Remote Reading of Surgical Monitor`s Physiological Readings: An Image Processing Approach
As a result of the global effect of infectious diseases like COVID-19, remote patient monitoring has become a vital need. Surgical ICU monitors are attached around the clock for patients in critical care. Most ICU monitor systems, on the other hand, lack an output port for transferring data to an auxiliary device for post-processing. Similarly, strapping a slew of wearables to a patient for remote monitoring creates a great deal of discomfort and limits the patient’s mobility. Hence, a unique remote monitoring technique for the ICU monitor’s physiologically vital readings has been presented, recognizing this need as a research gap. This mechanism has been put to the test in a variety of modes, yielding an overall accuracy of close to 90%.
Remote Reading of Surgical Monitor`s Physiological Readings: An Image Processing Approach
As a result of the global effect of infectious diseases like COVID-19, remote patient monitoring has become a vital need. Surgical ICU monitors are attached around the clock for patients in critical care. Most ICU monitor systems, on the other hand, lack an output port for transferring data to an auxiliary device for post-processing. Similarly, strapping a slew of wearables to a patient for remote monitoring creates a great deal of discomfort and limits the patient’s mobility. Hence, a unique remote monitoring technique for the ICU monitor’s physiologically vital readings has been presented, recognizing this need as a research gap. This mechanism has been put to the test in a variety of modes, yielding an overall accuracy of close to 90%.
Comparative Analysis of Matrix Factorization Techniques for Collaborative Filtering for Recommendation Systems
The essential of improving user experience and engagement in e-commerce platform is the building of a recommendation system. In this study implementation and comparison of several matrix factorization models for an e-commerce platform is presented. For this study dataset was taken from an online marketing platform which was stored in a MongoDB database. Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), Alternating Least Squares (ALS), and Neural Network Matrix Factorization models were implemented and tested with the dataset. Performance of the models were evaluated by using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The model of neural network matrix factorization produced the lowest RMSE (0.002) and MAE (0.001). Consequently, it could potentially be recommended as the appropriate model. By delivering personalized recommendations to individual user preferences, this study aims to improve user engagement and satisfaction of the e-commerce platform.
An In-Depth Exploration of Annotation: Methods, Applications, and Implications
This paper delves into annotation, investigating its purpose, methods, applications, and implications. It aims to deepen understanding of annotation’s role across different domains. The study uses a systematic review, analyzing various literature and empirical studies on annotation. This will cover methods across text, image, and data annotation, discussing technological advancements and offering both qualitative and quantitative insights. This will identify manual, semi-automated, and fully automated annotation methods, discussing their strengths and limitations. It reveals annotation’s broad applications in natural language processing, computer vision, healthcare, and social sciences, highlighting its contributions to research and technology. The paper discusses challenges like inter-annotator agreement, bias, and scalability in annotation, and their impact on research outcomes, ethics, and machine learning model development. Finally, we dive into recent advancements in annotation tools, like machine learning-based methods and collaborative platforms, highlighted for their contributions to efficiency and accuracy.
Comprehensive Analysis of Segmentation models for Multiclass Segmentation
Comparative Analysis of Matrix Factorization Techniques for Collaborative Filtering for Recommendation Systems
The essential of improving user experience and engagement in e-commerce platform is the building of a recommendation system. In this study implementation and comparison of several matrix factorization models for an e-commerce platform is presented. For this study dataset was taken from an online marketing platform which was stored in a MongoDB database. Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), Alternating Least Squares (ALS), and Neural Network Matrix Factorization models were implemented and tested with the dataset. Performance of the models were evaluated by using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The model of neural network matrix factorization produced the lowest RMSE (0.002) and MAE (0.001). Consequently, it could potentially be recommended as the appropriate model. By delivering personalized recommendations to individual user preferences, this study aims to improve user engagement and satisfaction of the e-commerce platform.