Pub Date : 2023-12-21DOI: 10.1080/01969722.2023.2296250
Tao Liu, Yifeng Peng, Ran Chen, Yi Lai, Haoxi Zhang, Edward Szczerbicki
Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the ...
{"title":"Learning Disentangled Representation for Chromosome Straightening","authors":"Tao Liu, Yifeng Peng, Ran Chen, Yi Lai, Haoxi Zhang, Edward Szczerbicki","doi":"10.1080/01969722.2023.2296250","DOIUrl":"https://doi.org/10.1080/01969722.2023.2296250","url":null,"abstract":"Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the ...","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138824462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1080/01969722.2023.2240647
Byron Guerrero-Rodriguez, Jaime Salvador-Meneses, Jose Garcia-Rodriguez, Christian Mejia-Escobar
AbstractThe hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses.Keywords: Clusteringlandslidesmeteorological dataMLPprecipitationrandom forestSOMSVMtime windows AcknowledgementsWe would like to express our gratitude to the Central University of Ecuador and FIGEMPA, which in the framework of the interinstitutional agreement with the University of Alicante, made this research work possible.Disclosure StatementThe authors declare that there is no conflict of interest regarding the publication of this paper.Data Availability StatementThe dataset and code used to support the findings of this study have been deposited in the GitHub repository (https://github.com/ByronGuerreroR/Improving-Landslides-Prediction).
{"title":"Improving Landslides Prediction: Meteorological Data Preprocessing Based on Supervised and Unsupervised Learning","authors":"Byron Guerrero-Rodriguez, Jaime Salvador-Meneses, Jose Garcia-Rodriguez, Christian Mejia-Escobar","doi":"10.1080/01969722.2023.2240647","DOIUrl":"https://doi.org/10.1080/01969722.2023.2240647","url":null,"abstract":"AbstractThe hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses.Keywords: Clusteringlandslidesmeteorological dataMLPprecipitationrandom forestSOMSVMtime windows AcknowledgementsWe would like to express our gratitude to the Central University of Ecuador and FIGEMPA, which in the framework of the interinstitutional agreement with the University of Alicante, made this research work possible.Disclosure StatementThe authors declare that there is no conflict of interest regarding the publication of this paper.Data Availability StatementThe dataset and code used to support the findings of this study have been deposited in the GitHub repository (https://github.com/ByronGuerreroR/Improving-Landslides-Prediction).","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-09DOI: 10.1080/01969722.2023.2247257
Syed Tahir Hussain Rizvi, Muhammad Yasir Latif, Muhammad Saad Amin, Achraf Jabeur Telmoudi, Nasir Ali Shah
Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion- or imputation-based methods are used that result in the reduction of data records or imputation of incorrect predicted value. Quality of imputed data can be significantly improved if missing values are generated accurately using machine learning algorithms. In this work, an analysis of machine learning-based algorithms for missing data imputation is performed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using a statistical deletion approach (List-wise Deletion (LD)) and ML-based imputation methods (KNN and SKNN) are then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate the effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms the LD-based approach and KNN method in terms of the effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT).
{"title":"Analysis of Machine Learning Based Imputation of Missing Data","authors":"Syed Tahir Hussain Rizvi, Muhammad Yasir Latif, Muhammad Saad Amin, Achraf Jabeur Telmoudi, Nasir Ali Shah","doi":"10.1080/01969722.2023.2247257","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247257","url":null,"abstract":"Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion- or imputation-based methods are used that result in the reduction of data records or imputation of incorrect predicted value. Quality of imputed data can be significantly improved if missing values are generated accurately using machine learning algorithms. In this work, an analysis of machine learning-based algorithms for missing data imputation is performed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using a statistical deletion approach (List-wise Deletion (LD)) and ML-based imputation methods (KNN and SKNN) are then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate the effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms the LD-based approach and KNN method in terms of the effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT).","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136108289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1080/01969722.2023.2247267
Sara Hsaini, Rabah Ammour, L. Brenner, M. E. H. Charaf, I. Demongodin, Dimitri Lefebvre
{"title":"A Reconfiguration Method for Muti-Robot Monitoring Patrols","authors":"Sara Hsaini, Rabah Ammour, L. Brenner, M. E. H. Charaf, I. Demongodin, Dimitri Lefebvre","doi":"10.1080/01969722.2023.2247267","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247267","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47526881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-06DOI: 10.1080/01969722.2023.2247261
Imane Haur, Jean-Luc Béchennec, Olivier H. Roux
{"title":"Model-Checking of Concurrent Real-Time Software Using High-Level Colored Time Petri Nets with Stopwatches","authors":"Imane Haur, Jean-Luc Béchennec, Olivier H. Roux","doi":"10.1080/01969722.2023.2247261","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247261","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42046305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1080/01969722.2023.2247266
F. Bendali, J. Mailfert, Eloise Mole Kamga, Alain Quilliot, H. Toussaint
{"title":"Models, Algorithms and Approximation Results for a Bi-Level Synchronized Knapsack Problem","authors":"F. Bendali, J. Mailfert, Eloise Mole Kamga, Alain Quilliot, H. Toussaint","doi":"10.1080/01969722.2023.2247266","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247266","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46663169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.1080/01969722.2023.2247263
Kürsat Çakal, M. Efe
{"title":"Cardiovascular Anomaly Detection with Heterogeneous Wave Segment Harmonization for Lightweight Systems","authors":"Kürsat Çakal, M. Efe","doi":"10.1080/01969722.2023.2247263","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247263","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44205445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1080/01969722.2023.2247260
Chaima Taieb, Takwa Tlili, I. Nouaouri, S. Krichen, H. Allaoui
{"title":"On Using Metaheuristics for the Allocation of Electric Vehicles to Charging Stations","authors":"Chaima Taieb, Takwa Tlili, I. Nouaouri, S. Krichen, H. Allaoui","doi":"10.1080/01969722.2023.2247260","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247260","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45461716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1080/01969722.2023.2247259
H. Tlijani, Ameni Jouila, K. Nouri
{"title":"Optimized Sliding Mode Control Based on Cuckoo Search Algorithm: Application for 2DF Robot Manipulator","authors":"H. Tlijani, Ameni Jouila, K. Nouri","doi":"10.1080/01969722.2023.2247259","DOIUrl":"https://doi.org/10.1080/01969722.2023.2247259","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41262918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：