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International Journal of Advances in Intelligent Informatics
Number of Followers: 7  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2442-6571
Published by Universitas Ahmad Dahlan Homepage  [18 journals]
  • Medoid-based shadow value validation and visualization

    • Authors: Weksi Budiaji
      Pages: 76 - 88
      Abstract: A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed.  Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV.
      PubDate: 2019-04-05
      DOI: 10.26555/ijain.v5i2.326
      Issue No: Vol. 5, No. 2 (2019)
       
  • Fast pornographic image recognition using compact holistic features and
           multi-layer neural network

    • Authors: I Gede Pasek Suta Wijaya, Ida Bagus Ketut Widiartha, Keiichi Uchimura, Muhamad Syamsu Iqbal, Ario Yudo Husodo
      Pages: 89 - 100
      Abstract: The paper presents an alternative fast pornographic image recognition using compact holistic features and multi-layer neural network (MNN). The compact holistic features of pornographic images, which are invariant features against pose and scale, is extracted by shape and frequency analysis on pornographic images under skin region of interests (ROIs). The main objective of this work is to design pornographic recognition scheme which not only can improve performances of existing methods (i.e., methods based on skin probability, scale invariant feature transform, eigenporn, and Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)) but also can works fast for recognition. The experimental outcome display that our proposed system can improve 0.3% of accuracy and reduce 6.60% the false negative rate (FNR) of the best existing method (skin probability and eigenporn on YCbCr, SEP), respectively. Additionally, our proposed method also provides almost similar robust performances to the MP-NF on large size dataset. However, our proposed method needs short recognition time by about 0.021 seconds per image for both tested datasets.
      PubDate: 2019-06-20
      DOI: 10.26555/ijain.v5i2.268
      Issue No: Vol. 5, No. 2 (2019)
       
  • Reversible data hiding method by extending reduced difference expansion

    • Authors: Zainal Syahlan, Tohari Ahmad
      Pages: 101 - 112
      Abstract: To keep hiding secret data in multimedia files, such as video, audio, and image considers essential for information security. Image, for instance, as the media aids data insertion securely. The use of insertion technique must ensure a reliable process on retaining data quality and capacity. However, a trade-off between the resulted image quality and the embedded payload capacity after the embedding process often occurs. Therefore, this research aims at extending the existing method of integrating confidential messages using the Reduced Difference Expansion (RDE), transform into a medical image by changing the base point, block size, and recalculating of difference. The results display that the proposed method enhances the quality of the stego image and capacity of the hidden message.
      PubDate: 2019-07-25
      DOI: 10.26555/ijain.v5i2.351
      Issue No: Vol. 5, No. 2 (2019)
       
  • Modified lambert beer for bilirubin concentration and blood oxygen
           saturation prediction

    • Authors: Pek Ek Ong, Audrey Kah Ching Huong, Xavier Toh Ik Ngu, Farhanahani Mahmud, Sheena Punai Philimon
      Pages: 113 - 122
      Abstract: Noninvasive measurement of health parameters such as blood oxygen saturation and bilirubin concentration predicted via an appropriate light reflectance model based on the measured optical signals is of eminent interest in biomedical research. This is to replace the use of conventional invasive blood sampling approach. This study aims to investigate the feasibility of using Modified Lambert Beer model (MLB) in the prediction of one’s bilirubin concentration and blood oxygen saturation value, SO2. This quantification technique is based on a priori knowledge of extinction coefficients of bilirubin and hemoglobin derivatives in the wavelength range of 440 – 500 nm. The validity of the prediction was evaluated using light reflectance data from TracePro raytracing software for a single-layered skin model with varying bilirubin concentration. The results revealed some promising trends in the estimated bilirubin concentration with mean ± standard deviation (SD) error of 0.255 ± 0.025 g/l. Meanwhile, a remarkable low mean ± SD error of 9.11 ± 2.48 % was found for the predicted SO2 value. It was concluded that these errors are likely due to the insufficiency of the MLB at describing changes in the light attenuation with the underlying light absorption processes. In addition, this study also suggested the use of a linear regression model deduced from this work for an improved prediction of the required health parameter values.
      PubDate: 2019-07-26
      DOI: 10.26555/ijain.v5i2.363
      Issue No: Vol. 5, No. 2 (2019)
       
