Low-Cost, LiDAR-Based, Dynamic, Flood Risk Communication Viewer by Debra F. Laefer, Evan O’Keeffe, Kshitij Chandna, Kim Hertz, Jing Zhu, Raul Lejano, Anh Vu Vo, Michela Bertolotto, Ulrich Ofterdinger

“…This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. …”
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Long-term satellite records of water clarity suggest increasing human activity influence in an inland lake by Jian Wang, Deyong Sun, Shengqiang Wang, Hailong Zhang, Yue Zhang, Miao He

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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. …”
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Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS by Polina Lemenkova

“…The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. …”
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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

“…We observed that ensemble and hybrid models with long short-term memory (LSTM) and support vector machines (SVM) are being more adopted for financial trends and price prediction. Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. …”
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Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design by Qiuling Tao, Xintong Yang, Longke Bao, Yuexin Zhou, Tao Yang, Yilu Zhao, Rongpei Shi, Zhifu Yao, Xingjun Liu

“…First, we construct two classification models using ML algorithms to predict the presence or absence of the L12 phase and other phases, respectively. …”
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ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements by Hezha H. MohammedKhan, Cascha Van Wanrooij, Eric O. Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed, Chenar Omer Ali Al Jaf

“…To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. …”
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Analysis of human brain RNA-seq data reveals combined effects of 4 types of RNA modifications and 18 types of programmed cell death on Alzheimer’s disease by Ke Ye, Xinyu Han, Mengjie Tian, Lulu Liu, Xu Gao, Qing Xia, Dayong Wang

“…An RNA modification-related programmed cell death risk score was calculated to predict the occurrence of AD. Results RPCD-related genes classified patients into subgroups with distinct clinical characteristics. …”
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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…It has been statistically demonstrated that TMB was related to the ability of neoantigen production and used to predict the efficacy of immunotherapy for various types of cancers. …”
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Characterizing and mapping the spatial variability of HIV risk among adolescent girls and young women: A cross-county analysis of population-based surveys in Eswatini, Haiti, and M... by Kristen N Brugh, Quinn Lewis, Cameron Haddad, Jon Kumaresan, Timothy Essam, Michelle S Li

“…Using geolocated survey data at enumeration clusters and high-resolution satellite imagery, we applied algorithms to predict the number and proportion of at-risk AGYW at hyperlocal levels.…”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…Finally, a random forest (RF) model is constructed to predict impurity rates by incorporating features such as mask pixel area, fitting length, fitting width and mask perimeter. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…According to the operational process of the algorithm given by this paper, a series of experiments are designed to validate the algorithm’s effectiveness. …”
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Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases by Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

“…Abstract N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardiovascular health. …”
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Deciphering the role of miR-71 in Echinococcus multilocularis early development in vitro. by Matías Gastón Pérez, Markus Spiliotis, Natalia Rego, Natalia Macchiaroli, Laura Kamenetzky, Nancy Holroyd, Marcela Alejandra Cucher, Klaus Brehm, Mara Cecilia Rosenzvit

“…Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. …”
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma by Zhen Chen, Yongjun Zhang

“…In addition, the IMMPS exhibited better prediction performance in comparison to 154 published gene signatures, suggesting that the IMMPS was an independent prognostic risk factor for evaluating the overall survival of LUAD patients. …”
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FrameBoost: Advanced Video Analytics With Inference Trigger Frame Selection via Tracking Error Estimation by Jin Mo Yang, Sunwook Hwang, Jeongjun Park, Saewoong Bahk

“…We identify key factors affecting IoU prediction performance and develop a lightweight solution that requires minimal computational resources. …”
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Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic by Cagatay Cebeci, Kasım Zor

“…The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. …”
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Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal by Soojeong Lee, Mugahed A. Al-antari, Gyanendra Prasad Joshi, Yeong Hyeon Gu

“…Therefore, we confirm that IPSG is efficiently trained to predict the complex nonlinear relationship between the feature vectors obtained from the photoplethysmography signal and the reference RR. …”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…Abstract Drought has been a major calamity due to climate change in recent years. Predicting drought has grabbed the attention of meteorologists and climate scientists, who study and look for modern techniques. …”
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Assessment of Melon Fruit Nutritional Composition Using VIS/NIR/SWIR Spectroscopy Coupled with Chemometrics by Dimitrios S. Kasampalis, Pavlos Tsouvaltzis, Anastasios S. Siomos

“…The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. …”
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