Deep-water seafloor geomorphic features of the Santos Basin, Southeastern Brazilian Margin, shown by analyses and integration of an extensive 3-D seismic data set by Cízia Mara Hercos, Simone Schreiner, Eugênio Taira Inácio Ferreira

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Deep-water seafloor geomorphic features of the Santos Basin, Southeastern Brazilian Margin, shown by analyses and integration of an extensive 3-D seismic data set by Cízia Mara Hercos, Simone Schreiner, Eugênio Taira Inácio Ferreira

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Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand by Chawarat Rotejanaprasert, Kawin Chinpong, Andrew B. Lawson, Richard J. Maude

“…This study compared spatiotemporal cluster detection methods using simulated and real dengue surveillance data from Thailand. …”
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Database anomaly detection model based on mining object-condition association rules by DAI Hua, QIN Xiao-lin, LIU Liang, BAI Chuan-jie

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Gaps and opportunities for data systems and economics to support priority setting for climate-sensitive infectious diseases in sub-Saharan Africa: A rapid scoping review. by Ellie A Delight, Ariel A Brunn, Francis Ruiz, Jessica Gerard, Jane Falconer, Yang Liu, Bubacarr Bah, Bernard Bett, Benjamin Uzochukwu, Oladeji K Oloko, Esther Njuguna, Kris A Murray

“…The aim of this study was to explore the role of data systems and economics in priority setting for CSID pandemic preparedness in sub-Saharan Africa. …”
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Cautiously optimistic: paediatric critical care nurses’ perspectives on data-driven algorithms in low-resource settings—a human-centred design study in Malawi by Margot Rakers, Daniel Mwale, Lieke de Mare, Lezzie Chirambo, Bart Bierling, Alice Likumbo, Josephine Langton, IMPALA Study team, Niels Chavannes, Hendrikus van Os, Job Calis, Kiran Dellimore, María Villalobos-Quesada

“…Abstract Background Paediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). …”
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A study on improved random forest-based anomaly detection of regional tariff data under distributed photovoltaic access by Shujun Ji, Kai Liu, Bo Ling, Jiadong Li, Jinteng Wang, Xun Ma

“…The model determines the training set and test set through mixed sampling. After delineating the range of anomalous regional electricity price data, the final classification of such anomalies is determined through voting mechanisms. …”
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Curvature-Based Change Detection in Road Segmentation: Ascending Hierarchical Clustering vs. K-Means by David Jaurès Fotsa-Mbogne, Addie Bernice Nguensie-Wakponou, Jean Michel Nlong, Marcellin Atemkeng, Maurice Tchuente

“…To mitigate scalability issues, it is beneficial to first group the data into homogeneous continuous sections. This approach aligns with the broader problem of change detection in a finite sequence of data indexed by a totally ordered set, which could represent either a time series or a spatial trajectory. …”
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Sample Inflation Interpolation for Consistency Regularization in Remote Sensing Change Detection by Zuo Jiang, Haobo Chen, Yi Tang

“…This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. …”
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Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing by Nuphar Avital, Idan Egel, Ido Weinstock, Dror Malka

“…The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. …”
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Deep learning-based anomaly detection for precision field crop protection by Cheng Wei, Yifeng Shan, MengZhe Zhen

“…AROS dynamically optimizes resource allocation based on real-time environmental feedback and multi-objective optimization, balancing yield maximization, cost efficiency, and environmental sustainability.ResultsExperimental evaluations demonstrate the effectiveness of our approach in detecting anomalies and improving decision-making in precision agriculture.DiscussionThis framework sets a new standard for sustainable and data-driven crop protection strategies.…”
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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data by Li-Rong Yang, Mei Yang, Liu-Lin Chen, Yong-Lin Shen, Yuan He, Zong-Ting Meng, Wan-Qi Wang, Feng Li, Zhi-Jin Liu, Lin-Hui Li, Yu-Feng Wang, Xin-Lei Luo

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An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. by Yansong Liu, Shuang Wang, He Sui, Li Zhu

“…Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. …”
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AG-Net3D: Embedding Attention Gate Into U-Net for 3D Seismic Data Fault Detection by Mingchun Chen, Hui Cao, Luofei Jia

“…Automatic fault detection of seismic data plays a remarkable role in ensuring the efficiency even effectiveness of oil and gas field exploration. …”
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Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data. by Jiaxin Fan, Xuran Wang, Rui Xiao, Mingyao Li

“…Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. …”
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A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting by Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park, Kihyeon Kwon

“…The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. …”
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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

“…This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject’s facial region of interest in each thermal frame. …”
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High resolution weld semantic defect detection algorithm based on integrated double U structure by Xiaoyan Li, Yi Wei, Zhigang Lv, Peng Wang, Liangliang Li, Mengyu Sun, Chu Wang

“…Then, a multi-image hybrid stitching technology was proposed to reconstruct the long weld into a standard size of 1500 × 1500, which not only expanded the WSCR data set, but also effectively improved the data imbalance problem. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. …”
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Complete reference genome and pangenome improve genome-wide detection and interpretation of DNA methylation using sequencing and array data by Zheng Dong, Joanne Whitehead, Maggie Fu, Julia L. MacIsaac, David H. Rehkopf, Luis Rosero-Bixby, Michael S. Kobor, Keegan Korthauer

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