Academic literature on the topic 'Diagnosis – Data processing – Congresses'
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Journal articles on the topic "Diagnosis – Data processing – Congresses"
Matsumoto, Koushi, Haruaki Sato, Syunji Ohtsuka, Nobutaka Yamada, and Goro Asano. "Computer-assisted data processing of pathogical diagnosis." Journal of Nippon Medical School 59, no. 1 (1992): 75–80. http://dx.doi.org/10.1272/jnms1923.59.75.
Full textŠverko, Zoran, Ivan Markovinović, Miroslav Vrankić, and Saša Vlahinić. "EEG data processing in ADHD diagnosis and neurofeedback." Engineering review 40, no. 3 (May 21, 2020): 116–23. http://dx.doi.org/10.30765/er.40.3.12.
Full textSakhno. "Integral data processing systems for functional diagnosis service." Biomedical Engineering 30, no. 1 (1996): 38. http://dx.doi.org/10.1007/bf02383400.
Full textSakhno, Yu F., and P. S. Kudryavtsev. "Integral data processing systems for functional diagnosis service." Biomedical Engineering 30, no. 1 (January 1996): 38–42. http://dx.doi.org/10.1007/bf02369227.
Full textPasichnyk, Natalya, Renat Rizhniak, and Hanna Deforzh. "Congresses of natural scientists and mathematicians in the “Bulletin of experimental physics and elementary mathematics” (1886–1917): Analysis of publications." History of science and technology 13, no. 2 (December 23, 2023): 280–310. http://dx.doi.org/10.32703/2415-7422-2023-13-2-280-310.
Full textJi, Jinjie, Qing Chen, Lei Jin, Xiaotong Zhou, and Wei Ding. "Fault Diagnosis System of Power Grid Based on Multi-Data Sources." Applied Sciences 11, no. 16 (August 20, 2021): 7649. http://dx.doi.org/10.3390/app11167649.
Full textS., R., Priyanka S., Jyoti B., and Priyanka K. "Skin Disease Diagnosis System using Image Processing and Data Mining." International Journal of Computer Applications 179, no. 16 (January 17, 2018): 38–40. http://dx.doi.org/10.5120/ijca2018916253.
Full textMotakabber, S. M. A., Mohammad Mominul Hoque, Md. Rafiqul Islam, Sany Ihsan, Gazi Zahirul Islam, and AHM Zahirul Alam. "MATLAB-Based Vibration Signal Processing for Fault Diagnosis." Asian Journal of Electrical and Electronic Engineering 3, no. 2 (September 30, 2023): 27–32. http://dx.doi.org/10.69955/ajoeee.2023.v3i2.52.
Full textBakhshi, Ali, Kobra Hajizadeh, Mohammad Reza Tanhayi, and Reza Jamshidi. "Diabetic retinopathy diagnosis using image processing methods." Advances in Obesity, Weight Management & Control 11, no. 5 (September 8, 2022): 132–34. http://dx.doi.org/10.15406/aowmc.2022.12.00375.
Full textXiao, Yang. "Application of Big Data in Electrical Engineering." Journal of Computing and Electronic Information Management 12, no. 3 (April 30, 2024): 22–27. http://dx.doi.org/10.54097/1cjvmpno.
Full textDissertations / Theses on the topic "Diagnosis – Data processing – Congresses"
Van, Boening Mark Virgil. "Call versus continuous auctions: An experimental study of market organization." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185542.
Full textBui, Bang Huy. "Development of algorithms for processing psychology data." Thesis, Queensland University of Technology, 1997. https://eprints.qut.edu.au/36007/1/36007_Bui_1997.pdf.
Full textNakamura, Carlos. "The effects of specific support to hypothesis generation on the diagnostic performance of medical students /." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102817.
Full textMoni, Mohammad Ali. "Clinical bioinformatics and computational modelling for disease comorbidities diagnosis." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708646.
Full textFrigo, Alessandro. "A procedure for the autonomic diagnosis of esophageal motor disorders from HRM data processing." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424419.
