ORCID Profile
0000-0002-3859-6706
Current Organisations
Institut Teknologi Sepuluh Nopember
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North Dakota State University
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Royal Melbourne Institute of Technology
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Publisher: IGI Global
Date: 2005
DOI: 10.4018/978-1-59140-560-3.CH120
Abstract: For several decades and especially with the preeminence of relational database systems, data is almost always formed into horizontal record structures and then processed vertically (vertical scans of files of horizontal records). This makes good sense when the requested result is a set of horizontal records. In knowledge discovery and data mining, however, researchers are typically interested in collective properties or predictions that can be expressed very briefly. Therefore, the approaches for scan-based processing of horizontal records are known to be inadequate for data mining in very large data repositories (Han & Kamber, 2001 Han, Pei, & Yin, 2000 Shafer, Agrawal, & Mehta, 1996).
Publisher: IOP Publishing
Date: 09-2018
Publisher: MDPI AG
Date: 15-04-2020
DOI: 10.3390/INFO11040210
Abstract: During the previous decades, intelligent identification of acronym and expansion pairs from a large corpus has garnered considerable research attention, particularly in the fields of text mining, entity extraction, and information retrieval. Herein, we present an improved approach to recognize the accurate acronym and expansion pairs from a large Indonesian corpus. Generally, an acronym can be either a combination of uppercase letters or a sequence of speech sounds (syllables). Our proposed approach can be computationally ided into four steps: (1) acronym candidate identification (2) acronym and expansion pair collection (3) feature generation and (4) acronym and expansion pair recognition using supervised learning techniques. Further, we introduce eight numerical features and evaluate their effectiveness in representing the acronym and expansion pairs based on the precision, recall, and F-measure. Furthermore, we compare the k-nearest neighbors (K-NN), support vector machine (SVM), and bidirectional encoder representations from transformers (BERT) algorithms in terms of accurate acronym and expansion pair classification. The experimental results indicate that the SVM polynomial model that considers eight features exhibits the highest accuracy (97.93%), surpassing those of the SVM polynomial model that considers five features (90.45%), the K-NN algorithm with k = 3 that considers eight features (96.82%), the K-NN algorithm with k = 3 that considers five features (95.66%), BERT-Base model (81.64%), and BERT-Base Multilingual Cased model (88.10%). Moreover, we analyze the performance of the Hadoop technology using various numbers of data nodes to identify the acronym and expansion pairs and obtain their feature vectors. The results reveal that the Hadoop cluster containing a large number of data nodes is faster than that with fewer data nodes when processing from ten million to one hundred million pairs of acronyms and expansions.
Publisher: IOP Publishing
Date: 09-2018
Publisher: IEEE
Date: 2016
Publisher: IOP Publishing
Date: 09-2018
Publisher: IEEE
Date: 2006
Publisher: Hindawi Limited
Date: 16-09-2022
DOI: 10.1155/2022/3227828
Abstract: Currently, speech recognition datasets are increasingly available freely in various languages. However, speech recognition datasets in the Indonesian language are still challenging to obtain. Consequently, research focusing on speech recognition is challenging to carry out. This research creates Indonesian speech recognition datasets from YouTube channels with subtitles by validating all utterances of downloaded audio to improve the data quality. The quality of the dataset was evaluated using a deep neural network. The time delay neural network (TDNN) was used to build the acoustic model by applying the alignment data from the Gaussian mixture model-hidden Markov model (GMM-HMM). Data augmentation was used to increase the number of validated datasets and enhance the performance of the acoustic model. The results show that the acoustic model built using the validated datasets is better than the unvalidated datasets for all types of lexicons. Utilizing the four lexicon types and increasing the data through augmentation to train the acoustic models can lower the word error rate percentage in the GMM-HMM, TDNN factorization (TDNNF), and CNN-TDNNF-augmented models to 40.85%, 24.96%, and 19.03%, respectively.
Publisher: Universitas Airlangga
Date: 26-04-2022
DOI: 10.20473/JISEBI.8.1.51-60
Abstract: Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four different CNN architectures to recognize license plate characters: AlexNet, LeNet-5, modified LeNet-5, and ResNet-50 architectures. We evaluated the performance based on their accuracy and computation time. We compared the deep learning methods with the Freeman chain code (FCC) extraction with support vector machine (SVM). We also evaluated the Otsu and the threshold binarization performances when applied in the FCC extraction method. Results: The ResNet-50 and modified LeNet-5 produces the best accuracy during the training at 0.97. The precision and recall scores of the ResNet-50 are both 0.97, while the modified LeNet-5’s values are 0.98 and 0.96, respectively. The modified LeNet-5 shows a slightly higher precision score but a lower recall score. The modified LeNet-5 shows a slightly lower accuracy during the testing than ResNet-50. Meanwhile, the Otsu binarization’s FCC extraction is better than the threshold binarization. Overall, the FCC extraction technique performs less effectively than CNN. The modified LeNet-5 computes the fastest at 7 mins and 57 secs, while ResNet-50 needs 42 mins and 11 secs. Conclusion: We discovered that CNN is better than the FCC extraction method with SVM. Both ResNet-50 and the modified LeNet-5 perform best during the training, with F measure scoring 0.97. However, ResNet-50 outperforms the modified LeNet-5 during the testing, with F-measure at 0.97 and 1.00, respectively. In addition, the FCC extraction using the Otsu binarization is better than the threshold binarization. Otsu binarization reached 0.91, higher than the static threshold binarization at 127. In addition, Otsu binarization produces a dynamic threshold value depending on the images’ light intensity. Keywords: Convolutional Neural Network, Freeman Chain Code, License Plate Character Recognition, Support Vector Machine
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 11-2014
Publisher: Elsevier BV
Date: 12-2020
Publisher: IEEE
Date: 10-2015
Publisher: MDPI AG
Date: 26-07-2022
DOI: 10.3390/APP12157524
Abstract: Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an utation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier.
Publisher: IEEE
Date: 10-2017
Publisher: AICIT
Date: 30-11-2011
Publisher: IEEE
Date: 06-2010
Publisher: Engineering and Technology Publishing
Date: 28-08-2013
Publisher: Inderscience Publishers
Date: 2020
Publisher: IEEE
Date: 09-2018
No related grants have been discovered for Taufik Fuadi Abidin.