Steganography in IoT: A Comprehensive Survey on Approaches, Challenges, and Future Directions by Maha Driss, Lamia Berriche, Safa Ben Atitallah, Siwar Rekik

“…The study ends with future research directions that underscore the importance of innovative steganographic techniques that strike a compromise between security, efficiency, and scalability in IoT applications.…”
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Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data by Munib Mesinovic, Peter Watkinson, Tingting Zhu

“…We compare standard supervised machine learning algorithms with novel tabular deep learning approaches and find that an integrated XGBoost model in our EHR time-series extraction framework (XMI-ICU) performs best. …”
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Prediction of volcanic fractures based on prestack azimuthal anisotropy: A case study of LFS area in southern Songliao Basin by LI Ning,MIAO He,CAO Kaifang

“…Anisotropic parameter inversion based on pre-stack azimuth gather seismic data is one of the primary methods for fracture prediction, among which two algorithms, RüGER approximate equation and Fourier series expansion, are more widely used. …”
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Combining Terrestrial Laser Scanning and Drone-Based Photogrammetry towards Improving Volume Calculations in Construction Projects by Sharafaddin Th. Muhammed, Fanar M. Abed

“…A low-cost drone was used for data acquisition, whereas Structure from Motion (SFM) and Multi View Stereo (MVS) algorithms were used for processing and analysing the photogrammetric data. …”
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A new method for voxelizing mesh phantoms through cross-sectional images by Raniele Costa de Matos dos Santos, Whoody Alem Wanderley Araripe Farias, José Wilson VIEIRA, Pedro Henrique Avelino de ANDRADE, Jefferson Melo Gonçalves PENA, Vanildo Júnior de Melo LIMA, Fernando Roberto de Andrad LIMA

“…Exposure Computational Models (ECMs) utilize algorithms for ionizing radiation sources and Monte Carlo (MC) codes to simulate radiation transport, its interaction with matter, and to assess the energy deposited in volumes of interest (VOIs) represented by a phantom. …”
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Efficiently Learning an Encoder That Classifies Token Replacements and Masked Permuted Network-Based BIGRU Attention Classifier for Enhancing Sentiment Classification of Scientific... by Muhammad Inaam Ul Haq, Khalid Mahmood, Qianmu Li, Ashok Kumar Das, Sachin Shetty, Majid Hussain

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High-Fidelity Universal Gates in the ^{171}Yb Ground-State Nuclear-Spin Qubit by J. A. Muniz, M. Stone, D. T. Stack, M. Jaffe, J. M. Kindem, L. Wadleigh, E. Zalys-Geller, X. Zhang, C.-A. Chen, M. A. Norcia, J. Epstein, E. Halperin, F. Hummel, T. Wilkason, M. Li, K. Barnes, P. Battaglino, T. C. Bohdanowicz, G. Booth, A. Brown, M. O. Brown, W. B. Cairncross, K. Cassella, R. Coxe, D. Crow, M. Feldkamp, C. Griger, A. Heinz, A. M. W. Jones, H. Kim, J. King, K. Kotru, J. Lauigan, J. Marjanovic, E. Megidish, M. Meredith, M. McDonald, R. Morshead, S. Narayanaswami, C. Nishiguchi, T. Paule, K. A. Pawlak, K. L. Pudenz, D. Rodríguez Pérez, A. Ryou, J. Simon, A. Smull, M. Urbanek, R. J. M. van de Veerdonk, Z. Vendeiro, T.-Y. Wu, X. Xie, B. J. Bloom

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Optimization of Traffic Flow by Allowing Private Cars to Merge into the Bus Lane at Specific Locations by Zhentao Yin, Kai Yuan, Shenshen Zheng

“…We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space-shared bus lane reduces private-car delays and total delays of the system. …”
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Identifying the optimal number of topics in text mining: a case study on reindeer pastoralism literature by Barbara Contiero, Øystein Holand, Giulio Cozzi

“…Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. …”
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Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market by Ikechukwu Kingsley Opara, Douglas Chinenye Divine, Yardjouma Silue, Umezuruike Linus Opara, Jude A. Okolie, Olaniyi Amos Fawole

