Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance.
{"title":"Semi-supervised learning for sentiment classification with ensemble multi-classifier approach","authors":"A. Aribowo, H. Basiron, Noor Fazilla Abd Yusof","doi":"10.26555/ijain.v8i3.929","DOIUrl":"https://doi.org/10.26555/ijain.v8i3.929","url":null,"abstract":"Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87161271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Olorunsola Stephen Olufunso, A. Evwiekpaefe, Martins E. Irhebhude
Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset.
{"title":"Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning","authors":"Olorunsola Stephen Olufunso, A. Evwiekpaefe, Martins E. Irhebhude","doi":"10.26555/ijain.v8i3.927","DOIUrl":"https://doi.org/10.26555/ijain.v8i3.927","url":null,"abstract":"Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75662877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning utilization to optimize block-structured communication systems has attracted tremendous attention from researchers. Nevertheless, owing to the extensive data transmission between the transmitter and the receiver, communication, in this case, is hard to establish and maintain effectively. As a solution for this, we first investigate typical end-to-end learning for a communication system, Generative Adversarial Network (GAN). Then, two problems associated with GAN-based systems, the gradient vanishing and overfitting, are reviewed. Subsequently, a residual aided GAN (RA-GAN) is proposed as means to overcome these problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. Finally, the numerical results performed in MATLAB for simulation and Codelabs for training have proven that the RA-GAN scheme has near-optimal performance and outperforms the conventional GAN scheme. Throughout this case study, readers can understand the issues that would occur when deep learning is applied to a communication system and possible approaches to address them.
{"title":"Deep reinforcement learning autoencoder with RA-GAN and GAN","authors":"Hoang-Sy Nguyen, Cong-Danh Huynh","doi":"10.26555/ijain.v8i3.896","DOIUrl":"https://doi.org/10.26555/ijain.v8i3.896","url":null,"abstract":"Deep learning utilization to optimize block-structured communication systems has attracted tremendous attention from researchers. Nevertheless, owing to the extensive data transmission between the transmitter and the receiver, communication, in this case, is hard to establish and maintain effectively. As a solution for this, we first investigate typical end-to-end learning for a communication system, Generative Adversarial Network (GAN). Then, two problems associated with GAN-based systems, the gradient vanishing and overfitting, are reviewed. Subsequently, a residual aided GAN (RA-GAN) is proposed as means to overcome these problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. Finally, the numerical results performed in MATLAB for simulation and Codelabs for training have proven that the RA-GAN scheme has near-optimal performance and outperforms the conventional GAN scheme. Throughout this case study, readers can understand the issues that would occur when deep learning is applied to a communication system and possible approaches to address them.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"227 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80166619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arisetra Razafinimaro, A. R. Hajalalaina, H. Rakotonirainy, Reziky Zafimarina
This article aims to apply machine learning algorithms to the supervised classification of optical satellite images. Indeed, the latter is efficient in the study of land use. Despite the performance of machine learning in satellite image processing, this can change but depends on the nature of the satellite images used. Moreover, when we use the satellite, then the reliability of one classifier can be different from the others. In this paper, we examined the performance of DT, SVM, KNN, ANN, and RF. Analysis factors were used to investigate further their importance for Sentinel 2, Landsat 8, Terra Modis, and Spot 5 images. The results show that the KNN showed the most interesting accuracy during the analysis of medium and low-resolution images with spectral bands lower or equal to 4, with a higher accuracy of about 93%. The RF completely dominated the other analysis cases, where the higher accuracy was about 94%. The classification accuracy is more reliable with high-resolution images than with the other resolution categories. However, the processing times of high-resolution images are much higher. Moreover, higher accuracy was often achieved with more expensive processing times. Besides, almost all machine learning algorithms suffered from the Hugs phenomenon during the analyses. So, before the classification with machine learning, some preprocessing is needed.
