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A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

Fahad Taha Al-Dhief, Nurul MurAzzah Abdul Latiff, Nik Noordini Nik Abd Malik, Naseer Sabri, Marina Mat Baki, Musatafa Abbas Abbood Albadr, Mazin Abed Mohammed
2020 IEEE Access  
INDEX TERMS Internet of Things, machine learning algorithms, the healthcare sector, voice pathology surveillance systems.  ...  IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring.  ...  ACKNOWLEDGMENT The authors would like to thank the Ministry of Higher Education (MOHE), Research Management Centre of Universiti Teknologi Malaysia (UTM) and School of Electrical Engineering, UTM, for  ... 
doi:10.1109/access.2020.2984925 fatcat:bvh66usrmvh5pe47hwypvtskdu

Introduction to the Issue on Automatic Assessment of Health Disorders Based on Voice, Speech, and Language Processing

Juan I. Godino-Llorente, Douglas O'Shaughnessy, Tan Lee, Najim Dehak, Claudia Manfredi
2020 IEEE Journal on Selected Topics in Signal Processing  
Results show an increase of accuracy taking Body Mass Index (BMI) as adversarial domain.  ...  of Health Disorders based on voice, JUAN I.  ...  He is a member of the International Speech Communication Association. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON  ... 
doi:10.1109/jstsp.2020.2978566 fatcat:32x76k4fnfhpbcpixz365muapm

Machine Learning for Brain Stroke: A Review

Manisha Sanjay Sirsat, Eduardo Fermé, Joana Câmara
2020 Journal of Stroke & Cerebrovascular Diseases  
A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019.  ...  Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients.  ...  voice signals using ML [45] LR, bagging and RF 1,568 voice features LR on imbalanced data AUC = 0.569-0.731 Estimating the body mass index (BMI) through voice signals can enhance early  ... 
doi:10.1016/j.jstrokecerebrovasdis.2020.105162 pmid:32912543 fatcat:5f7ps3qhwzej3pdhhxegxm65ny

Medical Deep Learning – A systematic Meta-Review [article]

Jan Egger, Christina Gsaxner, Antonio Pepe, Jianning Li
2020 arXiv   pre-print
They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies.  ...  With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information  ...  Acknowledgements This work received funding from the Austrian Science Fund (FWF) KLI 678-B31: Additional Information Competing financial interests: The authors declare no competing financial interests  ... 
arXiv:2010.14881v4 fatcat:56nrzawncnaopcpuzlzac5ceoy

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Hemantha Krishna Bharadwaj, Aayush Agarwal, Vinay Chamola, Naga Rajiv Lakkaniga, Vikas Hassija, Mohsen Guizani, Biplab Sikdar
2021 IEEE Access  
Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative.  ...  The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing.  ...  REINFORCEMENT MACHINE LEARNING Reinforcement learning is based on the methodology by which infants learn to interpret the world around them.  ... 
doi:10.1109/access.2021.3059858 fatcat:gwdku6me2fhzbfwf7agtt5vmse

Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

Sumit Majumder, Emad. Aghayi, Moein Noferesti, Hamidreza Memarzadeh-Tehran, Tapas Mondal, Zhibo Pang, M. Deen
2017 Sensors  
In this paper, we have presented a comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies.  ...  Healthcare personnel can also keep track of the overall health condition of the elderly in real-time and provide feedback and support from distant facilities.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s17112496 pmid:29088123 pmcid:PMC5712846 fatcat:53ct3376yzbhxj5k3fxcrc4ofe

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study

Giovanni Delnevo, Giacomo Mancini, Marco Roccetti, Paola Salomoni, Elena Trombini, Federica Andrei
2021 Sensors  
This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI).  ...  We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21072361 pmid:33805257 fatcat:gvlpqhlxobcjrpgodrilmoirce

Overview of the Artificial Intelligence Methods and Analysis of Their Application Potential [chapter]

Dalia Kriksciuniene, Virgilijus Sakalauskas
2022 Intelligent Systems Reference Library  
So the knowledge discovery from raw clinical data is a big challenge for healthcare system.  ...  Particular attention is paid to the diversity of Machine Learning and Artificial intelligence methods, analytical health data analysis models, its testing and evaluation capabilities.  ...  BMI (Body mass index) 27.8 to BMI 25.  ... 
doi:10.1007/978-3-030-79353-1_9 fatcat:3c6ditgfsbfofitka7r7lazdyy

