Reversible Spectral Speech Watermarking with Variable Embedding Locations Against Spectrum-Based Attacks by Xuping Huang, Akinori Ito

“…Significant progress in machine learning and speech synthesis has increased the potential for audio tampering. …”
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An overlapping sliding window and combined features based emotion recognition system for EEG signals by Shruti Garg, Rahul Kumar Patro, Soumyajit Behera, Neha Prerna Tigga, Ranjita Pandey

“….; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.…”
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Demonstration of high-reconfigurability and low-power strong physical unclonable function empowered by FeFET cycle-to-cycle variation and charge-domain computing by Taixin Li, Xinrui Guo, Franz Müller, Sukhrob Abdulazhanov, Xiaoyang Ma, Hongtao Zhong, Yongpan Liu, Vijaykrishnan Narayanan, Huazhong Yang, Kai Ni, Thomas Kämpfe, Xueqing Li

“…Furthermore, we show that the PUF is robust against parameter variations and resilient to machine learning (ML) attacks. These performances highlight the great promise of the FeFET-based strong PUF as a feasible IoT security solution.…”
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Trends and drivers of dissolved organic carbon in major Arctic rivers by Mingxin Song, Jue Huang, Desong Zhao, Yulei Mu

“…After comparing multiple empirical and machine learning models, the random forest (RF) model with best performance was selected. …”
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Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? by Donatien Legé, Pierre-Henri Murgat, Russell Chabanne, Kevin Lagarde, Clément Magand, Jean-François Payen, Marion Prud'homme, Yoann Launey, Laurent Gergelé

“…Adding more features to the input set and/or using nonlinear machine learning algorithms did not significantly improve classification performance. …”
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An annotated heterogeneous ultrasound database by Yuezhe Yang, Yonglin Chen, Xingbo Dong, Junning Zhang, Chihui Long, Zhe Jin, Yong Dai

“…However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. …”
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Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs by Hanlin Feng, Zitong Zhang, Chunlei Zhang, Chengcheng Zhong, Qiaoyu Ma

“…., to construct a lithology identification model using SVA-TCN. Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. …”
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Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions by Ying Jia He, Pin Lin Liu, Tao Wei, Tao Liu, Yi Fei Li, Jing Yang, Wen Xing Fan

“…Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. …”
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Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar by Justin E. Stopa, Chen Wang, Doug Vandemark, Ralph Foster, Alexis Mouche, Bertrand Chapron

“…These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. …”
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Extending TextAE for annotation of non-contiguous entities by Jake Lever, Russ Altman, Jin-Dong Kim

“…Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. …”
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Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model by Wangyang Hu, Chaolong Zhang, Laijin Luo, Shanhe Jiang

“…Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. …”
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A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME by Ahmed M. Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E. Petersen, Karim Lekadir, Gloria Menegaz

“…eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. …”
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Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning With Synthetic Pattern Generation by Xinguang Zhang, Shiyang Chen, Zhouhang Shao, Yongjie Niu, Li Fan

“…Lithographic hotspot detection is crucial for ensuring manufacturability and yield in advanced integrated circuit (IC) designs. While machine learning approaches have shown promise, they often struggle with detecting truly-never-seen-before (TNSB) hotspots and reducing false alarms on hard-to-classify (HTC) patterns. …”
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Nuclear Fusion Pattern Recognition by Ensemble Learning by G. Farias, E. Fabregas, I. Martínez, J. Vega, S. Dormido-Canto, H. Vargas

“…It is impossible to do a complete analysis of this data manually, and it is essential to automate this process. That is why machine learning models have been used to this end in previous years. …”
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Advances in Surface-Enhanced Raman Spectroscopy for Therapeutic Drug Monitoring by Huasheng Lai, Xinlan Wang, Menghan Qi, Hao Huang, Bingqiong Yu

“…We discuss the challenges faced by SERS for TDM, such as substrate signal reproducibility and matrix interference from complex biological samples, and explore solutions like digital colloid-enhanced Raman spectroscopy, enrichment detection strategies, microfluidic SERS, tandem instrument technologies, and machine learning-enabled SERS. These advancements address the limitations of traditional SERS applications and improve analytical efficiency in TDM. …”
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From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts by Arjun Neyyathala, Edvin Fako, Sandip De, Daria Gashnikova, Florian Maurer, Jan‐Dierk Grunwaldt, Stephan A. Schunk, Schirin Hanf

“…By employing local environment descriptors, unsupervised machine learning and density functional theory, the structure‐performance relationships are examined. …”
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Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…Several deep-learning and traditional machine-learning models have been used to detect AD. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations by Prashanth Choppara, Bommareddy Lokesh

“…The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). …”
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Approximate CNN Hardware Accelerators for Resource Constrained Devices by P Thejaswini, Gautham Suresh, V. Chiraag, Sukumar Nandi

“…The performance of our proposed architectures demonstrates significant acceleration and reduced power consumption compared to popular edge machine learning framework - TinyML TensorFlow Lite. FHA achieves a significant accuracy improvement of 6.2% along with a speedup of 1.07x and AFHA achieves accuracy enhancement of 4.3% and an impressive speedup of 1.42x.…”
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Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care. by Dirk H van Dalen, Angèle P M Kerckhoffs, Esther de Vries

“…<h4>Conclusion</h4>The identified potential predictors of being frail can be used as evidence-based input for machine learning based automated estimation of the probability of being frail using routine EHR data in the near future.…”
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