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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.
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Articles 9 Documents
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Search results for , issue "Vol 8, No 2 (2022): July 2022" : 9 Documents clear
Analysis of Color Features Performance Using Support Vector Machine with Multi Kernel for Batik Classification Edy Winarno; Wiwien Hadikurniawati; Anindita Septirini; Hamdani Hamdani
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. Each area, particularly Semarang in Central Java, has its own batik design. Unfortunately, due to a lack of knowledge, not all residents are able to recognize the types of Semarang batik.  Therefore, this study proposed an automated approach for classifying Semarang batik. Semarang batik was classified into five categories according to this method:  Asem Arang, Blekok Warak, Gamblang Semarangan, Kembang Sepatu, and Semarangan. Since color was able to distinguish batik patterns, it is necessary to analyze color features based on the color space in order to generate discriminative features.  Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier. The experiment was conducted using a local dataset of 1000 batik images classified into five classes (each class contains 200 images).  In order to evaluate the method, cross-validation was performed using a k-fold value of 10. The results showed that the proposed method could reach an accuracy of 1 in all SVM Kernels when employing the YIQ color space, which was consistent across all tests.
Incremental Multiclass Open-set Audio Recognition Hitham Jleed; Martin Bouchard
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i2.812

Abstract

Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data to update the existing models. Open-set recognition, however, requires the ability to recognize examples from known classes and reject examples from new/unknown classes. In this work, we propose a combination of multiclass open-set recognition and an incremental learning scheme in the audio recognition domain. We introduce incremental open-set multiclass support vector machine algorithms that can classify examples from seen/unseen classes, using incremental learning to increase the current model with new classes without entirely retraining the system. Comprehensive evaluations carried out on problems of multi-class open-set recognition showed promising performance for the proposed methods, compared with some representative previous methods
Optimizing Complexity Weight Parameter of Use Case Points Estimation using Particle Swarm Optimization Ardiansyah Ardiansyah
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

Among algorithmic-based software development effort estimation frameworks available, Use Case Points is one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of use case. This study investigates the potential of integrating particle swarm optimization with the Use Case Points framework, where PSO is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The accuracy and performance evaluation of the proposed model is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.
Cluster analysis and ensemble transfer learning for COVID-19 classification from computed tomography scans Lyubomir Gotsev; Ivan Mitkov; Eugenia Kovatcheva; Boyan Jekov; Roumen Nikolov; Elena Shoikova; Milena Petkova
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i2.817

Abstract

The paper aims to demonstrate the convergence and synergistic application of deep learning methods and techniques to improve approaches and solutions in COVID-19 computed tomography scans classification by addressing the data and protocol challenges found after analyzing related work. The study is set to test and experiment with the proposed strategy: data standardization and normalization to achieve proper contrast and resolution; k-means (clustering) and group shuffle split to avoid data leakage; augmentation and transfer learning to deal with limited sample size and over-fitting. All activities are implemented before applying ensemble learning. VGG-16, Densenet-201, Inception v3 are the pre-trained networks utilized to build base models for the suggested stacking model with a vector of their predictions fed into a meta-learner input. All four classifiers are measured and compared. Various confusion-matrix-based and weighted evaluation metrics are considered: accuracy, recall, precision, f-measure, specificity, and AUC. Critical measurements, such as negative prediction value, false-positive rate, false-negative rate, and false discovery rate, are also presented. The evaluated classifiers achieve high results with AUC between 0.95 to 1. However, the stacked method is the most reliable. The ensemble approach enhanced described strategy having three main advantages: outperforming the base models, reducing data pitfalls, and decreasing generalization error. It can serve as a baseline to increase the performance quality and mitigate the risk of bias in the field.
Feature selection using regression mutual information deep convolution neuron networks for COVID-19 X-ray image classification Tongjai Yampaka; Suteera Vonganansup; Prinda Labcharoenwongs
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

Coronavirus disease (COVID‐19) is a pandemic disease that has spread rapidly among people living in many countries.  The effective screening and immediate medical response for the infected patients are important to treat with stopping the spread of COVID‐19 disease.  Chest radiography (CXR) image is usually required for lung severity assessment.  However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification by feature selection technique using the regression mutual information deep convolution neuron networks (RMI Deep-CNNs).  The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images.  CXR images were comprehensively pre-trained using DCNNs to extract image features, then, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification.  These networks were compared for the classification of two different schemes (ResNet152V2 and InceptionV3). The classification accuracy, sensitivity, and specificity for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively.  In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification.
Contrast Enhancement for Improved Blood Vessels Retinal Segmentation Using Top-Hat Transformation and Otsu Thresholding Muhammad Arhami; Anita Desiani; Sugandi Yahdin; Ajeng Islamia Putri; Rifkie Primartha; Husaini Husaini
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

Diabetic Retinopathy is a diabetes complication that usually results in abnormalities in the retinal blood vessels of the eye, resulting in blurry vision, including blurry vision and blindness. Automatic segmentation of blood vessels in retinal images can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Top-hat transformation for the retinal image. The retinal image data used in the study were the Digital Retinal Images for Vessel Extraction (DRIVE) dataset generated by the fundus camera. The CLAHE and Top-hat transformation methods were used to increase the contrast of the retinal image and reduce noise so that blood vessels could be highlighted appropriately and the segmentation process could be facilitated. Otsu Thresholding was used to distinguish between blood vessel pixels and background pixels. The performance evaluation measures of the methods used are accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the average accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively, indicating that the proposed method was successful through blood vessels segmentation retinal images, especially for thick blood vessels.
Covid-19 Detection From Chest X-Ray Images: Comparison Of Well-Established Convolutional Neural Networks Models Muhammad Amir As'ari
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

Coronavirus disease 19 (Covid-19) is a pandemic disease that has already killed hundred thousands of people and infected millions more. At the climax disease Covid-19, this virus will lead to pneumonia and result in a fatality in extreme cases. COVID-19 reveals radiological signatures that can be easily detected using chest X-rays, which distinguishes it from other types of pneumonic disease.  Recently, there are several studies using the CNN model only focused on developing binary classifier that classify between Covid-19 and normal chest X-ray. However, no previous studies have ever made a comparison between the performances of some of the established pre-trained CNN models that involving multi-classes including Covid-19, Pneumonia and Normal chest X-ray. Therefore, this study focused on formulating an automated system to detect Covid-19 from chest X-Ray images by four established and powerful CNN models AlexNet, GoogleNet, ResNet-18 and SqueezeNet and the performance of each of the models were compared.  A total of 21,252 chest X-ray images from various sources were pre-processed and trained for the transfer learning-based classification task, which included Covid-19, bacterial pneumonia, viral pneumonia, and normal chest x-ray images. In conclusion, this study revealed that all models successfully classify Covid-19 and other pneumonia at an accuracy of more than 78.5%, and the test results revealed that GoogleNet outperforms other models for achieved accuracy of 91.0%, precision of 85.6%, sensitivity of 85.3%, and F1 score of 85.4%.
Deep Neural Network-based Physical Distancing Monitoring System with TensorRT Optimization Edi Kurniawan
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. Deep learning-based object detection is employed to detect people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) with Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. The results show that the inferencing speed in terms of Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones.
A Novel Hybrid Archimedes Optimization Algorithm for Energy-Efficient Hybrid Flow Shop Scheduling Dana Marsetiya Utama; Ayu An Putri Salima; Dian Setiya Widodo
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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Abstract

The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms. The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms.

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