Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

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Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU by Dongqing Han, Xin Yang, Guang Li, Shuangyin Wang, Zhen Wang, Jiandong Zhao

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Deep weighted survival neural networks to survival risk prediction by Hui Yu, Qingyong Wang, Xiaobo Zhou, Lichuan Gu, Zihao Zhao

“…Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. …”
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Identification of Osteosarcoma-Related Specific Proteins in Serum Samples Using Surface-Enhanced Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry by Jianli Gu, Jitian Li, Manyu Huang, Zhiyong Zhang, Dongsheng Li, Guoying Song, Xingpo Ding, Wuyin Li

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Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities by Guanci Yang, Siyuan Yang, Kexin Luo, Shangen Lan, Ling He, Yang Li

“…Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. …”
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Antimicrobial Resistance of Listeria monocytogenes from Animal Foods to First- and Second-Line Drugs in the Treatment of Listeriosis from 2008 to 2021: A Systematic Review and Meta... by Jaqueline Oliveira Dos Reis, Bruno Serpa Vieira, Adelino Cunha Neto, Vinicius Silva Castro, Eduardo Eustáquio de Souza Figueiredo

“…From the total of 302 identified studies, 16 met all the eligibility criteria from 2008 to 2021 and were included in this meta-analysis. They comprised a dataset of 1152 L. monocytogenes isolates, obtained from different animal food products, food processing environment, and live animals. …”
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Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation by Quentin Malé, Corentin J. Lapeyre, Nicolas Noiray

“…For these interpolation cases, the model approximates burning rates with low error even though the cases were not included in the training dataset. This a priori study shows that the proposed data-driven machine learning framework is able to address the challenge of modeling lean premixed $ {\mathrm{H}}_2 $ -air burning rates. …”
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Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system by M. Nuthal Srinivasan, Mohamed Yacin Sikkandar, Maryam Alhashim, M. Chinnadurai

“…In our case study, the ESACN model was applied to a dataset comprising 659 images across four classes: 178 images of Monkeypox, 171 of Chickenpox, 80 of Measles, and 230 of Normal skin conditions. …”
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Machine learning for predicting Chagas disease infection in rural areas of Brazil. by Fabio De Rose Ghilardi, Gabriel Silva, Thallyta Maria Vieira, Ariela Mota, Ana Luiza Bierrenbach, Renata Fiuza Damasceno, Lea Campos de Oliveira, Alexandre Dias Porto Chiavegatto Filho, Ester Sabino

“…We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. …”
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Towards better and unlinkable protected biometric templates using label‐assisted discrete hashing by Kiran Raja, R. Raghavendra, Christoph Busch

“…With auxiliary pseudousers, the performance of the proposed approach for user‐level protected template creation results in an EER of 2.5%, indicating very low performance degradation compared with the known enrolment dataset. Along with the set of experimental validation of the proposed approach, a security analysis of the proposed approach is presented to demonstrate the unlinkability of the biometric templates using a state‐of‐art unlinkability metric.…”
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HLA-G Expression Is an Independent Predictor for Improved Survival in High Grade Ovarian Carcinomas by M. J. Rutten, F. Dijk, C. D. Savci-Heijink, M. R. Buist, G. G. Kenter, M. J. van de Vijver, E. S. Jordanova

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Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system by Hany S. El-Mesery, Ahmed H. ElMesiry, Evans K. Quaye, Zicheng Hu, Ali Salem

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Development and Characterization of a New Solid Polymer Electrolyte for Supercapacitor Device by Theodore Azemtsop Manfo

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Effects of Direction and Index of Difficulty on Aiming Movements after Stroke by Paola Ribeiro Coqueiro, Sandra Maria Sbeghen Ferreira de Freitas, Cassandra Mendes Assunção e Silva, Sandra Regina Alouche

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A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna Q. Yonah

“…In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.…”
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Advancements in Predictive Analytics: Machine Learning Approaches to Estimating Length of Stay and Mortality in Sepsis by Houssem Ben Khalfallah, Mariem Jelassi, Jacques Demongeot, Narjès Bellamine Ben Saoud

“…The present study investigates the potential of machine learning (ML) models to predict these outcomes using a dataset of 1492 sepsis patients with clinical, physiological, and demographic features. …”
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Resilience in Crisis: A Longitudinal Study of Entrepreneurial Responses to COVID-19 pandemic by Yi Zhang, Xiaochen Bu

“…Limitations: The research based on a limited dataset consisting of only two companies, which may not be representative of the broader business landscape. …”
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Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Firstly，preprocess the collected raw load data and calculate the actual load value considering system energy loss； Secondly，the maximum information coefficient （MIC） is used to analyze the correlation between multiple loads and between multiple loads and weather factors，and to extract strongly correlated variables of multiple loads； Once again，the strongly correlated variables of multiple loads are substituted into CEEMDAN，and the load data is decomposed into stationary subsequences； Then，the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model，achieving collaborative prediction of multiple loads； Finally，the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City，Jilin Province，China. …”
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An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance by Borislava Toleva, Ivan Atanasov, Ivan Ivanov, Vincent Hooper

“…This paper proposes a novel methodology to perform classification in the case of heavy class imbalance, as observed in the PIMA diabetes dataset. The proposed methodology uses two novel steps, namely resampling and random shuffling prior to defining the classification model. …”
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Constructing Attention-LSTM-VAE Power Load Model Based on Multiple Features by Chaoyue Ma, Ying Wang, Feng Li, Huiyan Zhang, Yong Zhang, Haiyan Zhang

“…Finally, the Attention-LSTM-VAE model is constructed to capture the temporal variations laws of load. The dataset includes 2 years of load values and weather data collected in Caojiaping, Hunan Province, China. …”
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