Pub Date : 2023-07-08DOI: 10.1142/s0219622023500700
Shubham Dodia, B. Annappa, P. Mahesh
Lung cancer is known to be one of the leading causes of death worldwide. There is a chance of increasing the survival rate of the patients if detected at an early stage. Computed Tomography (CT) scans are prominently used to detect and classify lung cancer nodules/tumors in the thoracic region. There is a need to develop an efficient and reliable computer-aided diagnosis model to detect lung cancer nodules accurately from CT scans. This work proposes a novel kernel-based active-contour (KAC) SegNet deep learning model to perform lung cancer nodule detection from CT scans. The active contour uses a snake method to detect internal and external boundaries of the curves, which is used to extract the Region Of Interest (ROI) from the CT scan. From the extracted ROI, the nodules are further classified into benign and malignant using a Dense AlexNet deep learning model. The key contributions of this work are the fusion of an edge detection method with a deep learning segmentation method which provides enhanced lung nodule segmentation performance, and an ensemble of state-of-the-art deep learning classifiers, which encashes the advantages of both DenseNet and AlexNet to learn better discriminative information from the detected lung nodules. The experimental outcome shows that the proposed segmentation approach achieves a Dice Score Coefficient of 97.8% and an Intersection-over-Union of 92.96%. The classification performance resulted in an accuracy of 95.65%, a False Positive Rate, and False Negative Rate values of 0.0572 and 0.0289. The proposed model is robust compared to the existing state-of-the-art methods.
{"title":"KAC SegNet: A Novel Kernel-Based Active Contour Method for Lung Nodule Segmentation and Classification Using Dense AlexNet Framework","authors":"Shubham Dodia, B. Annappa, P. Mahesh","doi":"10.1142/s0219622023500700","DOIUrl":"https://doi.org/10.1142/s0219622023500700","url":null,"abstract":"Lung cancer is known to be one of the leading causes of death worldwide. There is a chance of increasing the survival rate of the patients if detected at an early stage. Computed Tomography (CT) scans are prominently used to detect and classify lung cancer nodules/tumors in the thoracic region. There is a need to develop an efficient and reliable computer-aided diagnosis model to detect lung cancer nodules accurately from CT scans. This work proposes a novel kernel-based active-contour (KAC) SegNet deep learning model to perform lung cancer nodule detection from CT scans. The active contour uses a snake method to detect internal and external boundaries of the curves, which is used to extract the Region Of Interest (ROI) from the CT scan. From the extracted ROI, the nodules are further classified into benign and malignant using a Dense AlexNet deep learning model. The key contributions of this work are the fusion of an edge detection method with a deep learning segmentation method which provides enhanced lung nodule segmentation performance, and an ensemble of state-of-the-art deep learning classifiers, which encashes the advantages of both DenseNet and AlexNet to learn better discriminative information from the detected lung nodules. The experimental outcome shows that the proposed segmentation approach achieves a Dice Score Coefficient of 97.8% and an Intersection-over-Union of 92.96%. The classification performance resulted in an accuracy of 95.65%, a False Positive Rate, and False Negative Rate values of 0.0572 and 0.0289. The proposed model is robust compared to the existing state-of-the-art methods.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131349842","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 : 2023-07-05DOI: 10.1142/s0219622023500621
Susmitha Alamuru, Sanjay Jain
In recent times, video event detection gained high attention in the researcher’s community, because of its widespread applications. In this paper, a new model is proposed for detecting different human actions in the video sequences. First, the videos are acquired from the University of Central Florida (UCF) 101, Human Motion Database (HMDB) 51 and Columbia Consumer Video (CCV) datasets. In addition, the DenseNet201 model is implemented for extracting deep feature values from the acquired datasets. Further, the Improved Gray Wolf Optimization (IGWO) algorithm is developed for selecting active/relevant feature values that effectively improve the computational time and system complexity. In the IGWO, leader enhancement and competitive strategies are employed to improve the convergence rate and to prevent the algorithm from falling into the local optima. Finally, the Bi-directional Long Short Term Memory (BiLSTM) network is used for event classification (101 action types in UCF101, 51 action types in HMDB51, and 20 action types in CCV). In the resulting phase, the IGWO-based BiLSTM network achieved 94.73%, 96.53%, and 93.91% accuracy on the UCF101, HMDB51, and CCV datasets.
