scholarly journals Proposal of an automatic tool for evaluating the quality of decision-making on Checkers player agents

2018 ◽  
Author(s):  
Matheus Prado Prandini Faria ◽  
Rita Maria Silva Julia ◽  
Lídia Bononi Paiva Tomaz
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Unsupervised Methods ◽  
Automatic Tool

Checkers player agents represent an appropriate case study for the best unsupervised methods of Machine Learning. This work presents a tool to measure the performance of these methods based on the quality of the decision making of these agents. The proposed tool, based on the data of movements performed in real games by the agents under evaluation, provides a statistical way of automatically comparing the coincidence rates between the decision making of the evaluated agents with those that the remarkable player agent Cake would do in the same situations. The tool was validated through tournaments between agents comparing their respective coincidence rates and their performance.

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.

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

2020 ◽  
pp. 095440542097051
Author(s):  
Raghad M Khorsheed ◽  
Omer Faruk Beyca
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Utility Theory ◽  
Predictive Maintenance ◽  
Manufacturing Operations ◽  
Pumping Stations ◽  
Bearing Failures ◽  
Temperature Levels ◽  
The Right

Bearings are the most widely used mechanical parts in rotating machinery under high load and high rotational speeds. Operating continuously under such harsh conditions, wear and failure are imminent. Developing defects give rise to even-higher vibration and temperature levels. In general, mechanical defects in a machine cause high vibration levels. Therefore, bearing fault identification and early detection enables the maintenance team to repair the problem before it triggers catastrophic failure in the bearing. Machine downtime is thus avoided or minimized. This paper explores the use of Machine Learning (ML) integrated with decision-making techniques to predict possible bearing failures and improve the overall manufacturing operations by applying the correct maintenance actions at the right time. The accuracy of the Predictive Maintenance (PdM) module has been tested on real industrial production datasets. The paper proposes an effective PdM methodology using different ML algorithms to detect failures before they happen and reduce pump downtime. The performance of the tested ML algorithms is based on five performance indicators: accuracy, precision, F-score, recall, and an area under curve (AUC). Experimental results revealed that all tested ML algorithms are successful and effective. Furthermore, decision making with utility theory has been employed to exploit the probability of failures and thus help to perform the appropriate maintenance interventions. This provides a logical framework for decision-makers to identify the optimum action with the maximum expected benefit. As a case study, the model is applied on forwarding pumping stations belonging to the Sewerage Treatment Company (STC), one of the largest sewage stations in Qatar.

Business Intelligence System Design and its Consequences for Knowledge Sharing, Collaboration, and Decision-Making

2020 ◽  
pp. 382-402
Author(s):  
Lapo Mola ◽  
Cecilia Rossignoli ◽  
Andrea Carugati ◽  
Antonio Giangreco
Keyword(s):  
Decision Making ◽  
Knowledge Sharing ◽  
Business Intelligence ◽  
Two Phase ◽  
Intelligence System ◽  
Business Intelligence Systems ◽  
Managerial Implications ◽  
Decision Making Processes

This exploratory study analyses the effects of the technical and organisational characteristics of business intelligence systems (BIS) on knowledge sharing, collaboration, and decision-making processes. The authors conducted a two-phase multi-method investigation. First, we surveyed 30 enterprises using BIS on a regular basis; then, we engaged in an in-depth case study with one of the respondent companies. Our results show that, on average, the technical and organisational characteristics of the BIS are positively associated with an increase in knowledge sharing, leading to an improvement in internal collaboration that subsequently brings improvement in the quality of decision-making. This case study adds that the way the BIS is designed and appropriated in organisations is important in obtaining such results is. A BIS being designed so that it can be appropriated by the general employee base is key in obtaining the desired organizational impacts. This suggests some requirements for BIS design that we will discuss in terms of theoretical and managerial implications.

