What is mobile-based application?5 answersA mobile-based application is a software designed for mobile devices that aims to address various health concerns. These applications play a crucial role in managing immunizations for children, aiding in maintaining their growth, and providing essential information on immunization and nutrition. Additionally, mobile applications have been utilized to combat the rising issue of childhood obesity by offering dietetic plans based on the Analytical Hierarchy Process (AHP), resulting in better outcomes compared to traditional methods. Furthermore, mobile applications have been instrumental in controlling weight, promoting physical activity, and improving dietary patterns among overweight children, with the potential to combat childhood obesity effectively. The use of mobile-based applications presents a convenient and accessible approach to address various health challenges in both children and adults.
Definition of mobile-based application?5 answersA mobile-based application refers to software designed to operate on mobile devices like smartphones, utilizing platforms such as Android or iOS. These applications have gained significant popularity, especially during the COVID-19 pandemic, where remote activities have become essential, including vaccine registration and healthcare management. The development of mobile applications involves various steps such as quick planning, design modeling, prototype construction, deployment, and feedback collection. These applications have been instrumental in improving healthcare services, as seen in studies focusing on the use of mobile apps for generating dietetic plans and enhancing care approaches for children with severe beta thalassemia, showcasing their impact on health outcomes and quality of life.
1.What functionalities of the mobile application may be described in terms of features?4 answersFunctionalities of mobile applications can be described in terms of features such as search capabilities, social media integration, responsive design, touch-friendly interfaces, feedback mechanisms, offline functionality. Additionally, smart city mobile applications offer features like monitoring city information, push notifications, geolocation-based alerts, feedback submission, home automation control, and access to home-related data. Studies on travel apps highlight the importance of information quality, source credibility, and app functionality in influencing users' perceptions and intentions to continue using them. Recommendations for mobile app development emphasize the significance of hybrid feature recommendation methods that combine explicit descriptions and implicit code information for effective feature extraction and enhancement.
What are the most recent papers regarding the importance of feature engineering for deep learning?5 answersFeature engineering is an important aspect of deep learning. It involves selecting or extracting relevant features from the data to improve model performance. In the context of melanoma diagnosis, a study suggests using handcrafted features and optimization algorithms to predict tumor size accurately. Another paper explores the impact of feature engineering on the prediction of remaining useful life (RUL) in various domains. It compares different feature selection and extraction techniques, such as principal component analysis (PCA) and stepwise regression (SR), to reduce the number of sensors while maintaining predictive performance. Additionally, a theoretical analysis of feature learning in deep learning models is presented. The study introduces the concept of an interaction tensor to analyze the relationship between data, features, and model performance. It provides insights into how features are distributed in data and how different models learn different features.
WHat is feature engineering in machine learning?5 answersFeature engineering in machine learning refers to the process of creating and selecting features from raw data to enhance the accuracy of machine learning models. It involves preparing the collected data for analysis by algorithms through steps such as encoding variables, treating outliers and missing values, binning, and variable transformation. Feature engineering is particularly important in real-time data streams where the data is constantly changing and the model needs to adapt quickly. Techniques for extracting and constructing features from raw data include time-based features, trend-based features, and error-based features. Feature engineering significantly improves the performance of classifiers and allows for more accurate predictions, as demonstrated in the case of predicting flight delays. It also plays a role in reducing the data size and training time of malware classifiers in the context of malware detection systems.
What is Feature Importance?5 answersFeature importance is an approach used in machine learning to explain the predictions made by models. It involves assigning importance scores to input features based on their contribution to the model's predictions. Different techniques exist for deriving these scores, but there is a lack of agreement between the assumptions behind these techniques and the expectations of data scientists. Feature importance is particularly relevant in the context of time series classification, where deep learning techniques are commonly used but lack interpretability. Current methods for feature importance in time series classification rely on replacing features with constant or random values, which can lead to unexpected results. To address this issue, a method based on Conditional Variational Autoencoder has been proposed to generate within-distribution inputs for evaluating feature importance. Additionally, a novel approach of re-partitioning the feature space into separable sets has been suggested to unify feature importance scores and overcome inconsistencies.