Effectiveness of mindfulness-based therapy, stress reduction in hypertension and prehypertension: a systematic review by D. I. Nozdrachev, M. N. Solovieva, K. A. Zamyatin

“…The systematic review was prepared according to the PRISMA algorithm with minor modifications. The search algorithm included articles in Russian and English, indexed in the Pubmed/MEDLINE and Cochrane Library databases. …”
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Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease by Ivar R. deVries, Judith O. E. H. vanLaar, Marieke B. van der Hout‐van der Jagt, Sally‐Ann B. Clur, Rik Vullings

“…More research is required to improve performance and determine the benefits to clinical practice.…”
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EDITORIAL: ARTIFICIAL INTELLIGENCE AND ITS TRANSFORMATIVE IMPACT ON SCIENTIFIC PUBLISHING by Abubakar Munir

“…Conventional software such as Turnitin and iThenticate have come a long way, using deep learning algorithms to identify more evolved instances of academic fraud, including paraphrasing plagiarism and AI-generated content. …”
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A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…Second, a semi-supervised ensemble (ESS) framework integrating dynamic pseudo-label screening and uncertainty bias correction (UBC) is established, which can dynamically select pseudo-labels based on local prediction performance improvement and reduce the influence of pseudo-labels’ uncertainty on the model. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…Recent advancements in Unmanned aerial vehicle (UAV) and sensor technologies have enabled high-throughput screening of salt-tolerant crops, offering a more efficient alternative. …”
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In the Refractory Hypertension “Labyrinth”. Focus on Primary Hyperaldosteronism by O. V. Tsygankova, T. I. Batluk, L. D. Latyntseva, E. V. Akhmerova, N. M. Akhmedzhanov

“…It should not only have made the diagnosis easy, but it could have also absolutely justified the surgical tactics. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. <i>Results</i>: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

“…Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. …”
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Identification method of roof rock interface based on response characteristics of drilling parameters by LI Dianshang, LIU Cancan, WANG Chuanbing, REN Bo, REN Shuai, KANG Zhipeng

“…Then, the accuracy of rock interface identification was analyzed using parameters such as penetration rate, revolution per minute, sound pressure level, and torque using the application of the change point detection algorithm, the strucchange model in RStudio software, and the decision tree algorithm. …”
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A FixMatch Framework for Alzheimer&#x2019;s Disease Classification: Exploring the Trade-Off Between Supervision and Performance by Al Hossain, Umme Hani Konok, MD Tahsin, Raihan Ul Islam, Mohammad Rifat Ahmmad Rashid, Mohammad Shahadat Hossain, Karl Andersson

“…While experienced medical professionals can often identify AD through conventional assessment methods, limited resources and growing patient populations make large-scale and rapid screening increasingly necessary. In this work, we explore whether the FixMatch algorithm&#x2014;a semi-supervised learning approach&#x2014;can aid in classifying Alzheimer&#x2019;s Disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) by using the ADNI fMRI dataset of 5,182 images. …”
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Factors Influencing Misinformation Propagation: A Systemic Review by HAN Xi, LIAO Ke

“…This study constructs an integrated model of the influencing factors for misinformation propagation, which can provide direction for targeted interventions and algorithm design to mitigate the spread of misinformation. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…Abstract In today’s digital age, the rapid increase in online users and massive network traffic has made ensuring security more challenging. Among the various cyber threats, phishing remains one of the most significant. …”
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Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. …”
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Gas adsorption meets geometric deep learning: points, set and match by Antonios P. Sarikas, Konstantinos Gkagkas, George E. Froudakis

“…Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. …”
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Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. by Stefano van Gogh, Subhadip Mukherjee, Jinqiu Xu, Zhentian Wang, Michał Rawlik, Zsuzsanna Varga, Rima Alaifari, Carola-Bibiane Schönlieb, Marco Stampanoni

“…Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. …”
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Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs. by Andrew McDonald, Anurag Agarwal, Ben Williams, Nai-Chieh Liu, Jane Ladlow

“…Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.…”
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Examination of Teacher Candidates’ Intercultural Sensitivity Levels by CART Analysis by Nesrin Hark Söylemez

“…The study was conducted on a voluntary basis. A relational screening model was employed to assess the intercultural sensitivity levels. …”
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Integrating status-neutral and targeted HIV testing in Zimbabwe: A complementary strategy. by Hamufare D Mugauri, Owen Mugurungi, Joconiah Chirenda, Kudakwashe Takarinda, Prosper Mangwiro, Mufuta Tshimanga

“…First tests were 65% more likely to test HIV positive (a95%CI: 1.43, 1.91) whilst screened patients were 3.89 times more likely to link to HIV prevention services (a95%CI: 3.05, 4.97), against 25.5% (n = 1,871) linkage among patients not screened.…”
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