Pub Date : 2023-11-02DOI: 10.4114/intartif.vol26iss72pp244-255
Saleh Nazal, Khamael Al-Dulaimi
Weed detection is considered the gold standard in smart agriculture field. An automated detection of weedprocedure is a complicated task, specifically detection of Rumex weed due to different real-world environmental conditions, including illumination, occlusion, overlapped, growth stage, and colours. Few works have doneto classify Rumex weed using machine learning. However, the performance is still not at the level required foragriculture communities and challenges have not been solved. This work proposes Region-Convolutional NeuralNetworks (RCNNs) and VGG16 model based on colour space information to classify Rumex weed from grassland.This paper is investigated the effectiveness of our proposed method over real-world images under different conditions. The findings have shown that the proposed method superior comparing with other AI existing techniques.The results demonstrate that the proposed method has an excellent adaptability over real-world images.
{"title":"Rumex Weed Classification Using Region-Convolution Neural Networks Based-Colour Space Information","authors":"Saleh Nazal, Khamael Al-Dulaimi","doi":"10.4114/intartif.vol26iss72pp244-255","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp244-255","url":null,"abstract":"Weed detection is considered the gold standard in smart agriculture field. An automated detection of weedprocedure is a complicated task, specifically detection of Rumex weed due to different real-world environmental conditions, including illumination, occlusion, overlapped, growth stage, and colours. Few works have doneto classify Rumex weed using machine learning. However, the performance is still not at the level required foragriculture communities and challenges have not been solved. This work proposes Region-Convolutional NeuralNetworks (RCNNs) and VGG16 model based on colour space information to classify Rumex weed from grassland.This paper is investigated the effectiveness of our proposed method over real-world images under different conditions. The findings have shown that the proposed method superior comparing with other AI existing techniques.The results demonstrate that the proposed method has an excellent adaptability over real-world images.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973188","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}
Pub Date : 2023-09-29DOI: 10.4114/intartif.vol26iss72pp223-243
None Vani Ashok, None Bharathi R K, None Sheela N
Quality evaluation of food products, agricultural produce to be specific, has gained momentum from past few decades due to the increased awareness among consumers across the world. This has resulted in the increased emphasis on the development and use of quality assessment techniques in food industry. Moreover, there is a need to automate the quality monitoring of agricultural produce like fruits and vegetables which is otherwise done manually in developing countries hence labor intensive, time consuming and subjective in nature. This paper presents an empirical analysis to build a rapid, robust, real-time, non-destructive computer vision based quality assessment model for mango fruits. The work employs the automatic disease classification of mango fruits based on machine and deep learning models. Firstly, the dataset of colored mango fruits images with 2279 images falling into three classes and another dataset of soft X-ray images of mango fruits with 572 images belonging to two quality classes are developed for detecting external and internal defects, respectively. The multilayer perceptron neural network (MLP NN) with two hidden layers, which may be considered as the starting point for deep learning technique, is proposed as machine learning model to classify the color images of mango fruits into one of three external quality classes with 95.1% accuracy and also to classify the soft X-ray images into two internal quality classes with 97.5% accuracy. In order to step out of feature engineering, actual deep learning convolutional neural network (CNN) models, a customized CNN model and pre-trained CNN models, VGGNet (VGG16) and DenseNet121 were also explored for mango disease classification. The maximum validation accuracy of custom CNN was found to be with 91.52% and 98.7% for color and augmented X-ray images, respectively. The classification accuracy of pre-trained models were found to be reasonably good for the color images but exhibited high variability in results and made it difficult to draw a general conclusion for the proposed datasets. However, the proposed MLP NN model based on few basic intensity and geometric features and also the proposed customized CNN model were found to be the best models and they outperform the state of the art reported in the literature.
