Publications by authors named "Sundararaj Suganthi"
Article Synopsis
- A COVID-19 detection and classification framework is developed using a combination of an optimized AlexNet convolutional neural network and a random forest classifier, utilizing a dataset from the Joseph Paul Cohen database.
- Image preprocessing techniques, specifically fuzzy gray level difference histogram equalization (FGLHE) and fuzzy stacking, are employed to enhance image quality and reduce noise before training the model.
- The proposed method (ADCNN-ASA-RFC) shows significant improvements in accuracy, specificity, and sensitivity compared to existing algorithms, demonstrating its effectiveness in accurately diagnosing COVID-19.
Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective.
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