learning
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
[PDF][PDF] Sentiment analysis in detecting sophistication and degradation cues in malicious web contents
Mobility, ease of accessibility, and portability have continued to grant ease in the adoption
rise of smartphones; while, also proliferating the vulnerability of users that are often …
rise of smartphones; while, also proliferating the vulnerability of users that are often …
[HTML][HTML] The development of phishing during the COVID-19 pandemic: An analysis of over 1100 targeted domains
To design preventive policy measures for email phishing, it is helpful to be aware of the
phishing schemes and trends that are currently applied. How phishing schemes and …
phishing schemes and trends that are currently applied. How phishing schemes and …
Advancements in Nanomaterials for Nanosensors: A Comprehensive Review
Nanomaterials (NMs) possess unique properties that render them highly suitable for
developing sensitive and selective nanosensors across various domains. This review aims …
developing sensitive and selective nanosensors across various domains. This review aims …
Spam-t5: Benchmarking large language models for few-shot email spam detection
This paper investigates the effectiveness of large language models (LLMs) in email spam
detection by comparing prominent models from three distinct families: BERT-like, Sentence …
detection by comparing prominent models from three distinct families: BERT-like, Sentence …
Predicting the ammonia nitrogen of wastewater treatment plant influent via integrated model based on rolling decomposition method and deep learning algorithm
K Yan, C Li, R Zhao, Y Zhang, H Duan… - Sustainable Cities and …, 2023 - Elsevier
Timely and accurate assessment of key sewage quality indicators based on deep learning
models has attracted much attention for intelligent wastewater treatment. Decomposition …
models has attracted much attention for intelligent wastewater treatment. Decomposition …
[HTML][HTML] Deep learning-based spam image filtering
Spam is some unwanted material that may be put in the form of images. While many
machine learning approaches are effective at detecting textual spam, this is not true for …
machine learning approaches are effective at detecting textual spam, this is not true for …
Machine learning algorithm-based spam detection in social networks
Many social media (SM) platforms have emerged as a result of the online social network's
(OSN) rapid expansion. SM has become important in day-to-day life, and spammers have …
(OSN) rapid expansion. SM has become important in day-to-day life, and spammers have …
A survey on imbalanced learning: latest research, applications and future directions
Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …
and machine learning. Despite continuous research advancement over the past decades …