Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study’s methodology is to comprehensively review around 165 researches, criticize and categories all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies and traffic government officials. It has a direct impact on drawing clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw a general attention to a new traffic management proposition approach. INDEX TERMS Artificial Intelligent, Data Mining; Intelligent Transportation; Machine Learning.

Hakeem is one of the e-health systems that were implemented in Jordan to support the health care and services of medical sectors by connecting public health hospitals all around Jordan. This study was carried out as a unique and first study to investigate the effectiveness of Hakeem health system according to the international health information systems standards. The Health Metrics Network (HMN) framework and standards for country health information systems are used to measure the Hakeem system from different perspectives to ensure that it meets its objectives to maintain the required level of medical services in Jordan compared to international systems. The study was conducted on 522 respondents of Hakeem system users from four hospitals and ten health centers in Jordan. Data were collected by a valid and reliable questionnaire and interviews and analyzed by SPSS software. The study results indicated that the conformity extent of Hakeem system’s components with HMN framework and standards are in different agreements percentages. Some components are with acceptable percentages such as the integration polices and data processing while other components are with moderate, low and very lower percentages that needed improvements. In the light of the results, the study introduces some recommendations to improve Hakeem system such as improving the technical support of entire system as well as the services used in pharmacy unit.

This paper proposes a health data collection and integration framework for the Jordanian health sector. It aims to strengthen the quality of Jordanian Health Systems (JHS) and ensure the provision of sustainable high quality e-health services for populations. The framework is developed to prepare the strategy involving current practices for health data collection based-on DLL and V-Model. We studied critical features associated with collection and integration processes through concentrating on analysing a set of actual data (cancer and diabetes in Jordan). The result of analysis demonstrates the needs and optimum data collection process for e-health data and directs all Jordanian Health Organisations (JHOs) to work within a common mechanism, which can reduce data inconsistency, enhance the JHS's capacity and sustainability, leverage the quality of the delivered health services and reduce the cost of healthcare.

Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforcement, and bad traffic light management are main factors that have led to traffic congestion. One of the most important problems in cities is the difficulty of further expanding the existing infrastructures. Having that in mind, the main accessible and available alternatives that could provide better management of the traffic lights is to use technological systems. There are many methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, and a few other technologies. This research is focused on reviewing all these existing methods and studies using a systematic literature review (SLR). The SLR was intended to improve the synthesis of research by introducing a systematic process. This article aims at analyzing and assessing the existing studies against selected factors of comparison. The study achieves these aims by analyzing 78 main studies. The research outcomes indicated that there are decent numbers of studies that have been proposed in the area of smart traffic light management. However, less attention has been paid on the possibility of investigating the use of live traffic data to improve the accuracy of traffic management.

Background and Objectives: The investigation of the psychosomatic symptoms in women residing in developing countries is still emerging. To be precise, the prevalence and correlates of severe fibromyalgia, depression, anxiety, and insomnia are understudied in Arab women, as these symptoms could relate to improper self-medication. This study mainly investigated the association between self-medication with analgesics and fibromyalgia, depression, anxiety, and insomnia symptoms among a community-based cohort of females in Jordan. Materials and Methods: We used a web-based cross-sectional study design. Fibromyalgia, depression, anxiety, and insomnia were assessed using validated scales. The used over-the-counter (OTC) painkillers were recorded. Results: Data were analyzed from 741 women, and fibromyalgia was screened in 16.4%, depression in 37.4%, anxiety in 27.8%, and insomnia in 38.3%. Fibromyalgia was associated with “married” (OR = 1.5, 95% CI = 1.017–2.305), “using OTC acetaminophen” (OR = 1.75, 95% CI = 1.15–2.69), “using herbal remedies” (OR = 2.02, 95% CI = 1.33–3.07), and “using antiseizure medications” (OR = 2.43, 95% CI = 1.38–4.28). Severe depression was significantly associated with “age” (OR = 0.97, 95% CI = 0.96–0.99), “high school education” (OR = 1.90, 95% CI = 1.21–2.98), “smoking” (OR = 1.72, 95% CI = 1.15–2.56), “OTC acetaminophen” (OR = 1.40, 95% CI = 1.02–1.92), “OTC non-steroidal anti-inflammatory drugs” (OR = 1.75, 95% CI = 1.15–2.65), and “antiseizures” (OR = 2.19, 95% CI = 1.30–3.70). Severe anxiety was significantly associated with “smoking” (OR = 2.08, 95% CI = 1.40–3.12), “OTC acetaminophen” (OR = 1.48, 95% CI = 1.06–2.06), and “antiseizure medications” (OR = 2.04, 95% CI = 1.22–3.41). Severe insomnia was significantly associated with “age” (OR = 0.98, 95% CI = 0.96–0.99), “high school education” (OR = 1.58, 95% CI = 1.01–2.47), “smoking” (OR = 1.51, 95% CI = 1.01–2.25), “OTC non-steroidal anti-inflammatory drugs” (OR = 1.74, 95% CI = 1.13–2.64), “antiseizure medications” (OR = 1.84, 95% CI = 1.09–3.11), and “No analgesics” (OR = 0.48, 95% CI = 0.32–0.71). Conclusions: Self-medication with analgesics is associated with a high burden of psychosomatic symptoms in Arab women, and awareness campaigns are required to guide self-medication behavior.