Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…The classification of cotton, cropland and non-cropland model produced with producers accuracy of 98%, 88% and 90%. …”
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Minimising surface-code failures using a color-code decoder by Asmae Benhemou, Kaavya Sahay, Lingling Lao, Benjamin J. Brown

“…Here we propose a decoder for the surface code that finds low-weight correction operators for errors produced by the depolarising noise model. The decoder is obtained by mapping the syndrome of the surface code onto that of the color code, thereby allowing us to adopt more sophisticated color-code decoding algorithms. …”
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ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention by Yanfei Chen, Tong Yue, Pei An, Hanyu Hong, Tao Liu, Yangkai Liu, Yihui Zhou

“…To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. …”
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Estimation of soil organic carbon content and dynamics in Mediterranean climate regions considering long-term monthly climatic conditions by Lei Shen, Wei Zhang, Duanqiang Zhai, Shuo Han, Shuang Tian

“…Results showed that the introduction of long-term average monthly climate data significantly improved the prediction accuracy of the models. The R2 value of the RF model was enhanced by 0.22, whilst the R2 of the LightGBM model improved by 0.20–0.21. …”
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Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes by Ashok Kumar Sah, Rabab H. Elshaikh, Manar G. Shalabi, Anass M. Abbas, Pranav Kumar Prabhakar, Asaad M. A. Babker, Ranjay Kumar Choudhary, Vikash Gaur, Ajab Singh Choudhary, Shagun Agarwal

“…Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. …”
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Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy by Miguel Martins, Miguel Mascarenhas, Maria João Almeida, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Joana Mota, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas-Saraiva, João Ferreira, Guilherme Macedo

“…Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. …”
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Quantitative Analysis of Sulfur Elements in Mars-like Rocks Based on Multimodal Data by Yuhang Dong, Zhengfeng Shi, Junsheng Yao, Li Zhang, Yongkang Chen, Junyan Jia

“…Furthermore, to optimize model performance, different feature selection algorithms were evaluated. …”
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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image by Jianhong Zhao, Huamin Yang, Yi Sui

“…By leveraging advanced computer vision algorithms, specifically a suitable model for long-term dendritic solidifications named Tang Rui Detect (TRD), the method achieves efficient and accurate detection and quantification of microstructure features. …”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

“…To address these challenges, this study proposes a completely new gridded precipitation generation scheme that integrates long-term daily observations from 3746 gauges with 11 key precipitation-related covariates. Building upon the improved inverse distance weighting interpolation method used in our previous dataset CHM_PRE V1, we integrated a machine learning algorithm – light gradient boosting machine (LGBM) – to incorporate precipitation-related covariates in a data-driven manner. …”
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Smart IoT Energy Optimisation and Localisation Monitoring for E-Bike Sharing by Mawada Mohamed, Siti Fauziah Toha, Md Ataur Rahman, Moh. Khairudin

“…However, existing systems face challenges such as limited input parameters for modeling, leading to inefficiencies in energy optimization algorithms and power assist mechanisms. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…Abstract Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. …”
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New insights into biomarkers and risk stratification to predict hepatocellular cancer by Katrina Li, Brandon Mathew, Ethan Saldanha, Puja Ghosh, Adrian R. Krainer, Srinivasan Dasarathy, Hai Huang, Xiyan Xiang, Lopa Mishra

“…Through human studies compiled with animal models and mechanistic insight in pathways such as the TGF-β pathway, the biological progression from chronic liver disease to cirrhosis and HCC can be delineated. …”
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Diagnosis and detection of bone fracture in radiographic images using deep learning approaches by Theyazn Aldhyani, Zeyad A. T. Ahmed, Bayan M. Alsharbi, Sultan Ahmad, Sultan Ahmad, Mosleh Hmoud Al-Adhaileh, Ahmed Hassan Kamal, Mohammed Almaiah, Jabeen Nazeer

“…By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture detection and diagnosis and proposes a system that improves accuracy.ConclusionThe findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.…”
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Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining by Negin Sadat Babaiha, Stefan Geissler, Vincent Nibart, Heval Atas Güvenilir, Vinay Srinivas Bharadhwaj, Alpha Tom Kodamullil, Juergen Klein, Marc Jacobs, Martin Hofmann-Apitius

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Applying Extensions of Evidence Theory to Detect Frauds in Financial Infrastructures by Luigi Coppolino, Salvatore D'Antonio, Valerio Formicola, Luigi Romano

“…In order to solve these flaws, a number of amendments and extensions of the initial DS model have been proposed in literature. In this work, we present a Fraud Detection System that classifies transactions in a Mobile Money Transfer infrastructure by using the data fusion algorithms derived from these new models. …”
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Solving the RNA design problem with reinforcement learning. by Peter Eastman, Jade Shi, Bharath Ramsundar, Vijay S Pande

“…Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. …”
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A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering by Yao Liang, Yao Liang, Yuwei Wang, Yuwei Wang, Hongjian Fang, Hongjian Fang, Feifei Zhao, Yi Zeng, Yi Zeng, Yi Zeng, Yi Zeng, Yi Zeng

“…The Differentiable Neural Computer (DNC) model, despite addressing these issues to some extent, still faces challenges such as algorithmic complexity, slow convergence, and limited robustness. …”
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Exploration of heterogeneity of treatment effects across exercise-based interventions for knee osteoarthritis by Paul A. Dennis, Livia Anderson, Cynthia J. Coffman, Sara Webb, Kelli D. Allen

“…Results: The regression tree model outperformed all 9 metalearner models. Tree results suggested group-based PT yielded the largest improvement in mean WOMAC score. …”
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CSTFNet: A CNN and Dual Swin-Transformer Fusion Network for Remote Sensing Hyperspectral Data Fusion and Classification of Coastal Areas by Dekai Li, Harold Neira-Molina, Mengxing Huang, Syam M.S., Yu Zhang, Zhang Junfeng, Uzair Aslam Bhatti, Muhammad Asif, Nadia Sarhan, Emad Mahrous Awwad

“…This article introduces a new hybrid model, CSTFNet, which combines an improved CNN module and dual-layer Swin transformer (DLST) to tackle these challenges. …”
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