Visual analysis of research trends in diabetes-associated dry eye via bibliometrics by Zhe Yang, Jia-Yi Jiang, Xiao-Hui Zhang

“…With high prevalence of dry eye in Asia, valuable resources like the Korea National Health and Nutrition Examination Survey (KNHANES) database offer crucial data for developing risk prediction models for DADE. Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.…”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…Also, it may be used for prediction of the course of stable angina as it was positively correlated with copeptin and Gensini score.…”
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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

“…In order to verify the adaptation’s reliability and precision, productivity is measured using the error histogram, changeover state, and prediction for addressing the MPV model. Visual representations are used to illustrate the investigation and compare results. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy by Yang Li, Yang Li, Yinan Ding, Yinan Ding, Jinghao Wang, Xiaoxia Wu, Xiaoxia Wu, Dinghu Zhang, Dinghu Zhang, Han Zhou, Han Zhou, Pengfei Zhang, Pengfei Zhang, Guoliang Shao, Guoliang Shao

“…Subsequently, machine learning algorithms were used to predict the classifications and prognoses. …”
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Analysis of Blockchain-Technology by Danylo Dvorchuk, Iryna Shpinareva

“…Future research should focus on developing dynamic sharding techniques, parallel consensus mechanisms, and predictive analytics for transaction management to advance blockchain's applicability in large-scale real-world scenarios. …”
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Towards Less Invasive Instruments For Cardiac Tissue Stabilizing by Moenkemoeller Raphael, Budde Leon, Martin Ulrich, Martens Andreas, Seel Thomas

“…Active motion compensation methods require complex control algorithms and expensive technology. We propose a novel design for a vacuum tissue stabilizer with expandable suction feet enabling a larger stabilized area and increased motion reduction. …”
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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

“…In this study, we explored the role of aging-related genes (ARGs) in UC pathogenesis and diagnosis prediction.Methods: Gene expression data were obtained from four independent datasets (GSE75214, GSE87466, GSE94648, and GSE169568) in the GEO database, and ARGs were derived from multiple public databases. …”
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The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks by Thomas Tasioulis, Evangelos Bagkis, Theodosios Kassandros, Kostas Karatzas

“…Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. …”
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Incidence and Predictors of Acute Kidney Injury Following Advanced Ovarian Cancer Cytoreduction at a Tertiary UK Centre: An Exploratory Analysis and Insights from Explainable Artif... by Elizabeth Ratcliffe, Ciara Devlin, Sarika Munot, Timothy Broadhead, Amudha Thangavelu, Michela Quaranta, David Nugent, Evangelos Kalampokis, Diederick De Jong, Alexandros Laios

“…Mortality rates were similar between patients with and without AKI. AI-driven algorithms highlighted the complexity of AKI prediction and provided individual risk profiles, enabling future stratification and prompting different frequencies of AKI monitoring following cytoreduction. …”
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Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing by Mohammed Alaa Ala'anzy, Raiymbek Zhanuzak, Ramis Akhmedov, Nader Mohamed, Jameela Al-Jaroodi

“…Simulations conducted using the iFogSim toolkit demonstrate that OLB achieves a 28% reduction in latency, a 15% improvement in network usage, a 20% reduction in execution time, a 25% decrease in energy consumption, and a 22% reduction in execution cost compared to existing methods, including the Load Balancing Scheme (LBS), Fog Node Placement Algorithm (FNPA), Load-Aware Balancing (LAB) scheme, and Mobile Edge Computing (MEC). …”
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A mechanism for the emergence of low-dimensional structures in brain dynamics by Claudio Runfola, Spase Petkoski, Hiba Sheheitli, Christophe Bernard, Anthony R. McIntosh, Viktor Jirsa

“…These range from linear algorithms to more intricate non-linear methods for manifold extraction. …”
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Optimizing power network expansion with pumped hydro energy storage using a multi-objective enhanced spider wasp optimizer approach by Mohamed M. Refaat, Saad F. Al-Gahtani, Hussain Bassi, Ziad M. Ali, Shady H. E. Abdel Aleem

“…The ESWO employs a variable reduction technique to simplify the problem complexity and improve performance. …”
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Genomic selection for meat quality traits based on VIS/NIR spectral information by Xi Tang, Lei Xie, Min Yan, Longyun Li, Tianxiong Yao, Siyi Liu, Wenwu Xu, Shijun Xiao, Nengshui Ding, Zhiyan Zhang, Lusheng Huang

“…Among them, the prediction accuracy of Strategy II for fat (%) even improved by 50.7% compared to traditional GS. …”
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Analysis of the Radiological Anatomy of the Proximal Femur after the Intramedullary Nailing of Trochanteric Fractures by Boris A. Maiorov, Igor G. Belenkiy, Gennadii D. Sergeev, Ivan A. Endovitskiy, Mariya A. Sergeeva, David A. Isakhanyan

“…To improve the quality of surgical treatment, it is worth paying an increased attention to the quality of the achieved reduction, implant selection, technical peculiarities of the fixation of types A2 and A3 fractures, improvement of preoperative planning algorithms, as well as development of criteria for intraoperative radiological assessment of the quality of the restoration of the proximal femur anatomy.…”
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Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers by Piya Prajumwongs, Attapol Titapun, Vasin Thanasukarn, Apiwat Jareanrat, Natcha Khuntikeo, Krit Rattanarak, Nisana Namwat, Poramate Klanrit, Arporn Wangwiwatsin, Jarin Chindaprasirt, Supinda Koonmee, Prakasit Sa-Ngiamwibool, Nattha Muangritdech, Sawanya Charoenlappanit, Janthima Jaresitthikunchai, Sittiruk Roytrakul, Watcharin Loilome

“…Feature selection on the training set identified 71 informative peptide mass fingerprints, which were then used to construct predictive models using SVM and RF algorithms. Visualization using heatmap, PLS-DA, and multiclass RF analysis showed clear separation between healthy individuals and HPB cancer patients, as well as among different HPB cancer subtypes. …”
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A Novel Approximation Method for Solving Ordinary Differential Equations Using the Representation of Ball Curves by Abdul Hadi Bhatti, Sharmila Karim, Ala Amourah, Ali Fareed Jameel, Feras Yousef, Nidal Anakira

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Psychometry of Latent Characteristics: Tools, Problems of Rank Information Processing, Solutions by V.G, Mitikhin

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The Role of Artificial Intelligence in Enhancing Diabetic Retinopathy Lesion Detection: A Review by Lakshmi K Santhiya, Sargunam B

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EBOC: Ensemble-Based Ordinal Classification in Transportation by Pelin Yıldırım, Ulaş K. Birant, Derya Birant

“…For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. …”
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