Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Future studies are encouraged to expand the use of machine learning models to unravel the complexities of arid climate hydrology further.…”
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Recurrent Neural Network-Based Autoencoder for Problems of Automat-ic Time Series Analysis at Power Facilities by Matrenin P.V., Khalyasmaa A.I., Potachits Y.V.

“…Thus, despite the large amount of data, there is a shortage of labeled data suitable for training, validating and testing the machine learning models. Labeling by an expert takes too much time, so there is an actual task to automatically identify data fragments that are potentially of interest. …”
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The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma by Atsuto Katano

“…Clinical studies have demonstrated notable benefits, such as a reduction in contouring time and improved dosimetric quality using machine learning–based optimization algorithms. …”
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Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

“…When combined with clinical variables in an interpretable machine learning model, GDF15 supports the early identification of patients at high risk for systemic cardiovascular events, facilitating personalized treatment strategies including multidisciplinary specialist referrals and aggressive cardiovascular risk reduction therapy. …”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…MethodsA retrospective analysis was conducted on data from women aged 70+ years with HER2-negative early-stage breast cancer treated at the French Léon Bérard Cancer Center between 1997 and 2016. Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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Research on Partial Least Squares Method Based on Deep Confidence Network in Traditional Chinese Medicine by Wang-ping Xiong, Tian-ci Li, Qing-xia Zeng, Jian-qiang Du, Bin Nie, Chih-Cheng Chen, Xian Zhou

“…This method mainly uses the deep learning model to extract the upper-level features of the original data, putting the extracted features into the partial least squares model for multiple linear regression and evading the problem that selects the number of principal components, continuously adjusting the model parameters until satisfied well-pleased accuracy condition. …”
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Leveraging Green Finance Innovation to Curb Pollution Emissions: Evidence From High-polluting Firms in China by Chunheng Fu, Qing Yu, Yi Liu, Xiaohui Xu

“…A double machine learning model is used to estimate the causal effects. …”
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A Framework for Segmentation and Classification of Cervical Cells Under Long-tailed Distribution by YANG Xiao na, LI Chao wei, SHAO Hui li, HE Yong jun

“…This framework first performs cell nucleus segmentation, uses U-Net as the base model for layer reduction, adds AG module, and uses ACBlock module instead of traditional standard convolutional blocks; then uses ResNeSt for coarse classification of segmented data, fuses manual features extracted based on physicians ′ experience and machine features extracted by ResNeSt network for fine classification , and uses active learning iteratively to expand the cervical cell categories and fuse the ACBlock module in the BBN model to process the long-tail data; finally, the diagnostic indexes of abnormal cells are refined and abnormal cells are screened according to the TBS diagnostic criteria and the physician ′s diagnostic experience. …”
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Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0 by Özlem Sabuncu, Bülent Bilgehan

“…The model was further compared with the PSO algorithm, demonstrating its superiority with a 7.5% reduction in total maintenance cost and a 6.3% decrease in total downtime. …”
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Text classification using SVD, BERT, and GRU optimized by improved Seagull optimization (ISO) algorithm by Yuanyuan Chen, Nan Sun, Yuanbang Li, Rong Peng, Abbas Habibi

“…Over time, techniques for text classification have progressed from rule-based methods to more advanced deep learning and machine learning approaches. Conventional approaches often struggled with the language intricacies, including issues like contextual links, polysemy, and the ambiguity among words. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Experimental results demonstrate that DEGCN achieves a cross-validated classification accuracy of 97.06% ± 2.04% on renal cancer data, outperforming conventional machine learning algorithms and state-of-the-art deep learning models. …”
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A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates by John Soltis, Michelle Ntampaka, Benedikt Diemer, John ZuHone, Sownak Bose, Ana Maria Delgado, Boryana Hadzhiyska, César Hernández-Aguayo, Daisuke Nagai, Hy Trac

“…We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev–Zeldovich observations. …”
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Beyond the numbers: App-enabled stroke prediction system for high-risk individuals in imbalanced datasets by Abrar Faiaz Eram, Aliva Sadnim Mahmud, Marwan Mostafa Khadem, Md Amimul Ihsan

“…Background:: Brain stroke, characterized by interrupted blood flow to the brain, poses significant mortality risks and quality-of-life impacts. While machine learning approaches show promise in stroke prediction, current research often relies on synthetic data to address dataset imbalance, potentially compromising real-world model performance in clinical settings. …”
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Potato leaves disease classification based on generalized Jones polynomials image features by Ala’a R. Al-Shamasneh

“…In order to diagnose potato diseases more accurately and quickly using a machine learning model, this study uses a new feature extraction method based on GJPs image features. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. …”
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Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home by Sinan Chen, Masahide Nakamura

“…In this study, abnormal behaviors in in-home elderly individuals were detected using high-precision pose detection technologies, such as the MoveNet model, based on machine learning. However, there was a discrepancy in pose detection accuracy between the standing and lying positions. …”
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Sample Weighting Methods for Compensating Class Imbalance in Elephant Flow Classification by Piotr Jurkiewicz, Robert Wojcik, Jerzy Domzal

“…However, the inherent class imbalance between elephant and mouse flows presents a challenge for machine learning models, often leading to poor classification accuracy. …”
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Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks by Haithem Ben Chikha, Alaa Alaerjan, Randa Jabeur

“…The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. …”
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Optimizing Euclidean Distance Computation by Rustam Mussabayev

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Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference by Deepika R., Pradeep Kumar T.S.

“…This study especially focuses on incorporating machine learning and deep learning techniques in the fog computing environment to leverage the overall diagnostic efficiency of dengue by promoting remote diagnosis and speedy treatment. …”
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