{"title":"An Automatic Non-Destructive External and Internal Quality Evaluation of Mango Fruits based on Color and X-ray Imaging with Machine Learning and Deep Learning Based Classification Models","authors":"None Vani Ashok, None Bharathi R K, None Sheela N","doi":"10.4114/intartif.vol26iss72pp223-243","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp223-243","url":null,"abstract":"Quality evaluation of food products, agricultural produce to be specific, has gained momentum from past few decades due to the increased awareness among consumers across the world. This has resulted in the increased emphasis on the development and use of quality assessment techniques in food industry. Moreover, there is a need to automate the quality monitoring of agricultural produce like fruits and vegetables which is otherwise done manually in developing countries hence labor intensive, time consuming and subjective in nature. This paper presents an empirical analysis to build a rapid, robust, real-time, non-destructive computer vision based quality assessment model for mango fruits. The work employs the automatic disease classification of mango fruits based on machine and deep learning models. Firstly, the dataset of colored mango fruits images with 2279 images falling into three classes and another dataset of soft X-ray images of mango fruits with 572 images belonging to two quality classes are developed for detecting external and internal defects, respectively. The multilayer perceptron neural network (MLP NN) with two hidden layers, which may be considered as the starting point for deep learning technique, is proposed as machine learning model to classify the color images of mango fruits into one of three external quality classes with 95.1% accuracy and also to classify the soft X-ray images into two internal quality classes with 97.5% accuracy. In order to step out of feature engineering, actual deep learning convolutional neural network (CNN) models, a customized CNN model and pre-trained CNN models, VGGNet (VGG16) and DenseNet121 were also explored for mango disease classification. The maximum validation accuracy of custom CNN was found to be with 91.52% and 98.7% for color and augmented X-ray images, respectively. The classification accuracy of pre-trained models were found to be reasonably good for the color images but exhibited high variability in results and made it difficult to draw a general conclusion for the proposed datasets. However, the proposed MLP NN model based on few basic intensity and geometric features and also the proposed customized CNN model were found to be the best models and they outperform the state of the art reported in the literature.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193056","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}
Pub Date : 2023-09-27DOI: 10.4114/intartif.vol26iss72pp202-222
Haseena Sikkandar, Saroja S, Suseandhiran N, Manikandan B
As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.
{"title":"An intelligent approach for anomaly detection in credit card data using bat optimization algorithm","authors":"Haseena Sikkandar, Saroja S, Suseandhiran N, Manikandan B","doi":"10.4114/intartif.vol26iss72pp202-222","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp202-222","url":null,"abstract":"As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting the fraud, identifying hidden correlations in data and reduced time for verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using full center loss function to improve fraud detection performance and stability. The proposed model is tested with Kaggle dataset and yields around 99% accuracy.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135534716","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}
Social media has become an integral part of people’s lives, resulting in a constant flow of information. However, a concerning trend has emerged with the rapid spread of fake news, attributed to the lack of verification mechanisms. Fake news has far-reaching consequences, influencing public opinion, disrupting democracy, fuelingsocial tensions, and impacting various domains such as health, environment, and the economy. In order to identify fake news with data sparsity, especially with low resources languages such as Arabic and its dialects, we propose a few-shot learning fake news detection model based on sentence transformer fine-tuning, utilizing no crafted prompts and language model with few parameters. The experimental results prove that the proposed method can achieve higher performances with fewer news samples. This approach provided 71% F1 score on the Algerian dialect fake news dataset and 70% F1 score on the Modern Standard Arabic (MSA) version of the same dataset, which proves that the approach can work on the standard Arabic and its dialects. Therefore, the proposed model can identify fake news in several domains concerning the Algerian community such as politics, COVID-19, tourism, e-commerce, sport, accidents, and car prices.
