Artificial intelligence applied to computer forensics essay

For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness.

Artificial intelligence applied to computer forensics essay

Dorian Mongel Digital transformation is about doing things differently, not just doing them better than everyone else. And because I come from the world of business process management technology development, I have a few thoughts about how bringing artificial intelligence into business process management can help you achieve both sides of this coin.

Many business process management BPM projects are aimed at cost reduction and efficiency improvement. This operational improvement approach is important, because doing things better is, well, better. But simply improving operational effectiveness does not necessarily provide strategic competitive advantage.

Machine learning could be applied during process execution to, for example, trigger a new process or reroute running processes according to predictions. Machine learning can also make recommendations — for a next best action, for example.

Artificial intelligence applied to computer forensics essay

These three examples depend on unique data generated by each process to make predictions. According to Wil van der Aalst, a professor in the department of mathematics and computer science at the Eindhoven University of Technology in the Netherlands, classic data mining techniques do not work for processes, because processes express behavior that is far too complex, with concurrency, loops, decisions and so on.

There is another area where AI can be applied using a more generic approach, independent of data. As process-based applications get more sophisticated, the effect of delays — or blocked business processes — remains a problem. Delays can be the result of human error, resource unavailability, peak workloads and external dependencies, among other factors.

Artificial intelligence applied to computer forensics essay

So it is possible to look at the process itself for prediction. How can we use data about a process itself to facilitate intelligent continuous improvement of process-based apps?

Choosing an Appropriate Model: Failures and Successes The model that is needed has a dual objective: Further, for processes, we need a model that does not depend on existing data. Although linear regression has the advantages of being easy to model and use, it requires good data! Consider techniques for process mining instead.

Conformance process mining is used when there is an a priori model. The existing model is compared with the process event logs; discrepancies between the logs and the model are analyzed. This model can be enhanced to make predictions. There is an a priori model, but it is not used to check conformance; instead, it is used to improve the existing model — to enable it to predict process duration, for example.

Enhanced process mining offers key advantages for process AI. The same algorithm can be used for prediction and recommendation, and it can be extended for further prediction use cases. So in short, an appropriate process mining algorithm for prediction can apply to any process model, and use the unique data stored in its particular event logs to enhance the process model using time stamps on events, calculating time remaining based on deadlines, computing average completion times, etc.

The person responsible could be alerted when a deliverable might be late, because a step in the process is flagged as taking too long. AI permits applications to analyze their own historical patterns and flag future constraints that are not easily perceived by a human observer.

Top use cases by function

And when some manual steps in the process might be found to be systematically taking longer than anticipated, user interface analysis data could be a handy way to improve predictions and recommendations. Ideally, the applied AI can recommend both immediate corrective actions for current applications and suggest redesigns for future applications.

Intelligent Continuous Improvement of Running Applications: Current Developments AI applied to process applications helps to anticipate future constraints and align them with available resources — that is, become proactive rather than reactive — and thus avoid bottlenecks and delays.

We can use process-flow pattern detection and process and business metrics predictions to guide corrective actions and updates of running applications. Applications can analyze their own historical patterns and flag future constraints that are not easily perceived by a human observer.

Some advanced decision automation capabilities can be based on both process-mining algorithms and machine learning techniques.The big question is just how much of a poet a computer is capable of becoming in order to recognize small but significant nuances the can mean the difference between a good essay and a great essay.


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Frequently, at real computer forensic examinations, ex- perts can’t determine beforehand which evidences will turn out to be the most relevant to the investigation of a crime.

Why AI and Business Process Automation Share a Bright Future