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Articles de revues sur le sujet « Medical machine »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

C, Liam. "Enhancing Diagnostic: Machine Learning in Medical Image Analysis." International Journal of Research Publication and Reviews 5, no. 5 (2024): 13013–16. http://dx.doi.org/10.55248/gengpi.5.0524.1458.

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Sellam, Abdelkrim, Boubakeur Dehiba, Mohamed B. Benabdallah, et al. "Vectorial Formalism of Polyphase Synchronous Machine With Permanents Magnets." Aceh International Journal of Science and Technology 2, no. 1 (2013): 1–7. http://dx.doi.org/10.13170/aijst.2.1.482.

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Abstract- This paper presents a mathematical model that transforms the real machine to fictitious machines and our goal is to simulate these and see the behavior of these machines in load. The polyphase machines are developed mainly in the field of variable speed drives of high power because increasing the number of phases on the one hand allows to reduce the dimensions of the components in power modulators energy and secondly to improve the operating safety. By a vector approach (vector space), it is possible to find a set of single-phase machine and / or two-phase fictitious equivalent to po

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Chetan, Bulla, Parushetti Chinmay, Teli Akshata, Aski Samiksha, and Koppad Sachin. "A Review of AI Based Medical Assistant Chatbot." Research and Applications of Web Development and Design 3, no. 2 (2020): 1–14. https://doi.org/10.5281/zenodo.3902215.

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<em>This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a program which allows the user to start a conversation with the machine. This is a platform focused on Artificial Intelligence (AI), which can be developed as messaging applications, web applications, or smartphone applications. A chatbot represents machine that answers ques

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Cartes-Velásquez, Ricardo. "Machine Learning and Medical Diagnosis." International Journal of Medical and Surgical Sciences 6, no. 4 (2019): 105–6. http://dx.doi.org/10.32457/ijmss.2019.031.

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Kiruba, J., R. Visalakshi, A. Vaishnavi, R. Ahalya, and RA Keerthi. "Medical Diagnosis using Machine Learning." Indian Journal of Public Health Research & Development 10, no. 4 (2019): 1337. http://dx.doi.org/10.5958/0976-5506.2019.00898.2.

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Sailaja, M., Abdul Ahad, K. Sivaramakrishna, and Ali Hussain. "Machine Learning Medical Resource Allocation." Journal of Physics: Conference Series 2089, no. 1 (2021): 012082. http://dx.doi.org/10.1088/1742-6596/2089/1/012082.

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Abstract In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes

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Wang, Qian, Yinghuan Shi, and Dinggang Shen. "Machine Learning in Medical Imaging." IEEE Journal of Biomedical and Health Informatics 23, no. 4 (2019): 1361–62. http://dx.doi.org/10.1109/jbhi.2019.2920801.

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Suzuki, Kenji, Pingkun Yan, Fei Wang, and Dinggang Shen. "Machine Learning in Medical Imaging." International Journal of Biomedical Imaging 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/123727.

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Fu, Geng-Shen, Yuri Levin-Schwartz, Qiu-Hua Lin, and Da Zhang. "Machine Learning for Medical Imaging." Journal of Healthcare Engineering 2019 (April 28, 2019): 1–2. http://dx.doi.org/10.1155/2019/9874591.

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Dolgin, Elie. "Medical devices: Managed by machine." Nature 485, no. 7398 (2012): S6—S8. http://dx.doi.org/10.1038/485s6a.

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Suzuki, Kenji, Luping Zhou, and Qian Wang. "Machine learning in medical imaging." Pattern Recognition 63 (March 2017): 465–67. http://dx.doi.org/10.1016/j.patcog.2016.10.020.

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Shen, Dinggang, Guorong Wu, Daoqiang Zhang, Kenji Suzuki, Fei Wang, and Pingkun Yan. "Machine learning in medical imaging." Computerized Medical Imaging and Graphics 41 (April 2015): 1–2. http://dx.doi.org/10.1016/j.compmedimag.2015.02.001.

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Giger, Maryellen L. "Machine Learning in Medical Imaging." Journal of the American College of Radiology 15, no. 3 (2018): 512–20. http://dx.doi.org/10.1016/j.jacr.2017.12.028.

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Wernick, Miles, Yongyi Yang, Jovan Brankov, Grigori Yourganov, and Stephen Strother. "Machine Learning in Medical Imaging." IEEE Signal Processing Magazine 27, no. 4 (2010): 25–38. http://dx.doi.org/10.1109/msp.2010.936730.

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Yan, Pingkun, Kenji Suzuki, Fei Wang, and Dinggang Shen. "Machine learning in medical imaging." Machine Vision and Applications 24, no. 7 (2013): 1327–29. http://dx.doi.org/10.1007/s00138-013-0543-8.

