Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. by Varun Tiwari, Kelly Thorp, Mirela G Tulbure, Joshua Gray, Mohammad Kamruzzaman, Timothy J Krupnik, A Sankarasubramanian, Marcelo Ardon

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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This analysis revealed 25 bioassays with statistically significant correlations to in vivo DART data. …”
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Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review. by Mohammad Moharrami, Parnia Azimian Zavareh, Erin Watson, Sonica Singhal, Alistair E W Johnson, Ali Hosni, Carlos Quinonez, Michael Glogauer

“…<h4>Background</h4>This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data.…”
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Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data by Zhijie Liang, Ling Wang, Jianyu Su, Bo Sun, Daifa Wang, Juan Yang

“…Furthermore, crucial brain channels for interference detection are pinpointed through grid search, and alterations in vital brain regions (R-Broca and L-Broca) are unveiled via association rule analysis. By integrating fNIRS, deep learning, and data mining techniques, this study delves into cognitive interference in English learning, providing valuable insights for educational neuroscience and data mining research.…”
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Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device by Kei Suzuki, Tipporn Laohakangvalvit, Midori Sugaya

“…Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. …”
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Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, Yucheng Zhang

“…This study employs multiple machine learning models to assess gully erosion susceptibility in this region. …”
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Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data by Dev Dinesh, Shashi Kumar, Sameer Saran

“…The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.…”
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Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications by Sita Rani, Raman Kumar, B. S. Panda, Rajender Kumar, Nafaa Farhan Muften, Mayada Ahmed Abass, Jasmina Lozanović

“…Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. …”
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FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold by Xin Wu, Jingjing Xu, Kuan Li, Jianping Yin, Jian Xiong

“…Through theoretical analysis and extensive experiments, we have fully proven that FldtMatch can overcome the negative impact of unbalanced data. …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. …”
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Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials by Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian

“…A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. …”
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Accurate Mapping of Downed Deadwood in a Dense Deciduous Forest Using UAV-SfM Data and Deep Learning by Steffen Dietenberger, Marlin M. Mueller, Boris Stöcker, Clémence Dubois, Hanna Arlaud, Markus Adam, Sören Hese, Hanna Meyer, Christian Thiel

“…Key objectives included testing the deep learning (DL) model’s performance at area, length, and object levels and benchmarking its accuracy against a traditional object-based image analysis (OBIA) method. …”
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Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production by Nesrine Chaali, Carlos Manuel Ramírez-Gómez, Camilo Ignacio Jaramillo-Barrios, Sarah Garré, Oscar Barrero, Sofiane Ouazaa, John Edinson Calderon Carvajal

“…This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. …”
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Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters by Loukas Katikas, Sofia Reizopoulou, Paraskevi Drakopoulou, Vassiliki Vassilopoulou

“…These data were then assigned to the EU EMODnet seabed habitats using local ecological knowledge. …”
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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining by Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, N. Kushal Kumar, Tsuyoshi Adachi, P.S. Paul, Youhei Kawamura

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Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context by Marcelo Bueno, Carlos Baca García, Nilton Montoya, Pedro Rau, Hildo Loayza

“…Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. …”
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Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud by Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, Ayad M. Fadhil Al-Quraishi, Joseph Omeiza Alao, Hussein Almohamad, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…The study presented a novel approach for estimating biomass in subtropical regions using remote sensing data set and machine learning models in Google platform. …”
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Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

“…For generalized anxiety, the LightGBM’s prediction for the State-Trait Anxiety Inventory-trait led to an AUROC of 0.819. In the same analysis, models using only physiological features had AUROCs of 0.626, 0.744, and 0.671, whereas models using only acoustic features had AUROCs of 0.788, 0.823, and 0.754.ConclusionsThis study showed that a ML algorithm using integrated multimodal data can predict upper tertile anxiety symptoms in patients with SAD with higher performance than acoustic or physiological data obtained during a VR session. …”
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Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data by Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova, Guido Lemoine, Klaus Deininger

“…Additionally, a novel transfer learning approach enables reliable classification in conflict-affected areas with limited ground-truth data.We achieved high classification accuracy across the 14 major crop types in Ukraine and abandoned land, validated through F1-scores exceeding 90 % for most classes. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…Additionally, several data-sampling techniques, including oversampling, undersampling, Random Over-Sampling Examples (ROSE), and Synthetic Minority Over-sampling Technique (SMOTE), were utilized to address the issue of imbalanced data. …”
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