Pub Date : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp2323-2332
Idriss Moumen, J. Abouchabaka, N. Rafalia
The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.
{"title":"Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction","authors":"Idriss Moumen, J. Abouchabaka, N. Rafalia","doi":"10.11591/ijai.v13.i2.pp2323-2332","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2323-2332","url":null,"abstract":"The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234409","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp1608-1618
Ilham Zharif Mustaqim, Hasna Melani Puspasari, Avita Tri Utami, Rahmad Syalevi, Y. Ruldeviyani
The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.
{"title":"Assessing public satisfaction of public service application using supervised machine learning","authors":"Ilham Zharif Mustaqim, Hasna Melani Puspasari, Avita Tri Utami, Rahmad Syalevi, Y. Ruldeviyani","doi":"10.11591/ijai.v13.i2.pp1608-1618","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1608-1618","url":null,"abstract":"The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231284","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp2011-2018
Felcia Bel, Sabeen Selvaraj
Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.
医疗保健是物联网（IoT）中非常重要的应用领域。本研究旨在提供一种新颖的组合特征选择（FS）方法，如基于树的单变量（UV）方法（TB）、基于最小绝对收缩选择算子（LASSO）的递归特征消除（RFE）方法、基于遗传算法（GA）的互信息（MI）方法和基于单变量的嵌入式方法（EM）。适合物联网医疗数据的机器学习算法是逻辑回归（LR）和支持向量机（SVM）。每种组合方法都被应用到机器学习算法中，以找到预后的最佳分类器。计算了逻辑回归和支持向量机的所有组合特征选择方法的各种性能指标后发现，使用递归消除特征选择方法进行精确分类，并将 LASSO 应用于逻辑回归，比所有其他组合方法取得了更好的性能，具有高准确性、高灵敏度和高曲线下面积。数据分析得出的结论是，在比较了所有其他与 LR 和 SVM 分类器相结合的方法后，使用 LR 特征选择方法的 RFE+LASSO 将为基于物联网的心脏病医疗数据集提供更好的整体性能。
{"title":"Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis","authors":"Felcia Bel, Sabeen Selvaraj","doi":"10.11591/ijai.v13.i2.pp2011-2018","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2011-2018","url":null,"abstract":"Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231337","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp2143-2154
Shabeena Lylath, Laxmi B. Rananavare
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.
{"title":"Autism spectrum disorder identification with multi-site functional magnetic resonance imaging","authors":"Shabeena Lylath, Laxmi B. Rananavare","doi":"10.11591/ijai.v13.i2.pp2143-2154","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2143-2154","url":null,"abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229420","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp2354-2363
Khalif Amir Zakry, Mohamad Syahiran Soria, Irwandi Hipni Mohamad Hipiny, Hamimah Ujir, Ruhana Hassan
Wildlife videography is an essential data collection method for conducting research on animals. The video recording process of an animal like the Chelonia Mydas turtle in its natural habitat requires the setting up of special camera traps or by performing complex camera movement to capture the animal in frame whilst the cameraman maneuvers over uneven terrain while filming. The result is hours of footage that only have the presence of the intended subject in it for seconds whilst the rest is background footage; or noisy and blurry footage that has only several usable frames among thousands of noisy and unusable ones. This presents a problem that deep learning models can help to assist, especially in detecting a wildlife subject and extracting usable data from hours of noise and background footage. This paper proposes the use of machine learning models to detect and extract wildlife images of Chelonia Mydas turtles to help prune through hundreds and thousands of frames from several video footages. Our paper shows that utilizing a custom model with various confidence scores can label and crop out images in noisy field video recordings of Chelonia Mydas turtles with up to 99.89% of output images correctly cropped and labeled.
{"title":"Chelonia mydas detection and image extraction from noisy field recordings","authors":"Khalif Amir Zakry, Mohamad Syahiran Soria, Irwandi Hipni Mohamad Hipiny, Hamimah Ujir, Ruhana Hassan","doi":"10.11591/ijai.v13.i2.pp2354-2363","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2354-2363","url":null,"abstract":"Wildlife videography is an essential data collection method for conducting research on animals. The video recording process of an animal like the Chelonia Mydas turtle in its natural habitat requires the setting up of special camera traps or by performing complex camera movement to capture the animal in frame whilst the cameraman maneuvers over uneven terrain while filming. The result is hours of footage that only have the presence of the intended subject in it for seconds whilst the rest is background footage; or noisy and blurry footage that has only several usable frames among thousands of noisy and unusable ones. This presents a problem that deep learning models can help to assist, especially in detecting a wildlife subject and extracting usable data from hours of noise and background footage. This paper proposes the use of machine learning models to detect and extract wildlife images of Chelonia Mydas turtles to help prune through hundreds and thousands of frames from several video footages. Our paper shows that utilizing a custom model with various confidence scores can label and crop out images in noisy field video recordings of Chelonia Mydas turtles with up to 99.89% of output images correctly cropped and labeled.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232204","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp1969-1979
Faisal Fahmi, Rizqon Fajar, Sigit Tri Atmaja, Erwandi Erwandi, D. Rahuna
Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.
