Nowadays technology is changing from day to day in almost all fields, that too much in the medical field. There are some techniques that are still continuing for a long time due to their popularity and their robustness. One among them is X-ray which is used for detecting the fractures in a bone. In some rural areas where the medical facility is poor it very difficult to have sufficient orthopaedics for treatment. Hence a computerized effective and robust X-Ray image classification technique is proposed which is the initial step for fracture detection. In this work a combination of high boost filtering technique, Fuzzy C means clustering, Statistical feature extraction technique along with different kinds of classifiers like Support Vector Machine (SVM), Convolution neural network, and Back-Propagation neural network (BPNN). A detailed comparison is done with the accuracy rates with all classifiers where the Convolution neural network gives an accuracy rate of 94.2% when compared to other neural networks. Hence Convolution neural network (CNN) is considered
Agriculture is the main source of income, food, employment, and livelihood for most rural people in India. Several crops can be destroyed yearly due to a lack of technical skills and changing weather patterns such as rainfall, temperature, and other atmospheric parameters that play an enormous role in determining crop yield and profit. Therefore, selecting a suitable crop to increase crop yield is an essential aspect of improving real-life farming scenarios. Anticipating crop yield is one of the major concerns in agriculture and plays a critical role in global, regional, and field decision-making. Crop yield forecasting is based on crop parameters and meteorological, atmospheric, and soil conditions. This paper introduces a crop recommendation and yield prediction system using a Hybrid Moth Flame Optimization with Machine Learning (HMFO-ML) model. The presented HMFO-ML method effectively recommends crops and forecasts crop yield accurately and promptly. The proposed model used a Probabilistic Neural Network (PNN) for crop recommendation and the Extreme Learning Machine (ELM) method for the crop yield forecasting process. The HMFO algorithm was used to improve the forecasting rate of the ELM approach. A wide-ranging simulation analysis was carried out to evaluate the HMFO-ML model, showing its advantages over other models, as it exhibited a maximum R2 score of 98.82% and an accuracy of 99.67%.
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