{"title":"Fake News Detection in Low Resource Languages using SetFit Framework","authors":"Amin Abdedaiem, Abdelhalim Hafedh Dahou, Mohamed Amine Cheragui","doi":"10.4114/intartif.vol26iss72pp178-201","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp178-201","url":null,"abstract":"Social media has become an integral part of people’s lives, resulting in a constant flow of information. However, a concerning trend has emerged with the rapid spread of fake news, attributed to the lack of verification mechanisms. Fake news has far-reaching consequences, influencing public opinion, disrupting democracy, fuelingsocial tensions, and impacting various domains such as health, environment, and the economy. In order to identify fake news with data sparsity, especially with low resources languages such as Arabic and its dialects, we propose a few-shot learning fake news detection model based on sentence transformer fine-tuning, utilizing no crafted prompts and language model with few parameters. The experimental results prove that the proposed method can achieve higher performances with fewer news samples. This approach provided 71% F1 score on the Algerian dialect fake news dataset and 70% F1 score on the Modern Standard Arabic (MSA) version of the same dataset, which proves that the approach can work on the standard Arabic and its dialects. Therefore, the proposed model can identify fake news in several domains concerning the Algerian community such as politics, COVID-19, tourism, e-commerce, sport, accidents, and car prices.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264761","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}
Pub Date : 2023-09-14DOI: 10.4114/intartif.vol26iss72pp160-177
Yan Gao, Amin Rezaeipanah
Advances in technology have led to advances in breast cancer screening by detecting symptoms that doctors have overlooked. In this paper, an automatic detection system for breast cancer cases based on Internet of Things (IoT) is proposed. First, using IoT technology, direct medical images are sent to the data repository after the suspicious person's visit through medical equipment equipped with IoT. Then, in order to help radiologists, interpret medical images as best as possible, we use four pre-trained convolutional neural network models including InceptionResNetV2, InceptionV3, VGG19 and ResNet152. These models are combined by an ensemble classifier. Also, these models are used to accurately predict cases with breast cancer, healthy people, and cases with pneumonia by using two datasets of X-RAY and CT-scan in a three-class classification. Finally, the best result obtained for CT-scan images belongs to InceptionResNetV2 architecture with 99.36% accuracy and for X-RAY images belongs to InceptionV3 architecture with 96.94% accuracy. The results show that this method leads to a reduction in daily visits to medical centers and thus reduces the pressure on the medical care system. It also helps radiologists and medical staff to detect breast cancer in its early stages.
{"title":"An Ensemble Classification Method Based on Deep Neural Networks for Breast Cancer Diagnosis","authors":"Yan Gao, Amin Rezaeipanah","doi":"10.4114/intartif.vol26iss72pp160-177","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp160-177","url":null,"abstract":"Advances in technology have led to advances in breast cancer screening by detecting symptoms that doctors have overlooked. In this paper, an automatic detection system for breast cancer cases based on Internet of Things (IoT) is proposed. First, using IoT technology, direct medical images are sent to the data repository after the suspicious person's visit through medical equipment equipped with IoT. Then, in order to help radiologists, interpret medical images as best as possible, we use four pre-trained convolutional neural network models including InceptionResNetV2, InceptionV3, VGG19 and ResNet152. These models are combined by an ensemble classifier. Also, these models are used to accurately predict cases with breast cancer, healthy people, and cases with pneumonia by using two datasets of X-RAY and CT-scan in a three-class classification. Finally, the best result obtained for CT-scan images belongs to InceptionResNetV2 architecture with 99.36% accuracy and for X-RAY images belongs to InceptionV3 architecture with 96.94% accuracy. The results show that this method leads to a reduction in daily visits to medical centers and thus reduces the pressure on the medical care system. It also helps radiologists and medical staff to detect breast cancer in its early stages.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135487148","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}
Pub Date : 2023-09-10DOI: 10.4114/intartif.vol26iss72pp146-159
Etienne Feukeu, Sumbwanyambe Mbuyu
Vehicular Ad Hoc Networks (VANETs) were created more than eighteen years ago with the aim of reducing accidents on public roads and saving lives. Achieving this goal depends on VANET mobiles exchanging Road State Information (RSI) with their surroundings and acting on the received RSI. Therefore, it is essential to ensure that transmitted messages are accurately received. This requires controlling the quality of the sharing medium or link while considering Channel State Information (CSI), which provides information on channel quality and Signal to Noise Ratio (SNR). The process of adjusting the payload based on the CSI is known as Link Adaptation (LA). While several LA works have been published in VANETs, few have considered the effect of relative node mobility. This work presents a link adaptation strategy for VANETs that uses a Neural Network (NN) and the Levenberg-Marquardt Algorithm (LMA). While accounting for Doppler Shift effect induced by the relative velocity, the simulation results demonstrate that the NN approach outperforms its peers by 77%, 115% and 853% in terms of the transmitted errors, model efficiency and throughput respectively, compared to Cte, ARF, and AMC algorithms.
