Delta ecosystem has always captivated the interests of scientists around the world because of its variety of landforms which are subjected to periodic or abrupt changes.

The problem of decorrelation leading to loss of coherence has been a major source of concern to identify the various problems of erosion and deposition in the delta. In this study, Permanent Scatter Interferometric SAR (PsInSAR) technique was used to identify the Permanent Scatter Candidates (PSCs) to explore its potential in identifying displacement based on the coherence of various features in the delta during the dry and wet periods.

A new approach to the reconstruction of pseudo-quad-polarized data from hybrid polarimetric data has been presented in this research. The algorithm is based on certain assumptions which were validated upon testing the aptness of the results and their comparison with true optical images of the region under study.

Network embedding has got enormous attention in recent past for their wide range of applications across different types of networks. This paper mainly includes a simple and novel model which is used for better node embeddings with respect to community detection in social networks. We use existing algorithms (mainly community detection algorithm) and Representation Learning (RL) techniques to find better embeddings that assist in better community detection.

Jhalawar, Kota, Baran, Chittorgarh, Bhilwara and Sri Ganganagar have been identified as the most important districts in the state of Rajasthan for the production of a particular variety of Oranges – Kinnow. As is the case with other grown fruits in India, the productivity of this crop too has suffered due to a list of teething troubles.

Visual saliency is an important problem in the field of cognitive science and computer vision with applications such as surveillance, adaptive compressing, detecting unknown objects and scene understanding. In this paper, we propose a small and sparse neural network model for performing salient object segmentation that is suitable for use in mobile and embedded applications. Our model is built using depthwise separable convolutions and bottleneck inverted residuals which have been proven to perform very memory efficient inference and can be easily implemented using standard functions available in all deep learning frameworks.

Studies have been carried out to standardize induced luteolysis model systems ƵƟůŝnjŝnŐ female monkeys and pregnant rats. In monkeys, ĂĚmŝnŝƐƚrĂƟŽn of a single ŝnũĞcƟŽn of GnRH receptor antagonist, Cetrorelix (CET; 150 µg/kg BW s.c.,), on day 7 of the luteal phase led to profound decrease in serum progesterone (P4) cŽncĞnƚrĂƟŽn within 24 h (3.6 ± 1.1 vs 0.8 ± 0.2 ng/ml before and 24 h post CET, rĞƐƉĞcƟvĞůy p<0.05), and followed by the premature onset of menses 96 h later.

Using Artificial Intelligence Algorithms on Satellite Data feeds to do real-time analysis of Quality and Quantity of Agricultural yields of a certain location. We use data from 3 separate satellites including HyperSpectral Imaging data analysis for this process. It is used to Automate agricultural loans.

The philosophical foundation of the paper is on algorithmic applications on human data collected via non-evasive touchless fashion to generate medical insights which can be used as a template or recommendation or triage factor for medical professionals by acting as Diagnostic Decision Support system for doctors. All these algorithms are all Machine Learning Algorithms using Artificial Neural Networks.

The advent of machine learning has impacted even in the field of financial knowledge outsourcing. The fundamental process of outsourcing is always cost reduction which is enhanced by the introduction of software robots which do the work of the financial analyst. This paper examines the various aspects and methodologies of robotic process automation in the field of financial knowledge outsourcing.