Skin disease recognition and observing is a major challenge looked by the medical industry. Because of expanding contamination and utilization of lousy nourishment, the tally of patients experiencing skin related issues is expanding at a quicker rate. Well-being isn’t the main concern, however unfortunate skin hurts our certainty. Customary and appropriate skin checking is a significant advance towards early discovery of any destructive or starting changes in skin that may bring about skin disease. Machine learning methods can add to the improvement of capable frameworks which can order various classes of skin illnesses. To identify skin maladies, first, it is required to separate the skin and non-skin. In this paper, five diverse machine learning algorithms have been chosen and executed on skin infection data set to anticipate the exact class of skin disease. Out of a few machine learning algorithms, we have worked on Random forest, naive bayes, logistic regression, kernel SVM and CNN. A similar examination dependent on confusion matrix parameters and training accuracy has been performed and delineated utilizing graphs. It is discovered that CNN is giving best training precision for the right expectation of skin diseases among all selected.
Due to its growth rate and strength, bamboo's versatility is huge. Bamboo has been developed to replace hardwood naturally. But it can be difficult to recognize a bamboo as many appear in a cluster or singular. Each bamboo type has its applications. Because of the utility of bamboo, we have worked in Random Forest, naive bays, logistic regression, the SVM-kernel, CNN, and ResNET, amongst several machine-learning algorithms. A similar test was carried out and delineated using graphs based on uncertainty matrix parameters and training accuracy. In this paper, we have used the data of following five species such as Phyllostachys nigra, Bambusa vulgaris ‘Striata‘, Dendrocalamus giganteu, Bambusa ventricosa, and Bambusa tulda which are generally found in north India. We trained, tested and validated the species from datasets using different machine learning and deep learning algorithms.
Healthcare 4.0 (H4.0) is the term corresponding to Industry 4.0 (I4.0). Like other industries, the healthcare industry has also gone through several technological changes and got nourished with them. The health care system of many countries is not mature enough to swiftly transform from its current state to the H4.0 state. Even the matured healthcare systems are facing several challenges while adopting H4.0 practices. The implementation of H4.0 has many challenges (or barriers) to be overcome. This paper has attempted to understand and analyze the challenges that the current Indian hospital management system faces while implementing H4.0. This study uses an integrated approach to the decision-making process to understand the importance of these barriers from the perspective of the Indian health care system. With the help of the Analytical Hierarchy Process (AHP), Interpretative Structural Modeling (ISM) and MICMAC analysis, the paper has mapped the importance of barriers which need to be overcome for to make implementation of H4.0 possible.
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