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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Nurul Amelina Nasharuddin
Universiti Putra Malaysia
Computer Science & IT

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A colour-based building recognition using support vector machine Mas Rina Mustaffa; Loh Weng Yee; Lili Nurliyana Abdullah; Nurul Amelina Nasharuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.11609

Abstract

Many applications apply the concept of image recognition to help human in recognising objects simply by just using digital images. A content-based building recognition system could solve the problem of using just text as search input. In this paper, a building recognition system using colour histogram is proposed for recognising buildings in Ipoh city, Perak, Malaysia. The colour features of each building image will be extracted. A feature vector combining the mean, standard deviation, variance, skewness and kurtosis of gray level will be formed to represent each building image. These feature values are later used to train the system using supervised learning algorithm, which is Support Vector Machine (SVM). Lastly, the accuracy of the recognition system is evaluated using 10-fold cross validation. The evaluation results show that the building recognition system is well trained and able to effectively recognise the building images with low misclassification rate.
Classifying date fruits using the transfer learning model Alia Nadzirah Mohd Adnan; Nurul Amelina Nasharuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25924

Abstract

Date palm trees originate in many tropical regions of the world and produce dates. Each variety can be differentiated through the shape, texture, size, and colour of the fruits. People have difficulties visualising and recognising the types of date fruits because they have many varieties and species. An Android-based mobile application is being proposed to help users quickly identify the dates based on their images and expand their knowledge of dates. The date fruit species classification mobile application categorises nine different varieties of date fruits, namely Ajwa, Medjool, Rutab, Nabtat Ali, Meneifi, Galaxy, Sugaey, Shaishe, and Sokari. The classification, which is based on a transfer learning technique from a pre-trained neural network, achieved a 94.2% accuracy rate. The mobile application features a user-friendly graphical interface that makes it easy to use and understand. Users can learn about different date fruit varieties and improve knowledge retention through a mini game. The application’s usability, usefulness, and interface design were confirmed through the user acceptance survey.
Co-Authors Alia Nadzirah Mohd Adnan Lili Nurliyana Abdullah Loh Weng Yee Mas Rina Mustaffa