The most common difficulty that every teacher faces in class room is to take the attendance of the students one by one in each and every class. For the time being many automated systems has been proposed for taking student attendance. In this paper, I introduced an automated student attendance system based on the use of unique techniques for face detection and recognition. This system automatically detects the student when he or she enters the classroom and recognizes that specific student and marks the student's attendance. This method also focuses on the specific features of different attributes such as the face, eye and nose of humans. In order to evaluate the performance of different face recognition system, different real-time situations are considered. This paper also suggests the technique for handling the technique such as spoofing and avoiding student proxy. This system helps track students compared to traditional or current systems and thereby saves time.

Outbreak prediction is a way to predict the epidemic potentials of diseases using the pattern of medication sales values. Successful prediction might result in being cautious of the outbreak of diseases and taking necessary measures to prevent the predicted outcome. As medication sales values are too random, the analysis of medication correlation is one of the most interesting and challenging parts for the researchers. The major objective of this proposed research method is to analyze medication drug sales values for a certain period of a pharmaceutical company using statistical methods. It is also the intent of this research to make a comparative analysis of the output generated by the deep learning model with the real sales values of a month. Our method successfully predicts the outbreak potential of diseases with competent accuracy, so that we will have enough time to take precautions and prevent future pandemics through precautionary measures.

Inspired by the human capability, zero-shot learning research has been approaches to detect object instances from unknown sources. Human brains are capable of making decisions for any unknown object from a given attributes. They can make relation between the unknown and unseen object just by having the description of them. If human brain is given enough attributes, they can assess about the object. Zero-shot learning aims to reach this capability of human brain. First, we consider a machine to detect unknown object with training examples. Zero-shot learning approaches to do this type of object detection where there are no training examples. Through the process, a machine can detect object instances from images without any training examples. In this paper, we develop a dynamic system which will be able to detect object instances from an image that it never seen before. Which means during the testing process the test image will completely unknown from trained images. The system will be able to detect completely unseen objects from some bounded region of given images using zero shot learning approach. We approach to detect object instances from unknown class, because there are lots of growing category in the world and the new categories are always emerging. It is not possible to limit objects in this fast-forwarding world. Again, collecting, annotating and training each category is impossible. So, zero-shot learning will reduce the complexity to detect unknown objects.

Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and one image class of images depicting healthy crops. To offer the suggested model the capability to identify and classify diseases from a variety of angles, the photographs that help compensate for the collection were flipped at a variety of different perspectives. These findings provide evidence that CNN can be applied to increase the precision with which diseases in wheat crops are identified.

Outbreak prediction is a way to predict the epidemic potentials of diseases using the pattern of medication sales values. Successful prediction might result in being cautious of the outbreak of diseases and taking necessary measures to prevent the predicted outcome. As medication sales values are too random, the analysis of medication correlation is one of the most interesting and challenging parts for the researchers. The major objective of this proposed research method is to analyze medication drug sales values for a certain period of a pharmaceutical company using statistical methods. It is also the intent of this research to make a comparative analysis of the output generated by the deep learning model with the real sales values of a month. Our method successfully predicts the outbreak potential of diseases with competent accuracy, so that we will have enough time to take precautions and prevent future pandemics through precautionary measures.