{"title":"Land cover classification based optical satellite images using machine learning algorithms","authors":"Arisetra Razafinimaro, A. R. Hajalalaina, H. Rakotonirainy, Reziky Zafimarina","doi":"10.26555/ijain.v8i3.803","DOIUrl":"https://doi.org/10.26555/ijain.v8i3.803","url":null,"abstract":"This article aims to apply machine learning algorithms to the supervised classification of optical satellite images. Indeed, the latter is efficient in the study of land use. Despite the performance of machine learning in satellite image processing, this can change but depends on the nature of the satellite images used. Moreover, when we use the satellite, then the reliability of one classifier can be different from the others. In this paper, we examined the performance of DT, SVM, KNN, ANN, and RF. Analysis factors were used to investigate further their importance for Sentinel 2, Landsat 8, Terra Modis, and Spot 5 images. The results show that the KNN showed the most interesting accuracy during the analysis of medium and low-resolution images with spectral bands lower or equal to 4, with a higher accuracy of about 93%. The RF completely dominated the other analysis cases, where the higher accuracy was about 94%. The classification accuracy is more reliable with high-resolution images than with the other resolution categories. However, the processing times of high-resolution images are much higher. Moreover, higher accuracy was often achieved with more expensive processing times. Besides, almost all machine learning algorithms suffered from the Hugs phenomenon during the analyses. So, before the classification with machine learning, some preprocessing is needed.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90763891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 provides radiological cues 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%.
{"title":"Covid-19 detection from chest x-ray images: comparison of well-established convolutional neural networks models","authors":"M. A. As’ari, Nur Izzaty Ab Manap","doi":"10.26555/ijain.v8i2.807","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.807","url":null,"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 provides radiological cues 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%.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82818255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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. There are two main challenges in this matter. First, new class discovery: the algorithm needs to not only recognize known classes but it must also detect unknown classes. Second, model extension: after the new classes are identified, the model needs to be updated. Focusing on this matter, 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 are carried out on both open set recognition and incremental learning. For open-set recognition, we adopt the openness test that examines the effectiveness of a varying number of known/unknown labels. For incremental learning, we adapt the model to detect a single novel class in each incremental phase and update the model with unknown classes. Experimental results show promising performance for the proposed methods, compared with some representative previous methods.
{"title":"Incremental multiclass open-set audio recognition","authors":"H. Jleed, M. Bouchard","doi":"10.26555/ijain.v8i2.812","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.812","url":null,"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. There are two main challenges in this matter. First, new class discovery: the algorithm needs to not only recognize known classes but it must also detect unknown classes. Second, model extension: after the new classes are identified, the model needs to be updated. Focusing on this matter, 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 are carried out on both open set recognition and incremental learning. For open-set recognition, we adopt the openness test that examines the effectiveness of a varying number of known/unknown labels. For incremental learning, we adapt the model to detect a single novel class in each incremental phase and update the model with unknown classes. Experimental results show promising performance for the proposed methods, compared with some representative previous methods.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"185 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86231091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tongjai Yampaka, S. Vonganansup, Prinda Labcharoenwongs
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 using feature selection technique by 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 the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance 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.
通常需要胸片(CXR)图像来评估肺的严重程度。然而，在COVID-19解释中，胸部x光片需要放射科专家的知识。本研究旨在利用回归互信息深度卷积神经元网络(RMI deep - cnn)的特征选择技术改进COVID-19 x射线图像分类。该数据集由219例COVID-19、500例病毒性肺炎和500例正常胸部x线图像组成。利用DCNNs对CXR图像进行全面的预训练，提取非常大的图像特征，从而降低模型的复杂性，减少模型的过拟合。因此，使用回归互信息选择关键特征，然后使用与softmax完全连接的层进行分类。对于两种可选系统的分类，比较了这些网络(ResNet152V2和InceptionV3)。两种方案的分类性能分别为92.21%、100%、90%和91.39%、100%、82.50%。此外，RMI deep - cnn不仅提高了准确率，而且减少了80%以上的可训练特征。该方法可显著提高COVID - 19分类的计算时间和模型精度。
{"title":"Feature selection using regression mutual information deep convolution neuron networks for COVID-19 X-ray image classification","authors":"Tongjai Yampaka, S. Vonganansup, Prinda Labcharoenwongs","doi":"10.26555/ijain.v8i2.809","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.809","url":null,"abstract":"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 using feature selection technique by 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 the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance 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.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81319954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Winarno, W. Hadikurniawati, Anindita Septiarini, H. Hamdani
Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. 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: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.
