SysSensory

2020 ◽  
Vol 13 (2) ◽  
pp. 60-74
Author(s):  
Helena Alvelos ◽  
Leonor Teixeira ◽  
Ana Luísa Ferreira Andrade Ramos ◽  
Ana Raquel Xambre
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
User Interfaces ◽  
Sensory Analysis ◽  
Food Industry ◽  
Information Technologies ◽  
Statistical Techniques ◽  
Organoleptic Characteristics ◽  
Innovative Tool

Sensory analysis is an area of the food industry to evaluate products' organoleptic characteristics. It encompasses a tasting process that produces large amounts of data used both in decisions about the products and to evaluate the tasters. In this context, some tools that usually support Industrial Engineering processes, can help making more reliable and timely decisions. The aim of this work is then to present a decision support system – SysSensory – developed to help food companies' deal with that data, by means of collecting, processing and visualizing it. Therefore, some statistical techniques incorporated in the system are explained, the specification of the system is described and some of the system's user interfaces are presented. SysSensory is considered a valuable contribute for researchers on Sensory Analysis, Statistics, as well as Information Technologies, and also for the food industry, for which it can be an innovative tool.

scholarly journals A Decision Support System Coupling Fuzzy Logic and Probabilistic Graphical Approaches for the Agri-Food Industry: Prediction of Grape Berry Maturity

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0134373 ◽  
Author(s):  
Nathalie Perrot ◽  
Cédric Baudrit ◽  
Jean Marie Brousset ◽  
Philippe Abbal ◽  
Hervé Guillemin ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Food Industry ◽  
Grape Berry ◽  
System Coupling

Clinical Decision Support System for Early Disease Detection and Management

2019 ◽  
pp. 108-155
Author(s):  
Likewin Thomas ◽  
Manoj Kumar M. V. ◽  
Annappa B.
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medical Error ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

scholarly journals The Multicriteria Assessment Methodology of the Decision Support System Implementation Effectiveness

2010 ◽  
Vol 2 (2) ◽  
pp. 81-100
Author(s):  
Lilianna Ważna ◽  
Tadeusz Krupa
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support System ◽  
Support System ◽  
Information Technologies ◽  
Assessment Methodology ◽  
System Implementation ◽  
Maximum Absolute Error ◽  
Implementation Effectiveness ◽  
Multicriteria Assessment

The Multicriteria Assessment Methodology of the Decision Support System Implementation EffectivenessThe multi-criteria assessment methodology of implementation effectiveness of information systems illustrated by an example of decision support system (DSS) realized in w information technologies is presented in the article. The assessment of DSS under consideration takes place using the knowledge recorded in the form of fuzzy neural network, collected in an enterprise, on the basis of earlier realized implementations of other information systems. A model of retrieved DSS is expressed by means of a set of functionalities serving business processes of the enterprise under consideration. A model of implementation undertaking determined by means of a set of preparatory actions for the implementation and a set of directly implementation and exploitation actions is built for the retrieved DSS as well. Furthermore, a vector determining a current and planned implementation state of a set of DSS functionalities in the enterprise at time moments, before and after the commencement of planned implementation of the retrieved DSS is built. A concept of trapezoidal fuzzy numbers is used in building DSS models. An adjustment of fuzzy parameters of DSS models takes place by means of geometrical method of maximum absolute error points. A presented methodology enables to execute a multi-criteria effectiveness assessment of planned undertaking in relation to subjective criteria established by the enterprise (preferred time, cost and values of priority indexes). Additionally, the knowledge collected on the basis of earlier realized implementations of information systems and applied imprecise description of parameters taking into account errors made in their estimation in the past is used.

Clinical Decision Support System for Early Disease Detection and Management

2021 ◽  
pp. 1035-1075
Author(s):  
Likewin Thomas ◽  
Manoj Kumar M. V. ◽  
Annappa B.
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Medical Error ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Statistical Techniques

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.

scholarly journals Systems Dynamics and Serious Video Games in an Appropriation Strategy of a Decision Support System for Small Livestock Farmers

2020 ◽  
Vol 14 (15) ◽  
pp. 4 ◽  
Author(s):  
Urbano Eliécer Gómez Prada ◽  
Martha Lucía Orellana Hernández ◽  
Jesús María Salinas Ibañez
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Simulation Model ◽  
Support System ◽  
Video Game ◽  
Information Technologies ◽  
Resistance To Change ◽  
Adoption Of Technology ◽  
Livestock Farmers ◽  
Small Livestock

This document presents a Decision Support System (DSS) aimed at small livestock farmers who have not made use of Information Technologies (IT) in their production systems. The DSS was built based on the finite difference equations of a simulation model in System Dynamics in whose definition the beneficiaries participated and also served as a base for the development of a serious video game. The DSS and the Serious video game is supported in a Web and Mobile Architecture. The simulation model and the serious video game are used as support tools in the training given to users to learn how to use the DSS. These three tools are the result of a doctoral research project, which used two methodologies during its execution: Design and Development Research and the Case Studies methodology. The tools were applied in an appropriation strategy with livestock farmers of the department of Santander in Colombia, where resistance to change and cultural attachment causes a low adoption of technology. The inclusion of gamification elements helps the user to understand the connection of these elements and their processes in a real farm, know the large volume of data managed by the DSS, enhance the process by making it more fun, improve the learning curve and provide useful data for tracking the use of the DSS. 16 months after the end of the training, the DSS has more than 13000 reported records about the activities of the farmers in their farms.

scholarly journals eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19

2022 ◽  
Vol 11 (1) ◽  
pp. 101
Author(s):  
Vinu Sherimon ◽  
P.C. Sherimon ◽  
Rahul V. Nair ◽  
Renchi Mathew ◽  
Sandeep M. Kumar ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
User Interfaces ◽  
Clinical Decision Support System ◽  
Medical Condition ◽  
Clinical Decision ◽  
Primary Data ◽  
X Rays

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

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