SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…Deep learning methodologies proffer more efficacious and automated solutions, but their segmentation accuracy is restricted by imprecise center offset predictions, particularly in intricate forest environments. …”
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Mediastinal staging lymph node probability map in non-small cell lung cancer by J. Bordas-Martinez, J.L. Vercher-Conejero, G. Rodriguez-González, P.C. Notta, C. Martin Cabeza, N. Cubero, R.M. Lopez-Lisbona, M. Diez-Ferrer, C. Tebé, S. Santos, M. Cortes-Romera, A. Rosell

“…This study aims to develop a predictive algorithm that combines EBUS, PET/CT and clinical data to provide a probability of malignancy. …”
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Proteomic analysis of blood plasma as a tool for personalized diagnosis of lung adenocarcinoma by D. N. Korobkov, A. S. Kononikhin, S. D. Semenov, H. L. Kordzaya, A. G. Brzhozovskiy, A. E. Bugrova, E. Yu. Vasilieva, D. Yu. Kanner, E. N. Nikolaev, A. A. Komissarov

“…Additionally, we identified three proteins that predict the presence of distant metastases among patients with LAC. …”
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NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care by Tarun Vats, Shrey Mehra, Uday Madan, Amit Chhabra, Akashdeep Sharma, Kunal Chhabra, Sarabjeet Singh, Utkarsh Chauhan

“…This integrative approach harnesses the advantages of NN’s ReLU activation and dropout regularization alongside the robustness of RF. By utilizing NN predictions as input features for RF training and refining RF through grid search with cross-validation, the ensemble model produces highly precise predictions, facilitating strategic crop cultivation for optimal yields across diverse environmental conditions. …”
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Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning by Jinyu Zheng, Xiaoyi Xu, Xianguo Chen, Xianshuai Li, Miao Fu, Yiping Zheng, Jie Yang

“…Three machine learning algorithms—Random Forest, LASSO, and SVM-RFE—were applied to identify key hub genes. …”
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An Improved Symbiosis Particle Swarm Optimization for Solving Economic Load Dispatch Problem by Jianxia Zhang, Jianxin Zhang, Feng Zhang, Minglu Chi, Linbin Wan

“…To realize the sustainable development of social economy, energy conservation and emission reduction has become one of the problems that must be considered in the current power system. …”
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Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

“…The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. …”
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Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features by Huan-Zhong Su, Zhi-Yong Li, Long-Cheng Hong, Yu-Hui Wu, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were subsequently utilized to construct predictive models employing five ML algorithms. The performance of the models was evaluated across a comprehensive array of learning metrics, prominently the area under the receiver operating characteristic curve (AUC). …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…A correction model that links pixel-wise errors with atmospheric and meteorological variables was taught using gradient-boosting algorithms. This model is then utilized to predict CAMS error in previously unobserved pixels across the Eastern Mediterranean, generating CAMS error fields. …”
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Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification by Xiaojuan Cai, Ya Zhou, Jie Ren, Jinrong Wei, Shiyu Lu, Hanbing Gu, Weizhe Xu, Xun Zhu

“…Conclusion: Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN.ConclusionOur developed thyroid nodule AI diagnostic model shows favorable predictive value. …”
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Integrative analysis of semaphorins family genes in colorectal cancer: implications for prognosis and immunotherapy by Jiahao Zhu, Benjie Xu, Zhixing Wu, Zhixing Wu, Zhiwei Yu, Shengjun Ji, Jie Lian, Haibo Lu

“…However, the prognostic value of SEMA-related genes in colorectal cancer (CRC) remains unclear.MethodsWe applied a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a SEMAs-related score (SRS). …”
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Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis by Abhay Nath, Om Roy, Priyanka Silveri, Sanskruti Patel

“…Our method applies residual connections with Squeeze-and-Excitation blocks along with hybrid attention systems and enhanced activation functions and optimization algorithms to boost gradient movement throughout feature extraction. …”
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Computational Evaluation and Multi-Criteria Optimization of Natural Compound Analogs Targeting SARS-CoV-2 Proteases by Paul Andrei Negru, Andrei-Flavius Radu, Ada Radu, Delia Mirela Tit, Gabriela Bungau

“…Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. …”
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Impacts of climate change on the global spread and habitat suitability of Coxiella burnetii: Future projections and public health implications by Abdallah Falah Mohammad Aldwekat, Niloufar Lorestani, Farzin Shabani

“…By 2090, a 44.56 % (range: 33–57.9 %) across the models, increase in suitable habitat is predicted, accompanied by a 27.66 % (range: 22.4–31.7 %) loss of current habitats. …”
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Comparison of light gradient boosting and logistic regression for interactomic hub genes in Porphyromonas gingivalis and Fusobacterium nucleatum-induced periodontitis with Alzheime... by Pradeep Kumar Yadalam, Shubhangini Chatterjee, Prabhu Manickam Natarajan, Carlos M. Ardila, Carlos M. Ardila

“…Logistic regression and light gradient boosting were used to predict interactomic hub genes, with outliers removed and machine learning algorithms applied.ResultsThe data were cross-validated and divided into training and testing segments. …”
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Machine learning-based identification of histone deacetylase-associated prognostic factors and prognostic modeling for low-grade glioma by Keshan Wen, Weijie Zhu, Ziyi Luo, Wei Wang

“…This study aims to develop a prognostic model based on HDAC-related genes to aid in risk stratification and predict therapeutic responses. Methods Expression data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify an optimal HDAC-related risk signature from 73 genes using 10 machine learning algorithms. …”
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A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

“…A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. …”
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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis by Stefano Rodinò, Giuseppe Rota, Matteo Chiodo, Antonio Corigliano, Carmine Maletta

“…Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). …”
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A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy by Sebastian Dinesen, Marianne G. Schou, Christoffer V. Hedegaard, Yousif Subhi, Thiusius R. Savarimuthu, Tunde Peto, Jakob K. H. Andersen, Jakob Grauslund

“…Abstract Introduction Existing deep learning (DL) algorithms lack the capability to accurately identify patients in immediate need of treatment for proliferative diabetic retinopathy (PDR). …”
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A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study by Yahan Zhang, Yi Chun, Hongyuan Fu, Wen Jiao, Jizhang Bao, Tao Jiang, Longtao Cui, Xiaojuan Hu, Ji Cui, Xipeng Qiu, Liping Tu, Jiatuo Xu

“…Then, we used 10 different machine learning algorithms to create a predictive model for anemia. …”
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