Appl. Comput. Intell. Soft Comput.最新文献_第7页

Train timetabling with passenger data and heterogeneous rolling stocks circulation on urban rail transit line 城市轨道交通线上具有乘客数据和异构车辆流通的列车调度

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.1007/s00500-022-07057-0

Yuhua Yang, M. Samà, D. Pacciarelli, S. Ni

引用次数: 2

Approximation of simplicial complexes using matroids and rough sets 用拟阵和粗糙集逼近简单复形

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.1007/s00500-022-07774-6

Abd El Fattah El Atik

引用次数: 0

Research on Great Wall section protection and user VR experience innovation based on GIS data visualization 基于GIS数据可视化的长城断面保护与用户VR体验创新研究

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.1007/s00500-023-08163-3

Yanzhen Wang, Xiaofen Wang, Lihua Han

引用次数: 0

A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification 面向数据分类特征选择的局部对立学习金正弦灰狼优化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4211312

Li Zhang

引用次数: 6

A high-quality feature selection method based on frequent and correlated items for text classification 基于频繁项和相关项的文本分类高质量特征选择方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.1007/s00500-023-08587-x

Heba Mamdouh Farghaly, Tarek Abd-El-Hafeez

引用次数: 0

An evolutionary intelligent control system for a flexible joints robot 柔性关节机器人的进化智能控制系统

Appl. Comput. Intell. Soft Comput. Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4280051

Alejandro Peña, Juan C. Tejada, J. D. González-Ruiz, L. Sepúlveda-Cano, F. Chiclana, Fabio Caraffini, M. Gongora

引用次数: 2

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers 使用机器学习技术和分类器进行预测建模的糖尿病疾病预测和类型分类

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-12-30 DOI: 10.1155/2022/7899364

B. Ahamed, Meenakshi S. Arya, S. Sangeetha, Nancy V. Auxilia Osvin

{"title":"Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers","authors":"B. Ahamed, Meenakshi S. Arya, S. Sangeetha, Nancy V. Auxilia Osvin","doi":"10.1155/2022/7899364","DOIUrl":"https://doi.org/10.1155/2022/7899364","url":null,"abstract":"The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"10 1","pages":"7899364:1-7899364:11"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72696030","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

Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing 基于聚类蚁群的移动群体感知边缘服务器定位策略

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-12-29 DOI: 10.1155/2022/2998385

A. A. Gad-Elrab, Amin Y. Noaman

{"title":"Clustering Ant Colony-Based Edge-Server Location Strategy in Mobile Crowdsensing","authors":"A. A. Gad-Elrab, Amin Y. Noaman","doi":"10.1155/2022/2998385","DOIUrl":"https://doi.org/10.1155/2022/2998385","url":null,"abstract":"Recently, edge-based mobile crowdsensing has become an important sensing technology that takes advantage of mobile devices to collect information about surroundings based on using a group of mobile edge servers that are deployed at the network edge as a link between users and the central server for data filtering and aggregation. Each user may collect multiple data types in mobile collective sensing. For facilitating data aggregation, the same data type carried by various users is assumed to be uploaded to the same mobile edge server. The main problem is determining the server which should be activated to process each data type for reducing the overall cost. In this paper, the problem is formulated as one form of the unqualified multicommodity facility location problem. To solve this problem, two edge-server location strategies are proposed, which use a clustering method for dividing the set of mobile users with data items into clusters and use the ant colony approach to select a mobile edge server for each data type in each cluster. Extensive simulations are conducted based on widely used real data sets. The simulation results show that the proposed strategy achieves better performance than the existing methods in terms of service and facility costs.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"1 1","pages":"2998385:1-2998385:17"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78053848","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

Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage 打击在线社交网络中的恶意内容审核规避:单词伪装的模拟与检测

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-12-27 DOI: 10.48550/arXiv.2212.14727

Álvaro Huertas-García, Alejandro Martín, J. Huertas-Tato, David Camacho

{"title":"Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage","authors":"Álvaro Huertas-García, Alejandro Martín, J. Huertas-Tato, David Camacho","doi":"10.48550/arXiv.2212.14727","DOIUrl":"https://doi.org/10.48550/arXiv.2212.14727","url":null,"abstract":"Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named\"pyleetspeak\"to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"23 1","pages":"110552"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91107222","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

Solving Partial Integro-Differential Equations via Double Formable Transform 用二重可成形变换求解偏积分微分方程

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-12-26 DOI: 10.1155/2022/6280736

B. Ghazal, Rania Saadeh, Abdelilah K. Sedeeg

引用次数: 2