Multi-Chain Multi-Leader Based Data Aggregationand and the Evaluation Metric QoDA

2005 ◽  
Author(s):  
Zhang Xi-huang ◽  
Xu Wen-bo
Keyword(s):  
Evaluation Metric

scholarly journals Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Frederick S. Vizeacoumar ◽  
Hongyu Guo ◽  
Lynn Dwernychuk ◽  
Adnan Zaidi ◽  
Andrew Freywald ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Cancer Recurrence ◽  
The Cancer Genome Atlas ◽  
Svm Classifier ◽  
Plasma Proteome ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers ◽  
Evaluation Metric

AbstractGastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

Towards a knowledge-based probabilistic and context-aware social recommender system

2017 ◽  
Vol 44 (4) ◽  
pp. 464-490 ◽  
Author(s):  
Luis Omar Colombo-Mendoza ◽  
Rafael Valencia-García ◽  
Alejandro Rodríguez-González ◽  
Ricardo Colomo-Palacios ◽  
Giner Alor-Hernández
Keyword(s):  
Latent Dirichlet Allocation ◽  
Evaluation Method ◽  
Context Aware ◽  
Average Precision ◽  
Case Scenario ◽  
Similarity Metric ◽  
Knowledge Based ◽  
Context Modelling ◽  
Evaluation Metric ◽  
Partial Implementation

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

MuSim: Mutation-based Fault Localization Using Test Case Proximity

2021 ◽  
Vol 31 (05) ◽  
pp. 725-744
Author(s):  
Arpita Dutta ◽  
Amit Jha ◽  
Rajib Mall
Keyword(s):  
Fault Localization ◽  
Test Case ◽  
Test Cases ◽  
Similarity Metrics ◽  
Localization Technique ◽  
Evaluation Metric

Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai.

scholarly journals Assessing the quality of ionospheric models through GNSS positioning error: methodology and results

GPS Solutions ◽  
2019 ◽  
Vol 24 (1) ◽  
Author(s):  
Adrià Rovira-Garcia ◽  
Deimos Ibáñez-Segura ◽  
Raul Orús-Perez ◽  
José Miguel Juan ◽  
Jaume Sanz ◽  
...  
Keyword(s):  
Satellite System ◽  
Ionospheric Delay ◽  
Single Frequency ◽  
Positioning Error ◽  
Dual Frequency ◽  
Error Sources ◽  
Ionospheric Models ◽  
Evaluation Metric ◽  
Global Navigation Satellite

Abstract Single-frequency users of the global navigation satellite system (GNSS) must correct for the ionospheric delay. These corrections are available from global ionospheric models (GIMs). Therefore, the accuracy of the GIM is important because the unmodeled or incorrectly part of ionospheric delay contributes to the positioning error of GNSS-based positioning. However, the positioning error of receivers located at known coordinates can be used to infer the accuracy of GIMs in a simple manner. This is why assessment of GIMs by means of the position domain is often used as an alternative to assessments in the ionospheric delay domain. The latter method requires accurate reference ionospheric values obtained from a network solution and complex geodetic modeling. However, evaluations using the positioning error method present several difficulties, as evidenced in recent works, that can lead to inconsistent results compared to the tests using the ionospheric delay domain. We analyze the reasons why such inconsistencies occur, applying both methodologies. We have computed the position of 34 permanent stations for the entire year of 2014 within the last Solar Maximum. The positioning tests have been done using code pseudoranges and carrier-phase leveled (CCL) measurements. We identify the error sources that make it difficult to distinguish the part of the positioning error that is attributable to the ionospheric correction: the measurement noise, pseudorange multipath, evaluation metric, and outliers. Once these error sources are considered, we obtain equivalent results to those found in the ionospheric delay domain assessments. Accurate GIMs can provide single-frequency navigation positioning at the decimeter level using CCL measurements and better positions than those obtained using the dual-frequency ionospheric-free combination of pseudoranges. Finally, some recommendations are provided for further studies of ionospheric models using the position domain method.

scholarly journals Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chansik An ◽  
Hyun Cheol Oh ◽  
Jung Hyun Chang ◽  
Seung-Jin Oh ◽  
Jung Mo Lee ◽  
...  
Keyword(s):  
Prognostic Score ◽  
External Validation ◽  
Vital Signs ◽  
Underlying Disease ◽  
Test Results ◽  
Additional Information ◽  
Patient Will ◽  
National Cohort ◽  
Evaluation Metric ◽  
Development And Validation

AbstractWe developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model’s performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website (https://nhimc.shinyapps.io/ih-psc/), where the results of the validation ongoing in our institution are periodically updated.

scholarly journals Analysis of new nosological models from disease similarities using clustering

2020 ◽  
Author(s):  
Lucía Prieto Santamaría ◽  
Eduardo P. García del Valle ◽  
Gerardo Lagunes García ◽  
Massimiliano Zanin ◽  
Alejandro Rodríguez González ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Molecular Data ◽  
Modern Medicine ◽  
Biological Information ◽  
Distributed Data ◽  
Feature Vectors ◽  
Dbscan Clustering ◽  
Disease Similarity ◽  
Starting Point ◽  
Evaluation Metric

AbstractWhile classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

scholarly journals Effective Capacity Analysis of Reconfigurable Intelligent Surfaces Aided NOMA Network

2021 ◽  
Author(s):  
Geng Li ◽  
Huiling Liu ◽  
Gaojian Huang ◽  
Xingwang Li ◽  
Bichu Raj ◽  
...  
Keyword(s):  
Multiple Access ◽  
Signal To Noise Ratio ◽  
Analytical Approximation ◽  
Base Station ◽  
Effective Capacity ◽  
Green Communication ◽  
Time Service ◽  
Delay Sensitive ◽  
Sixth Generation ◽  
Evaluation Metric

Abstract The future sixth generation (6G) is going to face the significant challenges of massive connections and green communication. Recently, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) have been proposed as two key technologies to address the above problems. Motivated by this fact, we consider a downlink RIS-aided NOMA system, where the base station seeks to communicate with two NOMA users with the aid of a RIS. Considering future network supporting real-time service, we investigate the system performance with the view of effective capacity (EC), which is an important evaluation metric of sensitive to delay sensitive system. Based on this basis, we derive the analytical expressions of the EC of the near and far users. To obtain more useful insights, we deduce the analytical approximation expressions of the EC in the low signal-to-noise-ratio (SNR) approximation by utilizing Taylor expansion. In order to compare, we provide the results of orthogonal multiple access (OMA). It is found that 1) The number of RIS components and the transmission power of the base station have important effects on the performance of the considered system model. 2) Compared with OMA, NOMA system has higher effective capacity due to the short transmission time.

scholarly journals Security Evaluation Metric of Windows-Based Information Security Products

2015 ◽  
Vol 8 (S8) ◽  
pp. 10 ◽  
Author(s):  
Kisoo Kim ◽  
Sangho Lee ◽  
Yeowung Yun ◽  
Jaemin Choi ◽  
Hyungjin Mun
Keyword(s):  
Information Security ◽  
Security Evaluation ◽  
Evaluation Metric
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