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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Yamin Thwe
Rajamangala University of Technology Thanyaburi
Computer Science & IT Electrical & Electronics Engineering

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Accurate fashion and accessories detection for mobile application based on deep learning Yamin Thwe; Nipat Jongsawat; Anucha Tungkasthan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4347-4356

Abstract

Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone.
Neuromarketing case study: recognition of sweet and sour taste in beverage products based on EEG signal features Yuri Pamungkas; Riva Satya Radiansyah; Padma Nyoman Crisnapati; Yamin Thwe
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26626

Abstract

Consumers’ acceptance of a food product hinges on its taste. Culinary practitioners typically conduct organoleptic tests to evaluate a food/beverage’s taste. Organoleptic tests have a subjective nature, making a clear description difficult. In this study, we suggest implementing a brain signal-based electroencephalogram (EEG) taste assessment system to evaluate consumer responses to the tastes of a drink, specifically sour and sweet. The system distinguishes flavors based on EEG data. These classifiers, including recurrent neural network (RNN), long-short term memory (LSTM), and gated recurrent unit (GRU), are utilized for the classification process. Total 35 participants’ EEG data were recorded for this study. Temporal (T3 and T4) and centro parietal (CP1 and CP2) channels are used for recording. EEG signal processing involves filtering, artefact elimination, and band decomposition into delta, theta, alpha, beta, and gamma frequencies. In the time domain of clean EEG data, mean absolute value, standard deviation, and variance are used for signal feature extraction. Several classifiers (RNN, LSTM, and GRU) will be fed with the signal feature values as input. An accuracy of 88.62% was achieved using LSTM in the classification. The RNN and GRU models achieved classification accuracies of 88.56% and 87.15% respectively.
Co-Authors Anucha Tungkasthan Nipat Jongsawat Padma Nyoman Crisnapati Riva Satya Radiansyah Yuri Pamungkas