Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…External validation using the REFRAIN cohort confirmed the robustness of model(AUC = 0.776). Conclusions The interpretable predictive model improves mortality risk stratification in ICU patients with hemorrhage, supporting clinicians in optimizing treatment plans and resource allocation. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. …”
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Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method by Yucheng Li, Yang Zhang, Deyu Jia, Song Gao, Muqun Zhang

“…This approach (1) eliminates object wave amplitude interference via dual-exposure superposition, establishing submicron linear mapping between fringe displacement and deformation amplitude; (2) introduces a fringe gradient-based direction detection algorithm resolving deformation vector ambiguity; and (3) implements an error-compensated fusion framework integrating theoretical modeling, MATLAB 2015b simulations, and experimental validation. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…The authors believe that the proposed model can be further improved and integrated with existing countermeasure techniques.…”
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The innovation path of VR technology integration into music classroom teaching in colleges and universities by Yupeng Han, Lin Han, Chun Zeng, Wei Zhao

“…To explore these possibilities, this study proposes an Intelligent Interactive Music Teaching (IIMT) model that combines VR technology with Deep Convolutional Generative Adversarial Networks and Deep Deterministic Policy Gradient algorithms. …”
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An Enhanced Phenology Dataset for Global Drylands from 2001 to 2019 by Yuqi Dong, Yu Zhou, Li Zhang, Feng Tian, Qiaoyun Xie, Yiyang Chen, Linlin Ruan, Bo Zhang

“…Existing land surface phenology (LSP) products exhibit low accuracy in drylands due to coarse spatiotemporal data sources and algorithms optimized for other ecosystems. Here we present the Global Dryland Phenology Dataset (GDPD) for 2001–2019, derived from daily 500-m two-band Enhanced Vegetation Index using MODIS NBAR data and an improved retrieval algorithm with dynamic, pixel-wise amplitude thresholds. …”
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Information and methodological aspects of developing platforms of spatial management of natural assets in resource limitations by V.V. Mykytenko, I.V. Dragan

“…It is planned to build, within the subsystems of functional and decision-making and development of communicative metaspace, which is included in the spatial management system, sustainable theoretical-analytical and applied models with a high degree of approximation of characteristics and comparative assessment of optimal natural assets for involvement within business ecosystems of Ukraine, available natural resources in the context of the formation of a national economy of the platform type as a result of strengthening the competitive positions of participants in sustainable management and increasing the effectiveness of spatial management. …”
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Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…Machine learning models, specifically Random Forest and C4.5, were optimized and evaluated using a 10-fold cross-validation approach. …”
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A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images by Hassaan Haider Syed, Muhammad Attique Khan, Usman Tariq, Ammar Armghan, Fayadh Alenezi, Junaid Ali Khan, Seungmin Rho, Seifedine Kadry, Venkatesan Rajinikanth

“…The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. …”
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Development of Adaptive Testing Method Based on Neurotechnologies by E. V. Chumakova, D. G. Korneev, M. S. Gasparian

“…However, it remains to answer the question of the need to improve the efficiency of an already implemented network, and, therefore, to conduct research on methods to improve the efficiency of networks, including finer tuning of parameters and learning algorithms, as well as architecture.A well-known and obvious drawback of using LSTMs is their exactingness in terms of equipment and resources, both during training (the training process takes a significant amount of time) and during startup, in our case, it is supplemented by increased requirements for the training sample and casts doubt on the advisability of further study of LSTM networks when solving this task.Conclusion. …”
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An artificial intelligence platform for predicting postoperative complications in metastatic spinal surgery: development and validation study by Weihao Jiang, Juan Zhang, Weiqing Shi, Xuyong Cao, Xiongwei Zhao, Bin Zhang, Haikuan Yu, Shengjie Wang, Yong Qin, Mingxing Lei, Yuncen Cao, Boyu Zhu, Yaosheng Liu

“…In addition, the final optimal model has been made available online as a freely accessible AI platform, and the URL was https://v37vx6dtwdyf4y4nn24cib.streamlit.app/ . …”
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Practical Recommendations for Artificial Intelligence and Machine Learning in Antimicrobial Stewardship for Africa by Tafadzwa Dzinamarira, Elliot Mbunge, Claire Steiner, Enos Moyo, Adewale Akinjeji, Kaunda Yamba, Loveday Mwila, Claude Mambo Muvunyi

“…The deployment of AI‐driven solutions presents unprecedented opportunities for optimizing treatment regimens, predicting resistance patterns, and improving clinical workflows. …”
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Research Progress and Applications of Artificial Intelligence in Agricultural Equipment by Yong Zhu, Shida Zhang, Shengnan Tang, Qiang Gao

“…The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. …”
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An End-to-End Solution for Large-Scale Multi-UAV Mission Path Planning by Jiazhan Gao, Liruizhi Jia, Minchi Kuang, Heng Shi, Jihong Zhu

“…The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. …”
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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride, Eric Sénéchal

“…Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. …”
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Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review by Johnderson Nogueira de Carvalho, Felipe Rodrigues da Silva, Erick Giovani Sperandio Nascimento

“…The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. …”
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2H-MoS2 lubrication-enhanced MWCNT nanocomposite for subtle bio-motion piezoresistive detection with deep learning integration by Ke-Yu Yao, Derek Ka-Hei Lai, Hyo-Jung Lim, Bryan Pak-Hei So, Andy Chi-Ho Chan, Patrick Yiu-Man Yip, Duo Wai-Chi Wong, Bingyang Dai, Xin Zhao, Siu Hong Dexter Wong, James Chung-Wai Cheung

“…Herein, we present an environmentally friendly, low-cost, and nonionic fabrication approach for a 2H-phase molybdenum disulfide (2H-MoS2)-enhanced multi-walled carbon nanotube (MWCNT) strain sensor, developed via a systematically optimized vacuum-assisted filtration process. This study is the first to validate the dual enhancement effect of MoS2, leveraging its shear-exfoliation properties to simultaneously improve strain gauge performance and mechanical robustness. …”
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