{"title":"Effective Video Event Detection Using Optimized Bidirectional Long Short-Term Memory Network","authors":"Susmitha Alamuru, Sanjay Jain","doi":"10.1142/s0219622023500621","DOIUrl":"https://doi.org/10.1142/s0219622023500621","url":null,"abstract":"In recent times, video event detection gained high attention in the researcher’s community, because of its widespread applications. In this paper, a new model is proposed for detecting different human actions in the video sequences. First, the videos are acquired from the University of Central Florida (UCF) 101, Human Motion Database (HMDB) 51 and Columbia Consumer Video (CCV) datasets. In addition, the DenseNet201 model is implemented for extracting deep feature values from the acquired datasets. Further, the Improved Gray Wolf Optimization (IGWO) algorithm is developed for selecting active/relevant feature values that effectively improve the computational time and system complexity. In the IGWO, leader enhancement and competitive strategies are employed to improve the convergence rate and to prevent the algorithm from falling into the local optima. Finally, the Bi-directional Long Short Term Memory (BiLSTM) network is used for event classification (101 action types in UCF101, 51 action types in HMDB51, and 20 action types in CCV). In the resulting phase, the IGWO-based BiLSTM network achieved 94.73%, 96.53%, and 93.91% accuracy on the UCF101, HMDB51, and CCV datasets.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114240231","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 : 2023-07-04DOI: 10.1142/s0219622023300057
Huchang Liao, Xiaowan Jin, Yong Shi, Gang Kou
{"title":"A bibliometric overview and visualization of the international journal of information technology and decision making between 2012 and 2022","authors":"Huchang Liao, Xiaowan Jin, Yong Shi, Gang Kou","doi":"10.1142/s0219622023300057","DOIUrl":"https://doi.org/10.1142/s0219622023300057","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243853","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 : 2023-06-30DOI: 10.1142/s0219622023500591
Meng-Meng Zhu, Junjun Mao, Wei Xu
Personalized individual semantics (PIS) is an important factor reflecting the personal habits of decision makers (DMs) and has been widely studied by scholars. Using criteria as a non-negligible information source in multi-criteria group decision making (MCGDM), how to extract PIS from it is a research gap to be solved. In addition, existing measurements of consensus are insufficiently sensitive to differences between individuals, while the current direction rules use a matrix as the unit of measurement, which is not detailed and precise enough. Therefore, this paper first constructs a PIS extraction model according to the principle that similar criteria have similar descriptions and mutually exclusive criteria have dissimilar descriptions. Secondly, the preference information of PIS is mingled with uncertainty and reliability of improved basic uncertain linguistic information (IBULI) as the data of the consensus reaching algorithm. The proposed consensus algorithm not only fully considers the dispersion of DMs in the consensus measurement stage, but also improves the objectivity of the consensus process through an adaptive feedback stage. Finally, the validity of the proposed model is verified by an example and comparative analysis of the selection of sustainable building materials.
{"title":"A Personalized Individual Semantic Extraction Model Based on Criterion for Adaptive Consensus Reaching Process Under Improved Basic Uncertain Linguistic Environment","authors":"Meng-Meng Zhu, Junjun Mao, Wei Xu","doi":"10.1142/s0219622023500591","DOIUrl":"https://doi.org/10.1142/s0219622023500591","url":null,"abstract":"Personalized individual semantics (PIS) is an important factor reflecting the personal habits of decision makers (DMs) and has been widely studied by scholars. Using criteria as a non-negligible information source in multi-criteria group decision making (MCGDM), how to extract PIS from it is a research gap to be solved. In addition, existing measurements of consensus are insufficiently sensitive to differences between individuals, while the current direction rules use a matrix as the unit of measurement, which is not detailed and precise enough. Therefore, this paper first constructs a PIS extraction model according to the principle that similar criteria have similar descriptions and mutually exclusive criteria have dissimilar descriptions. Secondly, the preference information of PIS is mingled with uncertainty and reliability of improved basic uncertain linguistic information (IBULI) as the data of the consensus reaching algorithm. The proposed consensus algorithm not only fully considers the dispersion of DMs in the consensus measurement stage, but also improves the objectivity of the consensus process through an adaptive feedback stage. Finally, the validity of the proposed model is verified by an example and comparative analysis of the selection of sustainable building materials.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116394966","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 : 2023-06-30DOI: 10.1142/s0219622023500736
Azam Seilsepour, R. Ravanmehr, R. Nassiri
{"title":"SSTSA: A self-supervised topic sentiment analysis using semantic similarity measures and transformers","authors":"Azam Seilsepour, R. Ravanmehr, R. Nassiri","doi":"10.1142/s0219622023500736","DOIUrl":"https://doi.org/10.1142/s0219622023500736","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123290904","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 : 2023-06-30DOI: 10.1142/s0219622023500724
Yousef Alqasrawi, Mohammad Azzeh, Yousef Elsheikh
{"title":"Analyzing the Role of Class Rebalancing Techniques in Software Defect Prediction","authors":"Yousef Alqasrawi, Mohammad Azzeh, Yousef Elsheikh","doi":"10.1142/s0219622023500724","DOIUrl":"https://doi.org/10.1142/s0219622023500724","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134144801","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 : 2023-06-28DOI: 10.1142/s0219622023500529
Baisakhi Banik, S. Alam, Avishek Chakraborty
In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results.
{"title":"A Novel Integrated Neutrosophic Cosine Operator Based Linear Programming ANP-EDAS MCGDM Strategy to Select Anti-Pegasus Software","authors":"Baisakhi Banik, S. Alam, Avishek Chakraborty","doi":"10.1142/s0219622023500529","DOIUrl":"https://doi.org/10.1142/s0219622023500529","url":null,"abstract":"In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results.","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128698525","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 : 2023-06-23DOI: 10.1142/s0219622023500712
Shefali Varshney, Rajinder Sandhu, P. K. Gupta
{"title":"An effective multi-criteria decision-making approach for allocation of resources in the fog computing environment","authors":"Shefali Varshney, Rajinder Sandhu, P. K. Gupta","doi":"10.1142/s0219622023500712","DOIUrl":"https://doi.org/10.1142/s0219622023500712","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134550320","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 : 2023-06-16DOI: 10.1142/s0219622023500669
E. Ahmed
{"title":"Big Data Analytics Implications On Central Banking Green Technological Progress","authors":"E. Ahmed","doi":"10.1142/s0219622023500669","DOIUrl":"https://doi.org/10.1142/s0219622023500669","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163141","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 : 2023-06-16DOI: 10.1142/s0219622023500682
Xing Yu, Chenya Liu, Weiguo Zhang
{"title":"Hedging salmon price risk based on fuzzy copula-GMM model","authors":"Xing Yu, Chenya Liu, Weiguo Zhang","doi":"10.1142/s0219622023500682","DOIUrl":"https://doi.org/10.1142/s0219622023500682","url":null,"abstract":"","PeriodicalId":257183,"journal":{"name":"International Journal of Information Technology & Decision Making","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115247587","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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