Intelligent Agents & Neural Networks

Our core intelligent technologies:

Intelligent Agents

Neural Networks

Artificial Intelligence

Some advantages of using artificial neural networks in data processing:

Below is a short description of the most important reasons why we use AI technologies

Organic Learning


Neural networks -- within the bounds of their data inputs and initial conditions -- can learn organically. They aren't limited entirely by what has been given to them in an expert system. Neural networks can generalize from their inputs, which makes them valuable for pattern recognition systems and for large-scale data analysis.

Nonlinear Data Processing


Nonlinear systems perform shortcuts to reach computationally expensive solutions and can infer connections between data points, rather than wait for records in a data source to be explicitly linked. This nonlinear short-cut mechanism is why neural networking techniques are valuable in commercial big-data analysis.

Fault Tolerance


In addition to the data processing advantages, artificial networks have the potential for high fault tolerance; when scaled across multiple machines and multiple servers, a neural network is able to route around missing data or servers and nodes that can't communicate.

Self-Debugging & Repair


If ANNs are asked for data that was in part of the network that is no longer communicating, they can regenerate large amounts of data by inference and using their organic learning traits, working forward from their current state. This is also a useful trait for networks that need to inform their users about the current state of the total network and effectively results in a self-debugging and diagnosing network.

Variable Pattern Recognition


Neural networks are very general and can capture a variety of patterns very accurately.

No base data assumptions


There is no need to assume an underlying input data distribution when programming a neural network

Focused on detecting links


Neural networks can detect all possible, complex nonlinear relationships between input and outputs.

No excessive training


Neural networks do not require excessive statistical training.

Selected use-cases:

Following are examples of use-cases of intelligent agents, ANNs or Artificial intelligence programms. Does any fit your case?

NEWSAGENTS GENERATING PERSONAL MEDIA

NewsAgent is a type of intelligent agent that is able to observe users when they are reading electronic newspapers and it has the capability of deducing the interesting subjects of particular users. Moreover, this agent enables users explaining their interests, but it is an optional way for informing only clear interests to personal agents. NewsAgent is an intelligent type of agent that has the capability of generating personal newspapers from particular user preferences extracted by observation and feedback. This agent generates personal newspapers using static word analysis for extracting a global classification and case-based reasoning for dynamic subclassification. The agent observes users by an applet with capabilities of detecting changes of pages. It also records the routine of reading newspapers of each user for analyzing readings in terms of their routines..

AI HELPING TACKLE THE INFORMATION OVERLOAD

The lack of effective information management tools has given rise to what is colloquially known as the information overload problem. Put simply, the sheer volume of information available to us via the Internet represents a very real problem. The potential of this resource is immediately apparent to anyone with more than the most superficial experience of using the www. Users can use programs that ‘learn to prioritize, delete, forward, sort, and archive mail messages on behalf of a user’. This is how agents can filter both mail and other data. Similarly, consumer agents can be responsible for representing the user’s interests in libraries or other places where information is consumed. They can maintain models of users, and use these models to assist them, by proactively providing information they require, and shielding them from information that is not of interest.

ELECTRONIC COMMERCE

Currently, commerce is almost entirely driven by human interactions; humans decide when to buy goods, how much they are willing to pay, and so on. But in principle, there is no reason why some commerce cannot be automated. By this, we mean that some commercial decision making can be placed in the hands of agents. Lest the reader suppose that this is fanciful, and that no sensible commercial organization would make their decision making (and hence money spending) the responsibility of a computer program, it should be remembered that this is precisely what happens today in the electronic trading of stocks and shares. Widespread electronic commerce is, however, likely to lie some distance in the future. In the near term, electronic trading applications are likely to be much more mundane and small scale.

AI FOR EXPENSE, AUDIT, FRAUD AND SUSPICIOUS ACTIVITY DETECTION

Agents can automate the research and reasoning that human auditors do and check for the legitimacy of every item on the expense. They can assign a risk score to it. Using artificial intelligence web scale and social data can be connected to the actual place, person, attendees, merchants & customers along with using signals from various sources our algorithms assign a risk score to every expense. AI can determine if the information that has been provided in the expense is actually valid and can provide the details of the fraud that may be associated with the expense.Suspicious activity detection is the process of reviewing all transactions for anomalies that may indicate foul play. Like KYC processes, detecting potential transaction issues requires the creation and analysis of a transaction profile. This profile determines attributes such as transaction type, payment methods, associated entities or persons, time, locations and values. Traditional rules-based approaches prescribe defined scenarios which systems can readily detect. The more difficult cases are those which on the surface are not obvious, but once uncovered, yield surprising connections.

