IAES International Journal of Artificial Intelligence最新文献_第7页

Automated COVID-19 misinformation checking system using encoder representation with deep learning models 使用编码器表示和深度学习模型的自动新冠肺炎错误信息检查系统

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp488-495

Mohamed Taha, Hala H. Zayed, Marina Azer, Mahmoud Gadallah

{"title":"Automated COVID-19 misinformation checking system using encoder representation with deep learning models","authors":"Mohamed Taha, Hala H. Zayed, Marina Azer, Mahmoud Gadallah","doi":"10.11591/ijai.v12.i1.pp488-495","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp488-495","url":null,"abstract":"Social media impacts society whether these impacts are positive or negative, or even both. It has become a key component of our lives and a vital news resource. The crisis of covid-19 has impacted the lives of all people. The spread of misinformation causes confusion among individuals. So automated methods are vital to detect the wrong arguments to prevent misinformation spread. The covid-19 news can be classified into two categories: false or real. This paper provides an automated misinformation checking system for the covid-19 news. Five machine learning algorithms and deep learning models are evaluated. The proposed system uses the bidirectional encoder representations from transformers (BERT) with deep learning models. detecting fake news using BERT is a fine-tuning. BERT achieved accuracy (98.83%) as a pre-trained and a classifier on the covid-19 dataset. Better results are obtained using BERT with deep learning models (LSTM), which achieved accuracy (99.1%). The results achieved improvements in the area of fake news detection. Another contribution of the proposed system allows users to detect claims' credibility. It finds the most related real news from experts to the fake claims and answers any question about covid-19 using the universal-sentence-encoder model.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48843855","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}

引用次数: 2

Null-values imputation using different modification random forest algorithm 采用不同修改的随机森林算法进行空值插值

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp374-383

Maad M. Mijwil, Alaa Wagih Abdulqader, Sura Mazin Ali, A. Sadiq

{"title":"Null-values imputation using different modification random forest algorithm","authors":"Maad M. Mijwil, Alaa Wagih Abdulqader, Sura Mazin Ali, A. Sadiq","doi":"10.11591/ijai.v12.i1.pp374-383","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp374-383","url":null,"abstract":"Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43441664","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}

引用次数: 1

Vehicle make and model recognition using mixed sample data augmentation techniques 使用混合样本数据增强技术的车辆制造和模型识别

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp137-145

T. Anwar, Seemab Zakir

引用次数: 2

Comparative study of optimization methods for optimal coordination of directional overcurrent relays with distributed generators 定向过流继电器与分布式发电机最优协调优化方法的比较研究

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp209-219

Zineb El Idrissi, Touria Haidi, Faissal Elmariami, Abdelaziz Belfqih

{"title":"Comparative study of optimization methods for optimal coordination of directional overcurrent relays with distributed generators","authors":"Zineb El Idrissi, Touria Haidi, Faissal Elmariami, Abdelaziz Belfqih","doi":"10.11591/ijai.v12.i1.pp209-219","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp209-219","url":null,"abstract":"Due to the growing penetration of distributed generators (DGs), that are based on renewable energy, into the distribution network, it is necessary to address the coordination of directional overcurrent relays (DOCR) in the presence of these generators. This problem has been solved by many metaheuristic optimization techniques to obtain the optimal relay parameters and to have an optimal coordination of the protection relays by considering the coordination constraints. In this article, a comparative study of the optimization techniques proposed in the literature addresses the optimal coordination problem using digital DOCRs with standard properties according to IEC60-255. For this purpose, the three most efficient and robust optimization techniques, which are particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are considered. Simulations were performed using MATLAB R2021a by applying the optimization methods to an interconnected 9-bus and 15-bus power distribution systems. The obtained simulation results show that, in case of distributed generation, the best optimization method to solve the relay protection coordination problem is the differential evolution DE. ","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132082","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}

引用次数: 0

Fuzzy C-means clustering on rainfall flow optimization technique for medical data 基于模糊c均值聚类的医疗数据降雨流优化技术

