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A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

Tamil Nadu, India, Asia, Algorithms, Cancer, Cancer Detection, Cyborgs, Emerging Technologies, Health and Medicine, Machine Learning, Oncology, Skin Cancer, Skin Neoplasms Keywords: Tamil Nadu; India; Asia; Algorithms; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Skin Cancer; Skin Neoplasms EN Tamil Nadu India Asia Algorithms Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Skin Cancer Skin Neoplasms 732 732 1 09/19/23 20230919 NES 230919 2023 SEP 19 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on Oncology - Cancer Detection. [Extracted from the article]

A recent study conducted by researchers at the Saveetha School of Engineering in Tamil Nadu, India, has proposed 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 approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. The incorporation of attention mechanisms and a depth-based variant search algorithm into the CNN model shows promise in improving the accuracy and robustness of liver tumor prediction, which can assist radiologists in diagnosis and treatment planning. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

A recent report from the School of Electrical Engineering in Tamil Nadu, India discusses the use of machine learning and computer vision in cancer classification. The researchers highlight the potential of these technologies in speeding up the diagnosis of cancer and improving treatment planning. The report focuses on nine major cancer types and examines the use of various imaging modalities and machine learning algorithms in their classification. The objective of the study is to provide researchers and medical experts with a comprehensive understanding of the current state of cancer classification using computer vision and machine learning, with the aim of enhancing cancer detection and therapy precision. [Extracted from the article]

Keywords: Tamil Nadu; India; Asia; Algorithms; Cyborgs; Digestive System Diseases and Conditions; Emerging Technologies; Gastroenterology; Health and Medicine; Hepatitis; Infectious Diseases and Conditions; Liver Diseases and Conditions; Machine Learning EN Tamil Nadu India Asia Algorithms Cyborgs Digestive System Diseases and Conditions Emerging Technologies Gastroenterology Health and Medicine Hepatitis Infectious Diseases and Conditions Liver Diseases and Conditions Machine Learning 699 699 1 03/27/23 20230328 NES 230328 2023 MAR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Hepatitis Weekly -- New research on Liver Diseases and Conditions - Hepatitis is the subject of a report. Keywords for this news article include: Tamil Nadu, India, Asia, Algorithms, Cyborgs, Digestive System Diseases and Conditions, Emerging Technologies, Gastroenterology, Health and Medicine, Hepatitis, Infectious Diseases and Conditions, Liver Diseases and Conditions, Machine Learning, Department of Computer Sciences and Engineering. [Extracted from the article]

A recent study conducted in Tamil Nadu, India, has focused on autism spectrum disorders (ASD) and their impact on behavior and communication abilities. The study highlights the increasing number of people being diagnosed with ASD and the high medical expenses associated with it. To address these challenges, the researchers propose a novel deep recurrent neural network algorithm for the detection of autism levels. The algorithm utilizes an artificial algae algorithm for feature extraction and an intelligent water droplet algorithm for obtaining optimal weights and biases. The experimental results show promising classification accuracy, sensitivity, and cost reduction. For more information, readers can refer to the research article published in the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. [Extracted from the article]