KYL AND AML

KNOW YOUR CUSTOMER and ANTI MONEY LAUNDERING - two recent booming cases of AI implementation. As regulators increasingly require greater oversight from institutions including closer monitoring for anti-money laundering (AML) compliance. Automation is the Key to AML Compliance Success and A.I. is a great way to achieve it. Suspicious Activity Detection, Case Management and Reporting, Know Your Customer (KYC) and Customer Due Diligence (CDD) are all fields tackled by A.I. Effective assessments will scan a variety of watch lists, including politically exposed persons (PEP), negative news, OFAC, known aliases, regulatory sanctions and criminal actions, and then track and identify changes over time. Persons and companies should be analyzed for citizenship, residency, criminal cases, corruption and any other geographic background that might indicate ties with countries, jurisdictions, regions or organizations that are under embargo, economic sanctions or other financial dealing that may be prohibited by governments or other law enforcement organization.The most significant challenge facing KYC and CDD processes is how to effectively reduce the ‘noise.’ Most systems produce numerous false positives, because they are not intelligent enough to differentiate immaterial anomalies from something more significant. ANNs can be trained in such a way, so as to reduce the noise.

IMPLEMENTATION OF ANN-BASED AI CONCEPTS TO THE SMART GRID

Smart grid deployment is a global trend, creating endless possibilities for the use of data generated by dynamic networks. The modern electricity distribution system gradually evolves into a very large and very complex structure in which ICT is getting more and more important role. It is nowadays most frequently referred to as smart grid. There is almost unlimited number of possible applications of ICT subsystems within the smart grid and one is not to say that smart grid is a fixed structure whose capabilities are finally set. A special offer of the ICT to the smart grid is artificial intelligence and particularly the artificial neural networks. The challenge is the transformation of this large volume of data into useful information for the electrical system. An example of this is the application of demand side management (DSM) techniques for the optimisation of power system management in real time. With load prediction or security monitoring ANNS can greatly contribute to the development of the smart grid toward an advanced, reliable and secure system. Moreover, ANNs can help in microgrids. Microgrids are characterized by self-sustainability, fault tolerance, reliability, security and power quality. To achieve these objectives, efficient, fast, and scalable optimization and control algorithms are required. These algorithms should be capable of processing information intelligently and taking critical decisions dynamically. Unlike, AI, various existing mechanisms fail to meet a lot of accompanying challenges: forecasting tasks, like renewable energy forecasting, storage forecasting and demand forecasting, that need intelligent rules; the use of new equipment like storage systems, where monitoring and mapping of faults to different fault conditions of the equipment is difficult; the inclusion of renewable energy sources, for which the calculation of generation units to be committed and the economic scheduling of these units for optimal operation is highly complex etc. But

MEDICAL IMAGE ANALYSIS

Given that neural networks have been widely reported in the research community of medical imaging, recent neural network developments have confimed their applicability in computer-aided diagnosis, medical image segmentation and edge detection toward visual content analysis, and medical image registration for its pre-processing and post processing. Known neural networks with fixed structures and training procedures could be applied to resolve a medical imaging problem; medical images could be analysed, processed, and characterised by neural networks; and neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.

DISEASE FORECASTING

Predicting future disease incidence is important and will be beneficial in the planning and management of a suitable policy to reduce the number of cases. Generally, information systems play a central role in the development of an effective and comprehensive approach to prevent, detect, respond, and manage infectious disease outbreaks in human. The application of artificial intelligence has motivated the use of artificial neural network in epidemiological area. Neural network can be used to learn the historical patterns of disease incidence to forecast future incidence. The advantages of neural network for supporting policy/decision makers in developing long term strategies regarding the number of disease incidence. Neural network has been established of their potentials in many domains related with medical forcasting and diagnosis disease, Although, Neural networks never replace the human experts instead they can helpful for decision making, classifying, screening and also can be used by domain experts to cross-check their diagnosis.