Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview by Imran Ul Haq, Mustafa Mhamed, Mohammed Al-Harbi, Hamid Osman, Zuhal Y. Hamd, Zhe Liu

“…By extracting novel characteristics from diverse medical data sources, advanced identification techniques known as multimodal learning may be applied, enabling algorithms to analyze data from various sources and eliminating the need to train each modality. …”
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Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods by Wei Guo, Hao Chen, Feng Wang, Yingjiao Chi, Wei Zhang, Shan Wang, Kezhu Chen, Hong Chen

“…BackgroundSeptic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. …”
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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks by Shayla Islam, S.M. Topazal, Mohammad Kamrul Hasan, Raenu AL Kolandaisamy, Huda Saleh Abbas, Nguyen Vo, Rahul Thakkar, Muhammad Attique Khan, Deepak Gupta, Dina Abdulaziz AlHammadi

“…The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. …”
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Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia by Minghui Wang, Huajian Xian, Xiaoli Xia, Wenjie Zhang, Zixuan Huang, Chaoqun Lu, Yuling Zheng, Yixin Wang, Shufeng Xie, Renyao Pan, YaoYifu Yu, Ruiheng Wang, Huijian Zheng, Guorui Huang, Han Liu

“…Results The ERCDI demonstrated strong prognostic and predictive performance for prognosis in AML patients. …”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…It employs machine learning algorithms to construct diagnostic models, including an optimal 8-gene model (GATA2, IL8, LAT, NOLC1, SMARCA5, SMC3, STX10, ZMIZ1), which demonstrates high predictive performance (AUC of 0.964 in training datasets and 0.884 in testing datasets). …”
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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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Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies by Seyyed Ali Hosseini, Ghasem Hajianfar, Brandon Hall, Stijn Servaes, Pedro Rosa-Neto, Pardis Ghafarian, Habib Zaidi, Mohammad Reza Ay

“…Abstract Purpose This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-small cell lung cancer (NSCLC). …”
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LEGAL REGULATION OF ACADEMIC TITLES IN GERMANY by A. Sokolov, O. Lakaev

“…This paper identiied the decentralized state of legal regulation of higher education with the deinition at the Federal level, the General principles of regulation of educational relations in higher education and detailed regulation in the legislation of the Federal States, where the establishment of scientiic (academic) ranks depends on the discretion of the legislator and embedded algorithm development career teacher through the gradual replacement of positions in the University and ful-illing the necessary qualiication requirements for their replacement. …”
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Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study by Maik JM Beuken, Melanie Kleynen, Susy Braun, Kees Van Berkel, Carla van der Kallen, Annemarie Koster, Hans Bosma, Tos TJM Berendschot, Alfons JHM Houben, Nicole Dukers-Muijrers, Joop P van den Bergh, Abraham A Kroon, Iris M Kanera

“…To identify a subset of distinct, minimally redundant, predictive variables, we used the statistically equivalent signature algorithm. …”
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Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field by Fan Jiang, Shikun Dai, Ying Zhang, Hongjun Tian

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Diagnosis and treatment of the functional dyspepsia: clinical guidelines of the Russian Gastroenterological Association by V. T. Ivashkin, I. V. Mayev, A. A. Sheptulin, T. L. Lapina, A. S. Trukhmanov, I. M. Kartavenko, V. A. Kiprianis, O. Z. Okhlobystina

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Essay Answer Detection System Uses Cosine Similarity and Similarity Scoring in Sentences by Siti Yuliyanti, Diah Hidayatul Ula

“…To improve accuracy and usability, future developments could use sophisticated text scoring algorithms and provide more features. This study highlights the importance of automated grading systems for optimizing essay scoring in educational settings while maintaining scalability and reliability. …”
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Additional lung – a rare malformation by S. A. Karavaeva, A. V. Podkamenev, T. V. Patrikeeva, M. M. Aleshugin, M. V. Golubeva, А. G. Li, R. A. Ti, D. A. Malekov, A. I. Tashchilkin, O. A. Murashova

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Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremit... by Jiawen Deng, BHSc, Myron Moskalyk, BHSc, MSc, Madhur Nayan, MD, PhD, Ahmed Aoude, MEng, MD, FRCSC, Michelle Ghert, MD, FRCSC, Sahir Bhatnagar, PhD, Anthony Bozzo, MSc, MD, FRCSC

“…LightGBM was identified as the best-performing algorithm for both outcomes. For 1-year mortality prediction without percent necrosis, LightGBM achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% confidence interval [CI] 0.70-0.86) during cross-validation and 0.72 on internal validation. …”
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Exposing Face Manipulation Based on Generative Adversarial Network–Transformer and Fake Frequency Noise Traces by Qiaoyue Man, Young-Im Cho

“…In recent years, with the application of GANs and diffusion generative network algorithms, many highly realistic synthetic images are emerging, greatly increasing the potential for misuse, and deepfakes have become a serious social concern. …”
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Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states by Prince Achankunju, Saroj Kumar Dash

“…This sophisticated estimation algorithm iteratively predicts and refines the state based on current estimates, combining model predictions with observed data to enhance accuracy, making it effective for estimating critical parameters in dynamic systems. …”
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Sparse long short-term memory for information fusion in wireless sensor networks by Yangfan Zhou, Mingchuan Zhang, Ping Xie, Junlong Zhu, Ruijuan Zheng, Qingtao Wu

“…Furthermore, the prediction accuracy of the sparse long short-term memory algorithm is almost the same as the other comparison algorithms.…”
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Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN by Yousuf Babiker M. Osman, Wei Li

“…A soft sensor model is studied to predict the BOD5 in a wastewater treatment plant to evaluate the proposed approach. …”
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How Re-Infections and Newborns Can Impact Visible and Hidden Epidemic Dynamics? by Igor Nesteruk

“…With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients.…”
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Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review by Abdulrahman Ahmed Aseri

“…AI-based technologies, such as machine learning algorithms, neural networks, and computer vision, are being increasingly utilized to interpret diagnostic imaging, predict implant outcomes, and customize treatment protocols tailored to individual patient needs. …”
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