Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

IEEE Reviews in Biomedical Engineering, Journal Year: 2022, Volume and Issue: 16, P. 5 - 21

Published: June 23, 2022

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is lack of model transparency, significantly limiting AI adoption real practice. To solve this problem, models need be explained users. Thus, we conducted comprehensive study Explainable (XAI) using PRISMA technology. Our findings suggest that XAI can improve performance, instill trust users, assist users decision-making. In systematic review, introduce common techniques their utility with specific examples application. We discuss evaluation results because it an important step maximizing value AI-based decision support systems. Additionally, present traditional, modern, advanced demonstrate evolution techniques. Finally, provide best practice guideline developers refer experimentation. also offer potential This hopefully, promote biomedicine healthcare.

Language: Английский

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Evaluating the Impact of COVID-19 on Society, Environment, Economy, and Education

Sustainability, Journal Year: 2021, Volume and Issue: 13(24), P. 13642 - 13642

Published: Dec. 10, 2021

The COVID-19 pandemic has caused drastic changes across the globe, affecting all areas of life. This paper provides a comprehensive study on influence in various fields such as economy, education, society, environment, and globalization. In this study, both positive negative consequences education are studied. Modern technologies combined with conventional teaching to improve communication between instructors learners. also greatly affected people disabilities those who older, these persons experiencing more complications their normal routine activities. Additionally, provided impacts world economies, business, agriculture, entertainment, tourism, service sectors. impact sectors is investigated some meaningful insights suggestions for revitalizing tourism sector. association globalization travel restrictions addition economic human health concerns, lockdown environmental investigated. During periods lockdown, amount pollutants air, soil, water was significantly reduced. motivates researchers investigate unexplored areas.

Language: Английский

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Text-Based Emotion Recognition Using Deep Learning Approach

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 8

Published: Aug. 23, 2022

Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards given goal, such as people, activities, organizations, services, subjects, products. Emotion detection subset of sentiment it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech facial expressions for recognition. However, in text tedious task cues are missing, unlike speech, tonal stress, expression, pitch, etc. To from text, several methods been proposed past using natural language processing (NLP) techniques: keyword approach, lexicon-based machine learning approach. there were some limitations with keyword- approaches they focus semantic relations. this article, we hybrid (machine + deep learning) model text. Convolutional neural network (CNN) Bi-GRU exploited techniques. Support vector used The performance approach evaluated combination three different types datasets, namely, sentences, tweets, dialogs, attains an accuracy 80.11%.

Language: Английский

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Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 13

Published: Feb. 1, 2022

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, deep learning-enabled drone is designed for mask detection distance monitoring. A one the unmanned systems that automated. This system mainly focuses on Industrial Internet Things (IIoT) monitoring using Raspberry Pi 4. automation sends alerts via speaker maintaining captures images detects persons faster regions with convolutional neural network (faster R-CNN) model. When persons, it their details respective authorities nearest police station. built model covers majority face different benchmark datasets. OpenCV camera utilizes 24/7 service reports daily basis 4 R-CNN algorithm.

Language: Английский

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Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 24659 - 24677

Published: Jan. 1, 2022

Metaheuristic algorithms are extensively utilized to find solutions and optimize complex industrial systems' performance. In this paper, metaheuristic predict the optimum value of operational availability a cooling tower in steam turbine power plant. These techniques have some flaws like poor convergence speed, being stuck local optima, premature convergence. For purpose, novel efficient stochastic model is proposed for that configured with six subsystems. The Markovian birth-death process develop Chapman-Kolmogorov differential-difference equations. All random variables statically independent, repairs perfect. Failure rates exponentially distributed, while repair follow arbitrary distribution. Steady-state (SSA) system derived concerning various failure rates. sensitivity analysis SSA also performed identify most critical component. Further, optimized using genetic algorithm (GA) particle swarm optimization (PSO) because they found be more suitable such types problems. It revealed PSO outperforms GA predicting towers used plants.

Language: Английский

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A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(1), P. 251 - 270

Published: Sept. 13, 2022

Language: Английский

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Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework

Contrast Media & Molecular Imaging, Journal Year: 2022, Volume and Issue: 2022(1)

Published: Jan. 1, 2022

Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers deep learning models automated screening suspected cases. An ensemble and Internet Things (IoT) based framework proposed Three well-known pretrained are ensembled. medical IoT devices to collect CT scans, diagnoses performed on servers. compared with thirteen competitive over four-class dataset. Experimental results reveal that ensembled model yielded 98.98% accuracy. Moreover, outperforms all in terms other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, 98.57% AUC. Therefore, can improve acceleration diagnosis.

Language: Английский

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Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

Mathematical Problems in Engineering, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 8

Published: Nov. 24, 2021

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons patient eyes, called diabetic retinopathy (DR). This first causes occlusion then rapid vision loss. The symptoms of are not conspicuous its early stage. featured by formation bloated structures retinal area microaneurysms. Because negligence, condition eye worsens into generation severe blots damage causing complete vision. Early screening monitoring DR reduce risk patients high possibilities. But detection classification human, challenging error-prone task, because complexity image captured color fundus photography. Machine learning algorithms armed some feature extraction techniques have been employed earlier detect classify levels DR. However, these provide below-par accuracy. Now, advent deep (DL) computer vision, it has become possible achieve DL models an abstraction human brain coupled eyes. To create model from scratch train cumbersome task requiring huge amount images. deficiency be patched up employing another technique transfer learning. In this, trained on metadata, learn features used hundreds classes enables professionals capable classifying unseen images proper grade or level acceptable paper proposed different classifiers correct class severity. We IDRD proven show

Language: Английский

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Delay-Driven Opportunistic Routing with Multichannel Cooperative Neighbor Discovery for Industry 4.0 Wireless Networks Based on Power and Load Awareness

Wireless Communications and Mobile Computing, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 12

Published: April 4, 2022

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and short Lifetime (NLife). To address these difficulties, an on-demand Power Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, latency to manage costs depending control packet flooding from destination node. However, exchange of packets target all nodes may impact network efficiency. As result, PLAEOR designed with Multichannel Cooperative Neighbor Discovery (MCND) locate nearby cooperative for each node path during transmission. Furthermore, when rate CBR 20 packets/sec, simulated results show that PLAEOR-MCND achieves 120 sec NLife J EC than state-of-the-art protocols.

Language: Английский

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Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 10

Published: Aug. 10, 2022

Corn has great importance in terms of production the field agriculture and animal feed. Obtaining pure corn seeds is quite significant for seed quality. For this reason, distinction that have numerous varieties plays an essential role marketing. This study was conducted with 14,469 images BT6470, Calipso, Es_Armandi, Hiva types licensed by BIOTEK. The classification carried out three stages. At first stage, deep feature extraction four performed pretrained CNN model SqueezeNet 1000 features were obtained each image. In second order to reduce these from SqueezeNet, separate selection processes Bat Optimization (BA), Whale (WOA), Gray Wolf (GWO) algorithms among optimization algorithms. Finally, last stages classified using machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), Neural Network (NN). mSVM achieved highest success 89.40%. as a result classifications through active selected (BA, WOA, GWO), 88.82%, 88.72%, 88.95%, respectively. accuracies tested stage are close other success. However, used selection, successful been fewer shorter time. results study, which inexpensive, objective, time processing types, present different perspective performance.

Language: Английский

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