Machine Learning

Machine learning algorithms, AI and IoT devices: the recipe for innovating the vending sector is CLEA, a concentrate of hi-tech, data analysis and customer experience.

With data monetization - the monetization of data, which is the process of strategic analysis and practical implementation that allows you to use data as a source of revenue - companies can transform data they have collected into monetizable value just like other assets. In fact, data monetization becomes a strategy that allows companies to virtuously recover the investments made on IoT and IIoT infrastructure, systems and platforms, accelerating their evolutionary path and designing new business scenarios.

Big data monetization - literally, data monetization, that is the process of strategic analysis and practical implementation that allows you to use data as a source of revenue, is one of the most rewarding objectives for any company that wishes to undertake a virtuous data-driven business path. However, its achievement can be hindered not only by a misinterpretation of the data already available but, above all, by a lack of correct investments in emerging technologies.

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system’s normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.

Maximum degree of automation, more flexibility in production, significantly lower personnel resources: innovative industrial companies hope for a prosperous future through Artificial Intelligence and Machine Learning.
And that is not all: PwC‘s consultants have
found out that such technologies are an
obligatory exercise in staying competitive.

ATMs are an easy target for fraud attacks, like card skimming/trapping, cash trapping, malware and physical attacks. Attacks based on explosives are a rising problem in Europe and many other parts of the world. A report from the EAST association shows a rise of 80% of such attacks between the first six months of 2015 and 2016. This trend is particularly worrying, not only for the stolen cash, but also for the significant collateral damages to buildings and equipment.