Motion-to-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users

Yumin Yao, Ling Song, Jin Ye
2020 Sensors  
The body mass index (BMI) is a simple and reliable index based on weight and height that is commonly used to identify and classify adults as underweight, normal, overweight (pre-obesity), or obese.  ...  In this paper, we propose a hybrid deep neural network for predicting the BMI of smartphone users, based only on the characteristics of body movement captured by the smartphone's built-in motion sensors  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/s20041134 pmid:32093013 pmcid:PMC7070876 fatcat:fgiz4sdvhnhyblkp54hj22dz54

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

Saurav Mishra
2022 Journal of Electronics Electromedical Engineering and Medical Informatics  
The paper also discusses about the implementation opportunities various AI technologies like Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision provide  ...  However, there are limitations and concerns on security of Protected Health Information which have to be addressed for a seamless amalgamation of AI systems into the healthcare domain.  ...  ., [154] implement the gradient boosting algorithm for early prediction of GD using clinical parameters like body mass index, maternal age, fasting glucose, and alanine aminotransferase.  ... 
doi:10.35882/jeeemi.v4i1.1 fatcat:j2zcn22rl5f77nmy7rmbpr76ma

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

Jan Weichert, Amrei Welp, Jann Lennard Scharf, Christoph Dracopoulos, Wolf-Henning Becker, Michael Gembicki
2021 Geburtshilfe und Frauenheilkunde  
The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.  ...  This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare  ...  AI-assisted voice control and speech recognition are based on similar principles, for instance, the Amazon Alexa, Google Home and Apple Siri voice assistants.  ... 
doi:10.1055/a-1522-3029 pmid:34754270 pmcid:PMC8568505 fatcat:ldfi4rutabdmxb6kr5ogt3rhd4

E-learning and ICTs Applications in Nutrition Science

Athanasios Drigas, Maria Karyotaki
2013 International Journal of Recent Contributions from Engineering, Science & IT  
Behaviour targeted applications should be appealing and intriguing, as they apply to many people with a variety of needs for a long period of time.  ...  Nutrition applications provide the means for automatic dietary intake and energy expenditure measurements as well as personalised counselling and educational services.  ...  Mass Index.  ... 
doi:10.3991/ijes.v1i2.3279 fatcat:wl6mrtegz5es5lhcq7axr5xdua

The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes [article]

Sebastian Spänig, Agnes Emberger-Klein, Jan-Peter Sowa, Ali Canbay, Klaus Menrad, Dominik Heider
2019 arXiv   pre-print
As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks.  ...  , e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities.  ...  Acknowledgments: We thank the students for answering the questionnaire.  ... 
arXiv:1903.12069v1 fatcat:tpmabx6ukzeahmhmsnbwlro52m

A Comprehensive Survey of the Internet of Things (IoT) and Edge Computing in Healthcare

Fatima Alshehri, Ghulam Muhammad
2020 IEEE Access  
INDEX TERMS Internet of Things (IoT), Internet of Medical Things (IoMT), edge computing, cloud computing, medical signals, smart health care, artificial intelligence.  ...  We survey this literature by answering several research areas on IoT and IoMT, AI, edge and cloud computing, security, and medical signals fusion.  ...  By identifying the source signal from the speech using linear prediction analysis, the proposed system could determine the voice disorder.  ... 
doi:10.1109/access.2020.3047960 fatcat:adbkd6gfg5dtnigtumglmtsecu

Machine Learning and Artificial Intelligence based Diabetes Mellitus Detection and Self-Management: A Systematic Review

Jyotismita Chaki, S. Thillai Ganesh, S.K Cidham, S. Ananda Theertan
2020 Journal of King Saud University: Computer and Information Sciences  
Thanks to advances in machine learning and artificial intelligence, which enables the early detection and diagnosis of DM through an automated process which is more advantageous than a manual diagnosis  ...  learning-based identification, classification, and diagnosis of DM, artificial intelligence-based intelligent DM assistant and performance measures.  ...  PIDD includes a variety of incomplete and impractical attributes, such as 0 plasma glucose and 0 body mass index.  ... 
doi:10.1016/j.jksuci.2020.06.013 fatcat:woewkyhb45ck3hy2yz726kvrga
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