"Since 2012 Airo Global Software has offered time-tested and field-proven technology platforms for developing and deploying empirical modeling solutions based on neural networks to enable Artificial Intelligence . Whatever kind of problem you face - prediction, classification, or pattern recognition - Airo Global Software can help you find the answer. Contact us with your data analysis, classification, and forecasting requirements. We offer complete solutions which can be implemented in embedded systems, desktop applications, enterprise frameworks as well as cloud based servers."

Today you can find Airo GLobal Software technology hard at work providing application solutions that span finance, marketing, manufacturing, pharmaceutical, government, and academic enterprises around the globe.

Data Analysis

  Preparing Data

The first step in developing empirical models is to obtain and prepare data. Typically this involves ensuring that there are no missing values, and then plotting data to get a quick view of possible trends and outliers.

During the data analysis phase of Predict Engine processing, missing field values are replaced by an appropriate value, depending on whether the field is numeric or alphanumeric. In addition, fields that would not be useful for modeling (e.g., sequence numbers that increment by a fixed value) are eliminated from further consideration.

 Data Noise

Most real-world systems yield data with varying degrees of noise, which may be the result of inefficiencies in physical systems, or the result of widely varying preferences and beliefs related to human influences. The Predict Engine implements two fundamental learning rules; the level of noise in data that you specify determines which rule is used during neural network training. For most modeling, the learning rule is an adaptive gradient rule. However, if you specify Very Noisy data, a Kalman Filter rule is employed.

Data-Analysis

 Data Transformations

The fundamental goal of empirical modeling is to map input values to output values. In situations which require supervised learning, a better mapping can be achieved by mathematically transforming raw values in ways that result in better matches between input value distributions and output value distributions. The Predict Engine incorporates a variety of transformations, such as log, exponent, square, square root, and others, that are applied to raw values in order to obtain a better correlation between input value distributions and output value distributions. The number of transformations applied depends on the option level selected.

 Selecting Input Variables

A critical element of any empirical modeling is the choice of inputs for the model. Very often there are inputs which are not fundamentally relevant, or which may even be detrimental to model performance (i.e., the inputs act as noise). The Predict Engine employs a genetic algorithm (GA) optimizer to identify the inputs which are most likely to produce the best model. Essentially, the GA explores different combinations of inputs and the effect they have on model performance (either a linear regression model or a limited neural network model serves as the fitness function). The set of inputs that occur most frequently in the best models are the inputs ultimately used to train the neural network. While the Predict Engine defaults to using the GA for variable selection, the feature can be turned off to permit performance comparisons with neural networks created using all available inputs.

 Training the Neural Network

Neural networks are trained to learn patterns and relationships in data. There are two fundamental types of training: supervised and unsupervised.

In unsupervised training, the relationships in data are not known, so the neural network identifies relationships through some type of metric - typically a distance metric. Unsupervised training is also referred to as competitive learning, since processing elements in the network "compete" to win (i.e., if the metric is a distance metric, the PE which is closest to the input data record is the winner). Each training record consists of record attributes - there are no output fields.

In supervised training, historical data provide a set of input-output pairs which reflect the relationships between inputs and outputs. Each record in the training dataset consists of a set of inputs and the associated output (or outputs).

Data Analytics

Data-Analytics

Your application generates a sea of data and critical business insights. We can help you stay afloat and gain insight.

WHAT IS SOFTWARE ANALYTICS?

Software Analytics refers to analytics specific to software systems and related software development processes. It aims at describing, predicting, and improving development, maintenance, and management of complex software systems.

It is about gathering billions and billions of metrics from your live production software, including user clickstreams, mobile activity, end user experiences and transactions, and then making sense of those—providing you with business insights. Software analytics includes Application Performance Management, but extends to User Behavior, Business Transactions, Customer Insights and much, much more..

What kind of things can I ask my applications?

We want you to be able to ask performance and application questions, but also business questions. Such as:

 How many people are live on our application right now ?

 How do response time trends impact customer adoption ?

 Who are our top customers at risk for churn ?

 What is the uptake of our newest product ?

