In today’s world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people […]
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%.
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 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.
To implement an AI-based GDM screening and outcome prediction platform for a diverse demographic population of South Asia by selecting Pakistan as a base case study.
Recently social media has been a widely used network that connects people around the world. Not only this but people sharing their life events, thoughts through posts, status updates all gather up as a big data resource. This resource is helpful in conducting various researches, analyses including big data and machine learning. In this study, we analyzed six mental health issues using Reddit’s data. The data obtained summarizes; Depression, Anxiety, Bipolar, Bipolar Disorder, Schizophrenia, Autism and Mental Health which is a general class which discusses mental health. Experimentation is done using various deep learning and NLP techniques applied for classification such as Convolutional Neural Network, Long-short term memory network, Gated Recurrent Unit, Bi- Long-short term memory network and Bi-Gated Recurrent Unit. In addition to these traditional techniques, pre-trained BERT model and …
A pipeline based on context-aware light-weight transformers with the goal of improving image quality without sacrificing the naturalness of the image, as well as reducing the inference time and size of the model. In this study, we trained a deep network-based transformer model on two standard datasets, i.e., Large-Scale Underwater Image (LSUI) and Underwater Image Enhancement Benchmark Dataset (UIEB), so that the network becomes more generalized, which subsequently improved the performance. Our real-time underwater image enhancement system shows superior results on edge devices. Also, we provide a comparison with other transformer-based methods.
Descriptive and Predictive Analysis of infectious diseases
Data mining is extraction of hidden, useful and precious data from large and high dimensional databases. This field is gaining significant recognition, particularly in medical domain, due to the availability of large amount of data, easily collected and stored via computer systems. Lung cancer patients undergo thoracic surgery which is a highly risky procedure. The […]
Heart failure is considered as one of the major reasons of death worldwide. The amount of deaths from heart failure surpasses the amount of deaths resulting from any other causes. This point makes heart disease as one of the most dangerous disease resulting in human deaths worldwide. Latest studies have concentrated on the usage of […]