Identifying highly-valued bank customers with current accounts based on the frequency and amount of transactions.

We hypothesize that highly-valued bank customers with current accounts can be identified by a high frequency of transactions in large amounts of money. To test our hypothesis, we employ machine learning predictive models to real data, including 407851 transactions of 4760 customers with current accounts in a local bank in Jordan. Thus, we exploit three clustering algorithms: density-based spatial clustering of applications with noise, spectral clustering, and ordering points to identify the clustering structure.

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion.

We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e.

Forecasting next-hour electricity demand in small-scale territories: Evidence from Jordan.

In exceptional times of wars, natural crises (e.g., snow storms), or hosting massive events (e.

Deletion Variants of Middle East Respiratory Syndrome Coronavirus from Humans, Jordan, 2015.

We characterized Middle East respiratory syndrome coronaviruses from a hospital outbreak in Jordan in 2015. The viruses from Jordan were highly similar to isolates from Riyadh, Saudi Arabia, except for deletions in open reading frames 4a and 3. Transmissibility and pathogenicity of this strain remains to be determined.