International Journal of Information Technology & Decision Making最新文献_第2页

KAC SegNet: A Novel Kernel-Based Active Contour Method for Lung Nodule Segmentation and Classification Using Dense AlexNet Framework KAC分割网:一种基于核的基于密集AlexNet框架的肺结节分割与分类新方法

International Journal of Information Technology & Decision Making Pub Date : 2023-07-08 DOI: 10.1142/s0219622023500700

Shubham Dodia, B. Annappa, P. Mahesh

{"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}

引用次数: 0

Effective Video Event Detection Using Optimized Bidirectional Long Short-Term Memory Network 基于优化双向长短期记忆网络的有效视频事件检测

International Journal of Information Technology & Decision Making Pub Date : 2023-07-05 DOI: 10.1142/s0219622023500621

Susmitha Alamuru, Sanjay Jain

{"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}

引用次数: 0

A bibliometric overview and visualization of the international journal of information technology and decision making between 2012 and 2022 2012年至2022年间国际信息技术与决策期刊的文献计量概述与可视化

International Journal of Information Technology & Decision Making Pub Date : 2023-07-04 DOI: 10.1142/s0219622023300057

Huchang Liao, Xiaowan Jin, Yong Shi, Gang Kou

引用次数: 0

A Personalized Individual Semantic Extraction Model Based on Criterion for Adaptive Consensus Reaching Process Under Improved Basic Uncertain Linguistic Environment 改进的基本不确定语言环境下基于准则的个性化个性化语义提取模型

International Journal of Information Technology & Decision Making Pub Date : 2023-06-30 DOI: 10.1142/s0219622023500591

Meng-Meng Zhu, Junjun Mao, Wei Xu

{"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}

引用次数: 0

SSTSA: A self-supervised topic sentiment analysis using semantic similarity measures and transformers 基于语义相似度度量和转换的自监督主题情感分析

International Journal of Information Technology & Decision Making Pub Date : 2023-06-30 DOI: 10.1142/s0219622023500736

Azam Seilsepour, R. Ravanmehr, R. Nassiri

引用次数: 0

Analyzing the Role of Class Rebalancing Techniques in Software Defect Prediction 类再平衡技术在软件缺陷预测中的作用分析

International Journal of Information Technology & Decision Making Pub Date : 2023-06-30 DOI: 10.1142/s0219622023500724

Yousef Alqasrawi, Mohammad Azzeh, Yousef Elsheikh

引用次数: 0

A Novel Integrated Neutrosophic Cosine Operator Based Linear Programming ANP-EDAS MCGDM Strategy to Select Anti-Pegasus Software 基于集成中性余弦算子的线性规划ANP-EDAS MCGDM策略选择反飞马软件

International Journal of Information Technology & Decision Making Pub Date : 2023-06-28 DOI: 10.1142/s0219622023500529

Baisakhi Banik, S. Alam, Avishek Chakraborty

引用次数: 4

An effective multi-criteria decision-making approach for allocation of resources in the fog computing environment 雾计算环境下有效的多准则资源分配决策方法

International Journal of Information Technology & Decision Making Pub Date : 2023-06-23 DOI: 10.1142/s0219622023500712

Shefali Varshney, Rajinder Sandhu, P. K. Gupta

引用次数: 0

Big Data Analytics Implications On Central Banking Green Technological Progress 大数据分析对中央银行绿色技术进步的启示

International Journal of Information Technology & Decision Making Pub Date : 2023-06-16 DOI: 10.1142/s0219622023500669

E. Ahmed

引用次数: 0

Hedging salmon price risk based on fuzzy copula-GMM model 基于模糊copula-GMM模型的三文鱼价格风险对冲

International Journal of Information Technology & Decision Making Pub Date : 2023-06-16 DOI: 10.1142/s0219622023500682

Xing Yu, Chenya Liu, Weiguo Zhang

引用次数: 0

International Journal of Information Technology &amp; Decision Making最新文献

International Journal of Information Technology & Decision Making最新文献