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Although many innovations have been achieved and natural disasters are well known to be extremely detrimental to persons and property, there is still no 100% assurance that alerts and real-time monitoring will work. Vulnerable communities sometimes relied on crude warning systems, such as flood gauges, observation towers, and local messengers, to deal with the unpredictable nature of floods. However, the effectiveness and reach of these strategies were constrained, leaving many people vulnerable to the disastrous effects of flooding. Therefore, the integration of cutting-edge technology and a system that is integrated and innovative overcomes the limits of conventional flood monitoring systems. The incorporation of WhatsApp, a widely used messaging service, into the flood monitoring and alerting process is a unique aspect of our system. We increase the reach and efficiency of our early flood warning system by combining standard SMS with WhatsApp messages. Additionally, our system includes sophisticated flood monitoring features that continuously monitor crucial parameters, including water levels. Administrators and authorized operators can respond quickly when the system sends alerts in reaction to aberrations from established thresholds. This invention bridges the gap between cutting-edge hardware and modern communication methods, representing a substantial advance in flood management technology. In conclusion, this research emphasizes how technology has the potential to improve catastrophe preparedness and response. It offers evidence of how innovation may be used to solve pressing problems and protect vulnerable areas from natural catastrophes, ultimately boosting resilience to flood events.

Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most serious illnesses that many individuals worldwide face. Accurate detection and effective treatment are of vital significance in lowering the death rate of breast cancer. Although researchers throughout the globe have offered many diagnostic approaches for the identification of this illness, these current methods still need additional refinement to assure the proper and efficient diagnosis of this disease. The purpose of this study is to make early and precise forecasts regarding breast cancer, which consider the second biggest cause of death in women globally. which in turn reduces the number of deaths around the world. In this study, we propose a methodology that utilises a soft voting classifier for diagnosing the type of breast cancer tumor, whether benign or malignant, based on three machine learning algorithms, namely, logistic regression, support vector machine, and decision tree. The Wisconsin Diagnostic Breast Cancer dataset was used to assess the proposed methodology. Before using this dataset, we balanced it using the SMOTE technique to eliminate bias and increase the size of the dataset. Modern studies have been surpassed by the proposed methodology, which has 99.3% accuracy, 100% precision, 98.46% recall, 99.2% F1 score, and an AUC of 0.992. Furthermore, it achieved an accuracy mean of 97.24% with 10-fold crossvalidation. [ABSTRACT FROM AUTHOR]

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Background: Medically unexplained somatic complaints are among the most common clinical presentations in primary care in developing countries and they are considerable burden for patients and health care system., Objectives: The aim of this study was to determine the prevalence of somatisation in comparison to psychologisation among a sample of Qatari patients who were visiting primary health care (PHC) centres and to investigate the clinical and socio-demographic characteristics of somatisers (STs) and psychologisers (PGs)., Method: The survey was conducted among PHC Qatari patients during the period from January to July 2007. About 2,320 patients were approached, of whom 1,689 agreed to participate and responded to the questionnaire. Among the studied Qatari patients, 404 patients were identified for clinical interview. The first stage of the study was conducted with the help of general practitioners, using the 12-item General Health Questionnaire. The second stage was carried out by a consultant using the Clinical Interview Schedule. A specific operational criterion was used to identify STs and PGs., Results: The prevalence rate of STs among the total studied sample was 12.4%, while the PGs were 11.5%. Among the identified psychiatric cases, the proportion of STs (52%) was higher than PGs (48%). Most of the diagnostic categories were more prevalent among PGs. The dissatisfaction at work and stressful life events within 12 months before the onset of the presenting symptoms were the three postulated determinants which were significantly more among STs than PGs., Conclusion: The prevalence of somatised mental disorder was little higher than the psychologised mental disorder. The prevalence of somatisation and psychologisation is comparable with other reported studies from the Middle-East and Western countries. Dissatisfaction at work and stressful life events were significantly higher among STs than PGs.