SSM-Net for Plants Disease Identification in Low Data Regime

Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.Comment: 5 pages, 7 Figure

France\u27s Financial Crisis: Analyzing the Role of the Finance Minister

The downfall of France\u27s Old Regime and the beginning of the French Revolution were largely caused by the financial crisis plaguing France. Since the Seven Year\u27s War, France\u27s finances had suffered and were spiraling out of control. The finances were kept largely by the country\u27s appointed finance minister. France would go through a host of these finance ministers up to the Revolution. The most notable was Jacques Necker who receives more detailed analysis. Tracing the administrations of these finance ministers helps explain an important factor leading to the French Revolution

A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting

Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance. Numerous publications published in the last five years have proposed diverse sets of objective loss functions to address cases such as biased data, long-term forecasting, multicollinear features, etc. In this paper, we have summarized 14 well-known regression loss functions commonly used for time series forecasting and listed out the circumstances where their application can aid in faster and better model convergence. We have also demonstrated how certain categories of loss functions perform well across all data sets and can be considered as a baseline objective function in circumstances where the distribution of the data is unknown. Our code is available at GitHub: https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.Comment: 13 pages, 23 figure

Women Education in India: An Analysis

Women education is an essential need to change their status in the society. Educated women can play a very important role in the society for socio-economic development. Education eliminates inequalities and disparities as the means of recovering their status within and out of their families. It is the key factor for women empowerment, prosperity, development and welfare. Education provides more strength to women. Such strength comes from the process of empowerment and empowerment will come from the education. Education plays a significant role in women empowerment inequality and vulnerability of women in the society in India. This paper is an effort to capture the emerging picture with respect to women’s education in India. Keywords: Women education, empowerment, opportunities

Regionalized Models with Spatially Continuous Predictions at the Borders

Creating maps of continuous variables involves estimating values between measurement locations scattered throughout a geographic region. These maps often leverage observed similarities between geographically close measurements, but may also make predictions using other geographic information such as elevation. The relationship between the available geographic information and the variable of interest can vary with location, especially when mapping large areas like a continent. A simple way to account for the changing relationship is to divide the space into different sub-regions and model the relationship at each region. The naive implementation of this approach has the side effect of making sudden changes in predictions at the borders of each region. This thesis describes a novel regional border smoothing method that allows for the formation of a continuous map built with regional models. The method is implemented and available to the public through the open source R package remap. Improvements in model accuracy are demonstrated using a national scale and a state scale dataset