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. The two segments of customers (generated from the clustering process) have different transactional characteristics. Our customer behavioral segmentation accuracy is, at best, 0.99 and at least 0.82. Likewise, we build three classification models using our segmented data: a neural network, a support vector machine, and a decision tree. Our predictive models have an accuracy of 0.97 at best and 0.90 at least. Our experimental results confirm that the frequency and amount of transactions of bank customers with current accounts are most likely sufficient indicators for recognizing those customers whom banks highly value. Our predictive models state that the two most critical indicators are the deposit and withdrawal transactions performed on ATMs. In contrast, the least significant indicators are the transactions of credit cards and credit cheques.

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., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98-99% in predicting vehicle travel time and traffic status on our study's street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study's street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.

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.g., international sports events), prior knowledge of hour-by-hour electricity demand might become critical for the concerned areas. Anticipating the next-hour demand within a bounded location might help cope with challenging situations. In this paper, we are concerned with the problem of electricity demand forecasting for the next hour within a small area in the context of the country of Jordan relying exclusively on historical electricity consumption. Existing electricity demand forecasting models predict electricity demand for the next day for the whole country of Jordan or for large cities using numerous indicators such as weather conditions and season-related features, together with historical electricity consumption. Our proposed solution consists of preprocessing the hourly-electricity-consumption data to rearrange it into time series of 25-hour length. Then, we applied supervised machine learning algorithms to build predictive models for electricity demand. Our methods proved effective to a certain degree in forecasting electricity demand using time series of historical electricity consumptions only. We employ several predictive machine learning models: neural network, decision tree, random forest, AdaBoost, support vector regression, nearest neighbors, linear regression, bayesian ridge, partial least squares, gradient boosting, and principal component regression. We found that the most critical indicators in our predictive models are the electricity consumption of the two hours preceding the target hour and the electricity consumption the day before in the hour (the same as the target hour) and the next hour. Our predictive models can efficiently forecast electricity demand in a small rural area in Jordan with an accuracy of nearly 97% at best and 92% at least. Our predictive models are constructed using historical data that includes the electricity consumption of a small Jordanian district for the years 2019-2021.

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.