Trustworthy Cloud Storage Data Protection based on Blockchain Technology

D, Jena Catherine Bel; Esther, C; B, Zionna Sen G; Tamizhmalar, D; Dhinakaran, D.; P, Anish T.

doi:10.1109/icecaa55415.2022.9936299

Cited by 19 publications

(6 citation statements)

References 13 publications

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“…Filters of varying sizes, called kernels, are used in every convolution for detecting features like edge, blur, etc. The filter slides over the input image, and the feature map as a matrix is acquired [22][23][24][25].…”

Section: Convolutional Layermentioning

confidence: 99%

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Banupriya¹,

Anusuya²

2023

Computers, Materials &Amp; Continua

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In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks (CNN) is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model (EDLM) for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent (SGD) optimizer. The EDLM's effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50. Experimental results show that the EDLM achieves higher average sensitivity by 8.28% for VGG16, by 7.03% for VGG19, by 5.58% for ResNet18, by 4.26% for ResNet 34, and by 2.04% for ResNet 50, respectively.

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Section: Convolutional Layermentioning

confidence: 99%

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Banupriya¹,

Anusuya²

2023

Computers, Materials &Amp; Continua

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“…The depth-based variant search algorithm is a powerful technique for optimizing the architecture of CNNs. By automatically searching for optimal network architecture, the algorithm can effectively capture the essential features of liver tumors in CT scans while minimizing the number of parameters and computational cost of the network [29][30][31][32]. This process is essential because manually designing optimal network architecture for liver tumor prediction can be challenging and time-consuming, especially given the complexity of CT scans.…”

Section: Depth-based Variant Search Algorithmmentioning

confidence: 99%

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

Kalaiselvi,

Anusuya

2023

CMC

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In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods.

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“…The inclusion of GBMs in the fish farm management system underscores the significance of data-driven decision support. This algorithm consistently fine-tunes and optimizes the feeding schedule to cater to the ever-evolving requirements of the fish population [42][43][44]. The result is not only improved fish health and productivity but also a more efficient resource allocation strategy that minimizes waste and maximizes the farm's economic and ecological sustainability.…”

Section: Gradient Boosting Machines (Gbms): Predicting Optimal Feedin...mentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

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In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

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Trustworthy Cloud Storage Data Protection based on Blockchain Technology

Cited by 19 publications

References 13 publications

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

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