Pub Date : 2023-01-10DOI: 10.1142/s0219622023500177
Jih-Jeng Huang, Chin-Yi Chen
{"title":"Generalized Analytic Network Process with Path Restriction by the Distance matrix and Transition Functions","authors":"Jih-Jeng Huang, Chin-Yi Chen","doi":"10.1142/s0219622023500177","DOIUrl":"https://doi.org/10.1142/s0219622023500177","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722512","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}
Pub Date : 2023-01-10DOI: 10.1142/s0219622023500189
Avishek Choudhury, S. Balasubramaniam, A.V. Pradeep Kumar, S. Karthikeyan, Sanjay Nakharu Prasad Kumar
Lung cancer accounts for about 7.6 million deaths annually worldwide. Early identification of lung cancer is essential for reducing preventable deaths. In this paper, we developed a Political Squirrel Search Optimization (PSSO)-based deep learning scheme for efficacious lung cancer recognition and classification. We used Spine General Adversarial Network (Spine GAN) to segment lung lobe regions where a Deep Neuro Fuzzy Network (DNFN) classifier forecasts cancerous areas. A Deep Residual Network (DRN) is also used to determine the various cancer severity levels. The Political Optimizer (PO) and Squirrel Search Algorithm (SSA) were combined to create the newly announced PSSO method. Experimental outcomes are assessed using the dataset of images from the Lung Image Database Consortium.
{"title":"PSSO: Political Squirrel Search Optimizer driven Deep learning for severity level detection and classification of Lung cancer","authors":"Avishek Choudhury, S. Balasubramaniam, A.V. Pradeep Kumar, S. Karthikeyan, Sanjay Nakharu Prasad Kumar","doi":"10.1142/s0219622023500189","DOIUrl":"https://doi.org/10.1142/s0219622023500189","url":null,"abstract":"Lung cancer accounts for about 7.6 million deaths annually worldwide. Early identification of lung cancer is essential for reducing preventable deaths. In this paper, we developed a Political Squirrel Search Optimization (PSSO)-based deep learning scheme for efficacious lung cancer recognition and classification. We used Spine General Adversarial Network (Spine GAN) to segment lung lobe regions where a Deep Neuro Fuzzy Network (DNFN) classifier forecasts cancerous areas. A Deep Residual Network (DRN) is also used to determine the various cancer severity levels. The Political Optimizer (PO) and Squirrel Search Algorithm (SSA) were combined to create the newly announced PSSO method. Experimental outcomes are assessed using the dataset of images from the Lung Image Database Consortium.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132325487","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}
Pub Date : 2023-01-06DOI: 10.1142/s0219622023500165
Selen Yucesoy Kahraman, Türkay Dereli, A. Durmuşoğlu
{"title":"Forty years of automated patent classification","authors":"Selen Yucesoy Kahraman, Türkay Dereli, A. Durmuşoğlu","doi":"10.1142/s0219622023500165","DOIUrl":"https://doi.org/10.1142/s0219622023500165","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811156","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}
Pub Date : 2023-01-06DOI: 10.1142/s0219622023500153
Soodabeh Amiri, S. Kheybari, M. Latifi, Negin Salimi, A. Labib
{"title":"Innovation and survival of traditional industries: Measuring barriers using the Best Worst Method","authors":"Soodabeh Amiri, S. Kheybari, M. Latifi, Negin Salimi, A. Labib","doi":"10.1142/s0219622023500153","DOIUrl":"https://doi.org/10.1142/s0219622023500153","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129588433","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}
Pub Date : 2023-01-05DOI: 10.1142/s021962202250095x
L. Lijesh, G. Arockia Selva Saroja
This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) | used for landslide identification | is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049[Formula: see text]J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.
