An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study

The effect of Gandharva Haritaki, Sudarshana Vati, Arogyavardhini Vati along with Punarnavadi Kashaya were studied clinically on a case of Hepatits - C by modern diagnostic tools with USG Abdomen and Hematological investigations. The review of the patient was done on daily basis in IPD and weekly basis after discharge, the clinical features like loss of appetite, heaviness of abdomen and general weakness were completely subsided during the 2nd month of treatment.

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Transfer of knowledge in chemical equipment reliability

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19892692 ◽

1989 ◽

Vol 54 (10) ◽

pp. 2692-2710 ◽

Cited By ~ 3

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060403 ◽

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.

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Prolonged Psychosis Associated With Interferon Therapy in a Patient With Hepatitis C: Case Study and Literature Review

Psychosomatics ◽

10.1016/s0033-3182(09)70848-5 ◽

2009 ◽

Vol 50 (5) ◽

pp. 538-542 ◽

Cited By ~ 4

Author(s):

Yi-Chun Cheng ◽

Chun-Chih Chen ◽

Ai-Sheng Ho ◽

Nan-Ying Chiu

Keyword(s):

Hepatitis C ◽

Literature Review ◽

Interferon Therapy

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Acute Allograft Rejection following Interferon Therapy for Hepatitis C in Recipients who have Returned to Dialysis after Kidney Transplant Failure: Case Study

The International Journal of Artificial Organs ◽

10.5301/ijao.5000359 ◽

2014 ◽

Vol 37 (11) ◽

pp. 803-808 ◽

Cited By ~ 1

Author(s):

Fabrizio Fabrizi ◽

Roberta D'Ambrosio ◽

Francesco Pallotti ◽

Luisa Berardinelli ◽

Piergiorgio Messa ◽

...

Keyword(s):

Hepatitis C ◽

Kidney Transplant ◽

Allograft Rejection ◽

Interferon Therapy ◽

Acute Allograft Rejection ◽

Transplant Failure

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A TRIPARTITE CLUSTERING ANALYSIS ON MICRORNA, GENE AND DISEASE MODEL

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720012400070 ◽

2012 ◽

Vol 10 (01) ◽

pp. 1240007 ◽

Cited By ~ 2

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.

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

Applied Sciences ◽

10.3390/app112210596 ◽

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.

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