Recommendation System for Research Studies Based on GCR

Dhinakaran, D.; Kumar, D. Arul; Dinesh, Siddarth; Selvaraj, D.; Srikanth, K.

doi:10.1109/mecon53876.2022.9751920

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Our HANT approach will not regularly perform trust and power evaluation; instead, the arbitrary function uses restricted verification parameters to designate a vulnerable node [40][41][42]. This procedure can be quickened for a local update, reducing the time required to stop communications.…”

Section: Average E2e Latencymentioning

confidence: 99%

Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

Sankar¹,

Revathi²,

Thiagarajan³

2023

Computer Systems Science and Engineering

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An ad-hoc sensor network (ASN) is a group of sensing nodes that transmit data over a wireless link to a target node, direct or indirect, through a series of nodes. ASN becomes a high-risk group for several security exploits due to the sensor node's limited resources. Internal threats are more challenging to protect against than external attacks. The nodes are grouped, and calculate each node's trust level. The trust level is the result of combining internal and external trust degrees. Cluster heads (CH) are chosen based on the anticipated trust levels. The communications are then digitally signed by the source, encoded using a key pair given by a trustworthy CH, decoded by the recipient, and supervised by verifications. It authenticates the technique by identifying the presence of both the transmitter and the recipient. Our approach looks for a trustworthy neighboring node that meets the trust threshold condition to authenticate the key produced. The companion node reaffirms the node's reliability by getting the public-key certification. The seeking sensor node and the certification issuer node must have a close and trusting relationship. The results of the proposed hybrid authentication using a node trustworthy (HANT) system are modeled and tested, and the suggested approach outperforms conventional trust-based approaches in throughput, latency, lifetime, and vulnerability methods.

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Section: Average E2e Latencymentioning

confidence: 99%

Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

Sankar¹,

Revathi²,

Thiagarajan³

2023

Computer Systems Science and Engineering

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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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“…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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Recommendation System for Research Studies Based on GCR

Cited by 12 publications

References 19 publications

Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

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

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

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