{"title":"Machine Learning Algorithm for a Link Adaptation strategy in a Vehicular Ad hoc Network","authors":"Etienne Feukeu, Sumbwanyambe Mbuyu","doi":"10.4114/intartif.vol26iss72pp146-159","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp146-159","url":null,"abstract":"Vehicular Ad Hoc Networks (VANETs) were created more than eighteen years ago with the aim of reducing accidents on public roads and saving lives. Achieving this goal depends on VANET mobiles exchanging Road State Information (RSI) with their surroundings and acting on the received RSI. Therefore, it is essential to ensure that transmitted messages are accurately received. This requires controlling the quality of the sharing medium or link while considering Channel State Information (CSI), which provides information on channel quality and Signal to Noise Ratio (SNR). The process of adjusting the payload based on the CSI is known as Link Adaptation (LA). While several LA works have been published in VANETs, few have considered the effect of relative node mobility. This work presents a link adaptation strategy for VANETs that uses a Neural Network (NN) and the Levenberg-Marquardt Algorithm (LMA). While accounting for Doppler Shift effect induced by the relative velocity, the simulation results demonstrate that the NN approach outperforms its peers by 77%, 115% and 853% in terms of the transmitted errors, model efficiency and throughput respectively, compared to Cte, ARF, and AMC algorithms.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072996","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}
Pub Date : 2023-09-06DOI: 10.4114/intartif.vol26iss72pp137-145
Vinodhini M, Sujatha Rajkumar, Mure Vamsi Kalyan Reddy, Vaishnav Janesh
Pneumonia, also known as bronchitis, is caused by bacteria, viruses, or fungi. Pneumonia can be fatal to an infected person because the lungs cannot exchange air. The disease primarily affects infants and people over the age of 65. Every year, nearly 4 million people are killed by the disease, which affects an estimated 420 million people. It is critical to detect and diagnose this condition as soon as possible. Diagnosing the condition using the patient's x-ray is the most effective method. Experienced radiologists will use a chest x-ray of the affected patient to make this informed decision. Recently, coronavirus is a contagious viral disease caused by the SARSCoV2 virus. This virus affects the human respiratory system. The virus also causes pneumonia (COVID pneumonia), which is far more dangerous than normal pneumonia. The main purpose of this task is to study and compare several deep learning enhancement techniques applied to medical x-ray and CT scan images for the detection of COVID19 (pneumonia). A convolutional neural network (CNN) is used to design a model that can distinguish between COVID19 pneumonia and normal pneumonia. In addition, image enhancement techniques (histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE)) have been processed against the dataset to find more efficient methods and models for detecting pneumonia. A dataset of 6432 CXRs were used - 576 COVID pneumonia CXRs, 1583 normal pneumonia CXRs, and 4273 healthy lung CXRs. Based on the results, it was observed that the equalized histogram and the equalized dataset of CLAHE run faster than the original dataset. This requires a computer-aided diagnosis (CAD) system that can distinguish between COVID pneumonia, normal pneumonia, and healthy lungs. In addition, the improved VGG16 achieved 96% accuracy in the detection of X-ray images of COVID19 - pneumonia.
