Search Results for author: Hirofumi Nonaka

Found 11 papers, 1 papers with code

Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips

no code implementations28 Aug 2020 Daiki Otaki, Hirofumi Nonaka, Noboru Yamada

This paper describes a method combining Bayesian optimization (BO) and a lamped-capacitance thermal circuit network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips.

Bayesian Optimization

Emotional Contribution Analysis of Online Reviews

no code implementations1 May 2019 Elisa Claire Alemán Carreón, Hirofumi Nonaka, Toru Hiraoka, Minoru Kumano, Takao Ito, Masaharu Hirota

In response to the constant increase in population and tourism worldwide, there is a need for the development of cross-language market research tools that are more cost and time effective than surveys or interviews.

BIG-bench Machine Learning

Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"

no code implementations25 Apr 2019 Elisa Claire Alemán Carreón, Tetsuro Ito, Hirofumi Nonaka, Minoru Kumano, Toru Hiraoka, Masaharu Hirota

Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country.

Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site

no code implementations24 Apr 2019 Elisa Claire Alemán Carreón, Hirofumi Nonaka, Toru Hiraoka

With an increasingly large number of Chinese tourists in Japan, the hotel industry is in need of an affordable market research tool that does not rely on expensive and time-consuming surveys or interviews.

General Classification Sentiment Analysis

Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

no code implementations15 Apr 2019 Elisa Claire Alemán Carreón, Hirofumi Nonaka, Asahi Hentona, Hirochika Yamashiro

In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months.

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