Fuzzy eigenvalue problems of structural dynamics using ANN

Jeswal, Sumit Kumar; Chakraverty, Snehashish

doi:10.1016/b978-0-12-822133-4.00010-4

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…In this study, 5 commonly used machine learning algorithms were adopted to achieve the best predictive model. These machine learning algorithms including RF, NB, RR, ANN, SVM were compared based on ROC curves and the best-performing prediction model was selected [23][24][25] .…”

Section: Machine Learningmentioning

confidence: 99%

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

Kasai

Miyoshi

Sato

et al. 2023

Preprint

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No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson’s correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99, p < 0.001) as well as in the validation cohort (0.92, p < 0.001) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data revealed that the PFS and OS in the high-prediction score group were significantly longer than those in the low-prediction score group. Univariate and multivariate analyses revealed that the radiomics prediction score could be an independent prognostic biomarker, and moreover significantly superior to serum SCC-Ag, the conventional tumor marker of ESCC. In conclusion, we have developed a novel and robust CT-based radiomics model using AI, which successfully predicts the CRT response as well as the prognosis for ESCC patients with high accuracy, non-invasiveness, and cost-effectiveness.

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Section: Machine Learningmentioning

confidence: 99%

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

Kasai

Miyoshi

Sato

et al. 2023

Preprint

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“…ANN works like the human brain's neural system, and it consists of nodes [27]. ANN is mainly of two types: Feed forward neural network and feedback neural network.…”

Section: ) K-nearest Neighbourmentioning

confidence: 99%

Detection of DDoS Attack on Smart Home Infrastructure Using Artificial Intelligence Models

Raja

Ezziane

et al. 2022

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

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The whole web world is concerned and constantly threatened by security intrusion. From the topmost corporate companies to the recently established start-ups, every company focuses on their network, system, and information security as it is the core of any company. Even a simple small security breach can cause a considerable loss to the company and compromises the CIA Triad (Confidentiality, Integrity, and Availability). Security concerns and hacking activities such as Distributed Denial of Service (DDoS) attacks are also experienced within home networks which could be saturated reaching a crashing point. This work focuses on using Artificial Intelligence (AI) and identifying suitable models to train, identify, and detect DDoS attacks. In addition, it aims to implement on smart home datasets and find the best model from those which performs with a high accuracy rate on the smart home dataset. The novelty of this project is identifying one best AI model among many of the existing models that works best on smart home datasets and in identifying and detecting DDoS attacks.

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Fuzzy-Affine Approach in Dynamic Analysis of Uncertain Structural Systems

Rout¹,

Chakraverty²

2021

Soft Computing in Interdisciplinary Sciences

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Fuzzy eigenvalue problems of structural dynamics using ANN

Cited by 5 publications

References 19 publications

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

Detection of DDoS Attack on Smart Home Infrastructure Using Artificial Intelligence Models

Fuzzy-Affine Approach in Dynamic Analysis of Uncertain Structural Systems

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