{"title":"Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques: Deep Learning","authors":"Vinodhini M, Sujatha Rajkumar, Mure Vamsi Kalyan Reddy, Vaishnav Janesh","doi":"10.4114/intartif.vol26iss72pp137-145","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp137-145","url":null,"abstract":"Pneumonia, also known as bronchitis, is caused by bacteria, viruses, or fungi. Pneumonia can be fatal to an infected person because the lungs cannot exchange air. The disease primarily affects infants and people over the age of 65. Every year, nearly 4 million people are killed by the disease, which affects an estimated 420 million people. It is critical to detect and diagnose this condition as soon as possible. Diagnosing the condition using the patient's x-ray is the most effective method. Experienced radiologists will use a chest x-ray of the affected patient to make this informed decision. Recently, coronavirus is a contagious viral disease caused by the SARSCoV2 virus. This virus affects the human respiratory system. The virus also causes pneumonia (COVID pneumonia), which is far more dangerous than normal pneumonia. The main purpose of this task is to study and compare several deep learning enhancement techniques applied to medical x-ray and CT scan images for the detection of COVID19 (pneumonia). \u0000A convolutional neural network (CNN) is used to design a model that can distinguish between COVID19 pneumonia and normal pneumonia. In addition, image enhancement techniques (histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE)) have been processed against the dataset to find more efficient methods and models for detecting pneumonia. A dataset of 6432 CXRs were used - 576 COVID pneumonia CXRs, 1583 normal pneumonia CXRs, and 4273 healthy lung CXRs. Based on the results, it was observed that the equalized histogram and the equalized dataset of CLAHE run faster than the original dataset. This requires a computer-aided diagnosis (CAD) system that can distinguish between COVID pneumonia, normal pneumonia, and healthy lungs. In addition, the improved VGG16 achieved 96% accuracy in the detection of X-ray images of COVID19 - pneumonia.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874939","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}
Pub Date : 2023-09-06DOI: 10.4114/intartif.vol26iss72pp124-136
Eunike Andriani Kardinata, Nur Aini Rakhmawati
Ontology is one form of structured representation of knowledge. Ontology is widely used and developed in information retrieval because of its ability to represent knowledge in a form that machines and humans can understand. With the increasing scale and complexity of ontology, there are more significant challenges in identifying extra-logical errors. Ontological development methods mostly use machine learning, which is at risk of missed extra-logical errors. To handle it, crowdsourcing is used, i.e. dividing a large job into several small jobs and hiring the masses to complete it. Data processing is usually done offline to take advantage of crowdsourcing, and batches are converted into online and incremental. Online incremental learning directly arranges an iterative model after a change is made by ensuring that the knowledge that has been obtained before is maintained. This study built an interactive medium to present the initial relationship between concept pairs. Crowdsourcing participants were asked to validate the relationship repeatedly until a specified accuracy value was reached. This study found that the crowdsourcing process was able to improve the model used in the relationship extraction process, from F1-Score 87.2% to 89.8%. Improvements using crowdsourcing achieve the same result as improvements by experts. Thus, crowdsourcing can correct extra-logical errors appropriately as an expert. In addition, it was also found that offline incremental learning using Random Forest resulted in higher model accuracy than incremental online learning using Mondrian Forest. The accuracy of the Random Forest model has a final accuracy of 90.6%, while the accuracy of the Mondrian Forest model is 89.7%. From these results, it was concluded that incremental online learning cannot provide better results than offline incremental learning to improve the meronymy relationship extraction process.
