Studies in Informatics and Control最新文献_第3页

A Hybrid Recommendation Model for Drug Selection 药物选择的混合推荐模型

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y202307

Qasem M. KHARMA, Qusai Y. SHAMBOUR, Abdelrahman H. HUSSEIN

{"title":"A Hybrid Recommendation Model for Drug Selection","authors":"Qasem M. KHARMA, Qusai Y. SHAMBOUR, Abdelrahman H. HUSSEIN","doi":"10.24846/v32i3y202307","DOIUrl":"https://doi.org/10.24846/v32i3y202307","url":null,"abstract":": Medical errors associated with medication pose significant threats to patients’ safety, primarily due to the abundance of drug information available on various online healthcare platforms, leading to challenges in identifying relevant drugs. To address this issue, drug recommendation systems have been developed to assist in selecting appropriate medications for specific medical conditions. Collaborative filtering approaches have been widely used to generate personalized recommendations for various applications. They are easy to implement, debug, and provide justifiable reasoning for recommended items, which is not readily accessible in several other recommendation approaches. Regardless of their success, they still need further enhancements to address challenges related to insufficient rating data, such as data sparsity and new item problems. This paper proposes a drug recommendation model that effectively employs drug taxonomy and multi-criteria collaborative filtering to tackle these challenges. Drug taxonomy enhances recommendation quality by offering a more organized and granular representation of drugs, while multi-criteria rating captures the patients’ preferences more accurately, enabling accurate recommendations that better match the patient’s specific preferences. Experiments conducted on a real-world drug multi-criteria rating dataset demonstrate that the proposed model outperforms baseline recommendation approaches in addressing these challenges and improving prediction accuracy and coverage, making it a valuable tool to assist patients in selecting relevant drugs for their specific medical conditions.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199248","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}

引用次数: 0

Deep Learning Model for Early Subsequent COPD Exacerbation Prediction 深度学习模型用于COPD早期急性加重预测

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y202309

Claudia ABINEZA, Valentina E. BALAS, Philibert NSENGIYUMVA

引用次数: 0

A Novel Trusted Intelligent Computing-aware Routing Service Offloading Method Based on MOPSO for Internet of Vehicles 一种基于MOPSO的车联网可信任智能计算路由服务分流新方法

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y202310

Huiyong LI, Furong WANG

引用次数: 0

Thermal Control of the New-borns Using a Cascade Approach 使用级联方法的新生儿热控制

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y2023011

Mohamed Aymen ZERMANI, Elyes FEKI, Abdelkader MAMI

引用次数: 0

Deep Constrained Clustering with Active Learning 基于主动学习的深度约束聚类

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y202301

Dan HUANG, Ran WEN, Boren DING, Junhua LI

{"title":"Deep Constrained Clustering with Active Learning","authors":"Dan HUANG, Ran WEN, Boren DING, Junhua LI","doi":"10.24846/v32i3y202301","DOIUrl":"https://doi.org/10.24846/v32i3y202301","url":null,"abstract":": Deep semi-supervised clustering approaches, which use supervised data to help the deep neural network acquire cluster-friendly representations, have improved clustering performance and simultaneously increased the semantic value of the clustering results. However, the majority of them cannot utilize both labeled and unlabeled data completely. Furthermore, in these methods, the supervised information is either passively acquired or randomly picked, which may be insufficient, redundant, and even decrease the performance of these models. This paper provides a deep semi-supervised clustering technique with active learning to address the problems mentioned above. The procedure is divided into two sections: model training and data labeling. In the model training section, the paired data is used to train the pseudo-Siamese network, and then the sub networks of the pseudo-Siamese network are fine-tuned using self-training. A new query strategy is devised in the data labeling part, which combines the traditional uncertainty query strategy with the deep Bayesian uncertainty query strategy. Finally, substantial tests are conducted to confirm the utility of the suggested approach on certain real-world data sets. The results of the tests demonstrate that both the suggested method and query strategy are practical.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199392","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}

引用次数: 0

Blockchain-based Decision Support System for Water Management 基于区块链的水管理决策支持系统

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y2023012

Bogdan-Ionuț PAHONȚU, Diana-Andreea ARSENE, Alexandru PREDESCU, Mariana MOCANU, Alexandru GHEORGHIȚĂ

引用次数: 0

Thermal Control of the New-borns Using a Cascade Approach 使用级联方法的新生儿热控制

4区计算机科学

Studies in Informatics and Control Pub Date : 2023-09-29 DOI: 10.24846/v32i3y202311

Mohamed Aymen ZERMANI, Elyes FEKI, Abdelkader MAMI

{"title":"Thermal Control of the New-borns Using a Cascade Approach","authors":"Mohamed Aymen ZERMANI, Elyes FEKI, Abdelkader MAMI","doi":"10.24846/v32i3y202311","DOIUrl":"https://doi.org/10.24846/v32i3y202311","url":null,"abstract":": This paper proposes a novel control method for the skin temperature of a preterm new-born and the air temperature and relative humidity of a preterm infant incubator. In the proposed control structure two generalized predictive controllers (GPC) are connected in series with a third GPC for the incubator humidity control. In this paper, a decoupling method is presented, which is meant to reduce the coupling between the infant`s skin temperature and the incubator air space humidity. In order to implement the new proposed controller, a prediction model for an infant incubator with an ultrasonic humidification system was developed. For this purpose, this system was divided into three compartments, namely the preterm infant, the incubator air space, and the active humidification system. Each compartment was modeled by a transfer function using chaotic particle swarm optimization. The effectiveness of the new control structure was compared with that of two other feedback loop-based controllers, namely a simple skin servo-control method (S-GPC) and a skin servo-control cascade method without decoupling (C-GPC). The obtained results demonstrate that the suggested controller performs better than S-GPC and C-GPC in terms of integral absolute error, settling time, response time and disturbance rejection.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135244002","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}

引用次数: 0

Automatic Detection of Biomarker Genes through Deep Learning Techniques: A Research Perspective 基于深度学习技术的生物标志物基因自动检测研究

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2023-06-28 DOI: 10.24846/v32i2y202305

R. Athilakshmi, S. Jacob, R. Rajavel

引用次数: 0

Algorithms for the Computation of Passive Robustness Margins in Railway Transport Systems 铁路运输系统中被动鲁棒裕度的计算算法

IF 1.6 4区计算机科学

Studies in Informatics and Control Pub Date : 2023-06-28 DOI: 10.24846/v32i2y202308

Anis Mhalla, S. C. Dutilleul

引用次数: 0

Comparison of Model Reference Control Schemes for Motor Speed Control Under Variable Load Torque 变负载转矩下电机转速控制模型参考控制方案的比较