Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

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A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning by Yimei Tian, Sunan Gao, Fan Zhang, Xuzhi Wan, Wei Jia, Jingjing Jiao, Yilei Fan, Yu Zhang

“…The seven machine learning models demonstrated strong predictive capabilities for internal urinary metabolite exposure and dietary exposure associations (average R > 0.6). …”
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An Exploration on Aperiodic Activities and Transient Oscillations During Semantic Processing: A Study With Wearable MEG by Xiaoyu Liang, Yuyu Ma, Ruonan Wang, Huanqi Wu, Changzeng Liu, Fuzhi Cao, Nan An, Min Xiang, Yueyang Zhai, Xiaolin Ning

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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

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CASSNet: Cross-Attention Enhanced Spectral–Spatial Interaction Network for Hyperspectral Image Super-Resolution by Zhanxu Zhang, Linzi Yang, Guanglian Zhang, Jiangwei Deng, Lifeng Bian, Chen Yang

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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

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Optimizing DD-SIMCA modeling for accurate classification of rice varieties via raman spectroscopy by Somaye Vali Zade, Hamed Sahebi, Adel Mirza Alizadeh, Behrooz Jannat, Hossein Rastegar, Solmaz Abedinzadeh, Fataneh Hashempour-Baltork, Amin Mousavi Khaneghah

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Machine learning-driven nanoparticle toxicity by Zied Hosni, Sofiene Achour, Fatma Saadi, Yangfan Chen, Mohammed Al Qaraghuli

“…This study presents a comprehensive machine learning-driven analysis to understand and predict the toxicity of nanoparticles (NPs), a crucial aspect in ensuring the safe application of nanotechnology in medicine, pharmaceuticals, biotechnology, and various other industries. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. …”
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A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection by U. Sumalatha, K. Krishna Prakasha, Srikanth Prabhu, Vinod C. Nayak

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New Approach to Drug Delivery in Ophthalmological Practice: Development of Composite Ophthalmological Solution for Drug Loading of Soft Contact Lenses by D. K. Naplekov, E. T. Zhilyakova, A. Yu. Malyutina, A. V. Bondarev, N. B. Demina, O. O. Novikov, R. A. Abramovich

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Multi-crop plant counting and geolocation using a YOLO-Powered GUI System by Renato Herrig Furlanetto, Nathan Schawn Boyd, Ana Claudia Buzanini

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Design and Evaluation of ACAFiB-APP, a clinical decision support system for anticoagulant considerations in patients with atrial fibrillation by Ramin Ansari, Sorayya Rezayi, Ali Asghar Safaei, Reza Mollazadeh, Eisa Rezaei, Mahboobeh Khabaz Mafinejad, Soha Namazi, Keyhan Mohammadi

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Generalization of neoantigen-based tumor vaccine by delivering peptide-MHC complex via oncolytic virus by Chenyi Wang, Yingjun Shi, Da Zhang, Yupeng Sun, Junjie Xie, Bingchen Wu, Cuilin Zhang, Xiaolong Liu

“…However, the application of this highly personalized strategy in the treatment of solid tumors is hindered by several obstacles, including very costly and time-consuming preparation steps, uncertainty in prediction algorithms and tumor heterogeneity. Universalization of neoantigen vaccine is an ideal yet currently unattainable solution to such limitations. …”
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Research and application of intelligent learning path optimization based on LSTM-Transformer model by Jinling Wang, Wandong Chai

“…In the global wave of digital learning, how to optimize personalized learning paths and improve learning efficiency has become a key issue to be solved urgently in the field of education. Based on this, this study proposes a hypothesis: the intelligent learning path optimization strategy based on the LSTM-Transformer model can achieve accurate prediction and personalized optimization of learners' learning paths with the help of deep learning technology. …”
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Comparing Blind Image Quality Metrics for Reliable Image Assessment by Cristian George Fieraru, Maria Biserica, Ioana Cristina Plajer, Mihai Ivanovici

“…Reliable image quality assessment is essential not only in digital photography but also as a key metric for evaluating the performance of algorithms and models designed for image quality enhancement or generation. …”
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Multiscale Modeling of Meandering Fluvial Reservoir Architecture Based on Multiple-Point Geostatistics: A Case Study of the Minghuazhen Formation, Yangerzhuang Oilfield, Bohai Bay... by Jia Li, Chengyan Lin, Xianguo Zhang, Chunmei Dong, Yannan Wei, Wenyan Ning, Changcheng Han, Wei Guo

“…The produced model conforms geologically realistically and enables the prediction of interwell permeability variation to enhance oil recovery.…”
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Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance by Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng

“…Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. …”
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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. by Daniel Walke, Daniel Steinbach, Sebastian Gibb, Thorsten Kaiser, Gunter Saake, Paul C Ahrens, David Broneske, Robert Heyer

“…<h4>Purpose</h4>Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. …”
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