Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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A method for high-accuracy localization of microseismic sources in mines based on finite element simulation by Dianyan NING, KaiPeng ZHU, Yongsheng ZHU, Shuxia YUAN, Zhenfang ZHOU

“…Presently, when used for acoustic source localization, the time difference of arrival (TDOA) method is facing issues including high algorithmic complexity, significant impacts of probe arrangement on the localization accuracy, and low adaptability to complex stratigraphic structures. …”
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Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu by Raga Saputra Heri Istanto, Fitra Abdurrachman Bachtiar, Achmad Ridok

“…These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%. …”
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Measuring mortality due to HIV-associated tuberculosis among adults in South Africa: Comparing verbal autopsy, minimally-invasive autopsy, and research data. by Aaron S Karat, Mpho Tlali, Katherine L Fielding, Salome Charalambous, Violet N Chihota, Gavin J Churchyard, Yasmeen Hanifa, Suzanne Johnson, Kerrigan McCarthy, Neil A Martinson, Tanvier Omar, Kathleen Kahn, Daniel Chandramohan, Alison D Grant

“…<h4>Methods and findings</h4>Decedents were enrolled into a trial of empirical TB treatment or a cohort exploring diagnostic algorithms for TB in South Africa. The WHO 2012 instrument was used; VA CoD were assigned using physician-certified VA (PCVA), InterVA-4, and SmartVA-Analyze. …”
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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. …”
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Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke by Yuqi Tang, Sixian Hu, Yipeng Xu, Linjia Wang, Yu Fang, Pei Yu, Yaning Liu, Jiangwei Shi, Junwen Guan, Ling Zhao

“…We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. …”
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Equitable and effective clinical guidance development and dissemination: trauma aims to lead the way by Deborah M Stein, Elliott R Haut, Eileen M Bulger, Vanessa P Ho, Andres M Rubiano, Ashley Farley, Lacey N LaGrone, Danielle J Wilson, Maria Michaels, Meghan B Lane-Fall, Michael A Person, Linda Reinhart

“…Suggestions include (1): professional societies collaborate rather than compete for guideline development.(2) Design primary clinical research for implementation, and where relevant, with guideline development in mind.(3) Select clinical topics for guideline development through systematic prioritization, with an emphasis on patient-centered outcomes.(4) Develop guideline authorship groups with a focus on transparency, equity of opportunity and diversity of representation.(5) Establish a plan for regular review and updating and provide the date the guideline was last updated for transparency.(6) Integrate options for adapting the guideline to local resources and needs at the time of development.(7) Make guidelines available on a platform that allows for open feedback and utilization tracking.(8) Improve discoverability of guidelines.(9) Optimize user-experience with a focus on inclusion of bedside-ready, mobile-friendly infographics, tables or algorithms when feasible.(10) Use open access and open licenses.(11) Disseminate clinical guidance via comprehensive and equitable communication channels. …”
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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. …”
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Potential distribution of endemic lizards from Brazilian restingas: The present announcing the end by Hugo Andrade, Luisa Maria Diele‐Viegas Costa Silva, Carlos Frederico Duarte Rocha, Antônio Jorge Suzart Argôlo, Eduardo José dos Reis Dias

“…Here, we used an ensemble of three modeling algorithms (Bioclim, GLM, and SVM). In predicting the effects of climate change on their future distributions, we used intermediate and pessimistic socio‐economic pathway scenarios (SSP3 70 and SSP5 85, respectively) considering projections for 2081–2100. …”
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Artificial Intelligence Performance in Cardiac Magnetic Resonance Strain Analysis for Aortic Stenosis: Validation with Echocardiography and Healthy Controls by Žygimantas Abramikas, Ieva Jasiukevičiūtė, Giedrė Balčiūnaitė, Sigita Glaveckaitė, Darius Palionis, Nomeda Valevičienė

“…However, the higher error rates in AS patients compared to controls underscore the need for more advanced AI algorithms to improve performance in cardiac pathology.…”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data by Jialiang Lin, Linjuan Huang, Weiming Li, Haijun Xiao, Mingmang Pan

“…Drug prediction highlighted Thymoquinone and Erlotinib as potential therapeutic candidates. Machine learning algorithms (SVM-RFE, LASSO and RF) identified BCL2 and 和FOXP2 as candidate hub DORGs for DFU diagnosis. …”
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Safety profile of ramelteon from the perspective of physicians and pharmacists: a 20-year real-world pharmacovigilance analysis based on the FAERS database by Jianhong Wu, Yujie Zhou, Linghe Qiu, Liang Liu, Fei Wang, Lili Zhen, Na Li

“…Signal detection was performed using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms. Results A total of 1,150 reports related to ramelteon were analyzed, covering 26 System Organ Classes (SOC) and 537 preferred terms (PT). …”
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Three-dimensional linkage analysis with digital PCR for genome integrity and identity of recombinant adeno-associated virus by Tam Duong, Michele Firmo, Chien-Ting Li, Bingnan Gu, Peng Wang

“…Particularly, we rigorously tested our algorithms by conducting experiments involving the mixing of seven DNA fragments to represent various AAV viral genome populations, including 3 single partials, 3 double partials, and 1 full-length genomes. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR. …”
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Surgical interventions in velopharyngeal dysfunction: comparative perceptual speech and nasometric outcomes for three techniques by Ryan Instrum, Agnieszka Dzioba, Anne Dworschak-Stokan, Murad Husein

“…We hope to aid in the eventual creation of treatment algorithms for VPD, allowing practitioners to tailor surgical technique selection to patient factors. …”
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Identification Exploring the Mechanism and Clinical Validation of Mitochondrial Dynamics-Related Genes in Membranous Nephropathy Based on Mendelian Randomization Study and Bioinfor... by Qiuyuan Shao, Nan Li, Huimin Qiu, Min Zhao, Chunming Jiang, Cheng Wan

“…<b>Methods:</b> Comprehensive bioinformatics analyses—encompassing Mendelian randomization, machine-learning algorithms, and single-cell RNA sequencing (scRNA-seq)—were employed to interrogate transcriptomic datasets (GSE200828, GSE73953, and GSE241302). …”
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Machine learning prediction of permeability distribution in the X field Malay Basin using elastic properties by Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, Abdul Halim Abdul Latif, Ida Bagus Suananda Yogi, Said Jadid A. Kadir

“…Several ensemble-based models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightBoost), Categorical Gradient Boosting (CatBoost), Bagging Regressor, Random Forest and Stacking, were evaluated for predictive performance, along with Multi-Layered Perceptron Neural Network algorithms. Additionally, Tree-structured Parzen Estimator-Bayesian optimization method was utilized to optimize the machine learning model's hyperparameters and improve accuracy. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
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Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling by Shanmugam Vijayakumar, Palanisamy Shanmugapriya, Pasoubady Saravanane, Thanakkan Ramesh, Varunseelan Murugaiyan, Selvaraj Ilakkiya

“…Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. …”
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