scholarly journals PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

2019 ◽  
Vol 9 (15) ◽  
pp. 3037 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Uriel Ramos-Deonati ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Overweight And Obesity ◽  
Process Data ◽  
Associated Conditions ◽  
Smart Health ◽  
Medical Recommendations ◽  
And Control

Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

Obesity management through Ayurveda - A Case Study

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Dheeraj Kumar Tyagi ◽  
Shivakumar .
Keyword(s):  
Clinical Findings ◽  
Dietary Habit ◽  
Unhealthy Lifestyle ◽  
Lifestyle Management ◽  
Before And After ◽  
Effect Of Treatment ◽  
And Control

Lifestyle disorders are one of the biggest threats for the population living unhealthy lifestyle. Sthoulya (Obesity) is one such disorder which creates lot of physical as well as mental disorder to the sufferer. Due to changing lifestyle, comforts and dietary habit lots of individuals changed their life totally. Obesity is a growing disease in developed and developing countries. Prevalence is drastically hike in past few years. Ayurveda, the science of life with which we can manage and control lots of lifestyle disorders. Focusing on dietary and lifestyle management along with treatment, we can overcome the hazards of obesity which is growing in a uncontrolled manner. The available data is based on the clinical findings only. Aim and objective: To assess the effect of “Guru Cha Atarpanam Chikitsa” in the management of Sthoulya. Setting: Swastharakshana evam Yoga, OPD and IPD, SDMCAH, Hassan. Method: Udwarthana, Parisheka, Shamana Aushadhis, Ahara, and Vihara was followed within the treatment duration and effect of treatment was assessed before and after treatment, advised for follow up. Results: The treatment adopted is effective in the management of Sthoulya and to improve the quality of life.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

scholarly journals A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

2021 ◽  
Vol 11 (13) ◽  
pp. 5826
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Coastal Zone ◽  
Extraction Process ◽  
Asia Minor ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency ◽  
Intelligent Tools ◽  
Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, and the different quality of language use across sources present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research aimed at the southern Balkans and the coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “expert–apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a machine learning (ML) algorithm, utilizing a combination of active learning (AL) and reinforcement learning (RL), and the expert is the human researcher. ML-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively, fetching a total number of 420 relevant ethnopharmacological documents in only 7 h versus an estimated 36 h of human-expert effort. Therefore, utilizing artificial intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

Venedig und der „wilde Osten“

2020 ◽  
Vol 100 (1) ◽  
pp. 208-260
Author(s):  
Christof Paulus ◽  
Albert Weber
Keyword(s):  
Middle Ages ◽  
Information Acquisition ◽  
Research Field ◽  
15Th Century ◽  
Late Middle Ages ◽  
Ranking Criteria ◽  
Areas Of Interest ◽  
And Control

AbstractVenice is considered the best-informed community of the late Middle Ages. The study examines the availability of information for the second half of the 15th century, particularly with regard to the key year 1462/1463, and as a case study concentrates on areas of the supposed Venetian periphery of interest, above all Hungary and the two principalities of Wallachia and Moldavia. The result is a thoroughly differentiated system of information acquisition, verification and control. Means of communication, as well as different areas of interest of the Serenissima, can be identified. A distinction is made between information maps and communication maps. The latter also include the distribution of news from the lagoon city exchanged with foreign envoys. During the period concerned, news was exchanged in an astonishingly liberal way, in turn integrating the Serenissima into the information networks of the other Italian states. The study thus places the „information commodity“ within the research field of late medieval gift exchange and patronage structures. In short, a thoroughly pragmatic Venetian approach to news acquisition and evaluation can be observed. Verification of the quality of the information obtained was subject not least to quantitative and ranking criteria. Ultimately, the informational power of Venice was based above all on its outstanding reputation among its contemporaries.

scholarly journals Antimalarial Drugs in Ghana: A Case Study on Personal Preferences

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 28
Author(s):  
Prince Yeboah ◽  
Arnold Donkor Forkuo ◽  
Obed Kwabena Offe Amponsah ◽  
Nana Ofori Adomako ◽  
Ahmad Yaman Abdin ◽  
...  
Keyword(s):  
Public Awareness ◽  
Modern Medicine ◽  
Herbal Remedies ◽  
Prefer Treatment ◽  
Adverse Side Effects ◽  
Serious Infection ◽  
General Weakness ◽  
And Control

Malaria is a serious infection affecting millions of people in Africa. Our study investigated the personal preferences and applications of antimalarial medicines in Ghana. Based on over 1000 questionnaires distributed in Ghana from January to May 2019, we noticed that although Western medications to fight this disease are widely available, most patients in Ghana prefer treatment with locally produced herbal remedies. This preference appears to be due to a combination of traditional venues for obtaining medicines “on the street” rather than in licensed pharmacies, trust in local and “green” products, extensive advertisement of such local products, and an inherent distrust of imported and synthetic or unnatural medicines. Going local and natural is a trend also observed in other countries across the globe, and adds to the acceptance or rejection of drugs regardless of their activity or toxicity. In fact, adverse side effects associated with herbal remedies, such as general weakness and swollen, sore mouth, do not seem to deter the respondents of this study in Ghana. We propose a combination of (a) increasing public awareness of the benefits of modern medicine and (b) an improvement and control of the quality of herbal remedies to raise the standard of malaria treatment in countries such as Ghana.

