scholarly journals The Borda Count as an Initial Threshold for Kemeny Ranking Aggregation

2021 ◽  
Author(s):  
Noelia Rico ◽  
Camino R. Vela ◽  
Raúl Pérez-Fernández ◽  
Irene Díaz
Keyword(s):  
Borda Count ◽  
Ranking Aggregation

Enzyme Function Classification Based on Borda Count Ranking Aggregation Method

2016 ◽  
pp. 75-85 ◽  
Author(s):  
Mahir M. Sharif ◽  
Alaa Tharwat ◽  
Aboul Ella Hassanien ◽  
Hesham A. Hefny ◽  
Gerald Schaefer
Keyword(s):  
Enzyme Function ◽  
Borda Count ◽  
Aggregation Method ◽  
Ranking Aggregation

Aiming for a Standardized Protocol for Preparing a Process Green Synthesis Report and for Ranking Multiple Synthesis Plans to a Common Target Product

2019 ◽  
Author(s):  
john andraos
Keyword(s):  
Green Synthesis ◽  
Block Diagram ◽  
Computational Method ◽  
Borda Count ◽  
Target Product ◽  
Material Efficiency ◽  
Synthesis Report ◽  
Common Target ◽  
Greenness Index ◽  
Multiple Synthesis

This paper proposes a standardized format for the preparation of process green synthesis reports that can be applied to chemical syntheses of active pharmaceutical ingredients (APIs) of importance to the pharmaceutical industry. Such a report is comprised of the following eight sections: a synthesis scheme, a synthesis tree, radial pentagons and step E-factor breakdowns for each reaction step, a tabular summary of key material efficiency step and overall metrics for a synthesis plan, a mass process block diagram, an energy consumption audit based on heating and cooling reaction and auxiliary solvents, a summary of environmental and safety-hazard impacts based on organic solvent consumption using the Rowan solvent greenness index, and a cycle time process schedule. Illustrative examples of process green synthesis reports are given for the following pharmaceuticals: 5-HT2B and 5-HT7 receptors antagonist (Astellas Pharma), brivanib (Bristol-Myers Squibb), and orexin receptor agonist (Merck). Methods of ranking synthesis plans to a common target product are also discussed using 6 industrial synthesis plans of apixaban (Bristol-Myers Squibb) as a working example. The Borda count method is suggested as a facile and reliable computational method for ranking multiple synthesis plans to a common target product using the following 4 attributes obtained from a process green synthesis report: process mass intensity, mass of sacrificial reagents used per kg of product, input enthalpic energy for solvents, and Rowan solvent greenness index for organic solvents.<br>

scholarly journals Aiming for a standardized protocol for preparing a process green synthesis report and for ranking multiple synthesis plans to a common target product

2019 ◽  
Vol 8 (1) ◽  
pp. 787-801
Author(s):  
John Andraos
Keyword(s):  
Green Synthesis ◽  
Block Diagram ◽  
Computational Method ◽  
Borda Count ◽  
Target Product ◽  
Material Efficiency ◽  
Synthesis Report ◽  
Common Target ◽  
Greenness Index ◽  
Multiple Synthesis

Abstract This paper proposes a standardized format for the preparation of process green synthesis reports that can be applied to chemical syntheses of active pharmaceutical ingredients (APIs) of importance to the pharmaceutical industry. Such a report is comprised of the following eight sections: a synthesis scheme, a synthesis tree, radial pentagons and step E-factor breakdowns for each reaction step, a tabular summary of key material efficiency step and overall metrics for a synthesis plan, a mass process block diagram, an energy consumption audit based on heating and cooling reaction and auxiliary solvents, a summary of environmental and safety-hazard impacts based on organic solvent consumption using the Rowan solvent greenness index, and a cycle time process schedule. Illustrative examples of process green synthesis reports are given for the following pharmaceuticals: 5-HT2B and 5-HT7 receptors antagonist (Astellas Pharma), brivanib (Bristol-Myers Squibb), and orexin receptor agonist (Merck). Methods of ranking synthesis plans to a common target product are also discussed using 6 industrial synthesis plans of apixaban (Bristol-Myers Squibb) as a working example. The Borda count method is suggested as a facile and reliable computational method for ranking multiple synthesis plans to a common target product using the following 4 attributes obtained from a process green synthesis report: process mass intensity, mass of sacrificial reagents used per kg of product, input enthalpic energy for solvents, and Rowan solvent greenness index for organic solvents.

Exploring the Uncertainty Space of Ensemble Classifiers in Face Recognition

2015 ◽  
Vol 29 (03) ◽  
pp. 1556002 ◽  
Author(s):  
Juan Luis Fernández-Martínez ◽  
Ana Cernea
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Majority Voting ◽  
Borda Count ◽  
Discrete Wavelet ◽  
Final Decision ◽  
Ensemble Classifiers ◽  
Classification Problems ◽  
Uncertainty Space

In this paper, we present a supervised ensemble learning algorithm, called SCAV1, and its application to face recognition. This algorithm exploits the uncertainty space of the ensemble classifiers. Its design includes six different nearest-neighbor (NN) classifiers that are based on different and diverse image attributes: histogram, variogram, texture analysis, edges, bidimensional discrete wavelet transform and Zernike moments. In this approach each attribute, together with its corresponding type of the analysis (local or global), and the distance criterion (p-norm) induces a different individual NN classifier. The ensemble classifier SCAV1 depends on a set of parameters: the number of candidate images used by each individual method to perform the final classification and the individual weights given to each individual classifier. SCAV1 parameters are optimized/sampled using a supervised approach via the regressive particle swarm optimization algorithm (RR-PSO). The final classifier exploits the uncertainty space of SCAV1 and uses majority voting (Borda Count) as a final decision rule. We show the application of this algorithm to the ORL and PUT image databases, obtaining very high and stable accuracies (100% median accuracy and almost null interquartile range). In conclusion, exploring the uncertainty space of ensemble classifiers provides optimum results and seems to be the appropriate strategy to adopt for face recognition and other classification problems.

Rank Level Fusion

2013 ◽  
pp. 69-79
Keyword(s):  
Information Aggregation ◽  
Regression Method ◽  
Borda Count ◽  
Fusion Method ◽  
Advantages And Disadvantages ◽  
Current State ◽  
Fusion Methods ◽  
Logistic Regression Method ◽  
Level Fusion ◽  
Voting Method

Rank level fusion is one of the after matching fusion methods used in multibiometric systems. The problem of rank information aggregation has been raised before in various fields. This chapter extensively discusses the rank level fusion methodology, starting with existing literature from the last decade in different application scenarios. Several approaches of existing biometric rank level fusion methods, such as plurality voting method, highest rank method, Borda count method, logistic regression method, and quality-based rank fusion method, are discussed along with their advantages and disadvantages in the context of the current state-of-the-art in the discipline.

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