Call for Abstract

General Information

An Artificial Neural Network (ANN) is an information taking care of perspective that is animated by the way natural tangible frameworks, for instance, the cerebrum, process information. The key part of this perspective is the novel structure of the information planning system. It is made out of a broad number of interconnected taking care of segments (neurons) filling in as one to handle specific issues.

ANNs are made out of different center points, which emulate natural neurons of human personality. The neurons are related by associations and they coordinate with each other. The centers can take enter data and perform essential undertakings on the data. The outcome of these exercises is passed to various neurons. The yield at each center point is called its institution or center point regard.

KNN is a kind of case based learning, or emotionless perceiving, where the most remote point is simply approximated locally and all count is surrendered until delineation.

  K-Nearest Neighbors is a champion among the most central and essential portrayal         figurings in Machine Learning. It has a place with the controlled learning space and discovers striking application in plot confirmation, data mining and impedance locale.

It is broadly pointless, in fact, conditions since it is non-parametric (it doesn't make any     secured assumptions about the course of data). KNN has for the most part high exactness. It is flexible in nature i.e. it is useful for classification or regression.


Machine learning teaches computers to do what comes naturally to humans and animals which they learn from experience. Machine learning algorithms use computational methods to gain information directly from data without relying on a predetermined equation as a model. As the number of samples available for learning increases, the performance of the machine increases.

Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are every day used to make difficult decisions in medical diagnosis, stock trading, energy load forecasting, and more.



 Support Vector Machine (SVM) is a classifier technique that performs arrangement of tasks by building hyper planes that isolates instances of various class names. SVM supports regression as well as classification tasks which can handle different types of variables. Support Vector Machines are based on the concept of decision planes that define decision boundaries.

Image Processing is a method to perform any operation on an image, either to get an enhanced image or to find out any characteristic about the image. It is kind of signal processing. Here the input is an image and the output can be an image or any feature of an image. Image Processing is basically used for visualization, image retrieval, image enhancement, etc.


Data Mining is a process mostly used for converting raw data into some useful information. Data mining is also known as Knowledge Discovery in Data. Data Mining focuses on large data sets & database. Data Mining is widely used in business, science research, security, etc.


A robot is a multitasking controller intended to move materials, devices or specific gadgets through programming to perform variety of tasks. A robot is a system that contains sensors, control frameworks, controllers, control supplies and programming all cooperating together to perform a task. Robots have supplanted humans in performing dreary assignments and perilous errands which people lean toward not to do or can't do.


Virtual Intelligence is a term given to computerized reasoning that exists inside a virtual world. Virtual Intelligence is the crossing point of Virtual Environment and Artificial Intelligence. Virtual Intelligence is a program intended to make PC frameworks less demanding to utilize. Virtual Intelligence is a thoroughly program based machine i.e. they are not mindful.


Watson created by IBM in 2010 as a question answering computing system. As a system, it is described as a heterogeneous ensemble of experts. The supercomputer IBM Watson consumes all the data & gives doctors the relevant information to a particular case, giving them access to better diagnostic information.


Drones also known as unmanned aerial vehicles (UAVs) which has no human pilot onboard, and is either controlled by a person on the ground or autonomously via a computer program. These stealth craft are becoming increasingly popular for war, military purposes, wildlife, atmospheric research to disaster relief and sports photography, archaeological sites, signs of illegal hunting and crop damage.


Deep learning also known as deep structured learning or hierarchical learning is part of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervisedsemi-supervised or unsupervised. ‘Deep Learning’ means using a neural network with several layers of nodes between input and output. The series of layers between input & output do feature identification and processing in a series of stages, just as our brains seem to.


In speech analysis, the voiced-unvoiced decision is performed in conjunction with the pitch analysis. The linking of voiced unvoiced (V-UV) decision to pitch analysis  results in unnecessary complexity, as well as makes it difficult for classification of short speech segments which are less than a few pitch periods in duration.

Pattern recognition is a part of machine learning that emphasis on the recognition of patterns. Pattern recognition systems are in many cases trained from supervised learning, but when no data are available other algorithms can be used to discover previously unknown patterns i.e. unsupervised learning.