Wireless Sensor Networks (WSNs) are extensively used in event monitoring and tracking, particularly in scenarios that require minimal human intervention. However, a key challenge in WSNs is the short lifespan of senso...
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Wireless Sensor Networks (WSNs) are extensively used in event monitoring and tracking, particularly in scenarios that require minimal human intervention. However, a key challenge in WSNs is the short lifespan of sensor nodes (SN), as continuous sensing leads to rapid battery depletion. In high-traffic areas, sensors located near the sink node exhaust their energy quickly, creating an energy-hole issue. As a result, optimizing energy usage is a significant challenge for WSN-assisted applications. To address this, this paper proposes an Energy-aware Routing and Cluster Head Selection in Wireless Sensor Network through an Attentive Dual Residual Generative Adversarial Network for golden search optimization algorithm in Wireless Sensor Network (EAR-WSN-ADRGAN-GSOA). This method involves selecting the Cluster Head (CH) using Attentive Dual Residual Generative Adversarial Network (ADRGAN), minimizing energy consumption, and reducing a number of dead sensor nodes. Subsequently, golden search optimization algorithm (GSOA) is employed to determine an optimal path for data transmission to the sink node, maximizing energy efficiency, and elongating sensor node lifespan. The proposed EAR-WSN-ADRGAN-GSOA method is simulated in MATLAB. The performance metrics, such as network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, and packet delivery ratio is examined. The proposed EAR-WSN-ADRGAN-GSOA demonstrates improved performance, achieving a higher average throughput of 0.93 Mbps, and lower average energy consumption of 0.39 mJ compared with the existing methods. These improvements have significant real-world implications for enhancing the efficiency and longevity of WSNs in applications, such as environmental monitoring, smart cities, and industrial automation.
Pneumonia causes a high rate of newborn morbidity and mortality. The challenge is accurately identifies respiratory disorders while overcoming the limitations of existing technologies such as low accuracy, delayed res...
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Pneumonia causes a high rate of newborn morbidity and mortality. The challenge is accurately identifies respiratory disorders while overcoming the limitations of existing technologies such as low accuracy, delayed response, and restricted scalability. To overcome this complication, Variational Onsager Neural Network optimized with golden search optimization algorithm fostered for Lung Disease Detection system in IoT (LDD-VONNCXR-IoT) is proposed. Initially, input CXR images are gathered from chest-X-ray Dataset. Then, pre-process the input CXR images using Two-way Recursive filtering (TWRF) for normalizing image and increasing the quality of the images. Afterwards, the preprocessed image is supplied to the feature extraction. Adaptive Synchro Extracting Transform (ASET) is employed to extract the statistical features. Finally, the extracted features are fed into Variational Onsager Neural Networks (VONN) which classifies the input CXR image into normal and pneumonia. The golden search optimization algorithm (GSOA) is used to optimize VONN that accurately detects the Lung Disease. The proposed LDD-VONN-CXR-IoT method is implemented. The performance metrics, like precision, accuracy, F1-score, Sensitivity, specificity, Error rate, ROC, computational time are examined. The proposed LDD-VONN-CXR-IoT approach attains 99.57%, 98.46%, and 98.13% for accuracy, F1 score, and precision respectively. These outcomes prove that this method for the Lung Disease Detection system in IoT is effectual tool to assist in clinical diagnosis. This method allows expertise to acquire exact results, thus providing the proper treatment.
One of the deadly diseases and a leading cause of death worldwide is lung cancer. Compound tomography (CT) is commonly used to identify tumors and it classifies the phase of cancer in the human body. Detection of canc...
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One of the deadly diseases and a leading cause of death worldwide is lung cancer. Compound tomography (CT) is commonly used to identify tumors and it classifies the phase of cancer in the human body. Detection of cancer disease in the lung at an early stage is quite difficult and is essential to increase the survival patient's rate. In this research paper, a deep learning (DL)-based optimization approach is developed for the detection of lung cancer and lung lobe segmentation using CT scan images. Initially, an adaptive wiener filter is used to pre-process the input images and the segmentation process is done by the pyramid scene parsing network (PSPNet) classifier which is effectually trained using the developed honey badger golden search optimization algorithm (HBGSO). Grid-based scheme is used to identify lung nodules and then the features are extracted. Finally, lung cancer detection is done by the Shepard convolutional neural networks (ShCNN) classifier and is trained using the proposed fractional HBGSO (FHBGSO) algorithm. The FHBGSO-based ShCNN outperforms with the highest accuracy of 93.4%, f-measure of 91.2% and precision of 89.8%. Thus, lung cancer is detected in the earlier stages by the devised scheme.
In order to realize the dynamic balance between the power supply of the integrated energy system and the change of multiple loads, a specific park is selected as the object to carry out the research on short-term fore...
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