We make custom analytics tools with Big data processing and Hadoop and other data processing methods

IOT (internet of things)

IOT

Our strategy

We are experimenting on making embedded system and applications to do and collect data and convert it into single click usable form with help of our cloud and Neural data processing applications and algorithms . We handle any kind of IoT development to make your business scalable to collect and transfer information’s .

What is Internet of Things?

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

The Internet of things (stylised Internet of Things or IoT) is the internetworking of physical devices, vehicles (also referred to as "connected devices" and "smart devices"), buildings and other items—embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. In 2013 the Global Standards Initiative on Internet of Things (IoT-GSI) defined the IoT as "the infrastructure of the information society. The IoT allows objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit. When IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020

The Internet of Things (IoT) starts with your things—the things that matter most to your business. IoT is about making your data come together in new ways. Tap into data with IoT dashboards. Uncover actionable intelligence. And modernise how you do business. Welcome to the Internet of your Things.

Network Architecture

Network Architecture

The architecture of a neural network is defined by its number of hidden layers and hidden units (also known as processing elements, or PEs).

A typical feed-forward neural network consists of an input layer, a small number (typically 1 to 4) of hidden layers, each with some number of hidden unit processing elements, and an output layer. The input layer connects to the available input data; the output layer produces a value that represents the output of the neural network - which can be used as the basis for decisions. Hidden unit processing elements contain differentiable non-linear functions which are the basis for learning.

This requirement to specify the number of hidden layers and number of hidden units per layer in traditional neural networks makes applying neural network technology somewhat difficult to implement using first generation tools.

A typical Self-Organizing Map (SOM) neural network consists of an input layer that is fully connected to the Kohonen layer, which contains processing elements. The processing units usually comprise a two-dimensional plane, although the Predict Engine supports creating SOMs with up to 4 conceptual dimensions. Before training can commence, you must specify the number of dimensions and the number of processing elements in each dimension. During training the weights of the processing element which is closest to the training data record currently being processed are adjusted. In the classical Self-Organizing Map implementation, training terminates after a set number of iterations through the training dataset.

Recognition and Detection

Recognition and Detection

We are specialized in detection and recognitions on various inputs and data formats Like Sound, Video, Speech , Image , Data Detection from Satellite data, Motion detection , Object detections , Face detection , Number plate detection etc

here are Some of our experiments to provide skills to robots
MOTION DETECTOT
PULSE DETECTION
BALL TRACKING
FACE RECOGNITION
NUMBER PLATE DETECTION

Video and Audio Processing

We do multimedia processing like Video processing, Audio processing , Image processing to find out information’s and transform information to valuable data

Neural network software developed by Airo GLobal Software offers sophisticated classification, analysis, and prediction technology that can be embedded in products and services and then deployed using the Internet or incorporated within the Internet infrastructure. Airo GLobal Software solutions are found in a wide variety of industry applications, including:

 energy generation, transmission, distribution, and demand forecasting;

 credit application and credit card transaction analysis;

 insurance and warranty claim analysis;

Video and Audio Processing

 medical research and health care outcomes analysis;

 securities trading and portfolio analysis and management;

 mailing-list management and targeted marketing;

 process modeling and control;

 natural resource exploration;

 production line management, quality control inspection, and equipment diagnostics.

 Audio and Video Processing

 OCR Solutions

 Speech and Sound Recognitions

 Language Detections

 Sentimence Analysis

 Auto Answer Applications

 Face detection and recognitions

 Finger print, RFID detections

 Number plate detection and recogntions

 Heart beat Detections

 Gender detection , Expression detection

 Motion detection

 Pluse Detection

 Object tracking

 Auto Drive car,

 Lane Detection

 Pedestrian Detection

Tools used

Big data and Hadoop , Opencv, Open MAX, EmguCV, RapidMiner (formerly known as YALE) WEKA , R-Programming, Orange,KNIME,NLTK ,Azure, IBM , Amazon, Google Cloud tools and Our proprietary solutions

For solutions and queries please mail us hello@airoglobal.com or schedule a conference