A Two Hop Relay Battery Aware Mote Scheme for Energy Redeemable and Network Lifespan Improvement in WSN

N, Achyutha Prasad; Guruprakash, C D

doi:10.35940/ijeat.a2204.109119

Cited by 14 publications

(21 citation statements)

References 7 publications

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“…The fine-tuned pretrained CNN that will be used in this study is described in section 4.2. CNN was implemented using Google Net [43] and Alex Net [44].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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ECG Biometric for Human Authentication using Hybrid Method

T G,

M N

2023

IJRITCC

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Recently there is more usage of deep learning in biometrics. Electrocardiogram (ECG) for person authentication is not the exception. However the performance of the deep learning networks purely relay on the datasets and trainings, In this work we propose a fusion of pretrained Convolutional Neural Networks (CNN) such as Googlenet with SVM for person authentication using there ECG as biometric. The one dimensional ECG signals are filtered and converted into a standard size with suitable format before it is used to train the networks. An evaluation of performances shows the good results with the pre-trained network that is Googlenet. The accuracy results reveal that the proposed fusion method outperforms with an average accuracy of 95.0%.

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“…The fine-tuned pretrained CNN that will be used in this study is described in section 4.2. CNN was implemented using Google Net [43] and Alex Net [44].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…GoogLeNet: GoogLeNet is regarded as the champion of the 2014 ImageNet Large-scale Visual Recognition Challenge (ILSVRC), an annual competition that assesses advancements in object categorization and identification [43]. The GooLeNet achieves 6.7% of the error rate when used with inception modules.…”

Section: Pretrained Networkmentioning

confidence: 99%

ECG Biometric for Human Authentication using Hybrid Method

T G,

M N

2023

IJRITCC

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“…However, there is an absence of multimodal and unimodal techniques, and improved convergence is required. Energy Efficient Cluster Head Selection using an Improved Version of the Grey Wolf Optimization (EECHIGWO) [38] deals with enhanced network stability and increased lifetime of the network. PSO [39] achieves improvement in network's lifetime and enhanced size of the network.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

“…Furthermore, the effectiveness of both the NMJSOA and traditional approaches was assessed in terms of Residual energy, delay, risk, alive nodes, the total number of packets transmitted to the BS, and distance. Moreover, the NMJSOA method was compared against state-of-the-art approaches like EECHIGWO [38] and OCHSPSO [39]. Additionally, it was contrasted with traditional algorithms including PSO, Spider Monkey Optimization (SMO), Grasshopper Optimization Algorithm (GOA), JSO, and NBO.…”

Section: Performance Analysismentioning

confidence: 99%

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

K B,

2023

IJEER

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Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc. However, it remains challenging to make optimal clustering process of WSN that makes the routing process more effective. Thereby, this work proposes a new Namib Merged Jelly Search Optimization Algorithm (NMJSOA) approach for the optimal CH selection in WSN by the different constraints. Initially, the sensor nodes are grouped together to form the clusters by the k-means clustering techniques. Subsequently, allocating the CH for each cluster by computing the weight function for each cluster depends on the conditions such as energy, delay, distance and security. According to the NMJSOA method, the Namib Beetle Optimization (NBO) searching position and the Jellyfish swam old position are added together to get the best optimum position. Finally, the performance of the suggested model is investigated over the conventional methods in terms of different performance measures.

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“…Making ensuring the contour evolution is not impeded by non-breast edges is difficult when using contour-based methods [4]. Both conventional techniques and pixel classification techniques based on machine learning were examined for region-based segmentation [15]. These techniques typically require additional manual involvement or a parameter selection process, which could result in variances between and between observers.…”

mentioning

confidence: 99%

A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

Murthy,

H V,

E V

et al. 2024

IJEER

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Segmentation is a technique for separating an image into discrete areas in order to separate objects of interest from their surroundings. In image analysis, segmentation—which encompasses detection, feature extraction, classification, and treatment—is crucial. In order to plan treatments, segmentation aids doctors in measuring the amount of tissue in the breast. Categorizing the input data into two groups that are mutually exclusive is the aim of a binary classification problem. In this case, the training data is labeled in a binary format based on the problem being solved. Identifying breast lumps accurately in mammography pictures is essential for the purpose of prenatal testing for breast cancer. The proposed TLA (Transfer Learning Approach) based CNN (Convolution Neural Network) –TLA based CNN aims to offer binary classification for rapid and precise breast cancer diagnosis (benign and malignant). In order to predict the sub-type of cancer, this exploration as used Deep Learning techniques on the Histogram of Oriented Gradient (HOG) - Feature extraction technique that creates a local histogram of the image to extract features from each place in the image with CNN classifier. This research work employs two well-known pre-trained models, ResNet-50 and VGG16, to extract characteristics from mammography images. The high-level features from the Mammogram dataset are extracted using a transfer learning model based on Visual Geometry Group (VGG) with 16-layer and Residual Neural Network with 50-layers deep model architecture (ResNet-50). The proposed model TLA based CNN has achieved 96.49% and 95.48% accuracy as compared to ResNet50 and VGG16 in the breast cancer classification and segmentation.

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A Two Hop Relay Battery Aware Mote Scheme for Energy Redeemable and Network Lifespan Improvement in WSN

Cited by 14 publications

References 7 publications

ECG Biometric for Human Authentication using Hybrid Method

ECG Biometric for Human Authentication using Hybrid Method

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

A Novel Transfer Learning Approach to Improve Breast Cancer Diagnosing on Screening Mammography

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