Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics by Saima Rashid, Ayesha Siddiqa, Fekadu Tesgera Agama, Nazeran Idrees, Mohammed Shaaf Alharthi

“…Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV’s progression and lack elucidation. …”
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Optimized CNN-Bi-LSTM–Based BCI System for Imagined Speech Recognition Using FOA-DWT by Meenakshi Bisla, Radhey Shyam Anand

“…EEG signal is enhanced using firefly optimization algorithm (FOA)–based optimized soft thresholding of high-frequency detail components obtained by DWT decomposition. …”
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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. …”
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Exact Quantum Algorithms for Quantum Phase Recognition: Renormalization Group and Error Correction by Ethan Lake, Shankar Balasubramanian, Soonwon Choi

“…Importantly, the error-correction threshold is proven to coincide exactly with the phase boundary. …”
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Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks by Zhijin Zhang, He Li, Lei Chen, Ping Han

“…To improve feature learning ability and accurately diagnose the faults of rolling bearings under a strong background noise environment, we present a new shrinkage function named leaky thresholding to replace the soft thresholding in the deep residual shrinkage networks (DRSNs). …”
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Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis by Yu Xue, Ying Zhou, Tingrui Wang, Huijuan Chen, Lingling Wu, Huayun Ling, Hong Wang, Lijuan Qiu, Dongqing Ye, Bin Wang

“…Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored. …”
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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem by Junhee Lee, Heechan Chae, Seungwook Son, Jongwoong Seo, Yooil Suh, Jonguk Lee, Yongwha Chung, Daihee Park

“…Then, the trained base model is improved through self-training, where a super-low threshold is applied to filter pseudo-labels. The experimental results show that the proposed system significantly improved the average precision (AP) from 36.86 to 90.62 under domain shift conditions, which achieved a performance close to fully supervised learning while relying solely on SLOT data. …”
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Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images by Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen, Shiqing Wei, Mingming Xu, Shanwei Liu

“…To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. …”
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Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics by Jinghong Pei BD, Jing Yu BD, Ping Ge BD, Liman Bao BD, Haowen Pang MS, Huaiwen Zhang MS

“…Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. …”
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Simultaneous multi-class detection of interplanetary space weather events by Nguyen Gautier, Bernoux Guillerme, Ferlin Antoine

“…Previous studies revealed the efficiency of deep-learning based methods for this task over traditional threshold-based techniques. …”
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Efficacy of different surgical approaches in the clinical and survival outcomes of patients with early-stage cervical cancer: protocol of a phase III multicentre randomised control... by Lei Li, Ming Wu, Xiaopei Chao, Shuiqing Ma, Xianjie Tan, Sen Zhong, Jinghe Lang, Aoshuang Cheng, Wenhui Li

“…However, the influencing factors of research centres and the learning curves of surgeons in these studies lacked sufficient evaluation. …”
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Multi-dimensional time series anomaly detection method based on VAE-WGAN by Xueyuan DUAN, Yu FU, Kun WANG

“…As the deficiency of learning ability of traditional semi-supervised depth anomaly detection model to unbalanced multidimensional data distribution and the difficulty of model training, a multi-dimensional time series anomaly detection method based on VAE-WGAN architecture was proposed.VAE was used as a generator of WGAN.The Wasserstein distance was used as a measure between the model fitting distribution and the real distribution of the data to be measured, complex and high-dimensional data distributions could be learned.A sliding window was applied to divide the time series, the normal sequence data were used to train the model.According to the abnormal score of the waiting test sequence in the trained model, the anomaly was judged with adaptive threshold technology.The experimental results show that the model is easy to train and stable, and has obvious improvement over the existing generative anomaly detection model in accuracy, recall rate, F1 score and other anomaly detection performance indicators.…”
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Oil Spill Detection using Convolutional Neural Networks and Sentinel-1 SAR Imagery by E. Kalogirou, E. Kalogirou, K. Christofi, K. Christofi, D. Makri, D. Makri, M. A. Iqbal, V. La Pegna, M. Tzouvaras, M. Tzouvaras, C. Mettas, C. Mettas, D. Hadjimitsis, D. Hadjimitsis

“…Preprocessing involved a thresholding technique to enhance feature extraction and improve classification precision. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Notably, age and WBC parameters demonstrated threshold effects, where optimal cutoff values enabled re-calibration of single-variable predictive scores. …”
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Research on the condition monitoring method of unmanned aerial vehicle based on improved multivariate state estimation technique by Hang Zhou, Jinju Zhou, Yunchen Li, Fanger Cai

“…Firstly, the IMSET constructs memory matrix (MM) by dynamic selection with incremental learning to improve the accuracy of estimation. Secondly, the exponentially weighted moving average (EWMA) is employed to mitigate the impact of measurement errors in condition vectors and then the threshold is set by probability distribution to reduce the dependence on human experience. …”
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Metformin Protective Effects in LPS-Induced Alzheimer's Disease Mice Model: NO-cGMP-KATP Pathway Involvement by Mojtaba Dolatshahi, Ali Khorsandinezhad, Behnam Ghorbanzadeh, Yousef Paridar, Donya Nazarinia

“…LPS treatment diminished the threshold of pain perception in hot-plate test, while metformin didn’t have any significant effect. …”
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Association between the stress hyperglycemia ratio and all-cause mortality in patients with hemorrhagic stroke: a retrospective analysis based on MIMIC-IV database by Wei Zhu, Wei Zhu, Dingke Wen, Lijuan Duan, Lijuan Duan, Chaofeng Fan, Chaofeng Fan, Yan Jiang, Yan Jiang

“…Restricted cubic spline analysis demonstrated non-linear associations between SHR and all-cause mortality at 28 and 90 days (p-non-linear < 0.05), while the overall trend remained significantly positive. The machine learning models identified SHR as a key predictor, with area under the curves (AUC) of 0.771 (28-day), 0.778 (90-day), and 0.778 (365-day).ConclusionThis study revealed threshold-dependent associations between the SHR and short- and long-term all-cause mortality in patients with hemorrhagic stroke. …”
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Fault Detection of Multi-Rate Two Phase Reactor Condenser System with Recycle Using Multiple Probabilistic Principal Component Analysis by Dhrumil Gandhi, Meka Srinivasarao

“…Common techniques used for fault detection include threshold-based detectors, statistical detectors, and machine learning-based detectors. …”
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Ukryte w języku aspekty przygotowania dzieci do szkoły by Grażyna Szyling

“…In this paper I reconstruct the image of school generated by children a short while before they cross their first educational threshold. I am interested in the aspect of school readiness hidden in language, which is anchored in culture, social life and children’s experiencing of the world. …”
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Multi-dimensional time series anomaly detection method based on VAE-WGAN by Xueyuan DUAN, Yu FU, Kun WANG

“…As the deficiency of learning ability of traditional semi-supervised depth anomaly detection model to unbalanced multidimensional data distribution and the difficulty of model training, a multi-dimensional time series anomaly detection method based on VAE-WGAN architecture was proposed.VAE was used as a generator of WGAN.The Wasserstein distance was used as a measure between the model fitting distribution and the real distribution of the data to be measured, complex and high-dimensional data distributions could be learned.A sliding window was applied to divide the time series, the normal sequence data were used to train the model.According to the abnormal score of the waiting test sequence in the trained model, the anomaly was judged with adaptive threshold technology.The experimental results show that the model is easy to train and stable, and has obvious improvement over the existing generative anomaly detection model in accuracy, recall rate, F1 score and other anomaly detection performance indicators.…”
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