Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li, Shiwei Wei

“…These findings provide a scientific basis and practical tools for managing lettuce downy mildew and resistance breeding while laying a foundation for broader applications of hyperspectral imaging in plant pathology.…”
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Assessing Ground Displacement in Oran City, Algeria: A Comparative Analysis of PSInSAR, SBAS, and Combined Method by Kamel Hasni, Bachir Gourine, Boudhane Boubaker, Slimani Mohammed El Amine

“…Advanced interferometric techniques, such as PSInSAR and SBAS provide a pertinent mean to observe this phenomenon by supplying dense displacement measurements for an extended period. However, while these algorithms have proven their effectiveness in some areas, their performance is less consistent in others. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. …”
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Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. …”
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Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification by Wei Huang, Dazhan Zhou, Le Sun, Qiqiang Chen, Junru Yin

“…We compared and analyzed seven advanced HSI classification algorithms, and experiments showed that APSFFT outperforms other state-of-the-art methods.…”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Ethical framework for responsible foundational models in medical imaging by Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Kumar Tomar, Linkai Peng, Sirui Li, Koushik Biswas, Ertugrul Aktas, Elif Keles, Matthew Antalek, Zheyuan Zhang, Bin Wang, Xin Zhu, Xin Zhu, Hongyi Pan, Deniz Seyithanoglu, Alpay Medetalibeyoglu, Vanshali Sharma, Vedat Cicek, Amir A. Rahsepar, Rutger Hendrix, Rutger Hendrix, A. Enis Cetin, Bulent Aydogan, Mohamed Abazeed, Frank H. Miller, Rajesh N. Keswani, Rajesh N. Keswani, Hatice Savas, Sachin Jambawalikar, Daniela P. Ladner, Amir A. Borhani, Concetto Spampinato, Concetto Spampinato, Michael B. Wallace, Michael B. Wallace, Ulas Bagci

“…By establishing clear guidelines for development and deployment, we aim to harness the transformative potential of foundational models while preserving the fundamental principles of medical ethics and patient-centered care.…”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Among them, PNs that remain stable for more than two years or whose pathological results suggest not being lung cancer are considered benign PNs (BPNs), while PNs that conform to the growth pattern of tumors or whose pathological results indicate lung cancer are considered malignant PNs (MPNs). …”
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Artificial intelligence in prostate cancer by Wei Li, Ruoyu Hu, Quan Zhang, Zhangsheng Yu, Longxin Deng, Xinhao Zhu, Yujia Xia, Zijian Song, Alessia Cimadamore, Fei Chen, Antonio Lopez-Beltran, Rodolfo Montironi, Liang Cheng, Rui Chen, Yuanyuan Ji

“…The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. …”
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Using triaxial accelerometry to detect hunts and kills by African wild dogs by James Redcliffe, Scott Creel, Scott Creel, Ben Goodheart, Ben Goodheart, Johnathan Reyes de Merkle, Johnathan Reyes de Merkle, Stephani S. Matsushima, Michelo Mungolo, Ruth Kabwe, Emmanuel Kaseketi, Will Donald, Adrian Kaluka, Clive Chifunte, Matthew S. Becker, Matthew S. Becker, Rory Wilson

“…Here, we used this method and then iteratively improved algorithms by testing them on observed hunts and kills of collared packs. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. …”
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Ethical and Legal Challenges of Implementing AI in Science and Math Education in Central Asia by Dilfuza M.Makhmudova, Xilola R. Sharipova, Nosirjon K. Hojiyev, Azamat E.Ergashev, Yulduzknon Kh.Satvaldieva, Khosiyat U.Mamatkulova, Egambergan M. Khudoynazarov

“…Additionally, 64% of respondents expressed serious concerns about student data privacy, while 71% supported the need for formal AI ethics training. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

“…AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. While the industry benefits from technological advancements, there are ongoing challenges such as concerns over data privacy and the need for constant updates to algorithms to keep pace with evolving market conditions.…”
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A Distributed Machine Learning Model for Blue and Green Water Resources With Transferable Applications in Similar Climatic Zones by Zhibin Li, Haroon Sahotra, Sajjad Ahmad, Wei Wang, Zhe Yang, Pute Wu, Eakalak Khan, La Zhuo

“…The modeling thoroughly accounts for the influence of human activities, incorporating 5 scales (grid, county, city, province, and cluster), 4 algorithms, and 2 model integration methods (Stacking and Bayesian). …”
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Can We Teach Machines to Select Like a Plant Breeder? A Recommender System Approach to Support Early Generation Selection Decisions Based on Breeders’ Preferences by Sebastian Michel, Franziska Löschenberger, Christian Ametz, Herbert Bistrich, Hermann Bürstmayr

“…The target trait was the retrospective binary classification of selected versus non-selected breeding lines during a period of five years, while the selection decisions of the breeder were predicted by various machine learning models. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). …”
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Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. Potential applications of joint parameter estimation are suggested.…”
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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…This leads to significant economic and time costs, while the complexity of instruments and algorithms further raises the barrier for DLD design and optimization. …”
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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Optimization results indicated a theoretical 4% productivity gain and a 3 percentage point yield improvement, while exposing critical design constraints in the HGMS chamber.These findings underscore the potential of digital twins to accelerate process optimization and reduce development costs through in-silico experimentation. …”
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