The inaccurate detection of diabetes and hypertension causes’ time wastage and a cost burden due to higher amounts of medicine intake and health problems. The previous works did not investigate machine learning (ML)-based diabetic and hypertension patient prediction by using multiple characteristics. This paper utilizes ML algorithms to predict the presence of diabetes and hypertension in patients. By analyzing patient data, including medical records, symptoms, and risk factors, the proposed system can provide accurate predictions for early detection and intervention. This paper makes a list of eighteen characteristics that can be used for data set preparation. With a classification accuracy of 93%, the Support Vector Machine is the best ML model in our work and is used for the diabetic and hypertension disease prediction models. This paper also gives a new mobile application that alleviates the time and cost burden by detecting diabetic and hypertensive patients, doctors, and medical information. The user evaluation and rating analysis results showed that more than sixty five percent of users declared the necessity of the proposed application features.

Building a personalized travel recommendation system is important to enhance the satisfaction and experience of travelers. Due to the lack of an efficient online-based tourist assistance system, tourists have faced several challenges in Bangladesh, such as difficulties in planning their trips and making informed decisions. To overcome the existing challenges, in this paper, a prediction model has been developed to predict the suitability of a travel destination based on the user’s preferences and some other relevant factors. Then the system offers personalized recommendations for the best local places to visit, hotels to stay in, transportation services, and travel agencies with the necessary details. This paper utilizes various machine learning classification algorithms to predict the best-suited travel destinations and local tourist spot recommendations for users based on their budget and preferences. The examined results verified that the random forest algorithm provides the best accuracy of 98 percent and is used for tourist place eligibility prediction. The user rating analysis visualized that the proposed mobile application received satisfactory remarks from more than 60 percent of reviewers regarding its effectiveness.

The rise in popularity of ride-sharing services and ride-booking systems has created new opportunities and challenges for security and safety. A useful system for passenger safety assistance using machine learning and mobile applications is missing from the existing work. This paper develops a data set regarding suspicious activity detection using a questionnaire. This paper selects a suitable machine learning model for suspicious activity prediction during a transport ride by examining support vector classifiers (SVC), random forest, MLP classifiers, decision trees, KNN, logistic regression, and Gaussian naive Bayes classifiers. The results showed that the SVC is most suitable, with 97% accuracy, for classifying suspicious activity predictions during transport riding. This paper provides a passenger safety mobile application with passenger and driver verification, application rating, suspicious activity prediction, suggestions regarding safety, location mapping, and trip booking features. The application evaluation results based on users’ comments showed that more than 55 percent of users supported the application's usability and effectiveness nature.