Integrated rapid risk assessment for dengue fever in settings with limited diagnostic capacity and uncertain exposure: Development of a methodological framework for Tanzania. by Matthias Hans Belau, Juliane Boenecke, Jonathan Ströbele, Mirko Himmel, Daria Dretvić, Ummul-Khair Mustafa, Katharina Sophia Kreppel, Elingarami Sauli, Johanna Brinkel, Ulfia Annette Clemen, Thomas Clemen, Wolfgang Streit, Jürgen May, Amena Almes Ahmad, Ralf Reintjes, Heiko Becher

“…We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity.…”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…A full end-to-end ML pipeline was implemented, encompassing data exploration, preprocessing, feature scaling, model training, and evaluation using stratified cross-validation and a separate test set. …”
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Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods by M. Hafidz Ariansyah, Sri Winarno

“…Researchers use machine learning that can explore large amounts of data sets to produce knowledge that is beneficial to science. …”
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Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates by Serge Zaugg, Camille Vögeli, Lena Märki, Clément Duckert, Edward A.D. Mitchell

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A Multi-Stage Framework for Kawasaki Disease Prediction Using Clustering-Based Undersampling and Synthetic Data Augmentation: Cross-Institutional Validation with Dual-Center Clinic... by Heng-Chih Huang, Chuan-Sheng Hung, Chun-Hung Richard Lin, Yi-Zhen Shie, Cheng-Han Yu, Ting-Hsin Huang

“…At a fixed recall rate of 95%, the model achieved a specificity of 97.5% and an F1-score of 53.6% on the CGMH test set, and a specificity of 74.7% with an F1-score of 23.4% on the KMUH validation set. …”
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Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis. by Peng Zhang, Xiaodong Tan, Daoqiang Zhang, Qi Gong, Xuefeng Zhang

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Assessing the Accuracy of Bed-Occupancy With a tina.care Bed Sensor in Hospital Wards and Home Care Settings: A Pilot Study by Tomas Kulhanek, Kvetoslava Hoskova, Jitka Feberova, Miroslav Malecha

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Anthozoan eDNA primer set characterizes Ctenophora and Medusozoa inhabiting mesophotic and deep waters off the Gulf Coast of the southern USA by Annemarie Wood, Luke McCartin, Luke McCartin, Luke McCartin, Santiago Herrera, Santiago Herrera, Santiago Herrera, Andrea M. Quattrini, Allen G. Collins, Allen G. Collins

“…Environmental DNA (eDNA) sequencing is a non-invasive approach that can detect organisms that are difficult to sample, enabling the DNA of understudied taxa to be sequenced. …”
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Improving Cancer Diagnosis in Alberta, Canada: A Qualitative Study of Emergency Department Healthcare Providers’ Perspectives on Diagnosing Cancer in the Emergency Setting by Anna Pujadas Botey, Cassandra Carrier, Eddy Lang, Paula J. Robson

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Imaging Magma Reservoirs From Space With Altimetry‐Derived Gravity Data by Hélène Le Mével

“…I find that most magmatic and hydrothermal systems create VGG anomalies with a characteristic wavelength and amplitude greater than the data uncertainty and are therefore detectable. The proposed approach consists in three main steps: (a) calculate the VGG from the two components of the deflection of the vertical, (b) calculate and remove the gravity contribution of the bathymetry interface using an independent bathymetry data set (e.g., acquired by multibeam echosounders) to obtain a VGG Bouguer gravity anomaly, (c) invert the Bouguer VGG anomaly to obtain a 3D density model. …”
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Evaluation of feeding practices in the first 12 months of life and nutritional status in an urban setting of a low-resource country: Beira, Mozambique by S. Calgaro, I. Avagnina, A. Vettor, A. Zin, A. Zin, C. Girotto, B. R. Cebola, A. R. Muhelo, I. Rosato, L. Da Dalt, G. Putoto, G. Verlato

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Impact of data bias on machine learning for crystal compound synthesizability predictions by Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

“…Despite using the same architecture for the machine learning model, we showcase how the model’s learning and prediction behavior differs once trained on distinct data. We use two data sets for illustration: a mixed-source data set that integrates experimental and computational crystal samples and a single-source data set consisting of data exclusively from one computational database. …”
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YOLOv3-A: a traffic sign detection network based on attention mechanism by Fan GUO, Yongxiang ZHANG, Jin TANG, Weiqing LI

