Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

2021 ◽  
Author(s):  
Sami Bourouis ◽  
Nizar Bouguila
Keyword(s):  
Data Analysis ◽  
Mixture Model ◽  
Medical Data ◽  
Learning Approach ◽  
Nonparametric Learning

scholarly journals Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2450
Author(s):  
Fahd Alharithi ◽  
Ahmed Almulihi ◽  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Nizar Bouguila
Keyword(s):  
Mixture Model ◽  
Hybrid Approach ◽  
Medical Data ◽  
Support Vector ◽  
Discriminative Learning ◽  
Learning Approach ◽  
X Rays ◽  
Intrinsic Nature ◽  
Discriminative Models ◽  
Dirichlet Mixture

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

2021 ◽  
pp. 175-204
Author(s):  
S. N. Kumar ◽  
A. Lenin Fred ◽  
L. R. Jonisha Miriam ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Medical Data

Penerapan Pendekatan Open-Ended Learning pada Pembelajaran Tematik di Masa Pandemi Covid’19

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Noer Maulidatul Leily ◽  
Mukni’ah
Keyword(s):  
Data Analysis ◽  
Case Study Research ◽  
Data Gathering ◽  
Learning Activity ◽  
Learning Approach ◽  
Analysis Model ◽  
Research Subjects ◽  
Structured Interviews ◽  
Learning Plan ◽  
The Government

Learning is an interaction process between learners and educators. At the covid '19 pandemic study activity is experiencing a lot of problems, students are struggling to understand the materials the teachers explain through an online system. Addressing the problem, the madrass sabielil muttaqien an open-ended learning approach to a learning activity that is carried out once a week with the system offline by keeping the health protocols recommended by the government. The research is intended to describe the steps of open-ended learning and action action at the madrasah ibtidaiyah sabileil muttaqien lesson year 2020-2021.The study a descriptive qualitative approach and the type of research is case study. Research subjects select using adhesive techniques. As for the data-gathering techniques in this essay using non-participant observations, semi-structured interviews, and documentation. Whereas data analysis the interactive data analysis model presented by miles and Huberman and the validity of the data using source and technical triangulation. The study concludes: 1) Preparation of an open-ended approach learning at Madrasah Ibtidaiyah Sabilil Muttaqin is a teacher constructing a learning device that is Silabus and described in the form of an invasive learning plan (RPP) by listing the open-ended question in the application of the learning plan (RPP). 2) The performance of open-ended learning approach works effectively and passionate learners follow the learning process. Learning activities consist of introductory activities, core activities, final or concluding activities and evaluation activities. For the core activity of the delivery of materials with the six stages of activity carried out by the teacher. ABSTRAK Pembelajaran merupakan proses interaksi antara peserta didik dengan pendidik. Dimasa pandemi Covid’19 kegiatan pembelajaran banyak mengalami permasalahan, peserta didik susah memahami materi yang dijelaskan oleh guru dengan system online. Menanggulangi permasalahan tersebut Madrasah Ibtidaiyah Sabielil Muttaqien menggunakan Pendekatan Open-Ended Learning pada kegiatan pembelajaran yang dilaksanakan satu minggu sekali dengan system offline (tatap muka) dengan tetap menjaga protokol kesehatan yang dianjurkan oleh pemerintah. Penelitian ini bertujuan untuk mendeskripsikan kegiatan pembelajaran dengan pendekatan Open-Ended Learning berjalan dengan efektif dan peserta didik semangat mengikuti proses pembelajaran. Untuk kegiatan inti berisi tentang penyampaian materi dengan enam tahap pendekatan Open-Ended Learning. Penelitian ini menggunakan pendekatan kualitatif deskriptif dan jenis penelitian adalah studi kasus. Penentuan subjek penelitian menggunakan teknik purposive. Adapun tehnik pengumpulan data dalam penelitian ini menggunakan observasi Non-Partisipan, wawancara semi terstruktur, dan dokumentasi. Sedangkan analisis data menggunakan model analisis data interaktif yang dikemukakan oleh Miles dan Huberman dan keabsahan datanya menggunakan triangulasi sumber dan triangulasi teknik. Penelitian ini memperoleh kesimpulan: 1) Persiapan Pendekatan Open-Ended Learning Pada Pembelajaran Tematik di Madrasah Ibtidaiyah Sabilil Muttaqin adalah guru menyusun sebuah perangkat pembelajaran yang berupa Silabus dan Rencana Pelaksanaan Pembelajaran (RPP). Dan membuat pertanyaan Open-Ended dalam isian Rencana Pelaksanaan Pembelajaran (RPP). 2) Pelaksanaan pembelajaran dengan pendekatan Open-Ended Learning berjalan dengan efektif dan peserta didik semangat mengikuti proses pembelajaran. Kegiatan pembelajaran terdiri dari kegiatan pendahuluan, kegiatan inti, kegiatan akhir atau penutup dan kegiatan evaluasi. Untuk kegiatan inti berisi tentang penyampaian materi dengan enam tahap kegiatan yang dilakukan oleh guru. Kata Kunci: pembelajaran, pandemi covid-19, pendekatan open-ended learning

scholarly journals Computing support for advanced medical data analysis and imaging

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Wojciech Wiślicki ◽  
Tomasz Bednarski ◽  
Piotr Białas ◽  
Eryk Czerwiński ◽  
Łukasz Kapłon ◽  
...  
Keyword(s):  
Data Analysis ◽  
Medical Data ◽  
Computing Support

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

Sign in / Sign up

Export Citation Format

Share Document