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 […]
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%.
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 + […]
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. […]
This study introduces a novel deep learning hybrid approach using various models like Vector autoregression(VAR), Long Short-Term Memory (LSTM) neural network, hybrid VAR-LSTM, ARIMA and various other classifiers for the three distinct regional locations namely SanJuan, Ahmedabad and Iquitos.
This thesis investigates the dynamics of crowdfunding success in Kickstarter’s technology category using a blend of network analysis and machine learning. By sourcing a comprehensive dataset from Webrobots.io, which includes detailed monthly data dumps of Kickstarter projects, this study focuses on United States (U.S.)-based technology projects to uncover patterns and factors contributing to successful crowdfunding […]
Spatio-temporal data mining techniques are used for crime analysis for their knowledge oriented and meaningful visual representation of crime incidents. Visual representation of crime patterns assist analysts with in-depth understanding of crime behavior with time and location. The representation can be made more knowledgeable and perceptible by incorporating details of socio-economic factor and areafis geographical […]
Data integration is a known technique to provide uniform view on sets of heterogeneous data sources. It not only provides better analysis of data but also facilitates users to query without any knowledge on the heterogeneous data sources. With the advent of Service Oriented Architecture and Cloud Computing it is possible for users to access […]
This research proposes the use of machine learning methods to classify dengue severity level for better management of patients and at the same time emergency healthcare services are not disturbed.
Our approach involves a fusion of deformable style transfer (DST), an optimization-centric technique that harmonizes the texture and geometry of a given content image to closely align with a chosen style image. This methodology is coupled with diffusion model inpainting, which is applied to content images originating from Calliar: an online dataset featuring handwritten Arabic calligraphy. Diverging from previous generative art methodologies, our approach presents aesthetically pleasing results, all the while preserving the integrity and structure of the Arabic script.