A Review of Virtual Tutoring Systems and Student Performance Analysis Using GPT-3 by Sireesha Prathigadapa, Salwani Binti Mohd Daud

“…Emphasis lies on the utilization of machine learning and deep learning models, along with the datasets utilized. …”
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Learning Quantum States and Unitaries of Bounded Gate Complexity by Haimeng Zhao, Laura Lewis, Ishaan Kannan, Yihui Quek, Hsin-Yuan Huang, Matthias C. Caro

“…We illustrate how these results establish fundamental limitations on the expressivity of quantum machine-learning models and provide new perspectives on no-free-lunch theorems in unitary learning. …”
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New physics through flavor tagging at FCC-ee by Admir Greljo, Hector Tiblom, Alessandro Valenti

“…Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. …”
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Methods for User-Controlled Synthesis of Blood Vessel Trees in Medical Applications: A Survey by Nikolaus Rauch, Matthias Harders

“…Various applications in medicine require geometric models of the underlying blood vessel networks. This ranges from anatomical visualizations, via surgical training systems, to machine learning-based anatomical segmentation frameworks. …”
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The Structural Stability of Enzymatic Proteins in the Gas Phase: A Comparison of Semiempirical Hamiltonians and the GFN-FF by Jarosław J. Panek

“…We selected two enzymatic proteins with great potential for applied use, the digestive enzyme trypsin and the cytochrome sterol demethylase, for which to develop gas-phase structural models. The employed levels of theory were semiempirical, density functional tight binding, and polarizable force-field techniques. …”
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Research on image generation technology based on deep learning by Li Jinchen

“…In the realm of image creation, deep learning stands out as an effective and valuable machine learning technique. Deep learning can automatically learn the intrinsic features of images， reaching the goal of generating high-quality images by utilizing multi-layer neural network models. …”
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Exploring the Cognitive Dimensions in Interpreting and AI

“…However, challenges such as the need for training AI models on diverse language pairs and the importance of maintaining the human interpreter's role and expertise should be considered. …”
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Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss by Yongsoo Park, Gregory C. Beroza

“…When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.…”
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ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYSIS IN CRIME PREVENTION AND COMBAT by George-Marius ȚICAL

“…The development of explainable predictive models, the reduction of biases, and the adoption of clear international regulations are essential. …”
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Mineral-Pore Integration: A new perspective by Shuheng Du

“…To bridge this gap, we propose the development of quantitative discrimination models using machine learning algorithms to decode mineral-pore coupling mechanisms. …”
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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

“…These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. …”
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Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential by Lingxian Zhang, Zuoqi Chen, Wenkang Gong, Congxiao Wang, Jing Xiong, Linxin Dong, Jingwen Ni, Yan Huang, Bailang Yu

“…This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). …”
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Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma by Yun Liang, Yun Liang, Min He, Wenqing Chen, Wenqing Chen, Lizhen Li, Lizhen Li, Yumeng Dong, Yumeng Dong, Gang Liang, Hui Huangfu, Zengyu Jiang, Zengyu Jiang, Sheng He, Sheng He, Sheng He

“…Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. …”
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High angular resolution diffusion-weighted imaging and higher order tractography of the white matter tracts in the anterior thalamic area: Insights into deep brain stimulation targ... by Ruhunur Özdemir, Kai Lehtimäki, Eetu Siitama, Timo Möttönen, Joonas Haapasalo, Soila Järvenpää, Hannu Eskola, Jukka Peltola

“…A more profound understanding of neuroanatomic characteristics may guide stereotactic implantation and subsequent programming to optimize outcomes.…”
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Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity. by Alexandre Guet-McCreight, Frank Mazza, Thomas D Prevot, Etienne Sibille, Etay Hay

“…We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. …”
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Development and validation of a nomogram for differentiating immune checkpoint inhibitor-related pneumonitis from pneumonia in patients undergoing immunochemotherapy: a multicenter... by Linli Duan, Guanglu Liu, Zijie Huang, Rong Chen, Di Mo, Yuxiao Xia, Jiazhu Hu, Mengzhang He

“…Utilizing the random forest machine learning method, optimal development and validation cohort allocation ratios (in a ratio of 8:2) were determined for the predictive model. …”
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Editorial by Teddy Surya Gunawan

“…This issue also highlights the integration of AI and machine learning in optimizing engineering systems. …”
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Maintenance techniques to increase solar energy production: A review by Fernando Martinez-Gil, Christopher Sansom, Aránzazu Fernández-García, Alfredo Alcayde-García, Francisco Manzano-Agugliaro

“…The study analyzes the rapid growth of solar energy and the challenges posed by environmental factors such as soiling, harsh climate conditions and hotspots, which reduce photovoltaic (PV) and concentrated solar power (CSP) system performance. Predictive models for solar energy generation and soiling detection, including artificial intelligence (AI) and machine learning (ML) algorithms and Internet of Things (IoT), are discussed as means for optimizing energy production and reducing maintenance costs. …”
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Industry 4.0 enabled calorimetry and heat transfer for renewable energy systems by Emmanuel O. Atofarati, Christopher C. Enweremadu

“…This review examines how these technologies improve thermal efficiency, enable real-time system monitoring, and support predictive maintenance across solar, wind, geothermal, and bioenergy applications. AI-driven models are discussed for optimizing complex heat transfer behaviors, while IoT frameworks facilitate continuous calorimetric data acquisition. …”
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Application of federated learning in predicting breast cancer by Chai Jiarui

“…During the local training process, the data is normalized and feature extracted, initially classified using support vector machines (SVM) or penalized logistic regression and optimized using stochastic gradient descent (SGD). …”
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