Role of IoT, image processing and machine learning techniques in weed detection: a review

Veeragandham, Syamasudha; Santhi, H.

doi:10.1504/ijitst.2022.122141

Cited by 4 publications

(2 citation statements)

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“…The typical applications of supervised learning are classification and regression tasks. Support vector machine (SVM) and k-nearest neighbor (kNN), on the other hand, are typical supervised learning algorithms [92]. The authors in [93] proposed a hybrid learning system that helps in dynamic channel tracking and channel estimation.…”

Section: Supervised Learningmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

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Section: Supervised Learningmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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show abstract

“…For diagnosing the heart diseases automatically using ECG images deep learning technique is used [8]. In recent studies, Image processing, Signal processing and Machine learning or Deep learning are widely used, especially in the medical field [9]- [11] . Most of the medical research carried out is based on signal data which is one-dimensional data using machine and deep learning techniques [12].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Automatic Cardiovascular Classification from Electrocardiogram images

Jothiaruna

2023

Preprint

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Cardiovascular disease is the primary reason of death within a short period because of a sudden blockage in blood vessels. Therefore, early diagnosis and treatments are necessary to reduce the disease severity. In this study, Image augmentation concepts are used for balancing the ECG images dataset classes. The balanced dataset contains 6322 images from five different classes are Normal, Abnormal Heartbeat, Myocardial Infraction (MI), Previous History of MI, and Covid-19 ECG images. The pre-trained models VGG-16, ResNet-50, and DenseNet-161 are used to train on ECG balanced dataset. The use of hyper-parameters will increase the accuracy of classifying cardiovascular disorders which includes learning rate, optimizer, and dropout. The mentioned pre-trained models reported the highest accuracy at learning rate = 0.00001 and dropout=0.3 with Adam optimizer. DenseNet model achieved 93.33% accuracy, 94.23% precision, 93.33% recall, and 93.30% F1-Score, which is higher in performance compared to ResNet-50 and VGG-16 pre-trained models.

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RETRACTED ARTICLE: LW-DN161: a cardiovascular disorder classification from 12 lead ECG images using convolutional neural network

Jothiaruna

Leema

2023

Soft Comput

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Role of IoT, image processing and machine learning techniques in weed detection: a review

Cited by 4 publications

References 0 publications

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

A Deep Learning Framework for Automatic Cardiovascular Classification from Electrocardiogram images

RETRACTED ARTICLE: LW-DN161: a cardiovascular disorder classification from 12 lead ECG images using convolutional neural network

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