ABSTRACT
An automated reminder mechanism is built in this Android-based application. It emphasizes the contact between doctors and patients. Patients can set a reminder to remind them when it is time to take their medicine. Multiple medications and timings, including date, time, and medicine description, can be programmed into the reminder by using image processing. Patients will be notified through a message within the system, as preferred by the patients. They have the option of looking for a doctor for assistance. In this COVID-19 pandemic situation where nurses have to remind the patients in the hospitals to take their medications, our application can be useful, alerting the patient every time of the day when he/she has to take the medicine and in what amounts. Also, all the necessary tests report and prescriptions can be saved on the cloud for later use. Patients will be provided with doctor contact information based on their availability. Also, patients will be notified of the expiry date of the medicine, and the former history of the medicines can be stored for further reference. The proposed system prioritizes a good user interface and easy navigation. Image processing will be accurate and efficient with the help of powerful CNN-RNN-CTC algorithm. It also emphasizes on a secure storage of the user's data with the help of the RSA algorithm for encryption and the gravitational search algorithm for secure cloud access. We attempted to create a Medical Reminder System that is cost-effective, time-saving, and promotes medication adherence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Early diagnosis of diseases is very critical for recovery. However, this is not always feasible due to the limited available staff or expensive and inadequate tools as we have witnessed in the recent COVID-19 pandemic. Lung diseases are life-threatening, but fortunately, they can be detected from X-ray images, which are cost-effective approaches. However, they need experts who are sometimes unavailable. Thus, using cutting-edge technology to diagnose diseases automatically and fast is the key solution to saving people's lives. In this research, deep learning techniques have been utilized to classify several lung diseases in a cost-saving, time-saving, and efficient manner. Examples of lung diseases studied in this research are COVID-19, Lung Opacity, Pneumonia, and Tuberculosis. Several pre-trained deep learning models have been employed for flat multi-class classification of these lung diseases instead of using binary classification to recognize one disease from normal cases, as most state-of-the-art studies carry out. The models' performance has been evaluated on imbalanced data of X-ray images with various resolutions and types. Finally, multiple measurements metrics have been utilized to evaluate the performance. The best accuracy achieved in this research is 95.643%. © 2022 IEEE.
ABSTRACT
A 2018 study of performance measures for the Utah Department of Transportation's (UDOT) Incident Management Team (IMT) program concluded that the program was cost effective and benefited Utah motorists. During the 2018 legislative session, UDOT received funding to expand its IMT program. To determine the benefits of expanding the IMT program, a comparison of performance measures for 2018 and 2020 incident data was conducted. In addition, data regarding the affected volume, the excess travel time, and the excess user cost associated with incident congestion were gathered. The effects of the COVID-19 pandemic affected traffic volumes during this study, and statistical analyses were utilized to account for volume differences between the two years. Results indicated that the expansion of the IMT program has allowed UDOT to respond more consistently to incidents and respond to a larger quantity of incidents over a larger coverage area and in extended operating hours. © ASCE.
ABSTRACT
Overhead costs in construction projects are costs that are borne and charged to the contractor to support the work. However, the amount of overhead costs for each project is different and is influenced by external factors such as environmental, socio-cultural, political, and the nature of the project location. Therefore, this study aims to identify the dominant external factors that affect construction project overhead costs and determine the percentage of overhead costs set aside by contractors from the direct costs of construction projects to anticipate the risks posed by overhead costs. In this study, a total of 30 questionnaires were collected from the contractors and a Likert scale of 1–5 was used to measure the level of influence of external factors on construction project overhead costs. Then, the collected data is processed using factor analysis techniques and produces three dominant external factors on construction project overhead costs, including (1) Economics, (2) Law, (3) Social-cultural and the impact of the COVID-19 pandemic. Regarding the percentage of overhead costs on construction projects, the results show that overhead costs on construction projects range from 6 to 10% of direct costs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The main issue of the paper is to examine risk of cryptocurrencies in the period of more intensive trading, from the beginning of 2017 up to mid-spring 2021, thus encompassing the Covid-19 crisis period. The three risk measures are employed: standard deviation, Value at Risk and Conditional Value at Risk. There are periods, like the crisis period, when parallel to the high and growing levels of the measured risk, the appropriate return-risk ratio is increasing even with the higher rates, showing that the risk-taking investment in cryptocurrencies was cost-effective. © 2021 Samo Drobne – Lidija Zadnik Stirn – Mirjana Kljajić Borštnar – Janez Povh – Janez Žerovnik