Full textLa procedura per la diagnosi di patologie della motilità intestinale non può prescindere da una conoscenza appropriata dei meccanismi fisiologici che regolano il trasporto del cibo ingerito all’interno dell’intestino. Una delle regioni più studiate del tratto gastrointestinale, infatti, è l’esofago: una struttura tubolare in grado di trasportare il cibo dalla bocca allo stomaco mediante una precisa sequenza di contrazioni delle fibre muscolari longitudinali e circonferenziali chiamata peristalsi. Sfortunatamente, alcune patologie e processi degenerativi sono in grado di alterare questo meccanismo, generando dolore toracico, reflusso gastro-esofageo, difficoltà nella deglutizione e/o carcinoma dell’esofago in un numero crescente di soggetti, costituendo un grave problema socio-sanitario. Attualmente, la diagnosi di disturbi della motilità esofagea si svolge analizzando i risultati di un particolare esame clinico chiamato Manometria ad Alta Risoluzione (High Resolution Manometry – HRM), che consente di misurare l’evoluzione temporale della pressione intra-esofagea in diverse posizioni lungo esofago mediante un catetere trans-nasale appositamente progettato. In letteratura sono stati proposti diversi modelli per l’interpretazione di dati da manometria, ma con risultati spesso insoddisfacenti a causa di una valutazione impropria della distribuzione eterogenea delle proprietà fisio-meccaniche dell’esofago e di una inadeguata definizione della loro relazione con i parametri di modello utilizzati. Inoltre, l’identificazione di tali parametri è stata fatta sulla base di dataset ridotti. Oggi, le linee guida per la diagnosi di disordini motori dell’esofago sono definite dalla Classificazione di Chicago (Chicago Classification – CC): un algoritmo gerarchico che individua la patologia sulla base di parametri specifici estratti dall’analisi di dati da HRM. Il punto debole della CC consiste nella necessità di personale specializzato per il calcolo dei parametri, introducendo inevitabilmente variabilità intra- e inter-operatore nei confronti della diagnosi effettuata. In questa ricerca è stata analizzata la motilità esofagea, con l’obiettivo di sviluppare un modello fisiologico in grado di interpretare risultati da esami di HRM. Tale modello è stato definito mediante parametri collegati direttamente a proprietà fisio-meccaniche specifiche dell’esofago, considerando la loro distribuzione eterogenea. Le attività hanno previsto l’implementazione di una procedura per l’individuazione automatica di disfunzioni motorie dell’esofago, basata sull’analisi di dati da HRM. Sono stati quindi definiti alcuni criteri oggettivi per supportare la figura del clinico durante l’attività diagnostica tradizionale di disordini motori dell’esofago. Il modello fisiologico è stato sviluppato per valutare la mappa pressoria generata dal passaggio di una generica onda di pressione. Con riferimento a tale modello, sono stati individuati i set di parametri ottimali per interpretare al meglio gli esami HRM di ciascuno dei soggetti di un training set composto da 229 pazienti e 35 volontari sani. Tutti i soggetti sono stati raggruppati in diverse categorie sulla base del corrispondente stato di salute: normali (73+35 soggetti), Acalasia I (34), Acalasia II (44), Acalasia III (7), ostruzione della giunzione gastro-esofagea (39), sfintere inferiore ipertensivo (9), esofago schiaccianoci (14) e Spasmo Esofageo Diffuso (9). I parametri così identificati sono stati analizzati statisticamente per valutare la loro distribuzione in ciascuna categoria. Le distribuzioni di tali parametri costituiscono la base per lo sviluppo della procedura di diagnosi automatica. Infatti, la condizione di salute di un generico paziente può essere determinata calcolando un “indice di similarità” definito appositamente per rappresentare numericamente l’affinità tra i parametri specifici del paziente e le distribuzioni dei parametri delle diverse categorie del training set. E’ stato così costituito un set preliminare di dati da manometria ad alta risoluzione, corrispondente a soggetti sani e patologici per sviluppare e testare il software sviluppato. L’adeguatezza del modello fisiologico per quanto riguarda l’interpretazione di dati da HRM è stata accertata valutando il coefficiente di determinazione R2 tra i dati sperimentali e i risultati di modello, il quale variava tra 83% e 96% nelle diverse categorie. L’applicazione del modello a ogni soggetto del training set ha permesso inoltre di valutare la distribuzione dei parametri in diverse condizioni di salute. A ulteriore sostegno dell’adeguatezza del modello, è stato osservato che le differenze nelle distribuzioni di parametri tra soggetti sani e patologici sono state riscontrate in corrispondenza delle regioni dell’esofago colpite dalle diverse patologie. Infine, l’affidabilità della procedura di diagnosi automatica è stata valutata analizzando la performance dell’algoritmo, il quale si è dimostrato in grado di individuare la diagnosi corretta nell’86% dei casi considerati. I risultati ottenuti indicano che gli strumenti computazionali sviluppati possono rappresentare un valido sostegno per il personale medico durante l’attività diagnostica tradizionale. Per quanto riguarda gli sviluppi futuri della ricerca, dal momento che le distribuzioni dei parametri costituiscono il fondamento della procedura di diagnosi automatica, le prestazioni del software possono essere migliorate considerando un training set più grande, condividendolo con altri centri di ricerca ed aggiornandolo continuamente. Inoltre, la procedura di diagnosi automatica può essere estesa e resa capace di effettuare diagnosi sulla base di ulteriori esami clinici in grado di fornire informazioni sulla conducibilità, morfometria e comportamento meccanico delle strutture biologiche coinvolte. Queste informazioni potrebbero quindi essere raccolte mediante un unico test clinico per ridurre costi di indagine e invasività per il paziente, e potrebbero essere svolti in contemporanea mediante una sonda endoscopica innovativa già in fase di sviluppo.
