Efficient MCDM Model for Evaluating the Performance of Commercial Banks: A Case Study

The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, the Grey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model's accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 with maximum accuracy of 98% outperforms other competitive approaches.

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“…Sample CT images from the above datasets are stated in Figs. 1 and 2 respectively [26][27][28][29][30][31][32].…”

Section: Proposed Multistage Feature Selection Algorithmmentioning

confidence: 99%

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Bhuvaneshwari¹,

Ahmed²,

Masud³

et al. 2023

Computer Systems Science and Engineering

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“…A smart contract is an agreement between a medical equipment supplier and a medical center or healthcare professional in the form of rules. They run on edge computing, so they are stored on a public database and cannot be changed [43][44][45][46][47][48][49]. We implement three major contracts in this work, including the registration smart contract, medical equipment orders smart contract, and medical waste handler smart contract.…”

Section: Smart Contractmentioning

confidence: 99%

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

Palanikkumar¹,

Kumar²,

Masud³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security, trustworthiness, and transparency. To overcome these issues, in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm (NBQ-BBOA). This proposed work is used to track, detect, and manage medical waste. To minimize the transferring cost of medical wastage from various nodes, the Q-Learning algorithm is used. The accuracy obtained for the Naïve Bayes algorithm is 88%, the Q-Learning algorithm is 82% and NBQ-BBOA is 98%. The error rate of Root Mean Square Error (RMSE) and Mean Error (MAE) for the proposed work NBQ-BBOA are 0.012 and 0.045.

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“…Transfer learning (TL) is a machine learning technique that involves reusing previously learned models for a new assignment [42,43]. Practically, reusing or transferring information from previous knowledge jobs for freshly learned activities does have the potential of increasing a supervised training agent's sample selection effectiveness.…”

Section: Transfer Learningmentioning

confidence: 99%

Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

Kumar¹,

Parthasarathi²,

Hogo³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Women from middle age to old age are mostly screened positive for Breast cancer which leads to death. Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy. Today all hospital brings high awareness and early detection technologies for breast cancer. This increases the survival rate of women. Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system. This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best characteristics. Initially, after successful learning of Convolutional Neural Network (CNN) algorithms, data augmentation is used to enhance the representation of the feature dataset. Then it uses BreastNet18 with fine-tuned VGG-16 model for pre-training the augmented dataset. For feature classification, Entropy controlled Whale Optimization Algorithm (EWOA) is used. The features that have been optimized using the EWOA were utilized to fuse and optimize the data. To identify the breast cancer pictures, training classifiers are used. By using the novel probability-based serial technique, the best-chosen characteristics are fused and categorized by machine learning techniques. The main objective behind the research is to increase tumor prediction accuracy for saving human life. The testing was performed using a dataset of enhanced Breast Ultrasound Images (BUSI). The proposed method improves the accuracy compared with the existing methods.

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Efficient MCDM Model for Evaluating the Performance of Commercial Banks: A Case Study

Cited by 11 publications

References 12 publications

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

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