A combined model based on stacked autoencoders and fractional Fourier entropy for hyperspectral anomaly detection

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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Combined Model-based Prediction on the Scale of Professional & Technical Talents in the End of the 13th Five-Year Plan Period of Guizhou, China

Proceedings of the 2018 International Workshop on Education Reform and Social Sciences (ERSS 2018) ◽

10.2991/erss-18.2019.23 ◽

2019 ◽

Author(s):

Junhui Kang ◽

Jianmin Wang ◽

Jie Wang ◽

Yulian Wang

Keyword(s):

Combined Model ◽

Plan Period ◽

Model Based

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An anomaly detection model based on neighborhood preserving

2010 International Conference on Intelligent Control and Information Processing ◽

10.1109/icicip.2010.5564280 ◽

2010 ◽

Author(s):

Weifeng Jia ◽

Ning Yang ◽

Bin Tong

Keyword(s):

Anomaly Detection ◽

Detection Model ◽

Model Based

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A SVR–ANN combined model based on ensemble EMD for rainfall prediction

Applied Soft Computing ◽

10.1016/j.asoc.2018.09.018 ◽

2018 ◽

Vol 73 ◽

pp. 874-883 ◽

Cited By ~ 22

Author(s):

Yu Xiang ◽

Ling Gou ◽

Lihua He ◽

Shoulu Xia ◽

Wenyong Wang

Keyword(s):

Combined Model ◽

Rainfall Prediction ◽

Model Based

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Radiomics Analysis Based on CT’s greater-omental caking for Predicting Pathological Grading of Pseudomyxoma Peritonei

10.21203/rs.3.rs-944581/v1 ◽

2021 ◽

Author(s):

Nan Zhou ◽

Ruixue Dou ◽

Xichao Zhai ◽

Jingyang Fang ◽

Jiajun Wang ◽

...

Keyword(s):

Roc Curve ◽

Pseudomyxoma Peritonei ◽

Diagnostic Efficiency ◽

Combined Model ◽

Clinical Model ◽

Model Based ◽

Preoperative Serum ◽

Imaging Observation ◽

Omental Caking ◽

The Impact

Abstract Purpose: The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater mental caking as the imaging observation index, a clinical model including clinical indexes, and a combination model of these two.Methods: A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and joint model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated.Results: The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power n for determining PMP pathological grade.Conclusion: The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grade, which is important in the treatment selection method and prognosis assessment.

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