Publications by authors named "Seelammal Chinnaperumal"
The research is aimed at filling the gap regarding the development of long-lasting, secure technologies that help build decentralized systems. Other consensus models, such as the Proof of Work (PoW), prevailing in cryptocurrencies, are known to be expensive in terms of energy, hence the development of enlightened models like Proof of Lightweight Hash, whereby while developing the model, an emphasis is placed on energy efficiency without compromising on security. At the same time, new technologies such as battery storage and electric vehicles are disrupting consumer habits where renewable energy is favored, and a decentralized energy market is promoted.
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- Recent advancements in identifying COVID-19 cases utilize methods like PCR testing and medical imaging techniques, with artificial intelligence and deep learning enhancing detection accuracy.
- The research focuses on various deep learning models, including ResNet152, VGG16, ResNet50, and DenseNet121, to automate the identification of COVID-19 and pneumonia from CT and X-ray images.
- The upgraded VGG16 model shows exceptional performance, achieving up to 99% accuracy in classifying different types of radiographic images and outperforming traditional diagnostic methods.
The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer.
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