AI- and IoT-Assisted Sustainable Education Systems during Pandemics, such as COVID-19, for Smart Cities

Sustainability, Journal Year: 2023, Volume and Issue: 15(10), P. 8354 - 8354

Published: May 21, 2023

The integration of AI and the IoT in education has potential to revolutionize way we learn. Personalized learning, real-time feedback support, immersive learning experiences are some benefits that can bring system. In this regard, research paper aims investigate how be integrated into sustainable order provide students with personalized during pandemics, such as COVID-19, for smart cities. study’s key findings report employed through learning. AI-powered algorithms used analyze student data create each student. This includes providing tailored content, assessments, align their unique style pace. Additionally, communicate a more natural human-like way, making experience engaging interactive. Another aspect obtained from is ability support. IoT-enabled devices, cameras microphones, monitor engagement feedback. then use these adapt real time. tablets laptops, collect process work, allowing automatic grading assignments assessments. technology facilitate remote monitoring which would particularly useful who cannot attend traditional classroom settings. Furthermore, also intelligent personal environments (PLEs) personalized, adaptive, experiences. combined algorithms, PLE student’s needs preferences. It concluded integrating people learn, support opening up new opportunities disadvantaged students. However, it will important ensure ethical responsible all have equal access technologies.

Language: Английский

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Effective Automated Medical Image Segmentation Using Hybrid Computational Intelligence Technique

BENTHAM SCIENCE PUBLISHERS eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 174 - 182

Published: Feb. 20, 2024

In biomedical domain, magnetic resonance imaging (MRI) segmentation is highly essential for the treatment or prevention of disease. The demand fast processing and high accurate results necessary medical diagnosis. This can be solved by using computational intelligence (CoIn) data processing. CoIn achieved well-known techniques such as fuzzy logic, genetic algorithm, evolutionary algorithms neural networks. complexity a image depends on characteristics well suitable algorithms. selection methods very important better because each algorithm outperforms different set. hybrid (H-CoIn) one solutions to overcome problem individual in segmentation. H-CoIn combination two more (like networks). drawbacks H-CoIn. process, variables objectives need optimized multi-objective optimization techniques, where simultaneously minimization maximization performed. this chapter, various algorithms' performance has been discussed detail compared with state-of-the-art techniques. H-Coin implemented large dataset attained an accuracy 98.89%. Further, reliable inter-observer intraobserver variability. 

Language: Английский

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SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic

Cluster Computing, Journal Year: 2021, Volume and Issue: 25(4), P. 2351 - 2368

Published: July 29, 2021

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, manufacturing and socio-economic operations that require human involvement have significantly intervened since beginning outbreak. As experienced, social-distancing lesson in potential new-normal world seems to force stakeholders encourage deployment contactless Industry 4.0 architecture. Thus, human-less or less-human keep these IoT-enabled ecosystems running without interruptions motivated us design demonstrate an intelligent automated framework. In this research, we proposed "EdgeSDN-I4COVID" architecture for efficient management during smart industry considering IoT networks. Moreover, article presents SDN-enabled layer, such as data, control, application, effectively automatically monitor data from a remote location. addition, convergence between SDN NFV provides control mechanism managing sensor data. Besides, it offers robust integration on surface devices required pandemic. Finally, justified above contributions through particular performance evaluations upon appropriate simulation setup environment.

Language: Английский

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A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms

EAI Endorsed Transactions on Pervasive Health and Technology, Journal Year: 2021, Volume and Issue: 7(29), P. e1 - e1

Published: Aug. 13, 2021

INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, impairment is irreversible and imperceptible up until disease reaches one later stages, demanding early detection initiation treatment in order ensure a good prognosis prolonged life. In this aspect, machine learning algorithms have proven be promising, points towards future diagnosis.OBJECTIVES: We aim apply different for purpose assessing comparing their accuracies other performance parameters chronic disease.METHODS: The ‘chronic dataset’ from repository University California, Irvine, has been harnessed, eight supervised models developed by utilizing python programming language disease.RESULTS: A comparative analysis portrayed among evaluating like accuracy, precision, sensitivity, F1 score ROC-AUC. Among models, Random Forest displayed highest accuracy 99.75%.CONCLUSION: observed that can contribute significantly domain predictive disease, assist developing robust computer-aided diagnosis system aid healthcare professionals treating patients properly efficiently.

