Qasem M. KHARMA, Qusai Y. SHAMBOUR, Abdelrahman H. HUSSEIN
: 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.
{"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}
Claudia ABINEZA, Valentina E. BALAS, Philibert NSENGIYUMVA
{"title":"Deep Learning Model for Early Subsequent COPD Exacerbation Prediction","authors":"Claudia ABINEZA, Valentina E. BALAS, Philibert NSENGIYUMVA","doi":"10.24846/v32i3y202309","DOIUrl":"https://doi.org/10.24846/v32i3y202309","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"64 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":"135243824","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}
{"title":"A Novel Trusted Intelligent Computing-aware Routing Service Offloading Method Based on MOPSO for Internet of Vehicles","authors":"Huiyong LI, Furong WANG","doi":"10.24846/v32i3y202310","DOIUrl":"https://doi.org/10.24846/v32i3y202310","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"48 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":"135244257","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}
{"title":"Thermal Control of the New-borns Using a Cascade Approach","authors":"Mohamed Aymen ZERMANI, Elyes FEKI, Abdelkader MAMI","doi":"10.24846/v32i3y2023011","DOIUrl":"https://doi.org/10.24846/v32i3y2023011","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"96 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":"135199390","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}
: 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.
{"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}
Bogdan-Ionuț PAHONȚU, Diana-Andreea ARSENE, Alexandru PREDESCU, Mariana MOCANU, Alexandru GHEORGHIȚĂ
: With significant use in the last few years, Blockchain has become one of the most important technologies of the last decade. Due to its new paradigm and its decentralized approach, Blockchain has become a game changer in almost all business domains. Water resources management is one of the areas that were updated with these new concepts. The aim of this paper is to present a decision support system, developed on top of the Ethereum Blockchain infrastructure which would facilitate the supplier`s internal flows related to the water network incident management and validation. The proposed solution integrates the following technology stacks: Cyber-Physical Systems, Crowd-Sensing, and Serious Gaming. This solution was implemented so as to work autonomously for some specific flows and take decisions by itself based on some specifically defined criteria and also to provide suggestions to validators and speed up the validation flows based on data stored exclusively on Blockchain.
{"title":"Blockchain-based Decision Support System for Water Management","authors":"Bogdan-Ionuț PAHONȚU, Diana-Andreea ARSENE, Alexandru PREDESCU, Mariana MOCANU, Alexandru GHEORGHIȚĂ","doi":"10.24846/v32i3y2023012","DOIUrl":"https://doi.org/10.24846/v32i3y2023012","url":null,"abstract":": With significant use in the last few years, Blockchain has become one of the most important technologies of the last decade. Due to its new paradigm and its decentralized approach, Blockchain has become a game changer in almost all business domains. Water resources management is one of the areas that were updated with these new concepts. The aim of this paper is to present a decision support system, developed on top of the Ethereum Blockchain infrastructure which would facilitate the supplier`s internal flows related to the water network incident management and validation. The proposed solution integrates the following technology stacks: Cyber-Physical Systems, Crowd-Sensing, and Serious Gaming. This solution was implemented so as to work autonomously for some specific flows and take decisions by itself based on some specifically defined criteria and also to provide suggestions to validators and speed up the validation flows based on data stored exclusively on Blockchain.","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":"44 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":"135244209","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}
: 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.
{"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}
{"title":"Automatic Detection of Biomarker Genes through Deep Learning Techniques: A Research Perspective","authors":"R. Athilakshmi, S. Jacob, R. Rajavel","doi":"10.24846/v32i2y202305","DOIUrl":"https://doi.org/10.24846/v32i2y202305","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46967409","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}
{"title":"Algorithms for the Computation of Passive Robustness Margins in Railway Transport Systems","authors":"Anis Mhalla, S. C. Dutilleul","doi":"10.24846/v32i2y202308","DOIUrl":"https://doi.org/10.24846/v32i2y202308","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45531878","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}
{"title":"Comparison of Model Reference Control Schemes for Motor Speed Control Under Variable Load Torque","authors":"Jessica Villalobos, F. Martell, Irma Y. Sanchez","doi":"10.24846/v32i2y202306","DOIUrl":"https://doi.org/10.24846/v32i2y202306","url":null,"abstract":"","PeriodicalId":49466,"journal":{"name":"Studies in Informatics and Control","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44908756","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}
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