“…Using historical data at the case study market from 2021 to 2023, different machine learning algorithms such as Random Forest, Gradient boosting, Decision tree, XGBoost, Extra tree and a Stacked Model were applied. …”
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The Application of Artificial Intelligence in Medical Diagnostics: Implications for Sports Medicine by Michał Bolek, Dominika Musialska, Aleksandra Kędzia, Bartosz Jagieła, Monika Fidyk, Magda Minkiewicz, Maciej Dyda

“…As AI continues to reshape healthcare delivery, it is essential to strike a balance between technology and compassionate care. …”
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Using Ray Tracing to Improve Bridge Monitoring With High-Resolution SAR Satellite Imagery by Zahra Sadeghi, Tim Wright, Andrew Hooper, Sivasakthy Selvakumaran

“…While satellite persistent scatterer synthetic aperture radar (SAR) interferometry is an effective technique to monitor the health of structures via selection of long-term coherent pixels, detailed interpretation of displacement measurements requires knowledge of which surfaces, the reflection is coming from. Ray tracing algorithms can be used to simulate SAR backscatter for structures, and link observed PS pixels to specific parts of structures. …”
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Embedded Anchors Coupled Low-Rank Tensor Learning for Multi-View Intrinsic Subspace Clustering by Yueyao Li, Yanying Mei, Zhenwen Ren, Bin Wu

“…The cubic time complexity of both graph construction and spectral clustering has greatly hindered the application of these algorithms to large-scale scenarios. Several research approaches have dramatically improved the clustering productivity by employing the anchor sampling mechanism. …”
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Decentralizing governance: exploring the dynamics and challenges of digital commons and DAOs by Mark Esposito, Mark Esposito, Terence Tse, Danny Goh

“…The conclusion calls for communitarian models that fuse algorithmic rule enforcement with human-centered deliberation to protect the emancipatory potential of blockchain governance. …”
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Trade-Offs Between Simplifying Inertial Measurement Unit–Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations... by Manu Airaksinen, Okko Räsänen, Sampsa Vanhatalo

“…The IMU data can be used for an algorithmic detection of different postures and movements, which may support more detailed assessments of complex behaviors, such as daily activities. …”
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Grid Search Based Hyperparameter-Tuned Deep Learning Model for Osteoporosis Diagnosis with Bi-Cubic Interpolation of X-Ray Images by Ruhul Amin, Md.Shamim Reza, Dewan Ahmed Muhtasim, Jungpil Shin, Md. Maniruzzaman, Md.Mahfujul Hasan

“…Even with the widespread use of deep learning (DL) and machine learning (ML) algorithms, the early diagnosis of osteoporosis patients can still be improved. …”
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Revolutionize 3D-Chip Design With Open3DFlow, an Open-Source AI-Enhanced Solution by Yifei Zhu, Zhenxuan Luan, Dawei Feng, Weiwei Chen, Lei Ren, Zhangxi Tan

“…<italic>Open3DFlow</italic>&#x2019;s open-source nature allows seamless integration of custom AI optimization algorithms. As a showcase, we leverage large language models (LLMs) to help the bonding pad placement. …”
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Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems by Prashant Nene, Dolly Thankachan

“…The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems. • Integrated Intelligence Stack: Combines RL-ENN, T-STFREP, FL-DEO, GNNHSCO, and Q-GAN-ESO into a unified architecture for real-time control, forecasting, decentralized optimization, network routing, and synthetic scenario generation. • Real-Time, Scalable, and Privacy-Preserving: Enables adaptive energy dispatch, federated optimization without compromising data privacy, and graph-based power routing, making it suitable for large-scale, smart grid deployments. • Proven Long-Term Performance: Achieved significant improvements over traditional methods (GA, PSO) with 27.5 % lower NPC, 18.2 % reduction in COE, and 30.2 % increase in battery life, validated using 30 years of meteorological data.…”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. …”
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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting by Agbassou Guenoukpati, Akuété Pierre Agbessi, Adekunlé Akim Salami, Yawo Amen Bakpo

“…This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. …”
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