{"title":"Analysis of color features performance using support vector machine with multi-kernel for batik classification","authors":"E. Winarno, W. Hadikurniawati, Anindita Septiarini, H. Hamdani","doi":"10.26555/ijain.v8i2.821","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.821","url":null,"abstract":"Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. 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: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78043738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lyubomir Gotsev, I. Mitkov, E. Kovatcheva, Boyan Jekov, R. Nikolov, E. Shoikova, Milena Petkova
The paper presents a brief analysis of publications utilizing the public SARS-CoV-2 dataset, consisting of patients’ computer tomography scans captured from Brazil hospitals and an experimental setup addressing the found data challenges. The analysis shows that all protocols, with one exception, suffer from data leakage arising from data organization where the patients and their images are not grouped. Each patient is represented with several scans. It can provide misleading results as data of the same individual may occur in both training and test sets. Furthermore, only one paper proposed ensemble learning utilizing as base models VGG-16, ResNet50, and Xception. Therefore, we proposed and experimented with the following strategy to mitigate the found risks of bias: data standardization and normalization to achieve proper contrast and resolution; k-means and group shuffle split to avoid data leakage; augmentation and ensemble transfer learning to deal with limited sample size and over-fitting. Compared with the earlier proposed ensemble approach, the current one stacks VGG-16, Densenet-201, and Inception v3, achieving higher accuracy (99.3 %), second in the related work, and most significantly, it applies augmentation and clustering analysis to avoid overestimation. In contrast, the paper also presented critical metrics in the medical domain: negative prediction value (99.55%), false positive rate (0.89%), false negative rate (0.42%), and false discovery rate (0.83%). The strategy has two main advantages: 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.
{"title":"Cluster analysis and ensemble transfer learning for COVID-19 classification from computed tomography scans","authors":"Lyubomir Gotsev, I. Mitkov, E. Kovatcheva, Boyan Jekov, R. Nikolov, E. Shoikova, Milena Petkova","doi":"10.26555/ijain.v8i2.817","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.817","url":null,"abstract":"The paper presents a brief analysis of publications utilizing the public SARS-CoV-2 dataset, consisting of patients’ computer tomography scans captured from Brazil hospitals and an experimental setup addressing the found data challenges. The analysis shows that all protocols, with one exception, suffer from data leakage arising from data organization where the patients and their images are not grouped. Each patient is represented with several scans. It can provide misleading results as data of the same individual may occur in both training and test sets. Furthermore, only one paper proposed ensemble learning utilizing as base models VGG-16, ResNet50, and Xception. Therefore, we proposed and experimented with the following strategy to mitigate the found risks of bias: data standardization and normalization to achieve proper contrast and resolution; k-means and group shuffle split to avoid data leakage; augmentation and ensemble transfer learning to deal with limited sample size and over-fitting. Compared with the earlier proposed ensemble approach, the current one stacks VGG-16, Densenet-201, and Inception v3, achieving higher accuracy (99.3 %), second in the related work, and most significantly, it applies augmentation and clustering analysis to avoid overestimation. In contrast, the paper also presented critical metrics in the medical domain: negative prediction value (99.55%), false positive rate (0.89%), false negative rate (0.42%), and false discovery rate (0.83%). The strategy has two main advantages: 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.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72509309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm 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 proposed model’s accuracy and performance evaluation metric 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. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. 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.
{"title":"Optimizing complexity weight parameter of use case points estimation using particle swarm optimization","authors":"A. Ardiansyah, R. Ferdiana, A. E. Permanasari","doi":"10.26555/ijain.v8i2.811","DOIUrl":"https://doi.org/10.26555/ijain.v8i2.811","url":null,"abstract":"Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm 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 proposed model’s accuracy and performance evaluation metric 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. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. 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.","PeriodicalId":52195,"journal":{"name":"International Journal of Advances in Intelligent Informatics","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87588937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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