Automatic verification method of progressive image model matching information based on machine learning

2021 ◽  
pp. 1-10
Author(s):  
Jie Yuan ◽  
Yuan Ji ◽  
Zhou Zhu ◽  
Liya Huang ◽  
Junfeng Qian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Design Method ◽  
Large Error ◽  
Model Matching ◽  
Target Image ◽  
Image Model ◽  
Verification Method ◽  
Low Performance

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.

scholarly journals Assessing automated CMR contouring algorithms using systematic contour quality scoring analysis

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Omer ◽  
A Amir-Khalili ◽  
A Sojoudi ◽  
T Thao Le ◽  
S A Cook ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Random Order ◽  
Standard Operating Procedure ◽  
Left Ventricular ◽  
Quality Score ◽  
Machine Learning Algorithms ◽  
Dice Similarity Coefficient ◽  
National Budget

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): SmartHeart EPSRC programme grant (www.nihr.ac.uk), London Medical Imaging and AI Centre for Value-Based Healthcare Background Quality measures for machine learning algorithms include clinical measures such as end-diastolic (ED) and end-systolic (ES) volume, volumetric overlaps such as Dice similarity coefficient and surface distances such as Hausdorff distance. These measures capture differences between manually drawn and automated contours but fail to capture the trust of a clinician to an automatically generated contour. Purpose We propose to directly capture clinicians’ trust in a systematic way. We display manual and automated contours sequentially in random order and ask the clinicians to score the contour quality. We then perform statistical analysis for both sources of contours and stratify results based on contour type. Data The data selected for this experiment came from the National Health Center Singapore. It constitutes CMR scans from 313 patients with diverse pathologies including: healthy, dilated cardiomyopathy (DCM), hypertension (HTN), hypertrophic cardiomyopathy (HCM), ischemic heart disease (IHD), left ventricular non-compaction (LVNC), and myocarditis. Each study contains a short axis (SAX) stack, with ED and ES phases manually annotated. Automated contours are generated for each SAX image for which manual annotation is available. For this, a machine learning algorithm trained at Circle Cardiovascular Imaging Inc. is applied and the resulting predictions are saved to be displayed in the contour quality scoring (CQS) application. Methods: The CQS application displays manual and automated contours in a random order and presents the user an option to assign a contour quality score 1: Unacceptable, 2: Bad, 3: Fair, 4: Good. The UK Biobank standard operating procedure is used for assessing the quality of the contoured images. Quality scores are assigned based on how the contour affects clinical outcomes. However, as images are presented independent of spatiotemporal context, contour quality is assessed based on how well the area of the delineated structure is approximated. Consequently, small contours and small deviations are rarely assigned a quality score of less than 2, as they are not clinically relevant. Special attention is given to the RV-endo contours as often, mostly in basal images, two separate contours appear. In such cases, a score of 3 is given if the two disjoint contours sufficiently encompass the underlying anatomy; otherwise they are scored as 2 or 1. Results A total of 50991 quality scores (24208 manual and 26783 automated) are generated by five expert raters. The mean score for all manual and automated contours are 3.77 ± 0.48 and 3.77 ± 0.52, respectively. The breakdown of mean quality scores by contour type is included in Fig. 1a while the distribution of quality scores for various raters are shown in Fig. 1b. Conclusion We proposed a method of comparing the quality of manual versus automated contouring methods. Results suggest similar statistics in quality scores for both sources of contours. Abstract Figure 1

scholarly journals Stencil control in the automatic insertion of a PIM Company

2020 ◽  
Vol 8 (10) ◽  
pp. 417-440
Author(s):  
Cláudio César Vasconcelos Barros ◽  
Jonas Gomes da Silva
Keyword(s):  
Qr Code ◽  
Process Data ◽  
Bar Code ◽  
Ishikawa Diagram ◽  
Manufactured Products ◽  
Quality Tools ◽  
A Company ◽  
Computerized System

The article evaluated the control of Stencil in the subprocess of Printing of the SMD line of a company located in the Industrial Pole of Manaus (PIM), to provide subsidies to develop a computerized system. With computerization, the focus of employees will be directed to the activities of production and quality of manufactured products, also, the collection of process data, done in real-time, will allow managers to better monitor and take actions in the process. To this end, a case study, bibliographic research of articles, dissertations, and theses involving the theme, and documentary research (forms, records, etc.) with the sectors involved were used. The descriptive statistics method was applied, quality tools were used, aimed at identifying and solving problems such as PDCA, Pareto, Ishikawa Diagram, flow chart, and 5W2H. A study of the activities related to the control of the Stencil was carried out, of the documentation used in the process, as well as of the factors and causes related to the effective Stencil control. Among the results, 24 causes affect the performance of the Stencil control, concluding that the main failures were human, due to the prioritization of production goals by the employees, leaving the other activities in the background, which is why the 24 guidelines proposed for the computerization of this process become relevant, some of which are: defining means to identify each Stencil using a bar code or QR code; do not allow the use of the Stencil if one of the activities unfinished in the process; stop production when an activity is not performed; digitize the documents used in this process; create an automatic notification to those responsible, when an action is necessary, etc.

Sign in / Sign up

Export Citation Format

Share Document