Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(2), С. 5893 - 5927

Опубликована: Май 29, 2023

Abstract Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps timely detection of any infectious disease (IDs) essential to management diseases prediction future occurrences. Many scientists scholars have implemented DL techniques for pandemics, IDs other healthcare-related purposes, these outcomes are with various limitations research gaps. For purpose achieving an accurate, efficient less complicated DL-based system therefore, this study carried out systematic literature review (SLR) on pandemics using techniques. The survey anchored by four objectives state-of-the-art forty-five papers seven hundred ninety retrieved from different scholarly databases was analyze evaluate trend application areas pandemics. This used tables graphs extracted related articles online repositories analysis showed that good tool pandemic prediction. Scopus Web Science given attention current because they contain suitable scientific findings subject area. Finally, presents forty-four (44) studies technique performances. challenges identified include low performance model due computational complexities, improper labeling absence high-quality dataset among others. suggests possible solutions such as development improved or reduction output layer architecture pandemic-prone considerations.

Язык: Английский

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A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification

IEEE Access, Год журнала: 2022, Номер 10, С. 113715 - 113725

Опубликована: Янв. 1, 2022

The skin lesion types result in delayed diagnosis due to high similarity early stages of the cancer. In this regard, deep learning algorithms are well-recognized solutions; however, these black box approaches lack trust as dermatologists unable interpret and validate decisions made by models. paper, an explainable artificial intelligence (XAI) based classification system is proposed improve accuracy. This will help make rational XAI model validated using International Skin Imaging Collaboration (ISIC) 2019 dataset. developed correctly identifies eight lesions (dermatofibroma, squamous cell carcinoma, benign keratosis, melanocytic nevus, vascular lesion, actinic basal carcinoma melanoma) with accuracy, precision, recall F1 score 94.47%, 93.57%, 94.01%, 94.45% respectively. These predictions further analyzed local interpretable model-agnostic explanations (LIME) framework generate visual that match a prior belief general explanation best practices. explainability integrated within our enhance its applicability real clinical practice.

Язык: Английский

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Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Systems, Год журнала: 2023, Номер 11(2), С. 107 - 107

Опубликована: Фев. 17, 2023

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across world. To slow spread COVID-19 virus, several measures have been adopted since start outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas smart cities requires modern technologies, such as deep learning transfer learning, computer vision automatic mask detection accurate control whether people wear correctly. This paper reviews progress research, emphasizing techniques. Existing datasets are first described discussed before presenting recent advances all related processing stages using a well-defined taxonomy, nature object detectors Convolutional Neural Network architectures employed their complexity, techniques that applied so far. Moving on, benchmarking results summarized, discussions regarding limitations methodologies provided. Last but least, future research directions detail.

Язык: Английский

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Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

Electronics, Год журнала: 2023, Номер 12(6), С. 1452 - 1452

Опубликована: Март 19, 2023

Resource allocation in smart settings, more specifically Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature fog computing. The demands users may alter over time, necessitating trustworthy resource administration. Effective management systems must be designed accommodate changing user needs. Fog devices don’t just run fog-specific software. link failures could brought on by absence centralised administration, device autonomy, wireless communication environment. Resources allocated managed effectively because majority are battery-powered. Latency-aware IoT applications, such as intelligent healthcare, emergency response, now pervasive a result enormous growth ubiquitous These services generate large amount data, which requires edge processing. flexibility on-demand for cloud can successfully manage these applications. It’s not always advisable applications exclusively cloud, especially latency-sensitive Thus, computing has emerged bridge between it supports. This typically how sensors connected. neighbouring control storage intermediary computation. In order improve environment reliability IoT-based systems, this paper suggests strategy. When assigning resources, latency energy efficiency taken into account. Users prioritise cost-effectiveness speed fog. Simulation was performed iFogSim2 simulation tool, performance compared with one existing state-of-the-art A comparison results shows that proposed strategy reduced 10.3% consumption 21.85% when

Язык: Английский

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A systematic review of COVID-19 transport policies and mitigation strategies around the globe

Transportation Research Interdisciplinary Perspectives, Год журнала: 2022, Номер 15, С. 100653 - 100653

Опубликована: Июль 18, 2022

Язык: Английский

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Investigating the Efficiency of Deep Learning Models in Bioinspired Object Detection

2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Год журнала: 2022, Номер unknown, С. 1 - 6

Опубликована: Июль 20, 2022

Object detection is the process of using a camera to track an object or group objects over time. It has numerous applications like human-computer interactions (HCI), security and surveillance, bioinspired approach, traffic control, public areas such as airports, subway stations, event centres. This application prompted extensive study in field computer vision for more than last decade now. Visual recognition which includes picture categorization, localization, detection, at heart all these gathered lot research attention. These visual identification algorithms have achieved extraordinary performance thanks considerable advancements neural networks, particularly deep learning (DL). Despite successes recorded through use DL models, experimental-based approach investigate models (BOD) still remain open issue. Thus, this paper investigates efficiency BOD six (6) metrics. Based on literature, eight common were selected experiment. Beetles Bee Morder hornet contained datasets that used images MATLAB 2018a. The results show CNN outperformed other 7 training time, accuracy, sensitivity, specificity, precision suggest efficient model can be considered taking into account focus project hand. modification models' layers architectures their under different scenarios was highlighted future scope study.

Язык: Английский

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Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

ParadigmPlus, Год журнала: 2023, Номер 4(1), С. 18 - 28

Опубликована: Апрель 27, 2023

Because of the flaws present university attendance system, which has always been time intensive, not accurate, and a hard process to follow. It, therefore, becomes imperative eradicate or minimize deficiencies identified in archaic method. The identification human face systems evolved into significant element autonomous attendance-taking due their ease adoption dependable polite engagement. Face recognition technology drastically altered field Convolution Neural Networks (CNN) however it challenges high computing costs for analyzing information determining best specifications (design) each problem. Thus, this study aims enhance CNN’s performance using Genetic Algorithm (GA) an automated face-based University system. improved accuracy with CNN-GA got 96.49% while CNN 92.54%.

Язык: Английский

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An ensemble learning approach for intrusion detection in IoT-based smart cities

Peer-to-Peer Networking and Applications, Год журнала: 2024, Номер unknown

Опубликована: Сен. 27, 2024

Язык: Английский

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Noise robust face super-resolution via learning of spatial attentive features

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(16), С. 25449 - 25465

Опубликована: Фев. 21, 2023

Язык: Английский

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Ensemble of deep transfer learning models for real-time automatic detection of face mask

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(16), С. 25131 - 25153

Опубликована: Фев. 1, 2023

Язык: Английский

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