Fusion of Mini-Deep Nets

Image classification and object recognition are some of the most prominent problems in computer vision. The difficult nature of finding objects regardless of pose and occlusions requires a large number of compute resources. Recent advancements in technology have made great strides towards solving this problem, and in particular, deep learning has revolutionized this field in the last few years. The classification of large datasets, such as the popular ImageNet dataset, requires a network with millions of weights. Learning each of these weights using back propagation requires a compute intensive training phase with many training samples. Recent compute technology has proven adept at classifying 1000 classes, but it is not clear if computers will be able to differentiate and classify the more than 40,000 classes humans are capable of doing. The goal of this thesis is to train computers to attain human-like performance on large-class datasets. Specifically, we introduce two types of hierarchical architectures: Late Fusion and Early Fusion. These architectures will be used to classify datasets with up to 1000 objects, while simultaneously reducing both the number of computations and training time. These hierarchical architectures maintain discriminative relationships amongst networks within each layer as well as an abstract relationship from one layer to the next. The resulting framework reduces the individual network sizes, and thus the total number of parameters that need to be learned. The smaller number of parameters results in decreased training time

Evaluation of Seawater Intrusion and Groundwater Quality in the Coastal Aquifers in Srikakulam District, A.P., India Using Electrical Conductivity Property

Seawater intrusion into coastal aquifers has emerged as a significant environmental threat that imperils water resources, especially groundwater resources, ecosystems, and human health. It is widely acknowledged that water plays an indispensable role in the maintenance of life. Groundwater is an important source, particularly in arid and semi-arid regions where surface water and precipitation are limited. Management of a safe and renewable supply of groundwater for drinking and agricultural purposes is one of the crucial aspects of sustainable development for any Nation. But the groundwater quality faces threats from urbanization, agricultural practices, industrial activities, climate changes, and groundwater parameters such as pH, Electrical Conductivity (EC), Total Dissolve Solids (TDS), fluoride, chloride, calcium, sulfate, and iron. In the present study, 13 coastal mandals viz., Ranastalam, Laveru, Etcherla, Srikakulam, Gara, Polaki, Santhabommali, Vajrapukotturu, Mandasa, Sompeta, Kanchili, Kaviti and Ichapuram in Srikakulam district, A.P., India, have been considered. The quality of the groundwater in these mandals has been assessed based on seawater intrusion into the coastal aquifers considering the EC and TDS parameters which help identifying seawater intrusion. Among all the 13 coastal mandals, the Gara, Polaki, Sompeta, Santabommali, and Ichapuram mandala are much influenced by seawater intrusion. The geology, geomorphology, climate, rainfall, and soil types of the study areas have been discussed. 61 water samples from bore wells of the 13 mandals have been collected for the present study. All the water samples were analyzed to determine the groundwater quality based on the EC and TDS of the water