Lembke, Benjamin. "Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification." Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158240.
Full textChou, Chuan-Ting. "Traditional Chinese medicine on-line diagnosis system." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3182.
Full textSubbiah, Arun. "Design and evaluation of a distributed diagnosis algorithm for arbitrary network topologies in dynamic fault environments." Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13273.
Full textFaremo, Sonia. "Medical problem solving and post-problem reflection in BioWorld." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84992.
Full textHeacock, Gregory. "An investigation of the role of virtual reality systems and their application to ophthalmic teaching, diagnosis and treatment." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287483.
Full textBooks on the topic "Diagnosis – Data processing – Congresses"
Symposium on Computer Applications in Veterinary Medicine (3rd 1985 Texas A & M University). Proceedings of the Third Symposium on Computer Applications in Veterinary Medicine: October 2-4, 1985. [College Station, Tex: The College, 1985.
Find full textS, Gero John, and International Conference on the Applications of Artificial Intelligence in Engineering (3rd : 1988 : Palo Alto, Calif.), eds. Artificial intelligence in engineering: Diagnosis and learning. Amsterdam: Elsevier, 1988.
Find full textGabrièle, Saucier, Ambler Tony, and Breuer Melvin A, eds. Knowledge based systems for test and diagnosis: Proceedings of the IFIP WG 10.5 International Workshop on Knowledge Based Systems for Test and Diagnosis, Grenoble, France, 27-29 September, 1988. Amsterdam: North-Holland, 1989.
Find full textU, Lemke H., ed. CAR '98, computer assisted radiology and surgery : proceedings of the 12th international symposium and exhibition, Tokyo, 24-27 June 1998. Amsterdam: Elsevier, 1998.
Find full textInternational Meeting on Clinical Laboratory Organization and Management (6th 1987 Noordwijkerhout, Netherlands). Laboratory data and patient care. New York: Plenum Press, 1988.
Find full textU, Lemke H., ed. CAR '96: Computer assisted radiology : proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy, Paris, June 1996. Amsterdam: Elsevier, 1996.
Find full textInternational Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy (1996 Paris, France). Computer assisted radiology: Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy, Paris, June 1996. Edited by Lemke H. U. Amsterdam: Elsevier, 1996.
Find full textNicholas, Ayache, Ourselin Sébastien, and Maeder Anthony, eds. Medical image computing and computer-assisted intervention: MICCAI 2007 : 10th international conference, Brisbane, Australia, October 29-November 2, 2007 : proceedings. Berlin: Springer, 2007.
Find full textInternational Conference on Medical Image Computing and Computer-Assisted Intervention (9th 2006 Copenhagen, Denmark). Medical image computing and computer-assisted intervention -- MICCAI 2006: 9th international conference Copenhagen, Denmark, October 1-6, 2006, proceedings. Berlin: Springer, 2006.
Find full text1947-, Dohi Takeyoshi, and Kikinis Ron, eds. Medical image computing and computer-assisted intervention-MICCAI 2002: 5th International Conference, Tokyo, Japan, September 25-28, 2002 : proceedings. Berlin: Springer, 2002.
Find full textBook chapters on the topic "Diagnosis – Data processing – Congresses"
Maojo, V., J. Sanandres, H. Billhardt, and J. Crespo. "Computational Intelligence Techniques in Medical Decision Making: the Data Mining Perspective." In Computational Intelligence Processing in Medical Diagnosis, 13–44. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1788-1_2.
