Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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MANAGEMENT AND ORGANIZATIONAL AND ECONOMIC CONDITIONS OF STRENGTHENING THE MARKETING ACTIVITY OF THE ENTERPRISE AND MAINTAINING EFFICIENT AGRO BUSINESS by L. Lomovskykh, M. Ponomarova, L. Chip, E. Krivosheya, O. Lisova

“…To strengthen marketing activities and conduct effective agribusiness, algorithms have been developed to gain a competitive advantage. …”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…The proposed approach combines conventional machine learning algorithms (Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and k-Nearest Neighbor (k-NN)) with ensemble methods (Bagging and Voting) in a layered fashion for detecting drought from satellite imagery. …”
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Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks by Mateus Basso, João Paulo da Ponte Souza, Guilherme Furlan Chinelatto, Luis Augusto Antoniossi Mansini, Alexandre Campane Vidal

“…Abstract Machine learning (ML) algorithms have been widely applied across geosciences for tasks such as data conditioning, resolution enhancement, and image classification. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. …”
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Treatment of infections in young infants in low- and middle-income countries: a systematic review and meta-analysis of frontline health worker diagnosis and antibiotic access. by Anne C C Lee, Aruna Chandran, Hadley K Herbert, Naoko Kozuki, Perry Markell, Rashed Shah, Harry Campbell, Igor Rudan, Abdullah H Baqui

“…For study question 1, meta-analysis showed that clinical sign-based algorithms predicted bacterial infection in young infants with high sensitivity (87%, 95% CI 82%-91%) and lower specificity (62%, 95% CI 48%-75%) (six studies, n = 14,254). …”
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The Systemic Immune‐Inflammation Index and Its Association With Biologic Therapy Switching and Response in Psoriasis by Gregg Murray, Niamh Kearney, Conor Smith, Kieran Carty, Yasmine Safta, Olwyn Conlon, Brian Kirby, Ali Alsharqi

“…Integrating SII into psoriasis treatment algorithms may enable clinicians to identify high‐risk patients early, optimise biologic selection, and improve long‐term outcomes. …”
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Postmarketing safety evaluation of pemetrexed using FAERS and JADER databases by Luo Lv, Xiangyang Wu, Yubo Ren, Yuli Guo, Haixiong Wang, Xiaofang Li

“…Disproportionality analysis was performed using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS). …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. In this work, we thoroughly investigate the synergy between neural operator designs and the physical property of Maxwell equations and introduce a physics-inspired AI-based FDTD prediction framework PIC2O-Sim. …”
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Exploring the role of hepsin in prostate cancer: bioinformatics, molecular Docking and molecular dynamics simulations by Hongying Peng, Dan Du, Zhonggui Hu, Zhiliang Xia

“…Using advanced computational techniques such as weighted gene co-expression network analysis (WGCNA), Lasso regression, and random forest algorithms, we pinpointed key genes involved in tumorigenesis. …”
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Novel HIV-1 recombinants spreading across multiple risk groups in the United Kingdom: the identification and phylogeography of Circulating Recombinant Form (CRF) 50_A1D. by Geraldine M Foster, John C Ambrose, Stéphane Hué, Valerie C Delpech, Esther Fearnhill, Ana B Abecasis, Andrew J Leigh Brown, Anna Maria Geretti, UK HIV Drug Resistance Database

“…<h4>Methods and results</h4>A total of 55,556 pol (reverse transcriptase and protease) sequences in the UK HIV Drug Resistance Database were analyzed using Subtype Classification Using Evolutionary Algorithms (SCUEAL). Overall 72 patients shared the same A1/D recombination breakpoint in pol, comprising predominantly MSM but also heterosexuals and injecting drug users (IDUs). …”
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Industry 6.0: methodology, tools, practice by Babkin Aleksandr, Liberman Irina, Klachek Pavel, Shkarupeta Elena

“…By analogy with genetic algorithms (where inheritance, mutation, selection, crossing over, etc. are used), the methodology and technology of DNA engineering of cybersocial metaecosystems include approaches of genetic engineering in an expanded synergetic, interdisciplinary format, which allows using the principles of convergent evolution and NBIC convergence. …”
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Design, Development and Implementation of iALERTS (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 Infection): A Research Protocol by Krishna Mohan Surapaneni, Manmohan Singhal, Ashish Joshi

“…This platform employs machine learning algorithms to predict the likelihood of PASC development among Coronavirus Disease-19 (COVID-19) survivors. …”
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Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES by Xueni Wang, Huihui Chen, Luqiao Wang, Wenguang Sun

“…While advanced machine learning algorithms are gaining recognition as effective tools for clinical prediction, their ability to predict all-cause mortality of MAFLD individuals remains uncertain. …”
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Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis by Yanan Li, Yuqiu Tan, Zengwen Ma, Zengwen Ma, Weiwei Qian, Weiwei Qian

“…Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. …”
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Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India by Anita Raj, Abhishek Singh, Nandita Bhan, Lotus McDougal, Nabamallika Dehingia, Julian McAuley

“…This paper machine learning algorithms paired with qualitative thematic analysis to identify new and potentially modifiable factors influencing MSV in India.Design, setting and participants This cross-sectional analysis of secondary data used data from in-person interviews with ever-married women aged 15–49 who responded to gender-based violence questions in the nationally representative 2015–2016 National Family Health Survey (N=66 013), collected between 20 January 2015 and 4 December 2016. …”
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The impact of an AI-focused ethics education program on nursing students’ ethical awareness, moral sensitivity, attitudes, and generative AI adoption intention: a quasi-experimenta... by Mohammad Abuadas, Zainab Albikawi, Ahmad Rayani

“…GAI technologies present ethical challenges related to patient privacy, algorithmic bias, and informed consent, underscoring the need for structured AI ethics education in nursing curricula. …”
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Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
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Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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