Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Geetha, B. T.; Mani, Prakash; Mayuri, A.V.R.; Jackulin, T.; Stalin, J. L. Aldo; Anitha, Varagantham

doi:10.1155/2022/2243827

Cited by 49 publications

(10 citation statements)

References 34 publications

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“…Te DL is a branch of the ML technique that employs a neural network (NN) system. A few technologies that belong to DL methods include recurrent neural network (RNN), convolutional neural networks (CNN), artifcial neural networks (ANN), autoencoder, and so on [10]. DL uses an NN system that resembles the human brain in making decisions and processing the data.…”

Section: Introductionmentioning

confidence: 99%

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Prabhakaran

Nedunchelian

2023

Computational Intelligence and Neuroscience

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Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Prabhakaran

Nedunchelian

2023

Computational Intelligence and Neuroscience

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“…Prediction and students' performance analysis are the two extensively discussed areas of research in educational systems [5]. Despite their disparate goals, the outcomes of performance analysis have a significant impact on predictive research.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students' grades and retention. Prediction of students' performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students' performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students' performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student's performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students' academic progress, with an accuracy of 96.49%.

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“…Very complex functions can be learned by combining such transformations. 5 Application of deep learning in health sector: deep learning models can analyze x-rays, MRIs, CT scans, and other medical pictures to make diagnosis in the field of medicine. Algorithms may analyze medical pictures and identify any anomalies or potential risks.…”

Section: Introductionmentioning

confidence: 99%

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Arumugham,

Sankaralingam,

Jayachandran

et al. 2023

Computational Intelligence

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Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end‐stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early‐stage CKD. The health care applications require interpretability over the predictions of the black‐box model to build conviction towards the model's prediction. Hence, the predictions of the DNN‐CKD model are explained by the local interpretable model‐agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN‐CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN‐CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.

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Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Cited by 49 publications

References 34 publications

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

An explainable deep learning model for prediction of early‐stage chronic kidney disease

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