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp180-188

A. J. Mabel Rani, C. Srivenkateswaran, M. Rajasekar, M. Arun

{"title":"Fuzzy C-means clustering on rainfall flow optimization technique for medical data","authors":"A. J. Mabel Rani, C. Srivenkateswaran, M. Rajasekar, M. Arun","doi":"10.11591/ijai.v12.i1.pp180-188","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp180-188","url":null,"abstract":"Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clustering by hybrid fuzzy C-means (FCM) clustering on rainfall flow optimization technique (RFFO), which is the normal flow and behavior of rainfall flow from one position to another position. FCM clustering algorithm is used to cluster the given medical data and RFFO is used to produce optimum clustering centroid. Finally, the clustering performance is also measured for the proposed FCM clustering on RFFO technique with the help of accuracy, random coefficient, and Jaccard coefficient for medical data set and find the risk factor of a heart attack.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45466757","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}

引用次数: 0

Automatic identification system-based trajectory clustering framework to identify vessel movement pattern 基于自动识别系统的轨迹聚类框架识别船舶运动模式

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp1-11

I Made Oka Widyantara, I Putu Noven Hartawan, Anak Agung Istri Ngurah Eka Karyawati, Ngurah Indra Er, Ketut Buda Artana

{"title":"Automatic identification system-based trajectory clustering framework to identify vessel movement pattern","authors":"I Made Oka Widyantara, I Putu Noven Hartawan, Anak Agung Istri Ngurah Eka Karyawati, Ngurah Indra Er, Ketut Buda Artana","doi":"10.11591/ijai.v12.i1.pp1-11","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp1-11","url":null,"abstract":"Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132078","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}

引用次数: 0

The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method 采用基于规则的推理方法，建立了供应链与专家系统相结合的药品配送新模型

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp295-304

Mufadhol Mufadhol, Mustafid Mustafid, Ferry Jie, Yuni Noor Hidayah

{"title":"The new model for medicine distribution by combining of supply chain and expert system using rule-based reasoning method","authors":"Mufadhol Mufadhol, Mustafid Mustafid, Ferry Jie, Yuni Noor Hidayah","doi":"10.11591/ijai.v12.i1.pp295-304","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp295-304","url":null,"abstract":"The medicine distribution supply chain is important, especially during the COVID-19 pandemic, because delays in medicine distribution can increase the risk for patients. So far, the distribution of medicines has been carried out exclusively and even some medicines are distributed on a limited basis because they require strict supervision from the Medicine Supervisory Agency in each department. However, the distribution of this medicine has a weakness if at one public Health center there is a shortage of certain types of medicines, it cannot ask directly to other public Health center, thus allowing the availability of medicines not to be fulfilled. An integrated process is needed that can accommodate regulations and leadership policies and can be used for logistics management that will be used in medicine distribution. This study will create a new model by combining supply chains with information systems and expert systems using the rule-based reasoning method as an inference engine that can be developed for medicine distribution based on a mobile hybrid system in the Demak District Health Office, Indonesia. So that a new framework model based on a mobile hybrid system can facilitate the distribution of medicines effectively and efficiently.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132080","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}

引用次数: 0

Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method 利用集合预报方法预测天气条件下登革热发病人数

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp496-504

Mursyidatun Nabilah, Raras Tyasnurita, Faizal Mahananto, Wiwik Anggraeni, Retno Aulia Vinarti, Ahmad Muklason

{"title":"Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method","authors":"Mursyidatun Nabilah, Raras Tyasnurita, Faizal Mahananto, Wiwik Anggraeni, Retno Aulia Vinarti, Ahmad Muklason","doi":"10.11591/ijai.v12.i1.pp496-504","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp496-504","url":null,"abstract":"Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. ","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132083","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}

引用次数: 0

Comparative evaluation for detection of brain tumor using machine learning algorithms 机器学习算法在脑肿瘤检测中的比较评价

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp469-477

S. Kareem, B. Abdulrahman, R. Hawezi, F. Khoshaba, Shavan K. Askar, K. Muheden, Ibrahim Shamal Abdulkhaleq

引用次数: 2

Mapping of extensible markup language-to-ontology representation for effective data integration 可扩展标记语言到本体表示的映射，以实现有效的数据集成

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp432-442

S. Haw, Lit-Jie Chew, D. S. Kusumo, P. Naveen, Kok-Why Ng

{"title":"Mapping of extensible markup language-to-ontology representation for effective data integration","authors":"S. Haw, Lit-Jie Chew, D. S. Kusumo, P. Naveen, Kok-Why Ng","doi":"10.11591/ijai.v12.i1.pp432-442","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp432-442","url":null,"abstract":"Extensible markup language (XML) is well-known as the standard for data exchange over the Internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43767863","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}

引用次数: 0