K-Neareest Neighbour- Real time application

In this article I would like to give a real time example scenario where KNN can be used. The K Nearest Neighbors algorithm (KNN) is an elementary but important machine learning algorithm. KNN can be used for both classification and regression predictive problems. The reason for the popularity of K Nearest Neighbors can be attributed to its easy interpretation and low calculation time.

k-NN is often used in search applications where you are looking for “similar” items; that is, when your task is some form of “find items similar to this one”. You’d call this a k-NN search.

The biggest use case of k-NN search might be Recommender Systems. Recommender Systems are used for recommending products, advertisements and media,etc.

For example, based on the residence location of a person we can recommend the person with a product that has the highest sales in that location. This can be further detailed with a more detailed example: If in a particular locality if there is a voltage fluctuations in the electricity supply for households, a resident of that locality can be recommended with a stabilizer when he purchases a refrigerator.

How Artificial Intelligence Works

How Artificial Intelligence Works

AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:

Machine Learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.

A Neural Network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.

Deep Learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.

Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Additionally, several technologies enable and support AI:

Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.

The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.

Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.

APIs, or application processing interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.

In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.

Terms frequently used in AI and ML :

Labeled data: Data consisting of a set of training examples, where each example is a pair consisting of an input and a desired output value (also called the supervisory signal, labels, etc)

Classification: The goal is to predict discrete values, e.g. {1,0}, {True, False}, {spam, not spam}.

Regression: The goal is to predict continuous values, e.g. home prices.

Dr. Siddhartha Ghosh

Professor in CSE

Vidya Jyothi Institute of Technology

Hyderabad

Article on Deep learning for Opinion Mining 

The developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field.

The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that  focuseson opinions and sentiments  as a major aspect of their central goal . opinion mining systems can be utilized for the creation and mechanized upkeep of survey and supposition accumulation sites, in which sentiments are persistently assembled from the Web and not confined to simply item audits, but rather likewise to more extensive subjects, for example, political issues and brand recognition.

Dr. D.Aruna Kumari

Professor ,CSE

Research head , AI plus research group 

Leading India. AI 

The AI, ML, DL research Initiative in VJIT - from Sept 2018

The research Group in AI, ML, DL @ VJIT is an initiative of CSE and IT Dept of Vidya Jyothi Institute of Technology, Hyderabad, Telangana. This research group nurtures the young minds towards AI, ML and DL along with deep research work in those areas. This initiative is a part of the LeadingIndia.AI initiative by Bannett University, Noida, India.
This group is active in :
1. Conducting regular meeting among stake holders for proper planning and implementation of the activities.
2. To do research and encourage and support young minds into research and mainly in the field of AI, ML and DL.
3. Conducting workshops for Staff and Students in the Area of AI, DL and ML
4. Helping Student in their project work in the field of AI, ML and DL
5. Creating learning content in the field of AI, ML and DL
6. Creating awareness in the field of AI, ML and DL
7. Publishing research paper in the field of AI, ML and DL
8. To conduct national and international level Conference in the field of AI, ML and DL
9. To help the students to write paper in the field of AI, ML and DL.
19. To work strongly with Departments to build syllabi in the field of AI, ML and DL
By -
Siddhartha Ghosh
Professor in CSE, VJIT
Head of Training and Placements
SPOC for LeadingIndia.AI Initiative