A Hybrid Approach of Verbal Decision Analysis and Machine Learning

2012 ◽  
pp. 126-131 ◽  
Author(s):  
Isabelle Tamanini ◽  
Plácido Rogério Pinheiro ◽  
Cícero Nogueira dos Santos
Keyword(s):  
Machine Learning ◽  
Decision Analysis ◽  
Hybrid Approach ◽  
Verbal Decision Analysis

scholarly journals A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Evandro Andrade ◽  
Samuel Portela ◽  
Plácido Rogério Pinheiro ◽  
Luciano Comin Nunes ◽  
Marum Simão Filho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Decision Analysis ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
World Health ◽  
Diagnostic Process ◽  
Icd 10 ◽  
Verbal Decision Analysis

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms’ composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

Hybrid Approach to Sentiment Analysis based on Syntactic Analy- sis and Machine Learning

2010 ◽  
Vol 14 (2) ◽  
pp. 159-181
Author(s):  
MUNPYO HONG ◽  
MIYOUNG SHIN ◽  
Shinhye Park ◽  
Hyungmin Lee
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Hybrid Approach

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

Agreement and Reliability Analysis of Machine Learning Scaling in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: Clinical Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Reliability Analysis ◽  
Gold Standard ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Hybrid Approach ◽  
Assessment Model ◽  
Lower Limbs ◽  
Neurological Deficits ◽  
Percent Agreement

BACKGROUND Assessing the symptoms of proximal weakness caused by neurological deficits requires expert knowledge and experienced neurologists. Recent advances in artificial intelligence and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. OBJECTIVE This study provides an agreement and reliability analysis of using an automated scoring system to evaluate proximal weakness by experts and non-experts. METHODS We collected 144 observations from acute stroke patients in a neurological intensive care unit to measure the symptom of proximal weakness of upper and lower limbs. A neurologist performed a gold standard assessment and two medical students performed identical tests as non-expert assessments for manual and machine learning-based scaling of Medical Research Council (MRC) proximal scores. The system collects signals from sensors attached on patients’ limbs and trains a machine learning assessment model using the hybrid approach of data-level and algorithm-level methods for the ordinal and imbalanced classification in multiple classes. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. In the reliability analysis, we analysed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the three observers’ scaling. RESULTS The mean percent agreement between the gold standard and the non-expert scaling was 0.542 for manual scaling and 0.708 for IoT-assisted machine learning scaling, with 30.63% enhancement. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The Krippendorff’s alpha of machine learning scaling increased to 0.445, with 61.82% improvement. CONCLUSIONS Automated scaling using sensors and machine learning provided higher inter-rater agreement and reliability in assessing acute proximal weakness. The enhanced assessment supported by the proposed system can be utilized as a reliable assessment tool for non-experts in various emergent environments.

scholarly journals Selection and Prioritization of Software Requirements Applying Verbal Decision Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20
Author(s):  
Paulo A. M. Barbosa ◽  
Plácido R. Pinheiro ◽  
Francisca R. V. Silveira ◽  
Marum Simão Filho
Keyword(s):  
Software Development ◽  
Decision Analysis ◽  
Decision Maker ◽  
Development Process ◽  
Software Requirements ◽  
Software Development Process ◽  
Next Release Problem ◽  
Support Tools ◽  
Verbal Decision Analysis ◽  
The Cost

During the software development process, the decision maker (DM) must master many variables inherent in this process. Software releases represent the order in which a set of requirements is implemented and delivered to the customer. Structuring and enumerating a set of releases with prioritized requirements represents a challenging task because the requirements contain their characteristics, such as technical precedence, the cost required for implementation, the importance that one or more customers add to the requirement, among other factors. To facilitate this work of selection and prioritization of releases, the decision maker may adopt some support tools. One field of study already known to solve this type of problem is the Search-Based Software Engineering (SBSE) that uses metaheuristics as a means to find reasonable solutions taking into account a set of well-defined objectives and constraints. In this paper, we seek to increase the possibilities of solving the Next Release Problem using the methods available in Verbal Decision Analysis (VDA). We generate a problem and submit it so that the VDA and SBSE methods try to resolve it. To validate this research, we compared the results obtained through VDA and compared with the SBSE results. We present and discuss the results in the respective sections.

scholarly journals Verbal Decision Analysis: Foundations and Trends

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Helen M. Moshkovich ◽  
Alexander I. Mechitov
Keyword(s):  
Cognitive Psychology ◽  
Organizational Behavior ◽  
Decision Analysis ◽  
Decision Process ◽  
Applied Mathematics ◽  
Processing System ◽  
Human Information Processing ◽  
Multiple Criteria Decision Analysis ◽  
Organizational Environments ◽  
Verbal Decision Analysis

The primary goal of research in multiple criteria decision analysis is to develop tools to help people make more reasonable decisions. In many cases, the development of such tools requires the combination of knowledge derived from such areas as applied mathematics, cognitive psychology, and organizational behavior. Verbal Decision Analysis (VDA) is an example of such a combination. It is based on valid mathematical principles, takes into account peculiarities of human information processing system, and fits the decision process into existing organizational environments. The basic underpinnings of Verbal Decision Analysis are demonstrated by early VDA methods, such as ZAPROS and ORCLASS. New trends in their later modifications are discussed. Published applications of VDA methods are presented to support the findings.

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