Intelligent Decision Technologies-Netherlands最新文献_第4页

Residual attention network based hybrid convolution network model for lung cancer detection 基于残差注意网络的混合卷积网络肺癌检测模型

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Intelligent Decision Technologies-Netherlands Pub Date : 2023-07-11 DOI: 10.3233/idt-230142

P. Balaji, Dr Rajanikanth Aluvalu, Kalpna Sagar

{"title":"Residual attention network based hybrid convolution network model for lung cancer detection","authors":"P. Balaji, Dr Rajanikanth Aluvalu, Kalpna Sagar","doi":"10.3233/idt-230142","DOIUrl":"https://doi.org/10.3233/idt-230142","url":null,"abstract":"Lung cancer is one of the dangerous diseases that cause shortness of breath and death. Automatic lung cancer disease identification is a challenging operation for researchers. This paper, presents an effective lung cancer diagnosis system using deep learning with CT images. It also decreases lung cancer’s misclassification. Initially, the input images are gathered from online resources. The collected CT images are given to the detection stage. Here, we perform the detection using a Multi Serial Hybrid convolution based Residual Attention Network (MSHCRAN). Using a deep learning framework lung cancer detection using CT images is effectively detected. The performance of the developed lung cancer detection system is compared to other conventional lung cancer detection models According to the analysis, the implemented deep learning-based detection of lung cancer system had a precision higher than 95.75% compared to CNN with 90.04%, ResNet with 89.62%, LSTM with 92%, and CRAN with 93.4% using dataset-1. The analysis with Dataset-2 shows a precision of 90.43% with CNN, ResNet with 90.12%, LSTM with 92%, and CRAN with 93.7%, with the proposed method precision of 95.8%.","PeriodicalId":43932,"journal":{"name":"Intelligent Decision Technologies-Netherlands","volume":"45 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90893469","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

Modified Euclidean-Canberra blend distance metric for kNN classifier kNN分类器的改进欧几里得-堪培拉混合距离度量

Intelligent Decision Technologies-Netherlands Pub Date : 2023-05-15 DOI: 10.3233/idt-220233

Gaurav Sandhu, Amandeep Singh, Puneet Singh Lamba, Deepali Virmani, Gopal Chaudhary

{"title":"Modified Euclidean-Canberra blend distance metric for kNN classifier","authors":"Gaurav Sandhu, Amandeep Singh, Puneet Singh Lamba, Deepali Virmani, Gopal Chaudhary","doi":"10.3233/idt-220233","DOIUrl":"https://doi.org/10.3233/idt-220233","url":null,"abstract":"In today’s world different data sets are available on which regression or classification algorithms of machine learning are applied. One of the classification algorithms is k-nearest neighbor (kNN) which computes distance amongst various rows in a dataset. The performance of kNN is evaluated based on K-value and distance metric used, where K is the total count of neighboring elements. Many different distance metrics have been used by researchers in literature, one of them is Canberra distance metric. In this paper the performance of kNN based on Canberra distance metric is measured on different datasets, further the proposed Canberra distance metric, namely, Modified Euclidean-Canberra Blend Distance (MECBD) metric has been applied to the kNN algorithm which led to improvement of class prediction efficiency on the same datasets measured in terms of accuracy, precision, recall, F1-score for different values of k. Further, this study depicts that MECBD metric use led to improvement in accuracy value 80.4% to 90.3%, 80.6% to 85.4% and 70.0% to 77.0% for various data sets used. Also, implementation of ROC curves and auc for k= 5 is done to show the improvement is kNN model prediction which showed increase in auc values for different data sets, for instance increase in auc values from 0.873 to 0.958 for Spine (2 Classes) dataset, 0.857 to 0.940, 0.983 to 0.983 (no change), 0.910 to 0.957 for DH, SL and NO class for Spine (3 Classes) data set and 0.651 to 0.742 for Haberman’s data set.","PeriodicalId":43932,"journal":{"name":"Intelligent Decision Technologies-Netherlands","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135140458","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

Multi-level graded facial emotion intensity recognition using MCANN for health care 基于MCANN的多层次面部情绪强度识别在医疗保健中的应用

Intelligent Decision Technologies-Netherlands Pub Date : 2023-05-15 DOI: 10.3233/idt-220301

