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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Noridayu Manshor
Universiti Putra Malaysia
Computer Science & IT

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Fuzzy encoding with hybrid pooling for visual dictionary in food recognition Mohd Norhisham Razali; Noridayu Manshor; Alfian Abdul Halin; Norwati Mustapha; Razali Yaakob
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp179-195

Abstract

Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by using MSER detector with SURF descriptor was encoded by using fuzzy encoding approach. Support vector machine (SVM) with linear kernel was employed to evaluate the effect of fuzzy encoding. The results of the experiments have demonstrated a noteworthy classification performance of fuzzy encoding approach compared to the traditional approach based on hard assignment and Fisher vector technique. The effects of uncertainty and plausibility were minimized along with more discriminative and compact visual dictionary representation.
The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis Yahaya Zakariyau Bala; Pathiah Abdul Samat; Khaironi Yatim Sharif; Noridayu Manshor
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.v22i4.25916

Abstract

This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a specific focus on the F1 score metric as evaluation criterion. Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). The choice of these algorithms reflects their prevalence in software defect prediction literature and their diversity. Through rigorous experimentation and analysis, the investigation unveils compelling evidence affirming the superiority of RF over its counterparts. The F1 score utilized as evaluation metric, capturing the delicate balance between precision and recall, essential in defect prediction scenarios. The nuanced examination of algorithmic efficacy provides practical insights for developers and practitioners navigating the challenges of cross-project defect prediction. By leveraging the rich and diverse AEEEEM dataset, this study ensures a comprehensive exploration of algorithmic influences across varied software projects. The findings not only contribute to the academic discourse on defect prediction but also offer practical guidance for real-world application, emphasizing the pivotal role of RF as a tool in enhancing predictive accuracy and reliability.
Co-Authors Alfian Abdul Halin Khaironi Yatim Sharif Mohd Norhisham Razali Norwati Mustapha Pathiah Abdul Samat Razali Yaakob Yahaya Zakariyau Bala