Challenges in Imputation of ICU Time-Series Data: A Comparison of Classical and Machine Learning Approaches by Favio Salinas, Marvin Agristean, Sobhan Moazemi, Steven Kessler, Bastian Dewitz, Hug Aubin, Artur Lichtenberg, Falko Schmid

“…Advanced imputation techniques, such as Bidirectional Recurrent Imputation for Time Series (BRITS), Self-Attention-based Imputation for Time Series (SAITS), and Multi-directional Recurrent Neural Network (M-RNN), show strong performance but are influenced by dataset characteristics like missing patterns and feature distributions. …”
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A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems by Asli Kaya Karakutuk, Ozer Ozdemir

“…On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. …”
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Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning by Daiyang Zhang, Enmiao Feng

“…By working with neural network architectures and relevant tools, performance patterns, successes, and new management. …”
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On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. …”
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Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay by Sahil Gupta, Sagar Gupta

“…Abstract Declining water quality poses serious environmental and public health risks, with chlorophyll-a serving as a key biological indicator of harmful algal blooms. This study evaluates the use of a Long Short-Term Memory (LSTM) neural network to forecast chlorophyll-a concentrations in the Chesapeake Bay, a critical estuarine ecosystem supporting over 17 million people. …”
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Integrating plot-based methods for monitoring biodiversity in island habitats under the scope of BIODIVERSA + project BioMonI: Tree monitoring in Terceira, Tenerife and Réunion Isl... by Paulo Borges, Leila Morgado, Rosalina Gabriel, Rui Elias, Mirana Gauche, Claudine Ah-Peng, Rüdiger Otto, Lea de Nascimento, Dominique Strasberg, Nathaly Guerrero-Ramírez, Holger Kreft, José María Fernández-Palacios

“…We are assembling data from BioMonI-Plot, a long-term vegetation plot network to understand biodiversity and ecosystem change. …”
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How well are implementation strategies and target healthcare professional behaviors reported? A secondary analysis of 204 implementation trials using the TIDieR checklist and AACTT... by Charlene Weight, Billy Vinette, Rachael Laritz, Meagan Mooney, Sonia A. Castiglione, Marc-André Maheu-Cadotte, Nikolas Argiropoulos, Kristin Konnyu, Christine E. Cassidy, Simonne E. Collins, Sonia Semenic, Nicola Straiton, Sandy Middleton, Natalie Taylor, Marie-Pierre Gagnon, Shuang Liang, Laura Crump, Olivia Di Lalla, Talia Meyers, Daniel N. Elakpa, Guillaume Fontaine

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Identification and Interpretation of Cultural Landscape Field from the Integral Conservation Perspective by Zhitong LIANG, Yan DU, Haiyan XIA

“…The actors involved in the overall operation and their relational network are highly generalized into an ontological model of the CLF, which consists of the subject, object, place, and the three-dimensional relationships among them. …”
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A Novel Aerosol Optical Depth Retrieval Method Based on SDAE from Himawari-8/AHI Next-Generation Geostationary Satellite in Hubei Province by Shiquan Deng, Ting Bai, Zhe Chen, Yepei Chen

“…Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. …”
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Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques by Sandy Putra Siregar, Imam Akbari, Poningsih Poningsih, Anjar Wanto, Solikhun Solikhun

“…Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. …”
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Programmed Cell Death-Ligand 1 Expression and Clinical Outcomes Among Patients with Resected, Early-Stage Non-Small Cell Lung Cancer: A Real-World Study by Parneet K. Cheema, Iqra Syed, Femida Gwadry-Sridhar, Muhammad Rakibuz-Zaman, Robin Sachdeva, Alec Pencz, Luna Zhan, Katrina Hueniken, Devalben Patel, Karmugi Balaratnam, Khaleeq Khan, Benjamin Grant, Brandon S. Sheffield, M. Elizabeth O. Locke, Daniel Moldaver, Mary Kate Shanahan, Geoffrey Liu, M. Sara Kuruvilla

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Performance Optimization and Field Validation of Post-Grouting Geopolymer Materials for Pile Foundations: Microstructural Insights and Environmental Durability by Chongchong He, Zhen Xu, Jing Wang, Pan Li, Yangyang Xia, Chao Zhang, Zhenpeng Chen, Wei He

“…The grout distribution pattern on the pile side exhibited a “compaction-splitting” mechanism. …”
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Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining Strategy by Rongbin Li, Jian Zhang, Fangming Deng

“…This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. …”
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MACHINE LEARNING AND ECONOMETRICS: BRIDGING THE GAP FOR ENHANCED ECONOMIC ANALYSIS by Jamiu Adeniyi Yusuf, Abdulkadri IDRIS, Azeez Olawale AKINLOLU

“…It illustrates how deep learning models, like neural networks, improve forecasting accuracy by capturing complex patterns in time series data. …”
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A Study on CNN-Based and Handcrafted Extraction Methods with Machine Learning for Automated Classification of Breast Tumors from Ultrasound Images by Mohamed Benaouali, Mohamed Bentoumi, Mansour Abed, Malika Mimi, Abdelmalik Taleb-Ahmed

“… In this paper, we present an efficient procedure for automatically classifying ultrasound images of benign and malignant breast tumors. We evaluated our approach using four openly available datasets and investigated two categories of feature extraction methods: handcrafted methods (Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG)) and methods based on convolutional neural network (CNN) models. …”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…The model was subsequently trained with sub-sample data containing 80% of the data while this part was further tested with 20% for verifying the generalizability and robustness of the model. Performance evaluation was conducted on the linear regression, random forest and artificial neural networks (ANN) through statistical metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). …”
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Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations by Travis McDevitt‐Galles, Arthur T. Degaetano, Sarah C. Elmendorf, John R. Foster, Howard S. Ginsberg, Mevin B. Hooten, Shannon LaDeau, Katherine M. McClure, Sara Paull, Erin Posthumus, Ilia Rochlin, Daniel Grear

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Use of the electronic nose to monitor the influences of modified atmosphere packaging on the storage of contaminated garlic by Alireza Makarichian, Ebrahim Ahmadi, Reza Amiri Chayjan, Doostmorad Zafari, Seyed Saeid Mohtasebi

“…Also, the non-destructive technology of the electronic nose (E-Nose) was used as a complementary solution in garlic quality monitoring. The data were evaluated by the Analysis of Variance (ANOVA), Principal Component Analysis (PCA), Backpropagation Neural Network (BPNN), Linear Discriminant Analysis (LDA), and Partial Least-Squares Regression (PLSR) methods. …”
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Machine learning technology in the classification of glaucoma severity using fundus photographs by Sukhumal Thanapaisal, Passawut Uttakit, Worapon Ittharat, Pukkapol Suvannachart, Pawasoot Supasai, Pattarawit Polpinit, Prapassara Sirikarn, Panawit Hanpinitsak

“…Glaucoma severity grading was based on the Hodapp-Parrish-Anderson (HPA) criteria incorporating the mean deviation value, defective points in the pattern deviation probability map, and defect proximity to the fixation point. …”
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Multi-source image feature extraction and segmentation techniques for karst collapse monitoring by Wenbo Lin, Xiao Li, Tingting Li

“…Traditional approaches often fall short in delivering timely and accurate evaluations of collapse risks.MethodsTo address these challenges, we propose the Integrated Karst Collapse Prediction Network (IKCPNet), a novel framework that combines multi-source imaging, geophysical modeling, and machine learning techniques. …”
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