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Erickson, Bradley J., Panagiotis Korfiatis, Zeynettin Akkus, and Timothy L. Kline. "Machine Learning for Medical Imaging." RadioGraphics 37, no. 2 (2017): 505–15. http://dx.doi.org/10.1148/rg.2017160130.

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Zhou, Huiyu, Jinshan Tang, and Huiru Zheng. "Machine Learning for Medical Applications." Scientific World Journal 2015 (2015): 1. http://dx.doi.org/10.1155/2015/825267.

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Judd, Robert M. "Machine Learning in Medical Imaging." JACC: Cardiovascular Imaging 13, no. 3 (2020): 696–98. http://dx.doi.org/10.1016/j.jcmg.2019.08.028.

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Desai, Ms Shruti, and Mr Rahul Erulan. "Medical Chatbot in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2101–3. http://dx.doi.org/10.22214/ijraset.2023.50502.

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Abstract: This article suggests a medical chatbot that uses machine learning methods to provide personalised healthcare assistance. The chatbot is intended to interact with patients, collect information about their symptoms, medical history, and other pertinent details, and give treatment guidance and recommendations. Natural language processing (NLP) algorithms are used in the suggested chatbot to comprehend patients' requests and provide pertinent answers. It also analyses medical documents and other data using machine learning algorithms to provide personalised health suggestions. To provid

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B, Suhas, Manoj A, and Vinay H S. "Applications of Machine Learning in Medicine." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 84–86. http://dx.doi.org/10.26562/ijiris.2023.v0903.07.

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As medical data and information technologies advance, an increasing number of practitioners are recognizing or planning to use artificial intelligence. Radically alter medical practice through the use of cutting-edge machine learning techniques. Research is now being done to determine how machine learning and predictive analysis might be used to tailor individual therapies. In order to create a medical model that can rapidly and reliably forecast new data, machine learning must first learn a large quantity of medical data and investigate the dependencies in data concentration. This allows for

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Salunke, Mahendra Balkrishna. "Ultra-Lightweight Block Cipher in Medical Internet of Things for Secure Machine-to-Machine Communication Using FPGA." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 236–51. http://dx.doi.org/10.47059/revistageintec.v11i4.2104.

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Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.

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Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed

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Yin, Zihan. "The Applications of Machine Learning Algorithms to the Field of Medical Diagnosis." Computer Life 12, no. 1 (2024): 42–43. http://dx.doi.org/10.54097/hemrvq08.

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With a large population base, the medical needs are also very large. Patients need a lot, and doctors have a big workload. In this case, the pressure of doctors increases accordingly. The competence and clinical experience of the doctor are at the heart of medical diagnosis. Accumulating experience requires massive practice and literature reading and comprehension. With the development of computer science and the deep study of machine learning theory, machines are gradually involved in the process of medical diagnosis. The application of machine learning algorithms has opened up a path for med

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Piyush, Piyush, Nasib Singh .., Priti Maheshwary, Shraddha V. .., Preeti .., and Piyush Kumar Pareek. "An Ensemble Machine Learning Method for Analyzing Various Medical Datasets." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 177–95. http://dx.doi.org/10.54216/jisiot.130114.

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In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method

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Finlayson, Samuel G., John D. Bowers, Joichi Ito, Jonathan L. Zittrain, Andrew L. Beam, and Isaac S. Kohane. "Adversarial attacks on medical machine learning." Science 363, no. 6433 (2019): 1287–89. http://dx.doi.org/10.1126/science.aaw4399.

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Xia, Yong, Zexuan Ji, Andrey Krylov, Hang Chang, and Weidong Cai. "Machine Learning in Multimodal Medical Imaging." BioMed Research International 2017 (2017): 1–2. http://dx.doi.org/10.1155/2017/1278329.

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Criminisi, A. "Machine learning for medical images analysis." Medical Image Analysis 33 (October 2016): 91–93. http://dx.doi.org/10.1016/j.media.2016.06.002.

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Rodriguez y Baena, F. "Man and the machine [medical robots]." Computing and Control Engineering 17, no. 5 (2006): 28–31. http://dx.doi.org/10.1049/cce:20060504.

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Greenstein, Shane. "Earning Stripes in Medical Machine Learning." IEEE Micro 39, no. 5 (2019): 126–28. http://dx.doi.org/10.1109/mm.2019.2932278.

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Bryan, Robert Nick, Christos Davatzikos, Edward H. Herskovits, Suyash Mohan, Jeffrey D. Rudie, and Andreas M. Rauschecker. "Medical Image Analysis: Human and Machine." Academic Radiology 27, no. 1 (2020): 76–81. http://dx.doi.org/10.1016/j.acra.2019.09.011.

