In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify...In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.展开更多
Flood control detention basins （DBs） can act as water quality control structures or best management practices （BMPs）. A key pollutant that DBs serve to settle out is particulate phosphorus, which adsorbs onto sedi...Flood control detention basins （DBs） can act as water quality control structures or best management practices （BMPs）. A key pollutant that DBs serve to settle out is particulate phosphorus, which adsorbs onto sediment. This study examines the sediment phosphorus concentration and its relationship with the particle size of sediment microcosms from pre- and post-rain event samples obtained from six DBs located in Clark County, Nevada. DBs were allotted a land use classification to determine if there was a correlation between the sediment phosphorus concentration and surrounding land use. The curve number method was used to calculate the runoff and subsequent phosphorus carried into the DB by the runoff. Our data show sediment phosphorus concentrations to he highest in soils from undeveloped areas. Runoff amount also plays a substantial role in determining the amount of phosphorus brought into the DB by sediment. This research has implications for improvement of water quality in arid regions.展开更多
文摘In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.
基金supported by the Urban Flood Demonstration Program of the United States Army Corps of Engineers(Grant No.W912HZ-08-2-0021)
文摘Flood control detention basins （DBs） can act as water quality control structures or best management practices （BMPs）. A key pollutant that DBs serve to settle out is particulate phosphorus, which adsorbs onto sediment. This study examines the sediment phosphorus concentration and its relationship with the particle size of sediment microcosms from pre- and post-rain event samples obtained from six DBs located in Clark County, Nevada. DBs were allotted a land use classification to determine if there was a correlation between the sediment phosphorus concentration and surrounding land use. The curve number method was used to calculate the runoff and subsequent phosphorus carried into the DB by the runoff. Our data show sediment phosphorus concentrations to he highest in soils from undeveloped areas. Runoff amount also plays a substantial role in determining the amount of phosphorus brought into the DB by sediment. This research has implications for improvement of water quality in arid regions.