{"title":"Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio","authors":"Faisal Fahmi, Rizqon Fajar, Sigit Tri Atmaja, Erwandi Erwandi, D. Rahuna","doi":"10.11591/ijai.v13.i2.pp1969-1979","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1969-1979","url":null,"abstract":"Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229915","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp1430-1442
Ramadiani Ramadiani, Muhammad Luthfi Fahrozi, Muhammad Labib Jundillah, Azainil Azainil
Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.
{"title":"Comparison of WASPAS and VIKOR methods to determine non-cash food assistance recipients","authors":"Ramadiani Ramadiani, Muhammad Luthfi Fahrozi, Muhammad Labib Jundillah, Azainil Azainil","doi":"10.11591/ijai.v13.i2.pp1430-1442","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1430-1442","url":null,"abstract":"Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230787","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp2451-2458
Ciara Nurdenara, Wikky Fawwaz Al Maki
Wayang orang performance is one of the Indonesian traditional cultures. The wayang orang players took about an hour to become a proper wayang orang since it takes time to have makeup and to find the appropriate costume before the performance is held. This problem can be solved by developing a computer-based simulation on applying makeup and traditional costume to the face and head of the wayang orang player, respectively. This task can be completed by using image translation. Therefore, people's images can be transformed into wayang orang images. This study aims to translate human faces into wayang orang by adding makeup and accessories using the U-GAT-IT with an unpaired dataset consisting of 1216 data trains and 240 data tests. The challenge of this research is to maintain the image background and the facial identity component in the input image. This research employs quantitative testing employ Kernel Inception Distance (KID), Frèchet Inception Distance (FID), and Inception Score (IS) to evaluate the quality of the output image obtained from the generator. The experimental results show that U-GAT-IT produces a better result than DCLGAN does according to the value of IS, FID, and KID. The IS, FID, and KID obtained by implementing U-GAT-IT are 2.414, 0.924, and 4.357, respectively.
{"title":"Image translation between human face and wayang orang using U-GAT-IT","authors":"Ciara Nurdenara, Wikky Fawwaz Al Maki","doi":"10.11591/ijai.v13.i2.pp2451-2458","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2451-2458","url":null,"abstract":"Wayang orang performance is one of the Indonesian traditional cultures. The wayang orang players took about an hour to become a proper wayang orang since it takes time to have makeup and to find the appropriate costume before the performance is held. This problem can be solved by developing a computer-based simulation on applying makeup and traditional costume to the face and head of the wayang orang player, respectively. This task can be completed by using image translation. Therefore, people's images can be transformed into wayang orang images. This study aims to translate human faces into wayang orang by adding makeup and accessories using the U-GAT-IT with an unpaired dataset consisting of 1216 data trains and 240 data tests. The challenge of this research is to maintain the image background and the facial identity component in the input image. This research employs quantitative testing employ Kernel Inception Distance (KID), Frèchet Inception Distance (FID), and Inception Score (IS) to evaluate the quality of the output image obtained from the generator. The experimental results show that U-GAT-IT produces a better result than DCLGAN does according to the value of IS, FID, and KID. The IS, FID, and KID obtained by implementing U-GAT-IT are 2.414, 0.924, and 4.357, respectively.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232096","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 : 2024-06-01DOI: 10.11591/ijai.v13.i2.pp1574-1584
Muhammad Arief, Made Gunawan, Agung Septiadi, Mukti Wibowo, V. Pragesjvara, Kusnanda Supriatna, Anto Satriyo Nugroho, Gusti Bagus, B. Nugraha, S. Supangkat
To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.
要生成具有良好性能的机器学习（ML）和深度学习（DL）架构，我们需要一个合适的数据集，用于开发过程中的训练和测试阶段。从知识发现和数据挖掘（KDD）杯 99 数据集开始，自 1998 年以来，已经有许多数据集被用于基于 ML 和 DL 的入侵检测系统（IDS）的训练和测试过程。由于可访问的数据集非常多，研究人员在选择使用哪个数据集时可能会遇到困难。因此，随着新数据集的定期创建，评估数据集是否适合要开展的研究的框架变得越来越重要。此外，鉴于近年来物联网（IoT）设备的日益普及以及物联网特定数据集的不断增加，为物联网数据集建立一个特定的框架至关重要。因此，本研究旨在为基于 ML 和 DL 的 IDS 开发一个评估物联网数据集的新框架。研究成果包括：第一，用于评估物联网数据集的新型框架；第二，将该新型框架与其他现有框架进行比较；第三，使用该新型框架对五个物联网数据集进行分析。
{"title":"A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems","authors":"Muhammad Arief, Made Gunawan, Agung Septiadi, Mukti Wibowo, V. Pragesjvara, Kusnanda Supriatna, Anto Satriyo Nugroho, Gusti Bagus, B. Nugraha, S. Supangkat","doi":"10.11591/ijai.v13.i2.pp1574-1584","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1574-1584","url":null,"abstract":"To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234476","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}
This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.
{"title":"Artificial intelligence and internet of things in manufacturing decision processes","authors":"Santo Wijaya, Lim Hermanto Rudy, Fransisca Debora, Rana Ardila Rahma, Arief Ramadhan, Yusita Attaqwa","doi":"10.11591/ijai.v13.i2.pp2185-2200","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2185-2200","url":null,"abstract":"This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235064","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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