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Lei, Qiyun. "Machine Learning in Medical Insurance Prediction." Advances in Economics, Management and Political Sciences 45, no. 1 (2023): 222–28. http://dx.doi.org/10.54254/2754-1169/45/20230270.

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Nowadays, the trend of an ageing society is more and more obvious. Accompanied with the huge population of the elderly, the medical insurance industry has more prospects and potential. As a result, more service and business operations of insurance companies are in need. With the analysis from past data, computer algorithms help a lot in predicting the new output values, aiding data-driven business decisions, ranking of influential factors and digital computerization. Through machine learning, the insurance companies are able to make a decision flatly in premiums without having unnecessary medi

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Waykole, Rashmi. "An Overview of Machine Learning Techniques for Diagnosing Diseases." International Journal of Advance and Applied Research 6, no. 25(B) (2025): 78–81. https://doi.org/10.5281/zenodo.15314487.

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<strong>Abstract</strong>:  These days, machine learning algorithms are crucial to the medical field, particularly when it comes to using medical databases to diagnose illnesses. Many companies utilize these techniques for the early prediction of diseases and enhance medical diagnostics. This paper aims to provide an overview of machine learning algorithms, including Naïve Bayes, logistic regression, support vector machines, K-nearest neighbor, K-means clustering, decision trees, and random forests, that are used for the identification and prediction of numerous diseases. Numerous ea

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Ayush, Mittal, Kumar Vijay, Jha Abhishek, Khanna Bhavuk, and Jayesh. "Identifying Suspicious Activities in Medical Insurance Claims Using Machine Learning." Journal of Advances in Computational Intelligence Theory 6, no. 1 (2023): 15–24. https://doi.org/10.5281/zenodo.10223997.

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<i>The research methodology involves collecting and preprocessing a comprehensive dataset comprising healthcare claims and associated fraud labels. Multiple machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks, are implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1 score are used to assess the effectiveness of each model.</i><i>The results of the study demonstrate that machine learning techniques exhibit considerable potential in healthcare fraud detection. The comparativ

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Kurnia, Hibarkah, Andini Putri Riandani, and Tri Aprianto. "Application of the Total Productive Maintenance to Increase the Overall Value of Equipment Effectiveness on Ventilator Machines." Jurnal Optimasi Sistem Industri 22, no. 1 (2023): 52–60. http://dx.doi.org/10.25077/josi.v22.n1.p52-60.2023.

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A ventilator machine is a medical device that plays an important role in handling Covid-19 cases during a pandemic. Covid 19 patients are arriving at a referral hospital in Jakarta, meaning that the hospital must prepare its medical equipment, including a ventilator machine. The ventilator machine experienced problems because the efficiency of the machine decreased so many patients waited in the ICU room. Machine effectiveness has an average value of 62.26% so it has an impact on disrupting patient services at home. The purpose of this research is to look for factors that cause the lack of eff

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Gupta, Manish, Ihtiram Raza Khan, B. Gomathy, and Ansuman Samal. "Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns." ECS Transactions 107, no. 1 (2022): 2559–73. http://dx.doi.org/10.1149/10701.2559ecst.

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Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framew

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Ujwala, Ghodeswar, Borkar Anchal, and Bagde Ashutosh. "Classification of Different Medical Images Using Neural Network Approach." Indian Journal of Science and Technology 15, no. 46 (2022): 2555–61. https://doi.org/10.17485/IJST/v15i46.1160.

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Abstract <strong>Objectives:</strong> This work aims to design model for classification and selection of medical images by using the Convolution Neural Network technique with higher accuracy. <strong>Methods:</strong> Classification of the digital images into relevant categories like X-ray, CT, MRI is implemented using convolution neural network. At the initial stage total 7560 different medical images are given as input. These images are applied to the classifier. These images are passed through different levels of CNN. <strong>Findings:</strong> This method identifie

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Prabowo, Herry Agung, Indra Maulana Fahturizal, and Hibarkah Kurnia. "Authorship Correction: Application of the Total Productive Maintenance to Increase the Overall Value of Equipment Effectiveness on Ventilator." Jurnal Optimasi Sistem Industri 23, no. 1 (2024): 120–29. http://dx.doi.org/10.25077/josi.v23.n1.p120-129.2024.

