Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…The encoder network extracts meaningful features from seismic signals while the projection head refines latent space representation. …”
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Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data by Abayomi Danlami Babalola, Kayode Francis Akingbade, Daniel Olakunle

“…Ensemble learning exhibits the highest overall accuracy, while SVM and ANN demonstrate strengths in specific aspects of prediction. …”
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Responsible AI in structural engineering: a framework for ethical use by Vagelis Plevris, Haidar Hosamo

“…This study addresses these issues through eight fictional but realistic case studies that illustrate plausible ethical dilemmas, such as algorithmic bias in predictive models and tensions between AI-generated recommendations and human engineering judgment. …”
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Among the DLTs: Holochain for the Security of IoT Distributed Networks—A Review and Conceptual Framework by Shereen Ismail, Raouf Mehannaoui, Eden Teshome Hunde, Hassan Reza

“…Holochain, an emerging distributed ledger technology (DLT), offers the benefits of blockchain while overcoming its limitations, such as the reliance on consensus algorithms and a globally synchronized ledger. …”
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Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk, Marko Valčić

“…The analysis reveals distinct variable-specific uncertainty trends with wind speed forecasts exhibiting significant temporal fluctuation (RMSE increasing from 0.5 to 4.0 m/s), while significant wave height forecasts degrade in a more stable and predictable pattern (from 0.2 to 0.9 m). …”
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Federation in Digital Twins and Knowledge Transfer: Modeling Limitations and Enhancement by Alexios Papacharalampopoulos, Dionysios Christopoulos, Olga Maria Karagianni, Panagiotis Stavropoulos

“…Two sequential cases are considered: the first one is used to study the type of knowledge that is required from modeling and federation; while the second one investigates the creation of a more suitable form of modeling.…”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…The proposed procedure ensures building models of outstanding predictive power while using small training datasets, which is beyond the capabilities of benchmark algorithms.…”
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An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data by Siqi Lai, Brian Deal

“…This study integrates big data (online reviews, images) and small data (survey results) with large language models (LLMs) and object detection algorithms to analyze public perceptions of urban parks in Stockholm, New York, and Shanghai. …”
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Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge‐Based Framework and Deep Learning by Parnian Ghaneei, Ehsan Foroumandi, Hamid Moradkhani

“…The results reveal that the combination of clustering algorithms with the PCA method may lead to loss of information while the nonlinear method (UMAP) can extract more informative features. …”
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A happy place to be in: how beliefs about living places influence depression in Chinese international student returnees by Ruining Jin, Meiling Yin, Qiang Shen, Tam-Tri Le

“…Depression levels were measured using the Patient Health Questionnaire-9 (PHQ-9), and Bayesian analysis with Markov Chain Monte Carlo (MCMC) algorithms was employed for statistical examination. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. …”
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A Novel Toolbox for Generating Realistic Biological Cell Geometries for Electromagnetic Microdosimetry by Mehrdad Saviz, A.H. Buchali Safiee, Elham Sharifi

“…However, biological shapes are often extremely irregular, while conventional geometrical modeling tools on the market cannot meet the demand for fast and efficient construction of irregular geometries. …”
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Impact of EU Laws on AI Adoption in Smart Grids: A Review of Regulatory Barriers, Technological Challenges, and Stakeholder Benefits by Bo Nørregaard Jørgensen, Saraswathy Shamini Gunasekaran, Zheng Grace Ma

“…The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. …”
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Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

“…With the accumulation of solar observation data and the development of data-driven algorithms, deep learning methods have been widely used to build solar flare prediction models. …”
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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis by V. S. G. S. Phaneendra Bottu, K. Ragavan

“…These systems can improve adaptable user interfaces and support music therapy. While prior research has explored algorithms for FER and their effectiveness in identifying emotions, existing solutions often lack optimal accuracy in pairing emotion recognition with music recommendations, particularly in real-world contexts with diverse user preferences. …”
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Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography. by Mahesh B Nagarajan, Paola Coan, Markus B Huber, Paul C Diemoz, Axel Wismüller

“…However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. …”
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Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…Experiments conducted on multiple public datasets, including MOT17, MOT20, and VisDrone-MOT, demonstrate that this method remarkably improves tracking accuracy while maintaining real-time performance. On the MOT17 test set, it achieves 63.7% in HOTA, 61.4 FPS in processing speed, and 79.4% in IDF1, outperforming current state-of-the-art tracking algorithms.…”
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Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions. by Yiye Zhang, Yufang Huang, Anthony Rosen, Lynn G Jiang, Matthew McCarty, Arindam RoyChoudhury, Jin Ho Han, Adam Wright, Jessica S Ancker, Peter Ad Steel

“…Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. …”
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Security and Privacy in the Internet of Everything (IoE): A Review on Blockchain, Edge Computing, AI, and Quantum-Resilient Solutions by Haluk Eren, Özgür Karaduman, Muharrem Tuncay Gençoğlu

“…Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. …”
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Optimized DINO model for accurate object detection of sesame seedlings and weeds by Yong Wang, ShunFa Xu, ZhenYuan Ye, KongHao Cheng

“…Specifically, $$\text {AP}_{S}$$ and $$\text {AP}_{M}$$ increased by 3.3% and 3.8%, respectively, while $$\text {AP}_{50}$$ and $$\text {AP}_{75}$$ increased by 2.3% and 3.2%. …”
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