No abstract

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are  several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network  can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is  a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the  neural network model has proposed.

Machine learning is becoming increasingly prevalent. However, in the discipline of Bioinformatics and Computational Biology, it is not a popular use case. Machine learning techniques are used in only a few technologies. The majority of the tools are built using deterministic techniques and algorithms. Deoxyribonucleic acid (DNA) is a biological macromolecule composed up of deoxyribonucleic acid. Its main function is to store data. Due to breakthroughs in sequencing technology, DNA sequence data is presently rising at an exponential pace, ushering the study of DNA sequences into the big data age. Machine learning is also a powerful tool for massive processing it learns on its own from large volumes of data. We've talked about machine learning techniques and how they can be used to improve genome sequencing accuracy. In our review we have also discussed about genome sequence for Mycobacterium Tuberculosis. Tuberculosis is because of the bacteria, Tuberculosis caused by Mycobacterium tuberculosis. TB is considered one of the leading the reasons for dying all over the world. MDR-TB is a form of germs that cause tuberculosis that is not susceptible to anti-TB medications such as isoniazid (INH) and rifampin (RMP).

In today’s world where data plays the very important role, we have various sources of pre-data like online books, equation analysis, encyclopedia, common-sense reasoning, common-sense knowledge, etc. The increasing capacity of pre-training language models have given knowledge intensive natural language processing (KI-NLP) a new boost for advanced functionalities for establishing a stable, flexible, robust and efficient model. Though pre-trained models have its own drawback for handling the KI-NLP tasks, we are here to discuss the challenges faced in this field. A wide variety of pre-trained language models enhanced with external knowledge sources have been proposed and are in rapid development to meet this difficulty. In this research we have also discusses the challenges in NLP in terms of generation of knowledge intensive models. We have also defined some mathematical model and its framework dependability for pre-training different language in NLP. Finally, we have also discussed about variety of literature reviews based on we intend to describe the present progress of pre-trained language model-based knowledge-enhanced models (PLMKEs) in this work by deconstructing their three key elements: information sources, knowledge-intensive NLP tasks, and knowledge fusion methods.

Automatic pest on plant detection in early stage is very essential for food quality control in the agriculture industry.  However, the visual method to identify pest on every plant by human is a cumbersome process and cannot be well suited in the agriculture field, because it is time consuming, less accurate and labor intensive. Pest on Plant and plant leaf disease are the major factors responsible for reducing the quality and quantity of food production. Detection at the earlier stage of pest growth and its killing would result in reducing its effect on plant and enhance the quality of food production. Various existing ways have been used to identify and classify pest on plant, but issues have not been resolved, and there is still a scope for improvement. This paper proposes a Deep Recursive Convolutional Neural Networks (DR-CNN) to improve the average running time and achieve high accuracy. DR-CNN model is integrating the convolution, ReLU and Max pooling Layer into single unit and call recursively.