Vels University

This study explores the antidiabetic potential of Turbinaria decurrens marine seaweed extract loaded solid lipid nanoparticles (SLNs) targeting the TGFBR1 signalling pathway using an integrated computational and experimental approach. A Random Forest based machine learning model was developed to predict TGFBR1 inhibitory activity, achieving a prediction accuracy of 91.02

The cognitive workload (CWL) triggers neural activity, which is crucial for understanding the brain’s response to mental stress or stimuli that induce stress. Electroencephalogram (EEG) signals were collected from a mental arithmetic task (MAT), simultaneous task EEG workload datasets, and segmented into 4-s intervals. These segmented signals were then transformed into images using time–frequency conversion methods (TF) called superlet transform (SLT). The resulting TF images were fed into convolutional neural networks (CNNs), such as VGG16, ResNet50, Xception, EfficientNetB0, AlexNet, GoogLeNet, SqueezeNet, VGG16 + LSTM, VGG16 + BiLSTM, FNet, gMLP, and multilayer perceptrons (MLP) mixer. CNN models were trained using the Adam optimizer to detect cognitive load. The preprocessing involved normalization, and scaling in both phases. Among the models tested, the SLT-based TFEEG with the MLP Mixer outperformed other CNN architectures. It helps reduce overfitting and vanishing gradients, enhances performance with new data, improves GPU acceleration, and reduces computational cost due to its simpler architecture. However, the SLT effectively handles non-stationary data through its adaptive multiresolution approach, making it ideal for EEG analysis. The proposed SLT + MLP Mixer achieved an accuracy of 98.69

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

This study investigated the enhancement of photovoltaic efficiency of dye-sensitized solar cells (DSSCs) through titanium dioxide (TiO2) anodes composite with iron oxide (Fe2O3). TiO2 with Fe2O3 at different weight percentages (0–30 wt

Brain tumors are a major worldwide health concern, which emphasizes the significance of prompt and accurate diagnosis for efficient treatment planning and management. Differentiating tumors from normal tissues is essential when assessing medical imaging. However, there are a number of challenges with traditional medical imaging techniques for brain tumor detection. Limited datasets, regulations on privacy limiting data sharing, and the requirement for specialized knowledge to correctly analyze medical images could all be obstacles to current approaches. A novel approach called Federated Learning with SNet-PC for Brain Tumor Detection and Classification (FL-SNet-PC) is proposed. This approach utilizes Federated Learning, which incorporates LP-pooled layer-assisted ShuffleNet (LP-SNet) and Parallel Convolutional Neural Network (PCNN) models. During local training, the SNet-PC method is used, which combines the LP-SNet and PCNN architectures. The local training pipeline has several stages, such as preprocessing, segmentation, and feature extraction. Initially, the input image undergoes preprocessing using the Wiener filtering technique to normalize the image. Then, precise segmentation is achieved using the Yeo-Johnson-based Balanced Iterative Reducing and Clustering Using Hierarchies (YJ-BIRCH) algorithm. After segmentation, feature extraction is done, where shape features, Grey-Level Co-Occurrence Matrix (GLCM) features, and Sobel Gradient-based Pyramid Histogram of Gradient Orientation (SG-PHOG) are captured from the segmented image. Once the local training process is completed, they are then sent to a central server for global aggregation. Finally, the global training process aids in detecting and classifying brain tumors effectively.