scholarly journals Initiative For Thyroid Cancer Diagnosis: Decision Support System For Anaplast Thyroid Cancer

2017 ◽  
Vol 1 (2) ◽  
pp. 48
Author(s):  
Jamil Ahmed Chandio ◽  
M. Abdul Rehman Soomrani ◽  
Attaullah Sehito ◽  
Shafaq Siddiqui
Keyword(s):  
Thyroid Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Confusion Matrix ◽  
Measurement Techniques ◽  
Research Area ◽  
Classification Problems ◽  
Biomedical Image ◽  
High Level

Due to the high level exposure of biomedical image analysis, Medical image mining has become one of the well-established research area(s) of machine learning. AI (Artificial Intelligence) techniques have been vastly used to solve the complex classification problems of thyroid cancer. Since the persistence of copycat chromatin properties and unavailability of nuclei measurement techniques, it is really problem for doctors to determine the initial phases of nuclei enlargement and to assess the early changes of chromatin distribution. For example involvement of multiple transparent overlapping of nuclei may become the cause of confusion to infer the growth pattern of nuclei variations. Un-decidable nuclei eccentric properties may become one of the leading causes for misdiagnosis in Anaplast cancers. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Decision Support System for Anaplast Thyroid Cancer” and it proposes a medical data preparation algorithm AD (Analpast_Cancers) which helps to select the appropriate features of Anaplast cancers such as (1) enlargement of nuclei, (2) persistence of irregularity in nuclei and existence of hyper chromatin. Proposed methodology comprises over four major layers, first layer deals with the noise reduction, detection of nuclei edges and object clusters. Second layer selects the features of object of interest such as nuclei enlargement, irregularity and hyper chromatin. Third layer constructs the decision model to extract the hidden patterns of disease associated variables and final layer evaluates the performance evaluation by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 97.2% with 10-k fold cross validation.

Methodologies of Legacy Clinical Decision Support System

2017 ◽  
Vol 2 (2) ◽  
pp. 20-37
Author(s):  
Meenakshi Sharmi ◽  
Himanshu Aggarwal
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Area

Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.

scholarly journals MACHoice: a Decision Support System for agricultural machinery management

2019 ◽  
Vol 4 (1) ◽  
pp. 305-321 ◽  
Author(s):  
M. Cunha ◽  
S.G. Gonçalves
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Production ◽  
Work Capacity ◽  
Production Costs ◽  
Modern Agriculture ◽  
Level Of Automation ◽  
High Level ◽  
User Friendly

AbstractMechanisation is a key input in modern agriculture, while it accounts for a large part of crop production costs, it can bring considerable farm benefits if well managed. Models for simulated machinery costs, may not replace actual cost measurements but the information obtained through them can replace a farm’s existing records, becoming more valuable to decision makers. MACHoice, a decision support system (DSS) presented in this paper, is a farm machinery cost estimator and break-even analyzer of alternatives for agricultural operations, developed using user-driven expectations and in close collaboration with agronomists and computer engineers. It integrates an innovative algorithm developed for projections of machinery costs under different rates of annual machine use and work capacity processing, which is crucial to decisions on break-even machinery alternatives. A case study based on the comparison of multiple alternatives for grape harvesting operations is presented to demonstrate the typical results that can be expected from MACHoice, and to identify its capabilities and limitations. This DSS offers an integrated and flexible analysis environment with a user-friendly graphical interface as well as a high level of automation of processing chains. The DSS-output consists of charts and tables, evidencing the differences related to costs and carbon emissions between the options inserted by the user for the different intensity of yearly work proceeded. MACHoice is an interactive web-based tool that can be accessed freely for non-commercial use by every known browser.

scholarly journals Social Assistance Decision Support System Using the Naive Bayes Method

2021 ◽  
Vol 1 (1) ◽  
pp. 85
Author(s):  
Moh. Syaiful Anam
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Social Assistance ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Naive Bayes Method