{"title":"Online Incremental Learning Based on Crowdsourcing For Indonesian Ontology Relation Extraction","authors":"Eunike Andriani Kardinata, Nur Aini Rakhmawati","doi":"10.4114/intartif.vol26iss72pp124-136","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp124-136","url":null,"abstract":"Ontology is one form of structured representation of knowledge. Ontology is widely used and developed in information retrieval because of its ability to represent knowledge in a form that machines and humans can understand. With the increasing scale and complexity of ontology, there are more significant challenges in identifying extra-logical errors. Ontological development methods mostly use machine learning, which is at risk of missed extra-logical errors. To handle it, crowdsourcing is used, i.e. dividing a large job into several small jobs and hiring the masses to complete it. Data processing is usually done offline to take advantage of crowdsourcing, and batches are converted into online and incremental. Online incremental learning directly arranges an iterative model after a change is made by ensuring that the knowledge that has been obtained before is maintained. This study built an interactive medium to present the initial relationship between concept pairs. Crowdsourcing participants were asked to validate the relationship repeatedly until a specified accuracy value was reached. This study found that the crowdsourcing process was able to improve the model used in the relationship extraction process, from F1-Score 87.2% to 89.8%. Improvements using crowdsourcing achieve the same result as improvements by experts. Thus, crowdsourcing can correct extra-logical errors appropriately as an expert. In addition, it was also found that offline incremental learning using Random Forest resulted in higher model accuracy than incremental online learning using Mondrian Forest. The accuracy of the Random Forest model has a final accuracy of 90.6%, while the accuracy of the Mondrian Forest model is 89.7%. From these results, it was concluded that incremental online learning cannot provide better results than offline incremental learning to improve the meronymy relationship extraction process.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874868","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}
Pub Date : 2023-06-09DOI: 10.4114/intartif.vol26iss72pp30-60
Alex Cevallos-Culqui, Claudia Pons, Gustavo Rodriguez
The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy.
An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots.
To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study.
The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.
{"title":"Semi-supervised learning models for document classification: A systematic review and meta-analysis","authors":"Alex Cevallos-Culqui, Claudia Pons, Gustavo Rodriguez","doi":"10.4114/intartif.vol26iss72pp30-60","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp30-60","url":null,"abstract":"The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy.
 An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots.
 To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study.
 The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158648","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}
Pub Date : 2023-05-24DOI: 10.4114/intartif.vol26iss71pp131-141
Yong Li, Zhiling Tang, Jun Yao
Accurate classification of wine quality may help to improve making technology of wine. For achieving more effective quality classification, a classification method named DE_PSO_SVM (dataset enhancement (DE)_particle swarm optimization (PSO)_support vector machine (SVM)) is proposed. The correlation between feature attributes and classification labels of wine samples were analyzed to achieve dimension reduction. DE was achieved by calculating the different weight sums of adjacent odd and even rows, both of which belong to the same class of samples. PSO was used to search for the optimal parameters of a Gaussian kernel function, which were substituted in the SVM model to classify wine. K-nearest-neighbor (KNN), random forest (RF) and classification and regression tree (CART) were also used to test the wine classification. In 7-fold cross-validation on three wine datasets, the average Precision, Recall, and F1score of DE_PSO_SVM were best. The results show that enhancing datasets with small samples and searching for the optimal super parameters by PSO improved the performance of the wine classification model.
{"title":"DE_PSO_SVM: An Alternative Wine Classification Method Based on Machine Learning","authors":"Yong Li, Zhiling Tang, Jun Yao","doi":"10.4114/intartif.vol26iss71pp131-141","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp131-141","url":null,"abstract":"Accurate classification of wine quality may help to improve making technology of wine. For achieving more effective quality classification, a classification method named DE_PSO_SVM (dataset enhancement (DE)_particle swarm optimization (PSO)_support vector machine (SVM)) is proposed. The correlation between feature attributes and classification labels of wine samples were analyzed to achieve dimension reduction. DE was achieved by calculating the different weight sums of adjacent odd and even rows, both of which belong to the same class of samples. PSO was used to search for the optimal parameters of a Gaussian kernel function, which were substituted in the SVM model to classify wine. K-nearest-neighbor (KNN), random forest (RF) and classification and regression tree (CART) were also used to test the wine classification. In 7-fold cross-validation on three wine datasets, the average Precision, Recall, and F1score of DE_PSO_SVM were best. The results show that enhancing datasets with small samples and searching for the optimal super parameters by PSO improved the performance of the wine classification model.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44139891","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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