“…To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background, based on the YOLOv3, a channel attention method for target detection and a spatial attention method based on semantic segmentation guidance were proposed to form the YOLOv3-A (attention) algorithm.The detection features in the channel and spatial dimensions were recalibrated, allowing the network to focus and enhance the effective features, and suppress interference features, which greatly improved the detection performance.Experiments on the TT100K traffic sign data set show that the algorithm improves the detection performance of small targets, and the accuracy and recall rate of the YOLOv3 are improved by 1.9% and 2.8% respectively.…”
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ANALYSIS OF EMOTION RECOGNITION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK MODELS by V. M. Goryaev, G. A. Mankaeva, T. B. Ochir-Goryaeva, A. B. Mantusov, V. V. Lidji-Garyaev, A. B. Kornyakov

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A Systematic Review of Deep Learning Approaches to Educational Data Mining by Antonio Hernández-Blanco, Boris Herrera-Flores, David Tomás, Borja Navarro-Colorado

“…Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. …”
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Real-Time Analysis of Basketball Sports Data Based on Deep Learning by Peng Yao

“…According to the extracted posture sequence, the basketball action of the set classification is recognized. In order to obtain more accurate and three-dimensional information, a multitraining target method can be used in training; that is, multiple indicators can be detected and feedback is provided at the same time to correct player errors in time; the other is an auxiliary method, which is compared with ordinary training. …”
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PHMD: An easy data access tool for prognosis and health management datasets by David Solís-Martín, Juan Galán-Páez, Joaquín Borrego-Díaz

“…With built-in metadata handling and task-specific experiment settings for diagnosis, prognosis, and detection, users can efficiently prepare and analyze data without needing to manage raw file formats or directories. …”
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Constructing View of Uncertain Data Provenance for Scientific Workflow in Cloud Computing by Haiyang Hu, Zhanchen Liu, Hua Hu

“…The view of data provenance in scientific workf1ow provides an approach of data abstraction and encapsu1ation by partitioning tasks in the data provenance graph（DPG）into a set of composite modu1es due to the data f1ow re1ations among them, so as to efficient1y decrease the work1oad consumed by researchers making ana1ysis on the data provenance and the time needed in doing data querying.Neverthe1ess, deve1oping and app1ying the scientific workf1ow systems in c1oud computing environments suffers the prob1em of uncertainty brought by the inaccuracy of data co11ection and unre1iabi1ity of data servers distributed in the internet.Concentrating on this scenario, the definitions of uncertain DPG and its sound view were presented first1y, and then a method for detecting the unsound view of DPG was proposed.A1so, a method for constructing sound and high-support view was presented, which is based on the data f1ow re1ations among the tasks and their first-order preceding tasks in the graph, and the 1oca1 expected support of the composite modu1es.A po1ynomia1-time a1gorithm was designed, and its maxima1 time comp1exity was a1so ana1yzed.Additiona11y, an examp1e and conduct comprehensive experiments were given to show the feasibi1ity and effectiveness of the method.…”
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Multi-step attack detection method based on network communication anomaly recognition by Ankang JU, Yuanbo GUO, Tao LI, Ziwei YE

“…In view of the characteristics of internal fixed business logic,inbound and outbound network access behavior,two classes and four kinds of abnormal behaviors were defined firstly,and then a multi-step attack detection method was proposed based on network communication anomaly recognition.For abnormal sub-graphs and abnormal communication edges detection,graph-based anomaly analysis and wavelet analysis method were respectively proposed to identify abnormal behaviors in network communication,and detect multi-step attacks through anomaly correlation analysis.Experiments are carried out on the DARPA 2000 data set and LANL data set to verify the results.The experimental results show that the proposed method can effectively detect and reconstruct multi-step attack scenarios.The proposed method can effectively monitor multi-step attacks including unknown feature types.It provides a feasible idea for detecting complex multi-step attack patterns such as APT.And the network communication graph greatly reduces the data size,it is suitable for large-scale enterprise network environments.…”
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Development of BIM Platform for Semantic Data Based on Standard WBS Codes by Dongwook Kim, Jose Matos, Son N. Dang

“…This study explores the integration of 4D BIM data within a Work Breakdown Structure (WBS) framework in a real-world setting. …”
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