Using Many Objective Bat Algorithms for Solving Many Objective Nonlinear Functions

In this paper, we have relied on the dominant control system as an important tool in building the group of leaders because it allows leaders to contain less dense areas, avoid local areas, and produce a more compact and diverse Pareto front. Nine standard nonlinear functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all the results of the proposed method and other methods in the same field of work

Early detection of breast cancer using mammography images and software engineering process

The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center

Prevalence of menstrual irregularities after coronavirus disease 2019 vaccination: A cross-sectional study in the Eastern Province, Saudi Arabia

BACKGROUND: Reports indicate that there are menstrual cycle disturbances following coronavirus disease 2019 (COVID-19) vaccination. The present study explored the prevalence of menstrual irregularities after COVID-19 vaccination and the association of menstrual irregularities with vaccine type, doses, immediate adverse effects, history of COVID-19 infection, and its severity. MATERIALS AND METHODS: For this cross-sectional study, 406 women of reproductive age completed an online survey about the postvaccine changes in their menstruation (cycle duration, bleeding days, and bleeding amount), COVID-19 vaccine history (doses, type of vaccine, and immediate adverse effects), history of COVID-19 infection, and its severity. Data was analyzed using SPSS; descriptive statistics were computed and Chi-square test, and binary logistic regression analysis were performed. RESULTS: Of the total 406 women, 45% reported postvaccine changes in their menstrual cycle. The most common menstrual change was increased dysmenorrhea (68%), followed by an increase in the length of the cycle (52%). There was a significant association between postvaccine menstrual changes and the age, marital status, and family history of menstrual irregularities. No association was observed between postvaccine menstrual changes and COVID-19 vaccine-and COVID-19 infection-related variables. As per the best-fit model of our predictors, the odds of having postvaccine menstrual changes were 0.41 times less in “single” women (confidence interval [CI] = 0.26–0.27; P < 0.001) and 1.714 times greater in women who had a “family history of menstrual irregularities” (CI = 1.092–2.690; P = 0.02), respectively. CONCLUSION: A substantial number of women complained of postvaccine menstrual changes regardless of their age, type of COVID-19 vaccine, doses, immediate adverse effects, and COVID-19 infection history/severity. Being “single” decreased the probability, whereas having a family history of menstrual irregularities increased the probability significantly of having postvaccine menstrual changes