JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS

Year: 2018, Volume: 1, Number: 1
Published : Jan 26, 2026

Comparison of Artificial Neural Networks and Genetic Programming Methods For Activity Recognition

Çağatay Berke Erdaş (1), Tunç Aşuroğlu (2), Koray Açıcı (3), Hasan Oğul (4)

(1) Bilgisayar Mühendisliği Bölümü, Başkent Üniversitesi, Ankara
(2) Bilgisayar Mühendisliği Bölümü, Başkent Üniversitesi, Ankara
(3) Bilgisayar Mühendisliği Bölümü, Başkent Üniversitesi, Ankara
(4) Bilgisayar Mühendisliği Bölümü, Başkent Üniversitesi, Ankara
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Abstract

With the widespread use of wearable sensors, the processing of raw data obtained from sensors has led to widely-used solutions to the problem of activity recognition. In this context, it is aimed to compare the performance of artificial neural network methods (ANN, RBFNN) and genetic programming (GP) methods over time, frequency and wavelet features extracted from the accelerometer data. The most successful classification performance achieved was 75.09% using 31 neurons in the hidden layer of the multilayer perceptron, using time attributes.

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