Ved Prakash Bhardwaj’s research while affiliated with University of Petroleum and Energy Studies and other places

Skin cancer is one of the most life-threatening diseases caused by the abnormal growth of the skin cells, when exposed to ultraviolet radiation. Early detection seems to be more crucial for reducing aberrant cell proliferation because the mortality rate is rapidly rising. Although multiple researches are available based on the skin cancer detection, there still exists challenges in improving the accuracy, reducing the computational time and so on. In this research, a novel skin cancer detection is performed using a modified falcon finch deep Convolutional neural network classifier (Modified Falcon finch deep CNN) that efficiently detects the disease with higher efficiency. The usage of modified falcon finch deep CNN classifier effectively analyzed the information relevant to the skin cancer and the errors are also minimized. The inclusion of the falcon finch optimization in the deep CNN classifier is necessary for efficient parameter tuning. This tuning enhanced the robustness and boosted the convergence of the classifier that detects the skin cancer in less stipulated time. The modified falcon finch deep CNN classifier achieved accuracy, sensitivity, and specificity values of 93.59%, 92.14%, and 95.22% regarding k-fold and 96.52%, 96.69%, and 96.54% regarding training percentage, proving more effective than literary works.

Cancer, by any means, is a significant cause of death worldwide. In the analysis of cancer disease, the classification of different tumor types is very important. This test initiates an attitude to the classification of cancer through the data in gene expression by modeling the support vector machine. Genetic material expression data of individual tumor types is designed by the SVM classifier, which tends to increase the potential of genetic data. Feature selection has long been considered a practical standard since its introduction in the field, and numerous feature selection methods have been used in an effort to reduce the input dimension while enhancing the classification performance. The proposed optimization has pertained to the gene expression data that selects the fusion factors for the hybrid kernel function in the SVM classifier and the genes as informative for cancer classification. The analysis of cancer classification is performed using colon cancer and breast cancer, and the performance of CoySVM is tested by taking the measures as precision, recall, and F-measure, and it achieves 87.598%, 95.669%, and 98.088% for colon cancer in addition to 93.647%, 92.984%, and 95% for breast cancer. It shows the best performance due to its highest classification in selected measures than the conventional methods.

ICMLIP-2020 is soliciting original, previously unpublished and high quality research papers addressing research challenges and advances in the tracks mentioned below. The maximum allowable length of the paper is 8 pages (including figures and references) for regular research articles strictly in SPRINGER SINGLE COLUMN FORMAT. Articles will be Double Blind reviewed by three experts to decide its suitability for publication in the conference. Acceptance of papers will be communicated to authors by email.