An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

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RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…To address these challenges, we propose the receptive field attention-guided YOLO (RFAG-YOLO) method, an advanced adaptation of YOLOv8 tailored for small-object detection in UAV imagery, with a focus on improving feature representation and detection robustness. To this end, we introduce a novel network building block, termed the receptive field network block (RFN block), which leverages dynamic kernel parameter adjustments to enhance the model’s ability to capture fine-grained local details. …”
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Highly efficient stacking ensemble learning model for automated keratoconus screening by Zahra J. Muhsin, Rami Qahwaji, Ibrahim Ghafir, Mo’ath AlShawabkeh, Muawyah Al Bdour, Saif Aldeen AlRyalat, Majid Al-Taee

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Extracting road maps from high-resolution satellite imagery using refined DSE-LinkNet by Prativa Das, Satish Chand

“…The experiments are performed on a publicly available dataset, DeepGlobe Road Extraction Challenge 2018, to show its efficacy over the D-LinkNet, winner of DeepGlobe Challenge 2018, by achieving IoU of 0.69 with lesser number of parameters and better computational complexity.…”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

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Prediction of acute pancreatitis severity based on early CT radiomics by Mingyao Qi, Chao Lu, Rao Dai, Jiulou Zhang, Hui Hu, Xiuhong Shan

“…CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. …”
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Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms by Manal Abdullah Alohali, Sultan Alahmari, Mohammed Aljebreen, Mashael M. Asiri, Achraf Ben Miled, Sami Saad Albouq, Othman Alrusaini, Ali Alqazzaz

“…It impacts a vast amount of computers. Researchers have proposed numerous malware detection solutions for the past few years. …”
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An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm by Fahd N. Al-Wesabi, Abeer A. K. Alharbi, Ishfaq Yaseen

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Parametric analysis of venturi-type microbubble generator and the bubble fragmentation dynamics by Yi Zhou, Jingyu Cui, Zhen Chen, Jiancong Liu, Lipeng He, Wei Fan, Mingxin Huo

“…Through a comprehensive analysis of suction performance, gas-phase transport modes, and bubble collapse kinetics, we analyzed structural parameters using computational fluid dynamics simulations. …”
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Volume of Fluid (VOF) Method as a Suitable Method for Studying Droplet Formation in a Microchannel by Felipe Santos Paes da Silva, Paulo Noronha Lisboa-Filho

“…This study implements the Volume of Fluid (VOF) method to investigate key physical parameters, including droplet size and the effect of the capillary number on fluid regimes, in droplet generation within a microchannel featuring a T-junction geometry. …”
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Comprehensibility and Prosody Ratings for Pronunciation Software Development by Paul Warren, Irina Elgort, David Crabbe

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Smart crop disease monitoring system in IoT using optimization enabled deep residual network by Ashish Saini, Nasib Singh Gill, Preeti Gulia, Anoop Kumar Tiwari, Priti Maratha, Mohd Asif Shah

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Numerical Study of Optimal Temperature Sensor Placement in Multi-Apartment Buildings with Radiant Floor Heating by Guiqiang Wang, Shilu Li, Haiman Wang

“…The proposed methodology is based on computational fluid dynamics (CFD) simulations of several typical scenarios and quantifies the relationship between the temperature field and the volume-averaged operating temperature to determine the optimal locations for temperature sensors. …”
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An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

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A Hybrid Image Registration for Large Global and Non-Linear Local Deformed Images by Issa W. AlHmoud, Balakrishna Gokaraju, Marwan Bikdash

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RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 by Wenjie Tang, Yangjun Deng, Xu Luo

“…This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. …”
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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…The training dataset consisted of experimental results from SOFC laboratory experiments, comprising 32,843 records with 47 control parameters. The study evaluated the effectiveness of input matrix dimensionality reduction using the following feature importance evaluation methods: mean decrease in impurity (MDI), permutation importance (PI), principal component analysis (PCA), and Shapley additive explanations (SHAP). …”
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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. …”
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Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions by Qun Li, Juntao Zhang, Licheng Hua, Songyin Fu, Chenjie Gu

“…Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. …”
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Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng, Yunguang Gao

“…The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). …”
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