Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
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Parameter Calculation of Steam Pipeline Based on Hybrid Modeling by Hongwei CHEN, Zherui MA, Chunwang LV, Yuqiang LIU, Wei ZHANG, Ruikun WANG

“…Based on the mechanism model, this paper establishes a data-driven error prediction model by virtue of the vector machine algorithm to predict the mechanism error caused by the mechanism model calculation, then uses the error prediction result of the model to correct the mechanism model calculation results. …”
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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…While the attributes that have an influence to determine the algorithm on the level of accuracy are Practice Questions, Quizzes, Mid-Semester Exams, and Final Exams. it is evident from our previous studies that the PSO-based neural network algorithm does have a very good advantage. based on ANN is a method that has calculations that build several units of interconnected connectivity, the ANN method with predictive accuracy can be an efficient and good tool for forecasting and classification research in the field of education. …”
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Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research by Parveen Kumar, Benu Chaudhary, Preeti Arya, Rupali Chauhan, Sushma Devi, Punit B. Parejiya, Madan Mohan Gupta

“…Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. …”
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Neural Network Approaches for Distributional Shifts in Environmental Sensors by Tobias Sukianto, Sebastian A. Schober, Cecilia Carbonelli, Simon Mittermaier, Robert Wille

“…Due to the distributional shift between the training and operational environment induced by sensor ageing and drift processes, the algorithms that predict air quality suffer from performance degradation during the products’ lifetime. …”
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Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

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Functional Connectivity Metrics in Temporal Lobe Epilepsy: A Machine Learning Perspective With MEG by M. V. Suhas, N. Mariyappa, A. Karunakar Kotegar, M. Ravindranadh Chowdary, K. Raghavendra, Ajay Asranna, L. G. Viswanathan, H. Anitha, Sanjib Sinha

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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The fault diagnosis algorithm was implemented using acoustic signals containing noise, present in the malfunctioning industrial machine investigation and inspection open dataset, and the fault prediction performance in noisy environments was examined based on forklift acoustic data using the VAE and DANN. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…By integrating the Mars-smallCNN feature extraction network with a Kalman filtering-based trajectory prediction module, the system attains 58.3% Multiple Object Tracking Accuracy (MOTA) and 62.1% Identity F1 Score (IDF1) in dense multi-object scenarios, representing an improvement of approximately 9.8 percentage points over the conventional YOLO+DeepSORT method. …”
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Research on the Exchange Rate Forecast of the Pound Sterling and the Dollar Based on Neural Networks by Chen Jiantao, Song Yining

“…During the training phase, the back-propagation algorithm is employed to reduce prediction errors, and rigorous cross-validation techniques are utilized to precisely evaluate the model's performance. …”
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Novel methods for selecting stock portfolio in conditions of uncertainty and forecasting with RR-DEA, ANFIS, FGP: A case study of Tehran stock exchange. by Mohammadmahdi Taheri, Amir Azizi, Emran Mohammadi, Abbas Saghaei

“…These selected stocks are then moved to the second stage, where the ANFIS algorithm is employed in MATLAB to predict the final closing prices and calculate the prediction error (RMSE). …”
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Novel multi-omics analysis revealing metabolic heterogeneity of breast cancer cell and subsequent development of associated prognostic signature by Peng Zhang, Cuicui Li, Fen Li, Jiezhong Wu, Kunpeng Hu, He Huang

“…Drug sensitivity prediction was performed via the OncoPredict tool. Functional assays investigated the oncogenic role of PDCD1 in breast cancer cell lines. …”
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UAV image analysis for detecting rice seedling gaps and gap effect on grain yield by Sanai Li, Yubin Yang, Jing Zhang, Lloyd T. (Ted) Wilson, Stanley Omar P.B. Samonte, Fugen Dou, Tanumoy Bera, Xin-Gen Zhou, Darlene Sanchez, Jing Wang

“…For instance, yield was predicted to decrease when gap fraction was above 4.5 % at 300°D > 10 °C, while even a smaller gap fraction (>0.5 %) led to yield reductions at 600°D. …”
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Development, optimization and modelling of performance parameters for remote-controlled mechatronic precision planter using RSM and Hybrid PSO-ANN model by Vimalsinh Vala, Rajvir Yadav, Bikram Jyoti, Ali Salem, Ajay K Roul, Rahul Potdar, Ankur Srivastava, Ahmed Elbeltagi

“…The integration of PSO with prediction models offered insights into enhancing precision planting efficiency and overall crop yield in mechatronic precision planters.…”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

“…These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. …”
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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“… This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights the intricate process involved in selecting the most suitable ML algorithm for predicting specific medical conditions, emphasizing the critical role of real-world data in testing and validation. …”
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Integrating Single‐Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma by Yuan Wang, Wei Su, Guangyao Zhou, Yijie Wang, Chunnuan Wu, Pengpeng Zhang, Lianmin Zhang

“…Ten machine learning algorithms were applied to develop and validate an ac4C‐related gene signature (ARGSig) for prognosis prediction across multiple independent cohorts. …”
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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

“…A Long Short-Term Memory (LSTM) neural network algorithm is employed, recognized for its strong predictive capability in time series data. …”
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Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization by Wei Wang, Lu-Lu Zhang, Jin-Jian Chen, Jian-Hua Wang

“…Robustness of the back estimated parameters is further illustrated by a forward prediction. Results show that the back-analyzed soil parameters can reasonably predict lateral displacements and give generally acceptable predictions of dynamic compaction for an adjacent location. …”
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Impact of Subjective and Objective Green Space Characteristics on Mental Health Benefits: An Explainable Machine Learning Approach by Ke LI, Yipei MAO, Yongjun LI

“…Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. …”
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