Health information technology is one of today's fastest-growing and most powerful technologies. This technology is used predominantly for predicting illness and obtaining medications quickly because visiting a doctor and performing pathological tests can be time-consuming and expensive. This has prompted many researchers to contribute by developing new disease prediction systems or improving existing ones. This paper presents a smartwatch-based prediction system for multiple diseases such as ischemic heart disease, hypertension, respiratory disease, hyperthyroidism, hypothyroidism, stroke, myocardial infarction, kidney failure, gallstones, diabetes, and dyslipidemia using machine learning algorithms; and it is called 'MedAi'. It comprises three core modules: a prototype smartwatch 'Sense O'Clock' equipped with eleven sensors to collect bodily statistics, a machine learning model to analyze the data and make a prediction, and a mobile application to display the prediction result. A dataset consisting of patient bodily statistics was obtained from a local hospital according to ethical guidelines, such as obtaining the prior consent of both patients and doctors. We employ several machine learning algorithms, including Support Vector Machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Random Forest (RF) to investigate the best performing algorithm. Experimentation using our dataset shows that the RF algorithm outperforms other machine learning algorithms such as SVM, KNN, XGBoost etc., in predicting aforementioned diseases with an accuracy of 99.4%. The system provides full-time assistance to the user by reporting his or her body condition and suggesting requisite remedies. It is a notable addition to early disease prediction systems and can predict multiple disease vulnerabilities before they reach an irrecoverable stage. Finally, we compare our method with the related existing methods.

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<p>Though in this modern society men and women are said to be given equal rights, having no gender discrimination, women are still considered weak and endlessly facing rape, domestic violence, eve-teasing, workplace violence, physical assault, cyberbullying even being killed. In South Asian countries, like Bangladesh, India, Pakistan there are thousands of cases regarding violence against women. The use of ICT including smartphone applications can be an aid in building awareness and support against these issues. Although several applications were developed worldwide, but not out there online for sensible use. In this paper, we aspire to present women with a technical solution by providing them a mobile application “SuperWomen” that is centered on urban and rural women of Asian countries and also available online. Not only women, but its voice-controlled siren feature is also a great help for physically challenged individuals, that works even on the lock screen. Survey feedback of this application shows that it takes care of each pre-incidental and post-incidental condition like an expert. This application provides user-friendly navigation for women with less technical knowledge. Its key features are secret button emergency SMS alert, location sharing with any social media, lawyer chatbot, pictorial self-defense techniques, etc. The effectiveness of this application has been justified through extensive literature review, application benchmark, performance evaluation, user survey, deployment in Google Play Store. We believe that this will bring a holistic resolution to safeguard suppressed women worldwide.<strong> </strong></p>

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