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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
Arjuna Subject : -
Articles 330 Documents
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Emotion brain-computer interface using wavelet and recurrent neural networks Esmeralda Contessa Djamal; Hamid Fadhilah; Asep Najmurrokhman; Arlisa Wulandari; Faiza Renaldi
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.432

Abstract

Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds.
Optimization of data resampling through GA for the classification of imbalanced datasets Filippo Galli; Marco Vannucci; Valentina Colla
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.409

Abstract

Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This paper overview a novel family of methods for the resampling of an imbalanced dataset in order to maximize the performance of arbitrary data-driven classifiers. The presented approaches exploit genetic algorithms (GA) for the optimization of the data selection process according to a set of criteria that assess each candidate sample suitability. A comparison among the presented techniques on a set of industrial and literature datasets put into evidence the validity of this family of approaches, which is able not only to improve the performance of a standard classifier but also to determine the optimal resampling rate automatically. Future activities for the improvement of the proposed approach will include the development of new criteria for the assessment of sample suitability.
Nonstandard optimal control problem: case study in an economical application of royalty problem Wan Noor Afifah Wan Ahmad; Suliadi Firdaus Sufahani; Alan Zinober; Azila M Sudin; Muhaimin Ismoen; Norafiz Maselan; Naufal Ishartono
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.357

Abstract

This paper's focal point is on the nonstandard Optimal Control (OC) problem. In this matter, the value of the final state variable, y(T) is said to be unknown. Moreover, the Lagrangian integrand in the function is in the form of a piecewise constant integrand function of the unknown state value y(T). In addition, the Lagrangian integrand depends on the y(T) value. Thus, this case is considered as the nonstandard OC problem where the problem cannot be resolved by using Pontryagin’s Minimum Principle along with the normal boundary conditions at the final time in the classical setting. Furthermore, the free final state value, y(T) in the nonstandard OC problem yields a necessary boundary condition of final costate value, p(T) which is not equal to zero. Therefore, the new necessary condition of final state value, y(T) should be equal to a certain continuous integral function of y(T)=z since the integrand is a component of y(T). In this study, the 3-stage piecewise constant integrand system will be approximated by utilizing the continuous approximation of the hyperbolic tangent (tanh) procedure. This paper presents the solution by using the computer software of C++ programming and AMPL program language. The Two-Point Boundary Value Problem will be solved by applying the indirect method which will involve the shooting method where it is a combination of the Newton and the minimization algorithm (Golden Section Search and Brent methods). Finally, the results will be compared with the direct methods (Euler, Runge-Kutta, Trapezoidal and Hermite-Simpson approximations) as a validation process.
Radial greed algorithm with rectified chromaticity for anchorless region proposal applied in aerial surveillance Anton Louise Pernez De Ocampo; Elmer Dadios
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.426

Abstract

In aerial images, human figures are often rendered at low resolution and in relatively small sizes compared to other objects in the scene, or resemble likelihood to other non-human objects. The localization of trust regions for possible containment of the human figure becomes difficult and computationally exhaustive. The objective of this work is to develop an anchorless region proposal which can emphasize potential persons from other objects and the vegetative background in aerial images. Samples are taken from different angles, altitudes and environmental factors such as illumination. The original image is rendered in rectified color space to create a pseudo-segmented version where objects of close chromaticity are combined. The geometric features of segments formed are then calculated and subjected to Radial-Greed Algorithm where segments resembling human figures are selected as the proposed regions for classification. The proposed method achieved 96.76% less computational cost against brute sliding window method and hit rate of 95.96%. In addition, the proposed method achieved 98.32 % confidence level that it can hit target proposals at least 92% every time.
Japanese sign language classification based on gathered images and neural networks Shin-ichi Ito; Momoyo Ito; Minoru Fukumi
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.406

Abstract

This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.
Improving stroke diagnosis accuracy using hyperparameter optimized deep learning Tessy Badriyah; Dimas Bagus Santoso; Iwan Syarif; Daisy Rahmania Syarif
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.427

Abstract

Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.
Trophic state assessment using hybrid classification tree-artificial neural network Ronnie Sabino Concepcion II; Pocholo James Mission Loresco; Rhen Anjerome Rañola Bedruz; Elmer Pamisa Dadios; Sandy Cruz Lauguico; Edwin Sybingco
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.408

Abstract

The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. This paper proposed a model of hybrid classification tree-artificial neural network (CT-ANN) to assess the trophic state based on the selected significant features. The classification tree was used as a multidimensional reduction technique for feature selection, which eliminates eight original features. The remaining predictors having high impacts are chlorophyll-a, phosphorus and Secchi depth. The two-layer ANN with 20 artificial neurons was constructed to assess the trophic state of input features. The neural network was modeled based on the key parameters of learning time, cross-entropy, and regression coefficient. The ANN model used to assess trophic state based on 11 predictors resulted in 81.3% accuracy. The modeled hybrid classification tree-ANN based on 3 predictors resulted to 88.8% accuracy with a cross-entropy performance of 0.096495. Based on the obtained result, the modeled hybrid classification tree-ANN provides higher accuracy in assessing the trophic state of the aquaponic system.
Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry Filippo Galli; Antonio Ritacco; Giacomo Lanciano; Marco Vannocci; Valentina Colla; Marco Vannucci
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.410

Abstract

Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization.
Serial and parallel implementation of Needleman-Wunsch algorithm Yun Sup Lee; Yu Sin Kim; Roger Luis Uy
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.361

Abstract

Needleman-Wunsch dynamic programming algorithm measures the similarity of the pairwise sequence and finds the optimal pair given the number of sequences. The task becomes nontrivial as the number of sequences to compare or the length of sequences increases. This research aims to parallelize the computation involved in the algorithm to speed up the performance using CUDA. However, there is a data dependency issue due to the property of a dynamic programming algorithm. As a solution, this research introduces the heterogeneous anti-diagonal approach, which benefits from the interaction between the serial implementation on CPU and the parallel implementation on GPU. We then measure and compare the computation time between the proposed approach and a straightforward serial approach that uses CPU only. Measurements of computation times are performed under the same experimental setup and using various pairwise sequences at different lengths. The experiment showed that the proposed approach outperforms the serial method in terms of computation time by approximately three times. Moreover, the computation time of the proposed heterogeneous anti-diagonal approach increases gradually despite the big increments in sequence length, whereas the computation time of the serial approach grows rapidly.
An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem Dana Marsetiya Utama; Dian Setiya Widodo; Muhammad Faisal Ibrahim; Shanty Kusuma Dewi
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.385

Abstract

This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.

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