Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…[Conclusion] The experiments verify that the WM-CTA model delivers optimal prediction performance with high computational efficiency. …”
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Kinematic analysis, methods and experimental studies of robot navigator movements on mecanum wheels by Sladkova L.A., Pshibiev A.M.

“…Machine learning and artificial intelligence, capable of making decisions on their own, have become indispensable tools for optimizing warehouse operations. …”

Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal by Kashif Faheem, Sagheer A. Onaizi, Muhammad S. Vohra

“…A response surface methodology (RSM)-based modeling exercise was used to optimize the adsorption process. …”
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A triple pronged approach for ulcerative colitis severity classification using multimodal, meta, and transformer based learning by Md. Nasim Ahmed, Dipta Neogi, Muhammad Rafsan Kabir, Shafin Rahman, Sifat Momen, Nabeel Mohammed

“…Furthermore, interpretability is enhanced via SHapley Additive exPlanations (SHAP), which interpret both local and global model behaviors, thereby fostering clinical trust in the model’s inferences. …”
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Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland by Afaq Khattak, Brian Caulfield

“…The SARIMA-BO–LSTM model achieves a Mean Absolute Error (MAE) of 742.99, Root Mean Squared Error (RMSE) of 1200, and R2 of 0.93, outperforming several baseline statistical and machine learning models. …”
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Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China by Quanshan Liu, Zongjun Wu, Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Zhihui Wang, Daozhi Gong, Yaosheng Wang, Lu Zhao, Renjuan Wei

“…The Black-winged Kite Algorithm (BKA) was employed to optimize both the Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) models. …”
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Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Different element quantitative models were constructed for each rock type. The kNN algorithm was selected using cross-validation to determine the optimal k value, and the key punishment parameter C and RBF width parameter γ of the SVM algorithm were determined using a grid search method. …”
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Application of Bayesian Neural Networks in Healthcare: Three Case Studies by Lebede Ngartera, Mahamat Ali Issaka, Saralees Nadarajah

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High spatial resolution crop type and land use land cover classification without labels: A framework using multi-temporal PlanetScope images and variational Bayesian Gaussian mixtu... by Minh Tri Le, Khuong H. Tran, Phuong D. Dao, Hesham El-Askary, Tuyen V. Ha, Taejin Park

“…This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.…”
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Scarf Adhesive Bonding of 3D-Printed Polymer Structures by Tiago F. R. Ribeiro, Raul D. S. G. Campilho, Ricardo F. R. Pinto, Ricardo J. B. Rocha

“…Additive manufacturing (AM) has swiftly emerged as a substitute for conventional methods such as machining and injection moulding. Its appeal is attributed to accelerated prototyping, improved sustainability, and the capacity to fabricate intricate shapes. …”
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Beyond N-Grams: Enhancing String Kernels With Transformer-Guided Semantic Insights by Nazar Zaki, Reem Alderei, Mahra Alketbi, Alia Alkaabi, Fatima Alneyadi, Nadeen Zaki

“…The rapid advancements in large language models (LLMs) have led to the generation of sophisticated AI-produced texts, posing significant challenges in distinguishing machine-generated content from authentic human writing. …”
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Burned Area Detection in the Eastern Canadian Boreal Forest Using a Multi-Layer Perceptron and MODIS-Derived Features by Hadi Mahmoudi Meimand, Jiaxin Chen, Daniel Kneeshaw, Mohammadreza Bakhtyari, Changhui Peng

“…This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. …”
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Performance of 5G Slicing With Access Technologies and Diversity: A Review and Challenges by Sinta Novanana, Adrian Kliks, Ajib Setyo Arifin, Gunawan Wibisono

“…5G slicing has become a key technology in 5G and beyond (B5G) for realizing IMT 2020 services, including enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). These advancements necessitate the support of advanced technologies and optimal solutions to satisfy the network requirements of IMT-2020 services. …”
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A comparative study on trajectory tracking control methods for automated vehicles by Dequan Zeng, Shicong Pan, Yinquan Yu, Yiming Hu, Jinwen Yang, Peizhi Zhang, Lu Xiong, Giuseppe Carbone, Letian Gao

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MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents by Moran Zhang, Qianqian Li, Shunhang Li, Binxian Sun, Zhuli Wu, Jinxuan Liu, Xingchao Geng, Fangyi Chen

“…<b>Methods:</b> MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. …”
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Research on artificial intelligence-driven container relocation problem for green ports by Sisi Zheng, Jin Sha, Yinying Kong, Yougan Wang

“…IntroductionContainer relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.MethodsThis study proposes a unified framework integrating an Intelligent Decision-Driven Model (IDDM), an Adaptive Data Generator (ADG), and an Optimization–Learning Closed-Loop Framework (OLCF).ResultsThe IDDM leverages heuristic search and machine learning within a multi-stage decision mechanism to mitigate the curse of dimensionality; in two-dimensional scenarios involving 50–100 containers, the model achieves an average response time of 9.83 ± 0.12 µs and reduces relocation operations by 61.68%. …”
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Deep learning and statistical approaches for area-based PM2.5 forecasting in Hat Yai, Thailand by Kasikrit Damkliang, Jularat Chumnaul

“…Among the tested models, the ConvLSTM1D-BiLSTM model, optimized with a 7-input window, 1-output window, and 6 strides, demonstrated the highest effectiveness, achieving a mean MAE of 2.05 and a mean R $$^2$$ 2 score of 0.68. …”
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Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks by Jiayu Mai, Haonan Lin, Xuezhen Hong, Zhenbo Wei

“…Models incorporating feature-optimized channel attention modules (f-CAM, f-ECA) achieved a classification accuracy of up to 90.28%, significantly outperforming traditional machine learning models (72–77%) and standard CNN models (83.33%). …”
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A Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint, with a... by Henrique Rabelo, Ayana Tsimiante, Yuri Binev, Florbela Pereira

“…Therefore, drugs targeting this axis can disrupt the interaction and enable immune cells to continue setting up a response against the cancer cells. (2) Methods: We have explored the immuno-oncological activity of NPs targeting the PD-1/PD-L1 immune checkpoint by estimating the half maximal inhibitory concentration (IC₅₀) through molecular docking scores and predicting it using machine learning (ML) models. The LightGBM (Light Gradient-Boosted Machine), a tree-based ML technique, emerged as the most effective approach and was used for building the quantitative structure–activity relationship (QSAR) classification model. (3) Conclusions: The model incorporating 570 spectral descriptors from NMR SPINUS was selected for the optimization process, and this approach yielded results for the external test set with a sensitivity of 0.74, specificity of 0.81, overall predictive accuracy of 0.78, and Matthews correlation coefficient (MCC) of 0.55. …”
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Research on the Combustion of Mixed Biomass Pellets in a Domestic Boiler by Penka Zlateva, Angel Terziev, Kalin Krumov, Mariana Murzova, Nevena Mileva

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