Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…In this study, five methodologies, including three artificial intelligence models (artificial neural networks [ANNs], support vector regression [SVR], random forest [RF]), a physical model, and a hybrid model, were evaluated for their ability to estimate the ROP on the basis of drilling data from a complex lithological area. …”
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AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model by Mojtaba Sirati-Amsheh, Elham Shabaninia, Ali Chaparian

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Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms by Li Qinsheng, Li Ming, Li Yuening, Zhao Xiufeng

“…A predictive pharmacological interaction model is integrated to quantify drug-target interactions, assess their systemic impacts, and simulate off-target effects. This approach also evaluates combinatorial drug effects, offering insights into the synergistic or antagonistic behaviors of multiple agents.ResultsBy incorporating these features, our method provides a comprehensive view of drug-induced changes, enabling accurate prediction of their effects on bone formation and resorption pathways.DiscussionExperimental results highlight the model’s potential to advance precision medicine, enabling the development of more effective and safer therapeutic strategies for managing bone health.…”
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Research on Pork Cut and Freshness Determination Method Based on Computer Vision by Shihao Song, Qiqi Guo, Xiaosa Duan, Xiaojing Shi, Zhenyu Liu

“…Next, five convolutional neural network models—VGGNet, ResNet, DenseNet, MobileNet, and EfficientNet—were selected for feature recognition experiments. …”
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Estimation of soil organic matter in mollisols based on artificial intelligence by Shihao Cui, Meng Zhou, He Yu, Xiongze Xie, Leilei Xiao, Jian Liu, Jinkuo Lin, Xiaobing Liu, Yueyu Sui, Jing Liu

“…This study employed an artificial intelligence method, based on deep neural networks (DNNs), to predict SOM content. In this method, relevant measurement values of soil nutrients, such as phosphorus, nitrogen and potassium, and the soil pH were used as input features for the model. …”
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Deep Neural Emulation of the Supermassive Black Hole Binary Population by Nima Laal, Stephen R. Taylor, Luke Zoltan Kelley, Joseph Simon, Kayhan Gültekin, David Wright, Bence Bécsy, J. Andrew Casey-Clyde, Siyuan Chen, Alexander Cingoranelli, Daniel J. D’Orazio, Emiko C. Gardiner, William G. Lamb, Cayenne Matt, Magdalena S. Siwek, Jeremy M. Wachter

“…Previously, Gaussian processes (GPs) and dense neural networks have been used to make such a connection by being built as conditional emulators; their input is some selected evolution or environmental SMBH binary parameters and their output is the emulated mean and standard deviation of the GWB strain ensemble distribution over many Universes. …”
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DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework by Arshak Rezvani, Nasrin Ravansalar, Mohammad Ali Akhaee, Andrew J. Greenshaw, Russell Greiner, Maryam S. Mirian, Muhammad Yousefnezhad, Martin J. McKeown

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A novel data processing approach to detect fraudulent insurance claims for physical damage to cars by Ahmet Yücel

“…Machine learning models were then developed, with predictors and composite variables selected based on standard feature selection procedures. Five machine learning models were used: Boosted Trees, Classification and Regression Trees, Random Forests, Artificial Neural Networks, and Support Vector Machines. …”
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Deep Learning-Based Surface Temperature Prediction for a Porous Radiant Burner Using Thermocouple-Calibrated Thermal Infrared Images by Hao-Yu Hsieh, Shenqyang Shy, Wei-Wun Wang, Yung-Chien Chou

“…Three CNN architectures, VGG-16, ResNet-50, and DenseNet-121, are evaluated as feature extractors within this framework. …”
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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification by Mouna Bouchane, Wei Guo, Shuojin Yang

“…The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). …”
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A novel approach for the detection of brain tumor and its classification via end-to-end vision transformer - CNN architecture by K. Chandraprabha, L. Ganesan, K. Baskaran

“…The models performance is evaluated according to various criteria, such as sensitivity, precision, recall, and specificity. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…Objective The integrity management of urban gas pipeline networks demands effective risk assessment methods. …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…The proposed framework is rigorously compared against six representative models—recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks-transformer graph neural network (CTGNet), MobileViT, and DeepsigNet—across multiple evaluation criteria. …”
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Characterisation of HER2‐Driven Morphometric Signature in Breast Cancer and Prediction of Risk of Recurrence by N. M. Atallah, S. Makhlouf, M. Nabil, A. Ibrahim, M. S. Toss, N. P. Mongan, E. Rakha

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Diet Engine: A real-time food nutrition assistant system for personalized dietary guidance by Asim Moin Saad, Md. Raihanul Haque Rahi, Md. Manirul Islam, Gulam Rabbani

“…This study introduces Diet Engine, an innovative smartphone application powered by machine learning that enhances health outcomes by providing immediate food classification and personalized dietary suggestions. The system features modules using deep learning (DL) and Convolutional Neural Networks (CNNs) to detect food, as well as textual analysis and natural language processing (NLP) to estimate components such as nutritional content. …”
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A Deep Learning-Driven Black-Box Benchmark Generation Method via Exploratory Landscape Analysis by Haoming Liang, Fuqing Zhao, Tianpeng Xu, Jianlin Zhang

“…Once the feature criteria are met, the resulting topological map point is used to train a neural network to produce a surrogate function that retains the desired landscape characteristics. …”
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kMoL: an open-source machine and federated learning library for drug discovery by Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Hiroaki Iwata, Aki Hasegawa, Takao Otsuka, Yasushi Okuno

“…It demonstrates extensive customization possibilities, advanced security features, straightforward implementation of user-specific models, and high adaptability to custom datasets without additional programming requirements. kMoL is evaluated through locally trained benchmark settings and distributed federated learning experiments using various datasets to assess the features and flexibility of the library, as well as the ability to facilitate fast and practical experimentation. …”
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Counterfactual and Prototypical Explanations for Tabular Data via Interpretable Latent Space by Simone Piaggesi, Francesco Bodria, Riccardo Guidotti, Fosca Giannotti, Dino Pedreschi

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A vision transformer based CNN for underwater image enhancement ViTClarityNet by Mohamed E. Fathy, Samer A. Mohamed, Mohammed I. Awad, Hossam E. Abd El Munim

“…To address these issues, ViT-Clarity, an underwater image enhancement module, is introduced, which integrates vision transformers with a convolutional neural network for superior performance. For comparison, ClarityNet, a transformer-free variant of the architecture, is presented to highlight the transformer’s impact. …”
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Leveraging LIME for Trustworthy Apple Quality Assessment by İsmail Kırbaş, Ahmet Çifci

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