  • Evolutionary deep belief networks with bootstrap sampling for imbalanced
           class datasets

    • Authors: A’inur A’fifah Amri, Amelia Ritahani Ismail, Omar Abdelaziz Mohammad
      Pages: 123 - 136
      Abstract: Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.
      PubDate: 2019-07-26
      DOI: 10.26555/ijain.v5i2.350
      Issue No: Vol. 5, No. 2 (2019)
       
  • A survey of graph-based algorithms for discovering business processes

    • Authors: Riyanarto Sarno, Kelly Rossa Sungkono
      Pages: 137 - 149
      Abstract: Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algorithm, i.e. Alpha Miner and its expansion. Other than that, this research also gives outline explanations about graph-based algorithms and their focus issues. Based on the evaluations, the graph-based algorithm has high performance and less time complexity than Alpha Miner algorithm.
      PubDate: 2019-07-30
      DOI: 10.26555/ijain.v5i2.296
      Issue No: Vol. 5, No. 2 (2019)
       
  • A comparison on classical-hybrid conjugate gradient method under exact
           line search

    • Authors: Nur Syarafina Mohamed, Mustafa Mamat, Mohd Rivaie, Shazlyn Milleana Shaharudin
      Pages: 150 - 159
      Abstract: One of the popular approaches in modifying the Conjugate Gradient (CG) Method is hybridization. In this paper, a new hybrid CG is introduced and its performance is compared to the classical CG method which are Rivaie-Mustafa-Ismail-Leong (RMIL) and Syarafina-Mustafa-Rivaie (SMR) methods. The proposed hybrid CG is evaluated as a convex combination of RMIL and SMR method. Their performance are analyzed under the exact line search. The comparison performance showed that the hybrid CG is promising and has outperformed the classical CG of RMIL and SMR in terms of the number of iterations and central processing unit per time.
      PubDate: 2019-07-31
      DOI: 10.26555/ijain.v5i2.356
      Issue No: Vol. 5, No. 2 (2019)
       
  • VIKOR multi-criteria decision making with AHP reliable weighting for
           article acceptance recommendation

    • Authors: Aji Prasetya Wibawa, Juwita Annisa Fauzi, Seno Isbiyantoro, Rahmat Irsyada, Dhaniyar Dhaniyar, Leonel Hernandez
      Pages: 160 - 168
      Abstract: DSS is built to support the solution recommendation of a problem. AHP and VIKOR are examples of DSS method. Due to VIKOR’s subjective weighting, this study combines the AHP and VIKOR approach to create a better and more reliable decision support system. The DSS is used to recommend article acceptance using five criteria: originality, quality, clarity, significance, and relevance. The results showed that AHP-VIKOR outperforms the performance of VIKOR. AHP weighting reliably replaces the subjective VIKOR’s initial weighting. The AHP-VIKOR result is more accurate and steadier than VIKOR. Thus, AHP-VIKOR can be presented as a proposed approach for creating a recommendation of scientific article acceptance.
      PubDate: 2019-04-03
      DOI: 10.26555/ijain.v5i2.172
      Issue No: Vol. 5, No. 2 (2019)
       
  • Tree-based mining contrast subspace

    • Authors: Florence Sia, Rayner Alfred
      Pages: 169 - 178
      Abstract: All existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bounds to identify inaccurate contrast subspaces for the given query object. This paper proposes a novel contrast subspace mining method that employs tree-based likelihood contrast scoring function which is not affected by the dimensionality of subspaces. The tree-based scoring measure recursively binary partitions the subspace space in the way that objects belong to the target class are grouped together and separated from objects belonging to other class. In contrast subspace, the query object should be in a group having a higher number of objects of the target class than other class. It incorporates the feature selection approach to find a subset of one-dimensional subspaces with high likelihood contrast score with respect to the query object. Therefore, the contrast subspaces are then searched through the selected subset of one-dimensional subspaces. An experiment is conducted to evaluate the effectiveness of the tree-based method in terms of classification accuracy. The experiment results show that the proposed method has higher classification accuracy and outperform the existing method on several real-world data sets.
      PubDate: 2019-07-23
      DOI: 10.26555/ijain.v5i2.359
      Issue No: Vol. 5, No. 2 (2019)
       
 
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