本文提出了一种利用物联网检测滑坡的方法。物联网的模拟是帮助收集数据的初步步骤。采用建议的水粒子灰狼优化算法(Water Particle Grey Wolf Optimization, WPGWO)进行路由。该方法将水循环算法(WCA)、粒子群算法(PSO)和灰狼算法(GWO)相结合。考虑了能量、链路成本、距离和时延，建立了新的适应度模型。维护路由是为了评估网络拓扑的可靠性。滑坡检测过程在物联网基站进行。在特征选择中，使用角距离。采用过采样方法丰富数据，采用WCA和PSO相结合的水循环粒子群优化(WCPSO)方法训练用于滑坡识别的深度残差网络(DRN)。提出的基于wcpso的DRN具有有效的性能，最高能量为0.049[公式:见文]J，通量为0.0495，准确率为95.7%，灵敏度为97.2%，特异性为93.9%。该方法具有较好的鲁棒性，并产生了全局最优解。对于所提出的WPGWO，将WCA、GWO和PSO联系起来，以提高确定最优路由的性能。通过与已有方法的比较，提出的基于wcpso的DRN具有较好的性能。
{"title":"Landslide Identification Using Optimized Deep Learning Framework Through Data Routing in IoT Application","authors":"L. Lijesh, G. Arockia Selva Saroja","doi":"10.1142/s021962202250095x","DOIUrl":"https://doi.org/10.1142/s021962202250095x","url":null,"abstract":"This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) | used for landslide identification | is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049[Formula: see text]J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133397448","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}
Pub Date : 2022-12-31DOI: 10.1142/s0219622022500936
Vasiliki Demertzi, Konstantinos Demertzis
Providing the same pedagogical and educational methods to all students is pedagogically ineffective. In contrast, the pedagogical strategies that adapt to the fundamental individual skills of the students have proved to be more effective. An important innovation in this direction is the adaptive educational systems (AESs) that adjust the teaching content on educational needs and students’ skills. Effective utilization of these approaches can be enhanced with artificial intelligence (AI) and semantic web technologies that can increase data generation, access, flow, integration, and comprehension using the same open standards driving the World Wide Web. This study proposes a novel adaptive educational eLearning system (AEeLS) that can gather and analyze data from learning repositories and adapt these to the educational curriculum according to the student’s skills and experience. It is an innovative hybrid machine learning system that combines a semi-supervised classification method for ontology matching and a recommendation mechanism that uses a sophisticated way from neighborhood-based collaborative and content-based filtering techniques to provide a personalized educational environment for each student.
{"title":"An Hybrid Ontology Matching Mechanism for Adaptive Educational eLearning Environments","authors":"Vasiliki Demertzi, Konstantinos Demertzis","doi":"10.1142/s0219622022500936","DOIUrl":"https://doi.org/10.1142/s0219622022500936","url":null,"abstract":"Providing the same pedagogical and educational methods to all students is pedagogically ineffective. In contrast, the pedagogical strategies that adapt to the fundamental individual skills of the students have proved to be more effective. An important innovation in this direction is the adaptive educational systems (AESs) that adjust the teaching content on educational needs and students’ skills. Effective utilization of these approaches can be enhanced with artificial intelligence (AI) and semantic web technologies that can increase data generation, access, flow, integration, and comprehension using the same open standards driving the World Wide Web. This study proposes a novel adaptive educational eLearning system (AEeLS) that can gather and analyze data from learning repositories and adapt these to the educational curriculum according to the student’s skills and experience. It is an innovative hybrid machine learning system that combines a semi-supervised classification method for ontology matching and a recommendation mechanism that uses a sophisticated way from neighborhood-based collaborative and content-based filtering techniques to provide a personalized educational environment for each student.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132237674","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}
Pub Date : 2022-12-30DOI: 10.1142/s021962202350013x
Qigan Shao, Huai-Wei Lo, J. Liou, G. Tzeng
{"title":"A data-driven model to construct the influential factors of online product satisfaction","authors":"Qigan Shao, Huai-Wei Lo, J. Liou, G. Tzeng","doi":"10.1142/s021962202350013x","DOIUrl":"https://doi.org/10.1142/s021962202350013x","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132540899","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}
Pub Date : 2022-12-30DOI: 10.1142/s0219622023500141
Melike Ilhan, Fatma Kutlu Gundogdu
{"title":"Evaluation Of Spaceport Site Selection Criteria Based on Hesitant Z-Fuzzy Linguistic Terms: A Case for Turkiye","authors":"Melike Ilhan, Fatma Kutlu Gundogdu","doi":"10.1142/s0219622023500141","DOIUrl":"https://doi.org/10.1142/s0219622023500141","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130692776","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}
Pub Date : 2022-12-29DOI: 10.1142/s0219622023500116
P. Patil, D. Parasar, S. Charhate
{"title":"Wrapper based feature selection and optimization enabled hybrid deep learning framework for stock market prediction","authors":"P. Patil, D. Parasar, S. Charhate","doi":"10.1142/s0219622023500116","DOIUrl":"https://doi.org/10.1142/s0219622023500116","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116137523","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}
Pub Date : 2022-12-29DOI: 10.1142/s0219622023500128
F. Talebi, A. Nazemi, Abdolmajid Abdolbaghi Ataabadi
{"title":"On uncertain mean-AVaR portfolio selection via an artificial neural network scheme","authors":"F. Talebi, A. Nazemi, Abdolmajid Abdolbaghi Ataabadi","doi":"10.1142/s0219622023500128","DOIUrl":"https://doi.org/10.1142/s0219622023500128","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"52 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120864664","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}
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