Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. …”
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On the Question of the Applicability of Artificial Intelligence Systems to Forensic Examination of Documents and Their Requisites by A. F. Kupin, A. S. Kovalenko

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RL-Based Vibration-Aware Path Planning for Mobile Robots’ Health and Safety by Sathian Pookkuttath, Braulio Felix Gomez, Mohan Rajesh Elara

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Cleft Lip and Palate Classification Through Vision Transformers and Siamese Neural Networks by Oraphan Nantha, Benjaporn Sathanarugsawait, Prasong Praneetpolgrang

“…Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models to handle complex, multimodal data and few-shot learning scenarios. Unlike previous studies that rely on single-modality data or traditional machine learning models, we uniquely fuse anatomical data from ultrasound images with functional data from speech spectrograms. …”
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Deep Neural Network-Based Detection of Modulated Jamming in Free-Space Optical Systems: Theory and Performance Under Atmospheric Fading by Manav R. Bhatnagar

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GT-STAFG: Graph Transformer with Spatiotemporal Attention Fusion Gate for Epileptic Seizure Detection in Imbalanced EEG Data by Mohamed Sami Nafea, Zool Hilmi Ismail

“…Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. …”
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Co-folding, the future of docking – prediction of allosteric and orthosteric ligands by Eva Nittinger, Özge Yoluk, Alessandro Tibo, Gustav Olanders, Christian Tyrchan

“…In drug discovery understanding protein structures is essential for comprehending their functions and interactions with drugs. Traditional methods like X-ray crystallography and cryo-electron microscopy have been used to solve these structures. …”
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Shared and distinct neural signatures of feature and spatial attention by Anmin Yang, Jinhua Tian, Wenbo Wang, Liqin Zhou, Ke Zhou

“…Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). …”
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Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks by Marzio Vallero, Emanuele Dri, Edoardo Giusto, Bartolomeo Montrucchio, Paolo Rech

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Movement Sensing Opportunities for Monitoring Dynamic Cognitive States by Tad T. Brunyé, James McIntyre, Gregory I. Hughes, Eric L. Miller

“…We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions.…”
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A Hybrid Multi-Strategy Differential Creative Search Optimization Algorithm and Its Applications by Yuanyuan Zhang, Longquan Yong, Yijia Chen, Jintao Yang, Mengnan Zhang

“…Additionally, the table reinforcement learning model (double Q-learning model) is integrated into the original algorithm to balance the probabilities between exploration and exploitation, thereby accelerating the convergence towards the global optimum. …”
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A comprehensive analysis through Mendelian randomization of immunological markers and sphingolipid metabolism by Dunpeng Yang, Wentian Zhang, Qibin Wang

“…The analysis was supplemented with single-cell expression profiling, functional enrichment analysis, and machine learning approaches. …”
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Exploring the Molecular Mechanism and Role of Glutathione S-Transferase P in Prostate Cancer by Shan Huang, Hang Yin

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TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code by Gary A. McCully, John D. Hastings, Shengjie Xu

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A novel multivariate decomposition-based hybrid model for interpretable multi-step-ahead daily reference evapotranspiration forecasting by Ali Matoog Obaid Lebawi, Mahnoosh Moghaddasi, Mehdi Mohammadi Ghaleni, Mansour Moradi

“…Each predictor was decomposed into Intrinsic Mode Functions (IMFs) to enhance the temporal characteristics of the data. …”
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Revolutionizing Epithelial Differentiability Analysis in Small Airway-on-a-Chip Models Using Label-Free Imaging and Computational Techniques by Shiue-Luen Chen, Ren-Hao Xie, Chong-You Chen, Jia-Wei Yang, Kuan-Yu Hsieh, Xin-Yi Liu, Jia-Yi Xin, Ching-Kai Kung, Johnson H. Y. Chung, Guan-Yu Chen

“…By integrating deep learning and image recognition techniques, we aim to analyze the differentiability of human small airway epithelial cells (HSAECs). …”
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Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks by Waqar Waheed, Qingshan Xu, Muhammad Aurangzeb, Sheeraz Iqbal, Saadat Hanif Dar, Z.M.S. Elbarbary

“…The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. …”
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Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms by Retinderdeep Singh, Sheifali Gupta, Heba G. Mohamed, Salil Bharany, Ateeq Ur Rehman, Yazeed Yasin Ghadi, Seada Hussen

“…The explainability component enables medical professionals to study how the model functions, which creates clear and proven segmentation outputs for better overall reliability. …”
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The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms by Leian Chen, Xiao Zhou, Yanan Qiao, Yu Wang, Zhi Zhou, Shuhong Jia, Yu Sun, Dantao Peng

“…Using surface-based morphometry (SBM), we created vertex-wise maps for group comparisons in terms of five measures: cortical thickness, fractal dimension, gyrification index, Toro’s gyrification index and sulcal depth respectively. Five machine learning (ML) models combining SBM parameters were established to predict AD. …”
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The impact of early life experiences on inhibitory control and working memory by Eva Dydenkova, Francis McGlone, Larisa Mayorova, Larisa Mayorova, Elena Nikolaeva

“…Dramatic environmental events during this period, such as history of institutionalisation, can disrupt optimal developmental pathways, leaving biological scars for life.MethodsThe focus of this study was to investigate the impact of institutionalisation on the development of inhibitory control and working memory in three groups of children matched for age (n = 130; 7.1 ± 2.0 years): (1) early institutionalised (n = 35; age of placement: 6.9 ± 10.6 months; duration of placement: 14.6 ± 10.4 months); (2) late institutionalised (n = 29; age of placement: 49.3 ± 30.6 months; the duration of placement: 16.0 ± 19.4 months); (3) never institutionalised (n = 66).ResultsResults showed that the early institutionalised group had the lowest scores on tests of inhibitory control (p = 0.03), working memory (p = 0.03) and retrieval-based learning (p = 0.04), while the results of the group of late institutionalised children do not differ significantly from never institutionalised.DiscussionThe existence of a sensitive period during the first 18 months of a child’s life is discussed, which determines the formation of the retrieval-based learning mechanism and of inhibiting ineffective experience, for which executive functions are responsible.…”
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