Full textNguyen, Phuong-Thao, Thanh-Hai Tran, Viet-Hang Dao, and Hai Vu. "Improving Gastroesophageal Reflux Diseases Classification Diagnosis from Endoscopic Images Using StyleGAN2-ADA." In Artificial Intelligence in Data and Big Data Processing, 381–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_30.
Full textSong, Yiping, Wei Ju, Zhiliang Tian, Luchen Liu, Ming Zhang, and Zheng Xie. "Building Conversational Diagnosis Systems for Fine-Grained Diseases Using Few Annotated Data." In Neural Information Processing, 591–603. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30111-7_50.
Full textArabi, Walid, Reda Yaich, Aymen Boudguiga, and Mawloud Omar. "Secure Data Processing for Industrial Remote Diagnosis and Maintenance." In Lecture Notes in Computer Science, 335–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68887-5_21.
Full textKhandelwal, Sarika, Harsha R. Vyawahare, and Seema B. Rathod. "Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease." In Data Analysis for Neurodegenerative Disorders, 95–109. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2154-6_5.
Full textZedda, Luca, Andrea Loddo, and Cecilia Di Ruberto. "A Deep Learning Based Framework for Malaria Diagnosis on High Variation Data Set." In Image Analysis and Processing – ICIAP 2022, 358–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06430-2_30.
Full textLi, Sijue, Gaoliang Peng, Daoyong Mao, Zhiyu Zhu, Mengyu Ji, and Yuanhang Chen. "Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 475–84. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_58.
Full textSelvi, G. Chemmalar, G. G. Lakshmi Priya, M. Sabrina, S. Sharanya, Y. Laasya, N. Sunaina, and K. Usha. "A Comprehensive Study of Data Pre-Processing Techniques for Neurological Disease (NLD) Detection." In Diagnosis of Neurological Disorders Based on Deep Learning Techniques, 7–27. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003315452-2.
Full textJaya, S., and M. Latha. "Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis." In Data Science and Innovations for Intelligent Systems, 111–38. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003132080-5.
Full textSingh, Sanjay Kumar, Aditya Khamparia, and Amit Sinha. "Explainable Machine Learning Model for Diagnosis of Parkinson Disorder." In Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI), 33–41. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1476-8_3.
Full textConference papers on the topic "Diagnosis – Data processing – Congresses"
Lin, Ruping, Jing Huang, Zhiguo He, Huishu Song, Xiaosheng Huang, and Yang Lin. "Research on Generator Test Data Processing and Fault Diagnosis." In 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10055187.
Full textYazdandoost, Mina, Ali Yazdandoost, Fakhri Akhoonili, and Farshid Sahba. "The diagnosis of lumbar disc disorder by MR image processing and data mining." In 2016 World Automation Congress (WAC). IEEE, 2016. http://dx.doi.org/10.1109/wac.2016.7583019.
Full textLi, Guang, Maolin Li, Dan Liu, Guanghua Xu, and Shiming Zhou. "Fault diagnosis of mechanical equipment based on data visualization." In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017. http://dx.doi.org/10.1109/cisp-bmei.2017.8302147.
Full textQin, Zhiwei, Zhao Liu, and Ping Zhu. "Aiding Alzheimer's Disease Diagnosis Using Graph Convolutional Networks Based on rs-fMRI Data." In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2022. http://dx.doi.org/10.1109/cisp-bmei56279.2022.9980159.
Full textLang, Haoxiang, Ying Wang, and Clarence W. de Silva. "Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42323.
Full textThai, S. M., H. S. Chong, C. K. Tan, S. J. Wilcox, J. Ward, and G. Andrews. "Monitoring and Diagnosis of Steel Reheating Burners." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67205.
Full textDai, Xiaoyan, Wencheng Xu, and Xia Liu. "Integration of remote sensing data and GIS for diagnosis of coastal ecosystem degradation: A case study of Chongming Dongtan wetlands, Shanghai, China." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100440.
Full textLuo, Zheng, Encheng Feng, Xiaojie Lin, and Wei Zhong. "Research on Coarse Granularity Data Sample Completion Method for District Heating System." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95250.
Full textRodrigues, Clayton Eduardo, Cairo Lúcio Nascimento Júnior, and Domingos Alves Rade. "Machine Learning Techniques for Fault Diagnosis of Rotating Machines Using Spectrum Image of Vibration Orbits." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1101.
Full textLoukis, E., P. Wetta, K. Mathioudakis, A. Papathanasiou, and K. Papailiou. "Combination of Different Unsteady Quantity Measurements for Gas Turbine Blade Fault Diagnosis." In ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/91-gt-201.
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