Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults by Zhenhua Niu

“…IntroductionFault diagnosis analysis of mechanical equipment is greatly significant for maintaining the production efficiency of enterprises. Traditional diagnostic methods have shortcomings in accuracy and robustness.MethodsTherefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using dropout methods, and proposes a hybrid diagnostic analysis model that combines improved autoencoder algorithms and signal reconstruction.ResultsThe experiment outcomes indicated that under the slow degradation mode of the bearing, the precision, recall, F1 score, and overall accuracy of the improved autoencoder model were 0.931, 0.933, 0.920, and 0.939, respectively, which were better than the pre-modified model. …”
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Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges by Sahar Shokrpour, AmirMehdi MoghadamFarid, Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Akbari, Mostafa Sarvizadeh

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Algorithms, Techniques, and Applications of Intelligent Diagnosis Using Dynamic Ultrasound: A Review by Jialong Liu, Jianfeng Zhang, Yuan Shao, Xiaolong Xiao, Shoujun Huang, Dexing Kong

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Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations by Constantinos Halkiopoulos, Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou

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Differential diagnosis algorithm of endogenous catatonia, catatonia-morphic and catatonia-mimicking states by D. N. Safonov

“…The aim is to develop pathomorphosis adapted clinical algorithm of endogenous catatonia differential diagnostics. …”
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Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis by Mario Muñoz, Adrián Rubio, Guillermo Cosarinsky, Jorge F. Cruza, Jorge Camacho

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Smartphone in medicine — from a reference book to a diagnostic system. Overview of the current state of the issue by A. A. Fedorovich, A. Yu. Gorshkov, A. I. Korolev, O. M. Drapkina

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Deep Learning-Based Algorithm for the Classification of Left Ventricle Segments by Hypertrophy Severity by Wafa Baccouch, Bilel Hasnaoui, Narjes Benameur, Abderrazak Jemai, Dhaker Lahidheb, Salam Labidi

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A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques by Farouk A. K. Al-Fahaidy, Belal Al-Fuhaidi, Ishaq AL-Darouby, Faheem AL-Abady, Mohammed AL-Qadry, Abdurhman AL-Gamal

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Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions by Joël Greffier, Maxime Pastor, Quentin Durand, Renaud Sales, Chris Serrand, Jean-Paul Beregi, Djamel Dabli, Julien Frandon

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Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death by A. V. Bykov, P. S. Azarova, S. A. Parkhomenko, A. V. Bykov, A. V. Polyakova, M. V. Alymova, A. V. Vinnikov

“…The basis is to improve the efficiency of predictive algorithms.Material and methods. This pilot, controlled, open-label, randomized, prospective clinical trial included 220 patients at risk of SCD and 150 patients without risk of SCD. …”
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Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review by Zhenli Chen, Jie Hao, Haixia Sun, Min Li, Yuan Zhang, Qing Qian

“…The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. …”
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Fracture parameter diagnostic method during staged multi-cluster fracturing based on distributed temperature sensing by Cao WEI, Haitao LI, Xiaohua ZHU, Nan ZHANG, Hongwen LUO, Kun TU, Shiqing CHENG

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Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes by Narainrit Karuna, Claire Tonry, Mark Ledwidge, Nadezhda Glezeva, Joe Gallagher, Ken McDonald, Chris J. Watson

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Validation of Replicable Pipeline 3D Surface Reconstruction for Patient-Specific Abdominal Aortic Lumen Diagnostics by Edoardo Ugolini, Giorgio La Civita, Moad Al Aidroos, Samuele Salti, Giuseppe Lisanti, Emanuele Ghedini, Gianluca Faggioli, Mauro Gargiulo, Giovanni Rossi

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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

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In-vitro diagnostic point-of-care tests in paediatric ambulatory care: A systematic review and meta-analysis. by Oliver Van Hecke, Meriel Raymond, Joseph J Lee, Philip Turner, Clare R Goyder, Jan Y Verbakel, Ann Van den Bruel, Gail Hayward

“…This difference was shown in randomised controlled trials in LMICs which included guidance on interpretation of POCT-CRP, specific training or employed a diagnostic algorithm prior to POC testing.<h4>Conclusion</h4>Overall, there is a paucity of evidence for the use of POCTs in paediatric ambulatory care. …”
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Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study by Emrah Aydın, Taha Eren Sarnıç, İnan Utku Türkmen, Narmina Khanmammadova, Ufuk Ateş, Mustafa Onur Öztan, Tamer Sekmenli, Necip Fazıl Aras, Tülin Öztaş, Ali Yalçınkaya, Murat Özbek, Deniz Gökçe, Hatice Sonay Yalçın Cömert, Osman Uzunlu, Aliye Kandırıcı, Nazile Ertürk, Alev Süzen, Fatih Akova, Mehmet Paşaoğlu, Egemen Eroğlu, Gülnur Göllü Bahadır, Ahmet Murat Çakmak, Salim Bilici, Ramazan Karabulut, Mustafa İmamoğlu, Haluk Sarıhan, Süleyman Cüneyt Karakuş

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Exploring the State-of-the-Art Algorithms for Brain Tumor Classification Using MRI Data by Joice J Anish, D Ajitha

“…This study is about the algorithms, datasets, and model designs that different researchers have utilized for classifying BT images. …”
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RUSSIAN-BELARUSIAN MULTICENTER GROUP STANDARD GUIDELINES FOR CHILDHOOD ACUTE LYMPHOBLASTIC LEUKEMIA FLOW CYTOMETRIC DIAGNOSTICS by I. A. Novikova, T. Yu. Verzhbitskaya, L. V. Movchan, G. A. Tsaur, М. V. Belevtsev, A. M. Popov

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