Towards creation of adversarial attacks for textual data from diverse domains

Deep learning is the foundation for various applications, including decision support, fraud detection, text categorization, machine translation, market research, and customer segmentation. Despite their widespread use, deep learning algorithms are frequently vulnerable to adversarial instances, in which legal inputs are manipulated in subtle and often invisible ways. Even the most complicated models may be tricked […]

Replication of Multi-Agent Reinforcement Learning for Hide & Seek Problem

Reinforcement learning generates policies based on reward functions, hyper-parameters. Slight changes in these can significantly affect results. The lack of documentation and reproducibility in Reinforcement learning research makes it difficult to replicate once-deduced strategies. While previous research has identified strategies using grounded maneuver, there is limited work in the more complex environments. The agents in […]

Improving Text-to-Image Generation with Multimodal Semantic Coherence in Adversarial Training

Research in the field of text to image generation has shown incredible momentum owing to the availability of more powerful natural language processing (NLP) models and generative networks. The quality of data representation learned by the generative models acts as a determining factor in the success of these models. Self-supervised learning augments the generative power […]

Enhancing Dysarthria Diagnosis: Leveraging Deep Learning Techniques with the TORGO Dataset

The model combines a SincNet layer, which uses band-pass filters based on the sinc function to extract audio features, with CNN and LSTM layers to capture spatial and temporal dynamics in speech signals. By integrating these components, the proposed model aims to learn features from raw audio and effectively handle sequential data. The study’s objectives include developing and evaluating the proposed model for dysarthria detection, comparing its performance with existing models, and examining factors contributing to its success. Additionally, the model’s robustness and generalization capabilities are tested on publicly available TORGO datasets and achieved an overall accuracy of 99%.

Multimodal Islamophobic Content Detection on Social Media using Deep Learning

Islamophobia or anti-Muslim antagonism is one prepotent yet dilapidated form of racism in our today’s world. The last couple of years have witnessed an immense surge in Islamophobic hate speech on social media nurturing and progressing violence and prejudice against Muslims and Islam. A growingly frequent expression of online hate speech is multimodal (text + […]

Mapping Network Features to attack profiles to enhance the Real time Intrusion Detection

The immeasurable amount of data in network traffic has increased its vulnerability. Therefore, monitoring and analyzing traffic for threat hunting is inevitable. Analyzing and capturing realtime network traffic is challenging due to privacy and space concerns. However, many simulated datasets are available. Machine-learning based intrusion detection systems are trained on these datasets for attack detection. […]