Analysis of the state of geometrization development and digital modeling in open-pit mining enterprises by M.S. Kunytska, D.S. Polishchyk, O.V. Shapochnikov

“…It was determined that their integrated application provides an increase in the accuracy of reserve assessment by 15–20 % and a reduction in operating costs by 10–15 %. Key development trends are identified: integration of digital technologies, improvement of methods of collecting and processing geospatial data, introduction of machine learning algorithms for risk prediction. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…The rapid increase in traffic data, along with the inherently dynamic characteristics of urban traffic, poses considerable challenges for traditional Machine Learning (ML) models, which often find it difficult to efficiently handle large-scale datasets. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

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Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Renuganth Varatharajoo, Shilpa Gite, Abdullah Alamri

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Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression by Lindsay L. Benster, Cory R. Weissman, Federico Suprani, Kamryn Toney, Houtan Afshar, Noah Stapper, Vanessa Tello, Louise Stolz, Mohsen Poorganji, Zafiris J. Daskalakis, Lawrence G. Appelbaum, Jordan N. Kohn

“…Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. …”
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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. …”
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Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High‐Strength Materials via Deep Reinforcement Learning by Yunhao Yang, Runnan Bai, Wenli Gao, Leitao Cao, Jing Ren, Zhengzhong Shao, Shengjie Ling

“…This study addresses the challenge by investigating the structure‐property relationships and stability of biomimetic 3D‐DFNS using large datasets generated through procedural modeling, coarse‐grained molecular dynamics simulations, and machine learning. …”
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Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model by Nasirul Mumenin, Maisha Mumtaz Rahman, Mohammad Abu Yousuf, Farzan M. Noori, Md Zia Uddin

“…Comparative analysis with standard machine learning models underscores the superior performance of the proposed framework. …”
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Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. by Guanqun Wang

“…Furthermore, four widely recognized machine learning methods are employed to classify the clustering results, achieving over 95% classification accuracy on the test set. …”
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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

“…However, when these machine learning methods are applied without careful consideration of data quality, model biases, and other critical factors, their potential is often compromised. …”
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On calculating structural similarity metrics in population-based structural health monitoring by Daniel S. Brennan, Timothy J. Rogers, Elizabeth J. Cross, Keith Worden

“…The improvement of inferences across populations uses the machine-learning technology of transfer learning. …”
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Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques by Lixing Bo, Jie Xu

“…Results from the study indicate significant improvements. The Transformer model achieved a reduction in Mean Absolute Error (MAE) from 15.8 to 8.2 and Root Mean Squared Error (RMSE) from 22.3 to 11.5, demonstrating enhanced forecasting accuracy. …”
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Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning by Alyaman H. Massarani, Mahmoud M. Badr, Mohamed Baza, Hani Alshahrani, Ali Alshehri

“…The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. …”
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Analysis of the 10-day ultra-marathon using a predictive XG boost model by Beat Knechtle, Elias Villiger, David Valero, Lorin Braschler, Katja Weiss, Rodrigo Luiz Vancini, Marilia S. Andrade, Volker Scheer, Pantelis T. Nikolaidis, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. …”
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Neutrosophic OWA-TOPSIS Model for Decision-Making in AI Systems with Large Volumes of Data by Juan Roberto Pereira Salcedo, Karla Melissa Ruiz Quezada, Edison Luis Cruz Navarrete, Pedro Manuel García Arias

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A Novel Data-Driven Method of Real-Time Transient Stability Assessment for AC/DC Hybrid Power Systems by Haifeng Li, Zhiwei Wang, Tao Jin, Xian Xu, Lin Shi

“…Next, transient stability assessment models are trained based on attention mechanisms, improved convolutional deep belief networks (CDBN), and multiple convolution-constrained Boltzmann machines to learn effective features from the input data adaptively. …”
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Adaptive neuro-fuzzy inference system optimization of natural rubber latex modified concrete’s mechanical Properties by Efiok Etim Nyah, David Ogbonna Onwuka, Joan Ijeoma Arimanwa, George Uwadiegwu Alaneme, G. Nakkeeran, Ulari Sylvia Onwuka, Chinenye Elizabeth Okere

“…Traditional laboratory testing for concrete properties is often time-consuming, costly, and prone to variability due to environmental and procedural inconsistencies. Machine learning techniques, such as ANFIS, offer a robust alternative by effectively modelling complex, nonlinear relationships in material behavior based on experimental data. …”
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A convolutional neural network model and algorithm driven prototype for sustainable tilling and fertilizer optimization by Sajeev Magesh

“…The machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. …”
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SMART-FL: Single-Shot Merged Adaptive Resource-Aware Tensor-Fusion for Efficient Federated Learning in Heterogeneous Cross-Silo Environments by Vineetha Pais, Santhosha Rao, Balachandra Muniyal

“…Federated Learning (FL) is an evolutionary approach for privacy-preserving distributed machine learning and is particularly significant in cross-silo settings where direct data sharing is restricted. …”
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