Nazmin Begum, A. Syed Mustafa

{"title":"Multi-level graded facial emotion intensity recognition using MCANN for health care","authors":"Nazmin Begum, A. Syed Mustafa","doi":"10.3233/idt-220301","DOIUrl":"https://doi.org/10.3233/idt-220301","url":null,"abstract":"Facial emotion recognition analysis is widely used in various social fields, including Law Enforcement for police interrogation, virtual assistants, hospitals for understanding patients’ expressions, etc. In the field of medical treatment such as psychologically affected patients, patients undergoing difficult surgeries, etc require emotional recognition in real-time. The current emotional analysis employs interest points as landmarks in facial images affected by a few emotions Many researchers have proposed 7 different types of emotions (amusement, anger, disgust, fear, and sadness). In our work, we propose a deep learning-based multi-level graded facial emotions of 21 different types with our proposed facial emotional feature extraction technique called as Deep Facial Action Extraction Units (DFAEU). Then using our Multi-Class Artificial Neural Network (MCANN) architecture the model is trained to classify different emotions. The proposed method makes use of VGG-16 for the analysis of emotion grades. The performance of our model is evaluated using two algorithms Sparse Batch Normalization CNN (SBN-CNN) and CNN with Attention mechanism (ACNN) along with datasets Facial Emotion Recognition Challenge (FERC-2013). Our model outperforms 86.34 percent and 98.6 percent precision.","PeriodicalId":43932,"journal":{"name":"Intelligent Decision Technologies-Netherlands","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136215667","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

Intelligent Decision Technologies: Proceedings of the 15th KES-IDT 2023 Conference 智能决策技术:第15届KES-IDT 2023会议论文集

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Intelligent Decision Technologies-Netherlands Pub Date : 2023-01-01 DOI: 10.1007/978-981-99-2969-6

引用次数: 0

Digital SDGs framework towards knowledge integration 面向知识整合的数字可持续发展目标框架

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Intelligent Decision Technologies-Netherlands Pub Date : 2022-11-20 DOI: 10.3233/idt-220276

Shuichiro Yamamoto

引用次数: 0

A novel ant colony optimization (ACO) based energy efficient multipath routing protocol for sensor communications 一种新型的基于蚁群优化的传感器通信节能多径路由协议

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Intelligent Decision Technologies-Netherlands Pub Date : 2019-03-25 DOI: 10.3233/IDT-170172

A. Nayyar, Rajeshwar Singh

引用次数: 0

Special Issue On: Optimization for Engineering, Science and Technology 特刊:工程、科学与技术的优化

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Intelligent Decision Technologies-Netherlands Pub Date : 2017-04-17 DOI: 10.3233/IDT-160272

P. Vasant, J. Watada

{"title":"Special Issue On: Optimization for Engineering, Science and Technology","authors":"P. Vasant, J. Watada","doi":"10.3233/IDT-160272","DOIUrl":"https://doi.org/10.3233/IDT-160272","url":null,"abstract":"Many modern engineering, science and technological problems inevitably faces problems of uncertainty in various aspects such as natural disaster, chaotic decision making, human resource availability, processing capability and constraints and limitations imposed by the authority. This problem has to be solved by a methodology which takes care of such uncertain information. As the analyst solves this problem, the decision maker and the implementer have to coordinate with the analyst for taking up a decision on a successful strategic and holistic decision making for final implementation. Such a complex problems in an imperfect world can be solved by the robust and flexible optimization methodologies. The objective of the special issue is to enlighten the researchers working on the development of innovative and novel techniques and methodologies to improve the performance of current development on the advanced algorithms related real world practical problems in the research areas of novel and modern optimization and its application in Engineering, Science and Technology. The special issue includes ten outstanding research papers in the field of optimization and its application in engineering and technology. The editors sincerely thank the referees’ and the authors’ for their marvelous contributing in making this special issue a successful publication. This special issue will contribute new body of the knowledge to the researchers across the planet.","PeriodicalId":43932,"journal":{"name":"Intelligent Decision Technologies-Netherlands","volume":"158 1","pages":"1"},"PeriodicalIF":1.0,"publicationDate":"2017-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77885950","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 genetic algorithm approach to global optimization of software cost estimation by analogy 基于类比的软件成本估算全局优化遗传算法

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Intelligent Decision Technologies-Netherlands Pub Date : 2013-01-01 DOI: 10.5555/2608538.2608543

MiliosDimitrios, StamelosIoannis, ChatzibagiasChristos

引用次数: 0

Main Branch Decision Tree Algorithm for Yield Enhancement with Class Imbalance 类不平衡下产量提高的主分支决策树算法

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Intelligent Decision Technologies-Netherlands Pub Date : 2012-05-01 DOI: 10.1007/978-3-642-29977-3_24

Chia-Yu Hsu, Chen-Fu Chien, Ya-Chun Lai

引用次数: 5

Manufacturing Intelligence to Forecast the Customer Order Behavior for Vendor Managed Inventory 利用制造智能预测供应商库存管理中的客户订单行为