The growing adoption of the Internet Things (IoT) has brought a significant increase in attacks targeting those devices. Machine learning (ML) methods have shown promising results for intrusion detection; however, scarcity IoT datasets remains limiting factor developing ML-based security systems scenarios. Static get outdated due to evolving architectures and threat landscape; meanwhile, testbe...

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal vertical system integration, internet of things have increased performance efficiency production lines manufacturing industry. Over past years, efforts been made to propose semantic models repr...

The classification of documents is one the problems studied since ancient times and still continues to be studied. With social media becoming a part daily life its misuse, importance text has started increase. This paper investigates effect data augmentation with sentence generation on performance in an imbalanced dataset. We propose LSTM based method, Term Frequency-Inverse Document Frequency ...

Algorithmic evaluation is a vital step in developing new approaches to machine learning and relies on the availability of existing datasets. However, real-world datasets often do not cover necessary complexity space required understand an algorithm’s domains competence. As such, generation synthetic fill gaps has gained attention, offering means evaluating algorithms when data unavailable. Exis...

Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach...

In this paper we propose a fuzzy rule generation approach based on granular computing using rough mereology (FRGAGCRM). The proposed system works in two phases. In the first phase, the pre-processing phase which use fuzzification methodology which map the numeric dataset into categorical dataset according to membership function described in this paper. In other hand, the second phase consists o...

The dbpedia Extraction Framework, the generation framework behind one of the Linked Open Data cloud’s central hubs, has limitations which lead to quality issues with the dbpedia dataset. Therefore, we provide a new take on its Extraction Framework that allows for a sustainable and general-purpose Linked Data generation framework by adapting a semantic-driven approach. The proposed approach deco...