Simultaneous Multi Voltage Aware Timing Analysis Methodology for SOC using Machine Learning

2020 ◽  
Author(s):  
Vishant Gotra ◽  
Srinivasa Kodanda Rama Reddy
Keyword(s):  
Machine Learning ◽  
Timing Analysis ◽  
Analysis Methodology

An Integrated Timing Analysis Methodology for Real-Time Systems

2011 ◽  
Author(s):  
Daniel Kästner ◽  
Marek Jersak ◽  
Christian Ferdinand ◽  
Peter Gliwa ◽  
Reinhold Heckmann
Keyword(s):  
Real Time ◽  
Timing Analysis ◽  
Real Time Systems ◽  
Analysis Methodology ◽  
Time Systems

scholarly journals Gene Signatures Research Involved in Cancer Using Machine Learning

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 19
Author(s):  
Jose Liñares-Blanco ◽  
Carlos Fernandez-Lozano
Keyword(s):  
Machine Learning ◽  
Human Beings ◽  
Huge Amount ◽  
Gene Signatures ◽  
Analysis Methodology ◽  
Computer Technologies ◽  
Genetic Signatures ◽  
Source Of Information ◽  
Omic Data

With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, in this case, is a branch of biology focused on the study of mRNA molecules, among others. The quantification of these molecules gives us information about the expression that a gene is having at a given moment. Having information on the expression of the approximately 20,000 genes harbored by human beings is a really useful source of information for the study of certain conditions and/or pathologies. In this work, patient expression -omic data data have been used to offer a new analysis methodology through Machine Learning. The results of this methodology were compared with a conventional methodology to observe how they differed and how they resembled each other. These techniques, therefore, offer a new mechanism for the search of genetic signatures involved, in this case, with cancer.

Mapping organizational complexity: a network-based approach to behavior through machine learning tools

2021 ◽  
pp. 34-51
Author(s):  
Sabrina Bagnato ◽  
Antonina Barreca ◽  
Roberta Costantini ◽  
Francesca Quintiliani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Organizational Behavior ◽  
Professional Growth ◽  
Univariate Analysis ◽  
Learning Tools ◽  
Organizational Complexity ◽  
Multivariate Technique ◽  
Analysis Methodology ◽  
Development Paths

The current uncertain, dynamic scenario calls for a systemic perspective when referring to organizational complexity and behavior. Our research contributes to the analysis of organizational complexity through multidimensional behavioral mapping. Our method uses machine learning tools to detect the interconnections between the different behaviors of a person in his/her operating context. First, the research project dealt with prototyping a model to read the organizational behavior, the related detection tool, and a data analysis methodology. It used machine learning tools and ended with a data visualization phase. We set our model to read the organizational behavior by comparing the literature benchmark theories with our field experience. The model was organized around 4 areas and 16 behaviors. These were the basis for singling out the indicators and the questionnaire items. The data analysis methodology aimed at detecting the interconnections between behaviors. We designed it by joining univariate analysis with a multivariate technique based on the application of machine learning tools. This led to a high-resolution network map through three specific steps: (a) creating a multidimensional topology based on a Kohonen Map (a type of unsupervised learning artificial neural network) to geometrically represent behavioral relationships; (b) implementing k-means clustering for identifying which areas of the map have behavior similarity or affinity factors; and (c) locating people and the various identified clusters within the map. The research highlighted the validity of machine learning tools to detect the multidimensionality of organizational behavior. Therefore, we could delineate the networking of the observed elements and visualize an otherwise unattainable complexity through multimedia and interactive reporting. Application in the field of research consisted of the design and development of a prototype integrated with our LMS platform via a plugin. Field experimentation confirmed the effectiveness of the method for creating professional growth and development paths. Furthermore, this experimentation allowed us to obtain significant data by applying our model to several sectors, namely pharmaceutical, TLC, banking, automotive, machinery, and services.

MLTimer: Leakage Power Minimization in Digital Circuits Using Machine Learning and Adaptive Lazy Timing Analysis

2018 ◽  
Vol 14 (2) ◽  
pp. 285-301 ◽  
Author(s):  
S. L. P. S. K. Patanjali ◽  
Milan Patnaik ◽  
Seetal Potluri ◽  
V. Kamakoti
Keyword(s):  
Machine Learning ◽  
Digital Circuits ◽  
Timing Analysis ◽  
Leakage Power ◽  
Power Minimization

EXPRESS: Visual Elicitation of Brand Perception

2021 ◽  
pp. 002224292199666
Author(s):  
Daria Dzyabura ◽  
Renana Peres
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Brand Management ◽  
Brand Personality ◽  
Abstract Art ◽  
Brand Associations ◽  
National Brands ◽  
The Core ◽  
Analysis Methodology ◽  
Brand Perception

Understanding consumers’ associations with brands is at the core of brand management. This is challenging since consumers can associate a brand with any number of objects, emotions, activities, sceneries, and concepts. This paper presents an elicitation platform, analysis methodology and results on consumer associations of US national brands. Our elicitation is direct, unaided, scalable, quantitative, and uses the power of visuals to depict a detailed representation of respondents’ relationships with a brand. The proposed platform, Brand Visual Elicitation Platform (B-VEP), allows firms to collect online brand collages created by respondents and analyze them quantitatively to elicit brand associations. The authors use the platform to collect 4,743 collages for 303 large US brands from 1,851 respondents. Using unsupervised machine-learning and image-processing approaches, they analyze the collages and obtain a detailed set of associations for each brand, including objects (animals, food, people), constructs (abstract-art, horror, delicious, famous, fantasy), occupations (musician, bodybuilding, baking), nature (beach, misty, snowscape, wildlife), and institutions (corporate, army, school). The authors demonstrate applications for brand management: obtaining prototypical brand visuals; relating associations to brand personality and equity; identifying favorable associations per category; exploring brand uniqueness through differentiating associations; and identifying commonalities between brands across categories for potential collaborations.

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