Covid-19 telah menjadi pandemi yang menyebar hampir ke seluruh penjuru dunia. Karena proses penularannya yang begitu cepat Dalam masa pandemi covid -19, pandemi ini menyebar ke seluruh sendi kehidupan dan salah satu yang paling menjadi perhatian adalah dibidang sosial ekonomi. Banyak terdapat bantuan Sosial (Bansos) yang disalurkan baik oleh pemerintah ataupun pihak swasta lain. Penelitian ini bertujuan untuk membuat sistem pendukung keputusan bantuan sosial menggunakan metode Naive Bayes, selanjutnya melakukan Analisa menggunakan tabel Confusion Matrix.  Dalam menyelesaikan masalah dengan menggunakan metode Naive Bayes dari hasil pembahasan yang dilakukan dapat ditarik kesimpulan Naive Bayes dan aturan yang dihasilkan memiliki tingkat akurasi tinggi (good) yaitu sebesar 73% dan Sementara nilai Precision sebesar 92% dan Recall sebesar 86%. Sehingga metode Naive Bayes dapat diterapkan dalam menentukan prediksi yang lebih banyak dan potensial aturan yang dihasilkan untuk membantu menentukan pemberian bantuan sosial.

scholarly journals A Statistical Model for Shutdowns due to Air Quality Control for a Copper Production Decision Support System

Organizacija ◽  
2015 ◽  
Vol 48 (3) ◽  
pp. 198-202
Author(s):  
Khalid Aboura
Keyword(s):  
Quality Control ◽  
Decision Support ◽  
Air Quality ◽  
Decision Support System ◽  
Statistical Model ◽  
Support System ◽  
Copper Production ◽  
Production Facility ◽  
Air Quality Control ◽  
High Level

Abstract Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.

scholarly journals Uncertainty Problem in Decision Support System for Means of Transport Maintenance Processes Performance Development / Problem Niepewności W Systemie Wsparcia Decyzyjnego Procesów Utrzymania W Stanie Zdatności Środków Transportu

2012 ◽  
Vol 23 (1) ◽  
pp. 173-192 ◽  
Author(s):  
Tomasz Nowakowski ◽  
Sylwia Werbińska-Wojciechowska
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Decision Support System ◽  
Uncertainty Analysis ◽  
Support System ◽  
Research Work ◽  
Research Area ◽  
Development Problem ◽  
Literature Overview ◽  
Performance Development

Abstract In this paper, the authors’ research work is focused on uncertainty analysis implementation in the developed DSS for transportation means’ maintenance processes performance. Thus, in the Introduction section, the transportation means’ maintenance processes issues and the uncertainty problem are described. Later, there is briefly literature overview in the research area discussed. In the next Section, the expert system for means of transport maintenance processes performance is also investigated. Following this, the uncertainty analysis is developed and the examples of expert system implementation are given. The work ends up with summary and directions for further research.

scholarly journals A Spatial Multi-criteria Decision Support System for Stress Recovery-Oriented Forest Management

2021 ◽  
pp. 171-184
Author(s):  
Irene Capecchi ◽  
Gianluca Grilli ◽  
Elena Barbierato ◽  
Sandro Sacchelli
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Support System ◽  
Forest Type ◽  
Well Being ◽  
Research Area ◽  
Stress Recovery ◽  
Recovery Potential ◽  
Urban Settlements

AbstractA solution to cope with chaotic urban settlements and frenetic everyday life is refuging in nature as a way to reduce stress. In general—in recent years—it has been scientifically demonstrated how natural areas are an important environment for psycho-physiological health. As a consequence, it is important to plan dedicated spaces for stress recovery in order to increase the well-being of people. With respect to forests, there is a growing interest in understanding the marketing and tourist potential of forest-therapy activities and policies. This paper develops a decision support system (DSS) for decision makers, based on geographic information system to define the suitability of forest areas to improve psychological and physiological human well-being. Innovative technologies such as electroencephalography (EEG) and virtual reality (VR) are applied to test human status. The DSS combines four sets of indicators in a multi-attribute decision analysis and identifies the areas with the largest stress-recovery potential. Two multi-attribute model—one in summer and one in winter—are elaborated to obtain a dynamic evaluation of suitability. Results show significant differences among forest type, forest management, altitude range, and season in terms of stand suitability. EEG and VR seem to be promising technologies in this research area. Strengths and weaknesses of the approach, as well as potential future improvement and implications for territorial marketing, are suggested.

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