Irrigation Water Quality Assessment Using Water Quality Index and GIS Technique in Pondicherry Region, South India

The utility of groundwater, irrespective of its availability, is essential for mankind. The efficacy of the coastal aquifer’s groundwater quality for agriculture purpose in the Pondicherry region was gauged by their hydrochemistry. 44 groundwater samples were collected during 4 different seasons namely, pre-monsoon (PRM), southwest monsoon (SWM), northeast monsoon (NEM) and post-monsoon (POM). The samples were measured for physico-chemical parameters like pH, EC, TDS, Na, K, Ca, Mg, Cl, HCO3, PO4, SO4 and NO3. The spatio temporal variations of EC indicates that the coastal groundwater were relatively saline except during PRM. The suitability of groundwater for irrigation is evaluated through various water quality parametrs such as Electrical Conductivity (EC), pH, Na%, sodium absorption ratio (SAR), residual sodium carbonate (RSC) and permeability index (PI). Na%, SAR, PI and EC values were spatially interporlated and integrated to determine the regions suitable for irrigation purpose. The study infers that the groundwater of the study area is suitable for irrigation except few samples’ locations along the western part, as they have attained an alarming stage and they are unsuitable for irrigation. Thus, proper management strategy for irrigation water source has to be developed and a preventive management practice to address this issue has to be implemented

Detection and classification of brain tumor using hybrid deep learning models

Abstract Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively