Now a days the facial skin problems can be identified and treated with the significant methods of computer-based technology and artificial intelligence. These skin diseases not only depress the infected person and cause psychological depression, but they may also lead to cause skin cancer. As the visual resolution is not so effective in skin disease images, medical experts with latest technical instruments are required for diagnosis of various types of diseases. These skin disease problems can be diagnosed automatically with the help of these computer aided system. In this paper, we suggested a Deep Convolutional Neural Network (CNN) which is an automated method for facial skin disease classification. In biomedical imaging-based decision making the importance of computer assisted diagnosis in deep learning is identified. ResNETs can be utilized for eliminating deep networks vanishing gradient problems. Various kinds of results may be obtained by the ResNET architectures having various activation functions, batch sizes, tested images and training stages. The activation functions ReLU and SELUare analyzed with the implementation of four network models are observed and by using same data sets image classification is done by residual learning. Highest accuracy is obtained by using ResNET with SELU instead of using residual block may lead to best accuracy rate of 97.01% for classification of various disease images.
Most of India's wealth and economy are derived from agriculture. Crop production price forecasting has always been a challenge for farmers. Climatological changes as well as other market variables have resulted in significant losses for farmers. Despite their best efforts, farmers are unable to sell their crops for the prices they want. A decision-assistance model for agricultural product price forecasting is being developed in this project. Farming decisions may be made using this method, which takes into consideration elements like yearly rainfall, WPI, and so on. A year's worth of forecasts are available from the technology. The system employs a machine learning regression approach known as decision tree regression.
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