Hybrid and dynamic clustering based data aggregation and routing for wireless sensor networks

Dhanaraj, Rajesh Kumar; Lalitha, K.; Shanmugam, Annaian; Khaitan, Supriya; Gupta, Punit; Goyal, Mayank Kumar

doi:10.3233/jifs-201756

Cited by 27 publications

(6 citation statements)

References 31 publications

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“…[23] Our system can categorize all the other multitemporal images chronologically without any labeling effort by exploiting the consistency of timeseries images and a domain adaption mechanism. [24] Our system obtains a classification accuracy that is comparable to what would be obtained with [25] supervised learning. [26] With the training samples for one temporal dataset, our system is still able to handle multitemporal remote sensing images, as mentioned Figure 1 with input data, preprocessing and methodology applied, and inception v3 and VGG16 model for image classification and accuracy.…”

Section: Introductionmentioning

confidence: 55%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

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Section: Introductionmentioning

confidence: 55%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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“…The proposed method was thoroughly tested with the Shenzhen and Montgomery datasets using metrics such as sensitivity, specificity, and accuracy, and it was discovered that the proposed method attained better accuracy when compared to state-of-the-art methods. The results of the proposed approach show a clear enhancement over the Ensemble Deep Learning [26], CNN [27], and Automatic Frontal Chest Radiograph Screening System [28]. The proposed approach shows an improved efficiency with sensitivity of 96.12%, specificity of 98.01%, accuracy 98.45% and F-Score 95.88% respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The F-Score is calculated by the average mean of the precision and recall. (11) Additionally, accuracy can be measured in terms of positive images and negative images as follows: (12) The proposed Stochastic Learning Based Artificial Neural Network (SL-ANN) Model is compared with Ensemble Deep Learning [26], CNN [27], and Automatic Frontal Chest Radiograph Screening System [28] in order to show the ability of the proposed technique to Stochastic Learning based optimization in a better way for the detection of tuberculosis system. The proposed system proves clear improvement over the current approaches due to the Stochastic Learning based optimization technique.…”

Section: B Performance Analysismentioning

confidence: 99%

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Stochastic Learning-Based Artificial Neural Network Model for an Automatic Tuberculosis Detection System Using Chest X-Ray Images

Urooj

Suchitra

Krishnasamy³

et al. 2022

IEEE Access

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Tuberculosis (TB) is still one of the most serious health issues today with a high fatality rate. While attempts are being made to make primary diagnosis more reliable and accessible in places with high tuberculosis rates, Chest X-rays has become a popular source. However, specialist radiologists are required for the screening process, which could be a challenge in developing countries. For early diagnosis of tuberculosis utilizing CXR images, a complete automatic system of tuberculosis detection can decrease the need for trained staff. Various deep learning and machine learning technologies have been introduced in recent years for examining digital chest radiographs for TB-related variances with the goal of reducing inter-class reader variability and reproducibility, as well as providing radiologic services in areas where radiologists are not available. Tuberculosis is sometimes misclassified as other conditions with similar radiographic patterns as a result of CXR images, resulting in inefficient therapy. The current approach, however, is limited to Computer-Aided Detection (CAD), which has only been evaluated with non-deep learning models. Deep neural networks open potentially new avenues for tuberculosis treatment. There are no peer-reviewed studies comparing the effectiveness of various deep learning systems in detecting TB anomalies, and none compare multiple deep learning systems with human readers. In this paper, the aim of the proposed method is to develop an efficient tuberculosis detection system based on stochastic learning with artificial neural network (ANN) model by random variations using Chest X-ray images. This approach can able to incorporate random functions into the network, either by assigning stochastic transfer functions to the network or by assigning stochastic weights to the network. This proposed method is to learn features from CXR images and optimize the parameters of an ANN model by randomly mixing the training dataset before each iteration, resulting in varied ordering of model parameter updates. Furthermore, in a neural network, model weights are frequently initialized at a random beginning point. By focusing on randomness functions with optimization, the proposed technique achieved great accuracy. The motivation of the proposed method is to detect abnormalities in CXR with the different levels of complexity of TB by strong or weak evidence with different deep geometric contexts such as shape, size, cavitation, and density. ANN's primary benefit is extracting hidden linear and non-linear relationships of high-dimensional and complex data. The proposed method was thoroughly tested with the Shenzhen and Montgomery datasets using metrics such as sensitivity, specificity, and accuracy, and it was discovered that the proposed method attained better accuracy when compared to state-of-the-art methods. The proposed method shows an improved efficiency with sensitivity of 96.12%, specificity of 98.01%, accuracy 98.45% and F-Score 95.88% respectively.

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“…e pooling layer in a convolutional block is used to reduce the data dimension and achieve something called spatial invariance, which means regardless of where the object is placed in the image, it identifies the object and classifies it. Authors in [25] discussed the tool flow mapping of CNN and FPGA based on compatibility performance density, arithmetic precision, and optimization objectives. Numerous FPGA designs are implemented for the CNN-based algorithms for high optimizations in RTL [26,27].…”

Section: Related Workmentioning

confidence: 99%

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Namburu

Sumathi

Raut

et al. 2022

Mobile Information Systems

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Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one’s health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121µW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power.

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Hybrid and dynamic clustering based data aggregation and routing for wireless sensor networks

Cited by 27 publications

References 31 publications

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Stochastic Learning-Based Artificial Neural Network Model for an Automatic Tuberculosis Detection System Using Chest X-Ray Images

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

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