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Correction of: https://josi.ft.unand.ac.id/index.php/josi/article/view/642 . DOI: 10.25077/josi.v22.n1.p52-60.2023 A ventilator machine is a medical device that plays an important role in handling Covid-19 cases during a pandemic. Covid 19 patients are arriving at a referral hospital in Jakarta, meaning that the hospital must prepare its medical equipment, including a ventilator machine. The ventilator machine experienced problems because the efficiency of the machine decreased so many patients waited in the ICU room. Machine effectiveness has an average value of 62.26% so it has an impact on

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Srinivas, Kanakala, and Prashanthi Vempaty. "Comparative analysis of heart failure prediction using machine learning models." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 297–305. https://doi.org/10.11591/ijict.v13i2.pp297-305.

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Heart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the

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Wankhede, Shrunkhala S., Mahima P. Adhale, Sejal T. Hiwase, Sayali D. Khedikar, and Tanushree S. Vaitage. "Pneumonia Diagnosis and Analysis System with Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37869.

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Pneumonia Diagnosis with Analysis System using Machine Learning”. Indicates that if the a Pneumonia is a major public health problem that causes a significant morbidity and mortality burden worldwide. Diagnosing pneumonia is often difficult and requires a combination of symptoms, physical examination, and ma ging studies. This research paper introduces the use of m machine learning to improve lung disease diagnosis and analysis, providing fast, accurate, and cost-effective solute for lung disease diagnosis. Recurrent Neural Network (RNN), and Support Vector Machines (SVM) to anal yet medical d

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Wolk, Krzysztof, and Krzysztof P. Marasek. "Translation of Medical Texts using Neural Networks." International Journal of Reliable and Quality E-Healthcare 5, no. 4 (2016): 51–66. http://dx.doi.org/10.4018/ijrqeh.2016100104.

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The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in whi

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Agarwal, Ritika. "Parkinson‘s Disorder: Taking a Step towards Homogenizing Machine Learning and Medical Science." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 6558–69. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020466.

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Hətəm qızı Mirzəzadə, İradə, Gülçin Gülhüseyn qızı Abdullayeva, and Həsənağa Rauf oğlu Nağızadə. "Mathematical machine usage in medical information systems." SCIENTIFIC WORK 65, no. 04 (2021): 75–85. http://dx.doi.org/10.36719/2663-4619/65/75-85.

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Biosystem of the human body is viewed as a whole. First of all adequate mathematical machine selection and class of biosystems needs to be assigned for creation of mathematical model of biological system. Biosystem has two types of appoach. One of them is supposed to be a simple approach, the other is likely to be very complex – indexed approach. Different biosystems with determination properties are usually described by differential and integral equations, linear and nonlinear algebra. In some cases, algebraic polynoms with timed argument are used for presenting determined biosystem dynamics.

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Jeong, Dae Seok. "4th Industrial Revolution & Machine Medical Ethics." Journal of Humanities 69 (May 30, 2018): 5–38. http://dx.doi.org/10.31310/hum.069.01.

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Takke, Kunal, Rameez Bhaijee, Avanish Singh, and Mr Abhay Patil. "Medical Disease Prediction using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 221–27. http://dx.doi.org/10.22214/ijraset.2022.42135.

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Abstract: There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medic

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Hou, Yuxuan, Yining Di, Zhong Ren, Yubo Tao, and Wei Chen. "Machine Learning Methods in Medical Image Compression." Journal of Computer-Aided Design & Computer Graphics 33, no. 8 (2021): 1151–59. http://dx.doi.org/10.3724/sp.j.1089.2021.18687.

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PEREZ CARETA, EDUARDO, RAFAEL GUZMÁN SEPÚLVEDA, JOSE MERCED LOZANO GARCIA, MIGUEL TORRES CISNEROS, and RAFAEL GUZMAN CABRERA. "CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING." DYNA 97, no. 1 (2022): 35–38. http://dx.doi.org/10.6036/10117.

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The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification base

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Bond, Koby, and Alaa Sheta. "Medical Data Classification using Machine Learning Techniques." International Journal of Computer Applications 183, no. 6 (2021): 1–8. http://dx.doi.org/10.5120/ijca2021921339.

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Ishiyama, Arai, Yamazaki, and Sendoh. "Spiral-type micro-machine for medical applications." Journal of Micromechatronics 2, no. 1 (2002): 77–86. http://dx.doi.org/10.1163/156856302766647161.

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El-Baz, Ayman, Georgy Gimel’farb, and Kenji Suzuki. "Machine Learning Applications in Medical Image Analysis." Computational and Mathematical Methods in Medicine 2017 (2017): 1–2. http://dx.doi.org/10.1155/2017/2361061.

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Suzuki, Kenji. "Pixel-Based Machine Learning in Medical Imaging." International Journal of Biomedical Imaging 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/792079.

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Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based

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