Automated Design of Classification Algorithms

Abstract Custom designed insoles are a niche product that is not always affordable to all who need them. When commercial insoles are fabricated using advanced technologies, the insoles in this study are assembled out of pre-cut modular components to keep the production cost down, hence their price. In this study, algorithms driven by a fuzzy inference were proposed in comparison with a decision tree in order to select the best component combination. One hundred and twelve subjects were recruited to collect foot data extracted from their foot images. Approximately 95% of 182 AI-designed insole pads were found in perfect agreement with the professional podiatrist’s decision with acceptable 5% deviation. Differences in the algorithms’ strength were also discussed. In addition to their superior performance, both algorithms allow the podiatrists to speed up the diagnosis and design phases. This approach, when integrated with applications of mobile devices for remotely retrieving foot data, will expand another simple yet effective customer-oriented product design service.

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Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms

Expert Systems with Applications ◽

10.1016/j.eswa.2018.03.030 ◽

2018 ◽

Vol 104 ◽

pp. 213-234 ◽

Cited By ~ 18

Author(s):

Thambo Nyathi ◽

Nelishia Pillay

Keyword(s):

Genetic Algorithm ◽

Genetic Programming ◽

Automated Design ◽

Grammatical Evolution ◽

Classification Algorithms

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Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

Journal of Psychophysiology ◽

10.1027//0269-8803.14.3.151 ◽

2000 ◽

Vol 14 (3) ◽

pp. 151-158 ◽

Cited By ~ 6

Author(s):

José Luis Cantero ◽

Mercedes Atienza

Keyword(s):

Rem Sleep ◽

Spectral Component ◽

Sleep Onset ◽

Quantitative Information ◽

Maximum Energy ◽

Alpha Activity ◽

The Other ◽

Classification Algorithms ◽

Microstructural Properties ◽

Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

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