A Survey on Autism Spectrum Disorder and E-Learning

Autism or mental imbalance is turmoil in the development and improvement of a mind or central nervous system that covers a huge range of skills, impairment and symptoms. The children who are experiencing autism (or mental imbalance) confront challenges in conveying and adjusting in the group as they experience difficulty in understanding what others feel and think. These days learning innovations changed instructive frameworks with amazing advancement of Information and Communication Technologies (ICT). Moreover, when these innovations are accessible, reasonable and available, they speak to more than a change for individuals with Autism Spectrum Disorder. In this paper, a writing study and foundation study is done on the Autism Spectrum Disorder (ASD) and E-Learning System for Autism Children

Marxist thought in Tamil Novels

Marxism, which produced the theory of communism, is very extensive. This field that originated in the West and grew up in Tamil novel literature, and Karl Marx and Friedrich Engels are the founders of Marxism, which has the principle of equality for the working class. The theory of reflection is the theory that is primarily in the literary theories advanced by Marxism. That is, the class conflicts in society cause crises in human lives. The economic inequality in society is the primary cause of social contradiction. Struggles erupt when the bourgeoisie exploits the working people. This article seeks to examine the struggles in Tamil novels published in the 21st century

Towards a Realization of the Condensed-Matter/Gravity Correspondence in String Theory via Consistent Abelian Truncation

We present an embedding of the 3-dimensional relativistic Landau-Ginzburg model for condensed matter systems in an $\mathcal{N}=6$, $U(N)\times U(N)$ Chern-Simons-matter theory (the ABJM model) by consistently truncating the latter to an abelian effective field theory encoding the collective dynamics of ${\cal O}(N)$ of the ${\cal O}(N^2)$ modes. In fact, depending on the VEV on one of the ABJM scalars, a mass deformation parameter $\mu$ and the Chern-Simons level number $k$, our abelianization prescription allows us to interpolate between the abelian Higgs model with its usual multi-vortex solutions and a $\phi^4$ theory. We sketch a simple condensed matter model that reproduces all the salient features of the abelianization. In this context, the abelianization can be interpreted as giving a dimensional reduction from four dimensions.Comment: 4 pages, revtex; reference added, typo corrected; added clarifying paragraphs at end of introduction and on pages 3-4. Version accepted to PR

A Non-Linear Controller for Forecasting the Rising Demand for Electric Vehicles Applicable to Indian Road Conditions

These days load forecasting is much more required  in order to reduce the wastage of energy. This paper is to implement & develop the idea of short term load forecasting by using Artificial Neural Network, the design of the neural network model, input data selection and Training & Testing by using short term load forecasting will be described in paper. For the EV load forecasting only 2 variables are being used as temperature and humidity to forecast the output as load. This type of designed ANN model will be mapped by using historical data of temperature and humidity (taken from meteorological sites), whereas it is being Trained & Tested by using historical data of loading of EV charging stations (Chetan maini ,Bangalore) of a particular area as Coimbatore to give the desired result. Training & Testing done by using large amount of historical data of weather conditions and loading data (kV). By the help of this model they can predict their daily loads (next day's load) by putting historical data in the acquired algorithm

DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches