scholarly journals Resources and expenditures management in the field of medical supplies using K-means clustering algorithm: A case study

2021 ◽  
Vol 23 (4) ◽  
pp. 51-60
Author(s):  
Fatemeh Ghaderi ◽  
Behrouz Moradhasel ◽  
◽  
Keyword(s):  
Clustering Algorithm ◽  
Medical Supplies

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

scholarly journals Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

2021 ◽  
Vol 10 (6) ◽  
pp. 403
Author(s):  
Jiamin Liu ◽  
Yueshi Li ◽  
Bin Xiao ◽  
Jizong Jiao
Keyword(s):  
Decision Making ◽  
Clustering Algorithm ◽  
Fuzzy Membership Function ◽  
Multi Criteria Decision Making ◽  
Landfill Site Selection ◽  
Spatial Distribution Characteristics ◽  
Criteria Weights ◽  
Spatial Decision Making ◽  
Complex Decision

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

scholarly journals An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study

2011 ◽  
Author(s):  
M. H. Marghny ◽  
Rasha M. Abd El-Aziz ◽  
Ahmed I. Taloba
Keyword(s):  
Hepatitis C ◽  
Clustering Algorithm ◽  
Evolutionary Clustering

A TRIPARTITE CLUSTERING ANALYSIS ON MICRORNA, GENE AND DISEASE MODEL

2012 ◽  
Vol 10 (01) ◽  
pp. 1240007 ◽  
Author(s):  
CHENGCHENG SHEN ◽  
YING LIU
Keyword(s):  
Gene Expression ◽  
Clustering Algorithm ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Disease Model ◽  
Relational Information ◽  
Microrna Gene ◽  
Research Findings ◽  
Family Based

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

scholarly journals Event Monitoring and Intelligence Gathering Using Twitter Based Real-Time Event Summarization and Pre-Trained Model Techniques

2021 ◽  
Vol 11 (22) ◽  
pp. 10596
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Yenming J. Chen ◽  
Yung-Lin Chuang
Keyword(s):  
Real Time ◽  
Data Science ◽  
Clustering Algorithm ◽  
Language Models ◽  
Event Monitoring ◽  
Data Set ◽  
Intelligence Gathering ◽  
Twitter Data ◽  
The Government

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the situation and possible trends of the event for making decisions. However, often in emergencies, the government or enterprises are usually unable to notify people in time for early warning and avoiding risks. A sensible solution is to integrate real-time event monitoring and intelligence gathering functions into their decision support system. Such a system can provide real-time event summaries, which are updated whenever important new events are detected. Therefore, in this work, we combine a developed Twitter-based real-time event detection algorithm with pre-trained language models for summarizing emergent events. We used an online text-stream clustering algorithm and self-adaptive method developed to gather the Twitter data for detection of emerging events. Subsequently we used the Xsum data set with a pre-trained language model, namely T5 model, to train the summarization model. The Rouge metrics were used to compare the summary performance of various models. Subsequently, we started to use the trained model to summarize the incoming Twitter data set for experimentation. In particular, in this work, we provide a real-world case study, namely the COVID-19 pandemic event, to verify the applicability of the proposed method. Finally, we conducted a survey on the example resulting summaries with human judges for quality assessment of generated summaries. From the case study and experimental results, we have demonstrated that our summarization method provides users with a feasible method to quickly understand the updates in the specific event intelligence based on the real-time summary of the event story.

An (s, S) Inventory Optimization Problem

2019 ◽  
pp. 187-206
Author(s):  
Gizem Sağol ◽  
Görkem Sariyer ◽  
Banu Yetkin Ekren ◽  
Mustafa Gökalp Ataman
Keyword(s):  
Inventory Management ◽  
Optimization Problem ◽  
Service Level ◽  
Inventory Model ◽  
Considerable Effort ◽  
Medical Products ◽  
High Service ◽  
Inventory Optimization ◽  
Medical Supplies

Inventory management is one essential lever to use the resources efficiently. However, managing inventories in hospitals is a challenging task because of the several issues: a high service level of medical supplies is required under the unpredictable demand, medical products constitute a significant portion of the overall costs, and the management of these supplies requires considerable effort to check the levels to track usage and to distribute them. Therefore, it is pertinence to apply operations research tools to cope with the managerial issues of the hospital inventory system. In this chapter, the authors implement an (s, S) inventory model by using simulation in a case study of a hospital in Izmir, Turkey. They aim to analyze the unpredictable nature of demand of medical supplies in this hospital and its implications on the developed inventory policy.

A Modular Product Design Method to Improve Product Social Sustainability Performance

2015 ◽  
Author(s):  
Junfeng Ma ◽  
Gül E. Okudan Kremer
Keyword(s):  
Product Design ◽  
Clustering Algorithm ◽  
Focal Point ◽  
Design Method ◽  
Social Sustainability ◽  
Social Responsibilities ◽  
Labor Time ◽  
Modular Product ◽  
Potential Synergy

Sustainability has been the emphasis of intense discussion over recent decades, but mostly focused on addressing critical aspects of environmental issues. An increasing awareness of social responsibilities and ever-shifting customer requirements have led manufacturers to consider social sustainability during the design phase in tandem with addressing environmental concerns; thus, design for social sustainability has evolved as a new product design direction. Modular product design (MPD), has been widely used in both academia and industry because of its significant benefits in design engineering. Because of the potential synergy, investigating design for social sustainability in association with MPD holds promise as a field of investigation. In this paper, we introduce a novel MPD approach that uses the elements of key component specification and product impact on social sustainability. The key components carry core technologies or have the highest sustainability effects in a product (i.e., the most costly or environmentally polluting parts). Product competitiveness strongly relies on a few key components that should be a focal point during product development. However, to the best of our knowledge, key components have not been well addressed in modular product design. In this paper, we employ labor time as an indicator to measure social sustainability. A heuristic-based clustering algorithm with labor time optimization is developed to categorize components into modules. A coffee-maker case study is conducted to demonstrate the applicability of the proposed methodology.

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