Language: Английский

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Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Interdisciplinary Sciences Computational Life Sciences, Journal Year: 2022, Volume and Issue: 14(2), P. 452 - 470

Published: Feb. 8, 2022

Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had significant influence on both economy and health infrastructure worldwide. This novel virus diagnosed utilising conventional method as RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. approach, however, produces lot false-negative erroneous outcomes. According to recent studies, COVID-19 can also be using X-rays, CT scans, blood tests cough sounds. In this article, we use machine learning predict diagnosis deadly virus. We present an extensive review various existing machine-learning applications diagnose from clinical laboratory markers. Four different classifiers along with technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) was utilized calculate gravity each feature it found eosinophils, monocytes, leukocytes platelets most critical parameters distinguished infection our dataset. These in conjunction improve sensitivity emergency situations such pandemic outbreak might happen due new strains The positive results indicate prospective automated framework could help clinicians medical personnel screen patients.

Language: Английский

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Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2022, Volume and Issue: 26(11), P. 5364 - 5371

Published: Aug. 10, 2022

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially the case of respiratory diseases. It provides a quick, cost-effective, reliable, non-invasive potential alternative option for COVID-19 ongoing pandemic scenario since subject's voice can be remotely recorded sent further analysis. The existing methods including RT-PCR, chest X-ray tests are not only costlier but also require involvement trained technician. present paper proposes novel scheme Asthma using Gradient Boosting Machine-based classifier. From speech samples, spectral, cepstral, periodicity features, as well spectral descriptors, computed then homogeneously fused to obtain relevant statistical features. These features subsequently used inputs Machine. various performance matrices proposed model been obtained thirteen sound categories' data collected from more than 50 countries five standard datasets accurate diagnosis diseases COVID-19. overall average accuracy achieved by stratified k-fold cross-validation test is above 97%. analysis demonstrates that under current scenario, gainfully employed physicians.

Language: Английский

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Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology

Published: Jan. 1, 2022

Internet of Things technology (IoT) is a fast-growing area computing, and it applicable to almost all human endeavor. The introduction IoT into medicine brought about the Medical (IoMT) that has really redefined smart healthcare systems globally, though its apprehension security threats risk especially in field second none. Though very challenging provide secured expansion using sensor medical domain but impart IoMT-based system can never be denied was greatly deployed various countries accordant with available facilities curb spread Covid-19 pandemic. But because sensitivity data critical information systems, continues posing several perilous challenges these keep growing. Therefore, this chapter discussed inherent opportunities facing data-driven solutions for IoMT. This will broaden research reassure users IoMT delivery.

Language: Английский

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BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 97505 - 97517

Published: Jan. 1, 2021

Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have carried out nationally globally to predict outbreak with varying levels dependability accuracy. Also, mobility restrictions had widespread impact on people's behavior such fear using public transportation (traveling unknown passengers closed area). Securing an appropriate level safety during situation is highly problematic issue that resulted from sector which hit hard by COVID-19. This paper focuses developing intelligent computing model for forecasting The autoregressive integrated moving average (ARIMA) machine learning used develop best twenty-one worst-affected states India six worst-hit countries world including India. ARIMA models are predicting daily-confirmed cases 90 days future values high incidence goodness-of-fit measures achieved 85% MAPE all above computational analysis will be able throw some light planning management healthcare systems infrastructure.

Language: Английский

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RETRACTED ARTICLE: Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic

Personal and Ubiquitous Computing, Journal Year: 2021, Volume and Issue: 28(S1), P. 9 - 9

Published: July 22, 2021

Life-threatening novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), also known as COVID-19, has engulfed the world and caused health economic challenges.To control spread of a mechanism is required to enforce physical distancing between people.This paper proposes Blockchain-based framework that preserves patients' anonymity while tracing their contacts with help Bluetooth-enabled smartphones.We use smartphone application interact proposed blockchain for contact general public using Bluetooth store obtained data over cloud, which accessible departments government agencies perform necessary timely actions (e.g., like quarantine infected people moving around).Thus, helps regular business day-to-day activities controlled keeps them safe from exposed people.The capable enough check COVID status after analyzing symptoms quickly observes (based on given symptoms) either this person or not.As result, Adaptive Neuro-Fuzzy Interference System (ANFIS) system predicts status, K-Nearest Neighbor (KNN) enhances accuracy rate 95.9% compared state-of-the-art results.

Language: Английский

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Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE

International Journal of Systems Assurance Engineering and Management, Journal Year: 2021, Volume and Issue: 15(10), P. 4649 - 4657

Published: June 23, 2021

Language: Английский

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