A Gaussian Process-Enhanced Non-Linear Function and Bayesian Convolution–Bayesian Long Term Short Memory Based Ultra-Wideband Range Error Mitigation Method for Line of Sight and No... by A. S. M. Sharifuzzaman Sagar, Samsil Arefin, Eesun Moon, Md Masud Pervez Prince, L. Minh Dang, Amir Haider, Hyung Seok Kim

“…A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. …”
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The performance of cylindrical solar still with hemispherical dome using circular fins in basin by Shahzanan Falah, Dhafer Manea Hachim, Wisam A. Abd Al-wahid

“…The research methodology involved the numerical testing of seven proposed models and the identification of the optimal variables for the solar still's performance. …”
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Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients by Bushra Mahdi Hasan, Zahraa Jasim Jaber, Ahmad Adel Habeeb

“…Local Binary Pattern (LBP) is a unique, efficient textural operator that finds widespread application in the area of computers such as biometric identification and detection of targets as feature extraction techniques. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Compared to traditional DNNs, the proposed method offers a more efficient approach to feature extraction, resulting in a notable enhancement in model performance, albeit with a moderate increase in computational complexity. …”
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FungiLT: A Deep Learning Approach for Species-Level Taxonomic Classification of Fungal ITS Sequences by Kai Liu, Hongyuan Zhao, Dongliang Ren, Dongna Ma, Shuangping Liu, Jian Mao

“…The internal transcribed spacer (ITS) region is widely used for fungal species classification and identification. However, most existing ITS databases cover limited fungal species diversity, and current classification methods struggle to efficiently handle such large-scale data. …”
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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“…Notably, K-Nearest Neighbors exhibited exceptional computational efficiency with a training time of 0.00 seconds. This study proposed an efficient model for prediction student performance. …”
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Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images by Carlos Sanchez, Feng Wang, Yongsheng Bai, Haitao Gong

“…Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. …”
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Research on Combustion State System Diagnosis Based on Voiceprint Technology by Jidong Yan, Yuan Wang, Liansuo An, Guoqing Shen

“…In summary, the combustion state diagnosis system based on CNN model combined with acoustic features has optimal performance, and the combination of step index P and frequency-domain monitoring in the flameback diagnosis can improve the accuracy of combustion state identification and safety control level, which provides an important theoretical basis and practical reference in the field of combustion state diagnosis and is of profound significance to ensure the safe and efficient operation of the combustion process.…”
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A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection by Siyuan Chen, Zhen Wang, Hao Wang, Shuai Wang, Yang Li, Yang Li, Bing Wang

“…The use of deep learning models in electrocardiogram (ECG) analysis has aided more accurate and efficient PVC identification. …”
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A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging by Sidrah Mumtaz, Mudassar Raza, Ofonime Dominic Okon, Saeed Ur Rehman, Adham E. Ragab, Hafiz Tayyab Rauf

“…The highest achievable outcomes were 98.9% with 5-fold and 99.2% with 10-fold cross validation, confirming the evidence that the identification of Leaf Blight is accurate, successful, and efficient.…”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. …”
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Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach by Thota Rishik Sai Santhosh, Sachi Nandan Mohanty, Nihar Ranjan Pradhan, Tauseef Khan, Morched Derbali

“…To address this issue, a deep learning-driven framework consisting of four pre-trained models viz DenseNet169, VGG-19, Xception, and EfficientNetV2B2 is developed to classify potential brain tumours from medical resonance images. …”
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Beam-like damage detection methodology using wavelet damage ratio and additional roving mass by Juliana C. Santos, Marcus V. G. de Morais, Marcela R. Machado, Ramon Silva, Erwin U. L. Palechor, Welington V. Silva

“…Numerical results showed that all proposed techniques are efficient techniques for damage identification in Timoshenko's beams concerning low computational cost and practical application. …”
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SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development by Ottavia Spiga, Ottavia Spiga, Ottavia Spiga, Anna Visibelli, Francesco Pettini, Bianca Roncaglia, Annalisa Santucci, Annalisa Santucci, Annalisa Santucci

“…Global properties emerged as the most influential feature group in prediction accuracy, followed by structural and sequence information. The model showed superior recall and F1-scores compared to existing computational approaches.DiscussionThese findings establish SHASI-ML as an efficient computational tool for prioritizing immunogenic candidates in Salmonella vaccine development. …”
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Research progress and prospects of coal and gas outburst and composite dynamic disaster warning systems in China by Zhigang ZHANG, Qinghua ZHANG, Jun LIU

“…Based on the current situation of coal and gas outburst and its composite dynamic disaster warning methods and systems, as well as the demand for intelligent warning in coal mines, this paper proposes future research prospects: to carry out research on the coupling mechanism of multiple disasters in deep and high-strength mining mines, develop a quantitative dynamic identification model of multiple indicator systems, and achieve integrated monitoring of multiple disasters and coupled disaster or single disaster classification warning; Carry out precise geological exploration and modeling, combined with technologies such as big data and cloud computing, to more accurately capture dynamic disaster indicator parameters and determine critical warning indicator values; By using mathematical methods, the generalized model for qualitative description of the occurrence mechanism of composite disasters is transformed into a quantitative mathematical model, forming a comprehensive warning model of theory, experience, and data, further improving the accuracy of the warning system.…”
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BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm by Qing Wang, Ning Yan, Yasen Qin, Xuedong Zhang, Xu Li

“…In recent years, object detection algorithms have gained widespread application in tomato disease detection due to their efficiency and accuracy, providing reliable technical support for crop disease identification. …”
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Research on Audit Risk Prediction in Enterprise Management Based on Optimized BP Neural Network Algorithm by Wang Mingming

“…Under the development of enterprise management intelligence, there are more and more studies on the identification and evaluation of audit risks, in order to accurately identify enterprise audit risks, enterprises have created an audit risk identification model with artificial intelligence algorithm as the core, which aims to identify enterprise audit risks with high quality and significantly improve audit efficiency. …”
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Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties by Hyeonbin Moon, Songho Lee, Wabi Demeke, Byungki Ryu, Seunghwa Ryu

“…Abstract Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for modeling and design of thermoelectric devices. However, nonlinear temperature dependence and coupled transport behavior make forward simulation and inverse identification challenging under sparse measurements. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…Abstract Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. …”
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Detecting command injection attacks in web applications based on novel deep learning methods by Xinyu Wang, Jiqiang Zhai, Hailu Yang

“…To address these challenges, a novel detection model, the Convolutional Channel-BiLSTM Attention (CCBA) model, is proposed, leveraging deep learning techniques to enhance the identification of web command injection attacks. …”
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