A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers

Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%.Comment: Paper has been presented at ICCCNT 2023 and the final version will be published in IEEE Digital Library Xplor

Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

This research paper explores the classification of knee osteoarthritis (OA) severity levels using advanced computer vision models and augmentation techniques. The study investigates the effectiveness of data preprocessing, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data augmentation using diffusion models. Three experiments were conducted: training models on the original dataset, training models on the preprocessed dataset, and training models on the augmented dataset. The results show that data preprocessing and augmentation significantly improve the accuracy of the models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the augmented dataset. Additionally, attention visualization techniques, such as Grad-CAM, are utilized to provide detailed attention maps, enhancing the understanding and trustworthiness of the models. These findings highlight the potential of combining advanced models with augmented data and attention visualization for accurate knee OA severity classification.Comment: Paper has been accepted to be presented at ICACECS 2023 and the final version will be published by Atlantis Highlights in Computer Science (AHCS) , Atlantis Press(part of Springer Nature

ANTIOXIDANT ACTIVITIES OF MARINE ALGAE: A REVIEW

ABSTRACT Oxidative stress is the result of an imbalance between pro-oxidant and antioxidant homeostasis that leads to the generation of toxic reactive oxygen species (ROS). The necessity of compounds with antioxidant activity is increasing as it is realized that the formation of reactive oxygen species (ROS) and reactive nitrogen species (RNS) have been linked in the pathogenesis of several human diseases such as atherosclerosis, diabetes mellitus, chronic inflammation, neurodegenerative disorders and certain types of cancer. The antioxidant activity of these compounds are mainly attributed to scavenging activity against superoxide and hydroxyl radicals, chelating ability, quenching singlet and triplet oxygen, and reducing power. It is important to develop, identify and utilize new source of safe and effective antioxidants of natural origin. Recently, much research attention has been focused on the free-radicalscavenging activity of metabolites from marine macro algae. Several studies have investigated the antioxidant activity of natural products in marine and freshwater algae. The marine environment is known as a rich source of chemical structures with numerous beneficial health effects. Among marine organisms, marine algae have been identified as an under-exploited plant resource, although they have long been recognized as valuable sources of structurally diverse bioactive compounds. Here summarized what are the compounds, methods and recent research on antioxidant activities of marine algae

Towards an understating of signal transduction protein interaction networks

Protein network analysis has witnessed a number of advancements in the past for understanding molecular characteristics for important network topologies in biological systems. The signaling pathway regulates cell cycle progression and anti-apoptotic molecules. This pathway is also involved in maintaining cell survival by modulating the activity of apoptosis through RAS, P13K, AKT and BAD activities. The importance of protein-protein interactions to improve usability of the interactome by scoring and ranking interaction data for proteins in signal transduction networks is illustrated using available data and resources

Analysis of Molecular Marker Compounds from Vitexagnus cactus Using the High Performance Liquid Chromatography and Evaporative Light Scattering Detector Techniques

Dried whole plant of Vitexagnus cactus was extracted with organic solvents in different ratios and analyzed using high performance liquid chromatography (HPLC) with evaporative light scattering (ELS) detector. The markers of interest in Vitexagnus castus were agnuside, casticin and vitexyl acetone. These standard molecular markers do not contain strong chromophores, hence it is difficult to identify them using the UV detection system in high performance liquid chromatography. ELS detector is regarded as a valuable alternative to UV detection system for identification of the compounds that do not contain strong chromophores. The results of the present study show that the ELS detection system is more efficient than the UV-visible detection system.Key words:  Vitexagnus castus, evaporative light scattering detector, high performanc