Planted Roofs Over Buildings

2022 ◽  
pp. 338-349
Author(s):  
Theodoros Galanis ◽  
Ploutarhos Kerpelis
Keyword(s):  
Decision Making ◽  
Poor Quality ◽  
School Building ◽  
Human Beings ◽  
Original Design ◽  
Advantages And Disadvantages ◽  
The Poor ◽  
Financial Benefits

Humans in addition with other factors have increased the environmental pollution of the planet. Many highly populated cities like Athens have problems with air quality due to the poor quality of construction, high temperatures in summer, noise, no existence of city plans, etc. The scope of this study is the investigation of urban towns' benefits using planted roofs. All types of planted roofs have many environmental, constructional, social, and financial benefits. The research suggests a method from the design, the study until the construction, using decision making, informing the citizens, and taking into account their opinion. The original design of buildings must have adopted an integrated energy strategy such as the solution of planted roofs so as to maximize the benefits to the environment and human beings. The research is specialized using a case study about a planted roof of an existing school building in Athens. The advantages and disadvantages of the usage are shown focusing on environmental, social, and construction aspects.

scholarly journals How Cognitive Biases Influence the Data Verification of Safety Indicators: A Case Study in Rail

Safety ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 69
Author(s):  
Burggraaf ◽  
Groeneweg ◽  
Sillem ◽  
van Gelder
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Decision Makers ◽  
Human Cognition ◽  
Quality Data ◽  
Verification Process ◽  
Support Decision Making ◽  
Good Quality Data

The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition.

Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

2020 ◽  
Vol 120 (6) ◽  
pp. 1149-1174 ◽  
Author(s):  
K.H. Leung ◽  
Daniel Y. Mo ◽  
G.T.S. Ho ◽  
C.H. Wu ◽  
G.Q. Huang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Service Providers ◽  
Series Data ◽  
Illustrative Case ◽  
Implementation Framework ◽  
Inference System ◽  
Content Type

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

scholarly journals Construction of Quality Indicators Based on Pre-Established Goals: Application to a Colombian Public University

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1075
Author(s):  
Vicente Liern ◽  
Sandra E. Parada-Rico ◽  
Olga Blasco-Blasco
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Gender Inequality ◽  
Multiple Criteria Decision Making ◽  
Similarity Measures ◽  
Public University ◽  
Strategic Decisions ◽  
Economic Disparity

This study creates indicators of adequacy and excellence based on multiple-criteria decision-making (MCDM) methods and fuzzy logic. The calculation of indicators presents two main difficulties: The nature of the data (numerical, interval, and linguistic values are mixed) and the objective of each criterion (which does not have to reach either the maximum or the minimum). A method is proposed, based on similarity measures with predetermined ideals, that is capable of overcoming these difficulties to provide easy-to-interpret information about the quality of the alternatives. To illustrate the usefulness of this proposed method, it has been applied to data collected from students across nine semesters at the Bucaramanga campus of the Industrial University of Santander in Colombia. This case study demonstrates that the proposed method can facilitate strategic decisions at an institution and open the way for the establishment of action policies regarding gender inequality and economic disparity, among other things.

scholarly journals Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method

2019 ◽  
Vol 16 (23) ◽  
pp. 4843 ◽  
Author(s):  
Zhi Wen ◽  
Huchang Liao ◽  
Ruxue Ren ◽  
Chunguang Bai ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Cold Chain ◽  
Solution Methods ◽  
Criteria Weights ◽  
Ranking Of Alternatives ◽  
And Storage ◽  
Selection Of

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses.

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.

scholarly journals Iterative Guided Machine Learning-Assisted Systematic Literature Reviews: A Diabetes Case Study

2021 ◽  
Author(s):  
John Zimmerman ◽  
Robin Soler ◽  
Lavinder James ◽  
Murphy Sarah ◽  
Atkins Charisma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Literature ◽  
Relevant Information ◽  
Text Analytics ◽  
Inclusion Criteria ◽  
Literature Reviews ◽  
Study Test ◽  
Model Training

Abstract Background: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and Machine Learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.Methods: In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. Results: the case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes. Conclusions: We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.

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