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

OTKRIVANJE I KLASIFIKACIJA BOLESTI USJEVA NA TEMELJU INFORMACIJSKOG HIBRIDNOG PRISTUPA

The objective of this paper to identify the diseases in the leaves of the all plants. Plant disease diagnosis helps to improve both the quality and quantity of crop productivity. In existing, to detect the diseases they used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. This work proposes an approach for the detection of leaf diseases based on the characterization of texture, shape and color properties. The detection of diseases which are detected using ISRC(improved sparse Representation Classifier) technique. First the GENABC clustering approach is applied to the input image to segment the affected area. Then extract the features from the affected area by using feature extraction techniques. In this paper Improved Transform Encoded Local Pattern used to extract the texture feature, Enhanced Gradient Feature (EGF) to extract the shape and Improved Color Histogram Techniques(ICH) are used to extract the color. And then these features are given to the ISRC classifier to get the exact type of disease on affected leaves. To analyze the performance of the proposed method we use four metrics. They are classification accuracy, error rate, precision value and recall value. From the analysis of experimental results, the ISRC method provides the best result than the existing approach.Cilj ovog rada je identificirati bolesti u listovima svih biljaka. Dijagnoza biljnih bolesti pomaže poboljšati kvalitetu i količinu produktivnosti usjeva. Za otkrivanje bolesti koriste se spektroskopske tehnike. Te tehnike su vrlo skupe i mogu ih koristiti samo obučene osoba. Ovaj rad predlaže pristup za otkrivanje bolesti listova na temelju karakterizacije svojstava teksture, oblika i boja. Otkrivanje bolesti koje se detektiraju uporabom ISRC tehnike. Najprije se primjenjuje GENABC klastering pristup na ulaznu sliku za segmentiranje pogođenog područja. Zatim se ekstrahiraju značajke sa zahvaćene površine pomoću tehnika ekstrakcije značajki. U ovom se radu koristi poboljšana transformirana enkodirana lokalna shema koja se koristi za izdvajanje značajki teksture, poboljšane značajke gradijenata (EGF) za izdvajanje oblika i poboljšane tehnike hektologije boja (ICH) za izdvajanje boje. Zatim se ove značajke daju ISRC klasifikatoru kako bi dobili točnu vrstu bolesti na zahvaćenom lišću. Za analizu izvedbe predložene metode koristimo četiri metrike. To su točnost klasifikacije, stopa pogrešaka, preciznost i vrijednost opoziva. Iz analize eksperimentalnih rezultata ISRC metoda daje bolji rezultat od postojećeg pristupa

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