Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

“…Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation. …”
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Computer vision-based road lane glass beads quality analysis system using ANN by Gi-sang Kang, Jong-Jae Lee, Jong Woo Kim, Seung Wan Seo, Hoki Ban

“…This system utilizes computer vision to detect the glass beads and extract their feature data. An Artificial Neural Network (ANN) is then used to classify the refractive index of the glass beads. …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…This review explores the integration of AI techniques, including machine learning (ML), deep learning (DL), and generative models with traditional computational methods such as molecular docking, quantum mechanics, and molecular dynamics simulations. It outlines the evolution of computational chemistry and the transformative role of AI in interpreting complex molecular data, automating feature extraction, and improving decision-making across the drug development pipeline. …”
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Attention-enhanced hybrid deep learning model for robust mango leaf disease classification via ConvNeXt and vision transformer fusion by Ebru Ergün

“…To further improve feature discrimination, a FAM inspired by squeeze-and-excitation networks is integrated into each stage of the backbone. …”
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Enhancing MANET Security Through Long Short-Term Memory-Based Trust Prediction in Location-Aided Routing Protocols by Saad Mohsen Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar

“…Mobile Ad-Hoc Networks (MANETs) face significant security threats due to their decentralized and dynamic topology, making them susceptible to malicious attacks. …”
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OGC API: Ldproxy, an Updated and Contemporary Approach to Geodata Provision in Disaster Mapping by L. F. P. Sulaeman, F.-J. Behr

“…However, it offers an mvt feature format that makes the feature data can be loaded more efficiently and quickly.…”
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An Efficient Aerial Image Detection with Variable Receptive Fields by Wenbin Liu, Liangren Shi, Guocheng An

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Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode by Deo Arpit, Korde Manish, Tiwari Anant, Jain Anant, Choudhary Akash

“…This study enhances financial risk evaluation by providing a flexible, sector-specific forecasting model. …”
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Deep Learning–Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach by Sara Abbasi, Khosro Rezaee

“…To enhance efficiency, we propose a novel architecture integrating a bottleneck attention module into a standard bidirectional long short‐term memory network (BiLSTM). This architecture, adaptable to a convolution bottleneck attention–BiLSTM (CBA‐BiLSTM), classifies signals using data from ankle, leg, and trunk sensors. …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. …”
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Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia by Abebe Temesgen Ayalew, Tarun Kumar Lohani

“…In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under different feature and crop types. The techniques are evaluated in terms of percentage in prediction accuracy. …”
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Efficient compression of encoder-decoder models for semantic segmentation using the separation index by Movahed Jamshidi, Ahmad Kalhor, Abdol-Hossein Vahabie

“…Abstract We present a novel approach to compressing encoder–decoder architectures, particularly in semantic segmentation tasks, by leveraging the Separation Index (SI)—a metric that quantifies how distinctly a network’s feature maps separate different classes at the pixel level. …”
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Urban pulse: measuring pace of life through public camera-based cadence analysis by Pei-Yu Ho, Ming-Kuang Chung, Ling-Jyh Chen

“…The Real-Time Image Collection Module autonomously gathers video streams from a network of public live cameras; the Pedestrian Feature Extraction Module employs the You Only Look Once version 7 (YOLOv7) model along with the Simple Online and Real-Time Tracking (SORT) algorithm to produce time series data of pedestrian characteristics; following this, the Pedestrian Cadence Estimation Module applies signal processing to these data to evaluate pedestrian cadence. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. …”
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Machine learning model optimization for compressional sonic log prediction using well logs in Shahd SE field, Western Desert, Egypt by Khaled Saleh, Walid M. Mabrouk, Ahmed Metwally

“…The methodology involves data preprocessing, feature selection, and training various regression models, including Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN). …”
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Optimising AI writing assessment using feedback and knowledge graph integration by Ci Zhang

“…In this work, the authors provide a novel framework for the effectiveness of AI writing assessment systems by embedding state-of-the-art deep learning networks, user feedback mechanisms, and knowledge graph frameworks. …”
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Semantic-aware style transfer for unsupervised aircraft pose estimation by Qifeng Luo, Danya Zhou, Zhenzhong Wei

“…The framework employs stylized images to train pose estimator and incorporates a pseudo-supervised segmentation loss to enhance cross-domain feature consistency. This loss utilizes pseudo-labels generated via our unsupervised segmentation approach, which exploits intra-image feature self-similarity and inter-image feature mutual similarity to produce accurate masks. …”
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Knowledge distillation-enhanced marine optical remote sensing object detection with transformer and dual-path architecture by Yubin Yuan, Yiquan Wu, Langyue Zhao, Yuqi Liu, Jinlin Chen

“…To balance detection accuracy with computational efficiency, a knowledge distillation framework is implemented where a compact student model learns distilled representations through multi-granularity alignment with a teacher network, encompassing intermediate feature guidance and output-level probability calibration. …”
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Autonomous UAV Detection of <i>Ochotona curzoniae</i> Burrows with Enhanced YOLOv11 by Huimin Zhao, Linqi Jia, Yuankai Wang, Fei Yan

“…By combining the lightweight visual Transformer architecture EfficientViT with the hybrid attention mechanism CBAM, we develop an enhanced YOLOv11-AEIT algorithm: (1) EfficientViT is employed as the backbone network, strengthening micro-burrow feature representation through a multi-scale feature coupling mechanism that alternates between local window attention and global dilated attention; (2) the integration of CBAM (Convolutional Block Attention Module) in the feature fusion neck reduces false detections through dual-channel spatial attention filtering. …”
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Mixture-of-experts graph transformers for interpretable particle collision detection by Donatella Genovese, Alessandro Sgroi, Alessio Devoto, Samuel Valentine, Lennox Wood, Cristiano Sebastiani, Simone Scardapane, Monica D’Onofrio, Stefano Giagu

“…By leveraging attention maps and expert specialization, the model offers insights into its internal decision-making, linking predictions to physics-informed features. We evaluate the model on simulated events from the ATLAS experiment, focusing on distinguishing rare Supersymmetric signal events from Standard Model background. …”
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