With recent developments in Artificial intelligence and deep learning every major field which is using computers for any type of work is trying to ease the work using deep learning methods. Deep learning is used in a wide range of fields due to its diverse range of applications like health, sports, robotics, education, etc. In deep learning, a Convolutional neural network (CNN) is being used in image classification, pattern recognition, Text classification, face recognition, live monitoring systems, handwriting recognition, Digit recognition, etc. In this paper, we propose a system for educational use where the recognition and solving process of mathematical equations will be done by machine. In this system for recognition of equations, we use a Convolutional neural network (CNN) model. The proposed system can recognize and solve mathematical equations with basic operations (-,+,/,*) of multiple digits as well as polynomial equations. The model is trained with Modified National Institute of Standards and Technology (MNIST) dataset as well as a manually prepared dataset of operator symbols (“-”,”+”, “/”, “*”, “(“, “)” ). Further, the system uses the RNN model to solve the recognized operations.

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In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.

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Lost Humans in our country and in different nations that they are known by everybody to be a significant social issue. These days distinguishing proof of a specific individual in the packed territory is a perplexing assignment. The human face assumes a significant part in our social communication, passing on individuals' personality. Face acknowledgment is an errand that people perform regularly and easily in their everyday lives. Face acknowledgment, as one of the essential biometric advances, turned out to be increasingly more significant inferable from fast advances in advances like computerized cameras, the Internet and cell phones, and expanded requests on security. For this, an answer is furnished on this with the assistance of a profound learning idea. Convolutional Neural Network (CNN) is utilized for the recognizable proof of an individual. The missing individual is distinguished utilizing different facial highlights. Face Detection assumes a significant part in this task. This framework tends to the structure of face acknowledgment framework by utilizing CNN technique. The CNN has been widely utilized for face acknowledgment calculations. It decreases the dimensionality of the picture, yet additionally holds a portion of the varieties in dataset of images.

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