Ensemble Federated Learning: An approach for collaborative pneumonia diagnosis

Applied Soft Computing, Год журнала: 2023, Номер 144, С. 110500 - 110500

Опубликована: Июнь 19, 2023

Federated learning is a very convenient approach for scenarios where (i) the exchange of data implies privacy concerns and/or (ii) quick reaction needed. In smart healthcare systems, both aspects are usually required. this paper, we work on first scenario, preserving key and, consequently, building unique and massive medical image set by fusing different sets from institutions or research centers (computation nodes) not an option. We propose ensemble federated (EFL) that based following characteristics: First, each computation node works with (but same type). They locally apply combining eight well-known CNN models (densenet169, mobilenetv2, xception, inceptionv3, vgg16, resnet50, densenet121, resnet152v2) Chest X-ray images. Second, best two local used to create model shared central node. Third, aggregated obtain global model, which nodes continue new iteration. This procedure continues until there no changes in models. have performed experiments compare our centralized ones (with without approach)\color{black}. The results conclude proposal outperforms these images (achieving accuracy 96.63\%) offers competitive compared other proposals literature.

Язык: Английский

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Augmenting IoT Healthcare Security and Reliability with Early Detection of IoT Botnet Attacks

Опубликована: Май 26, 2023

The rapid development and growth in Internet of Things technologies inspire the research community to utilize these devices for numerous types applications. with healthcare is one emerging domains that motivated enhance services by multiple center. task patient monitoring, fetching medical laboratory results, doctor prescriptions etc. can be easily handled using things gathered data aggregated at a server machine or cloud services. On other side, highly vulnerable cyber-attacks lead compromise information patients, doctors concerned teams. In this research, learning-based framework proposed security reliability early detection botnet attacks. attackers target hack generate denial-of-service attacks on critical technology assets. goal methodology secure all internet used center so identity should not breached. detected Machine learning models integration small chip inside devices, entire IoT process secured. approach analyzed random forest classifier technique as dataset taken attack contains unbalanced data. results are evaluated estimation metrics like precision, recall, accuracy F1 score.

Язык: Английский

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Deep Learning Meets Agriculture: A Faster RCNN Based Approach to pepper leaf blight disease Detection and Multi-Classification

Опубликована: Май 26, 2023

Pepper Leaf Blight Disease (PLBD) is a widespread plant ailment that has severe impact on pepper cultivation across the globe. The rapid detection and precise classification of PLBD severity levels are crucial for efficient disease control optimal agricultural productivity. present study introduces novel model based Faster region-based convolutional neural network (R-CNN) multi-classification in leaves. dataset used training testing consisted 10,000 images. model's performance was evaluated its accuracy accuracy, which were found to be 99.39% 98.38%, respectively. computational efficiency assessed determined sufficient deployment real-time applications. average inference time 0.23 seconds per image renders it appropriate high-throughput study's findings indicate faster RCNN successful method detecting classifying This potential enhance management crop yield farming.

Язык: Английский

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Analysis for diagnosis of pneumonia symptoms using chest X-ray based on MobileNetV2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (CLAHE)

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 90, С. 105857 - 105857

Опубликована: Дек. 15, 2023

Язык: Английский

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A Smart Approach to Coconut Leaf Spot Disease Classification using Computer Vision and Deep Learning Technique

Опубликована: Июль 14, 2023

Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose deep learning (DL)-based ResNext50 model for automated detection severity classification of CLS disease. Our leverages mode; trained tested on dataset images with six levels, ranging from healthy leaves critical severity. The proposed approach achieves high accuracy in detecting classifying the levels findings suggest that method successful properly identifying categorizing illness an rate 91.77% overall. strategy has been presented possibility significantly improve efficiency monitoring, ultimately leading better management strategies increased productivity industry.

Язык: Английский

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