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A major research subject in recent times is Alzheimer’s disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of Alzheimer’s disease (AD) is critical for effective treatment and monitoring the time period between normal aging’s unavoidable cognitive loss & dementia’s more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of Alzheimer’s disease (AD) has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNN) to identify Alzheimer’s disease have yielded promising results. Instead of adequately engaging with the lack of Real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer’s disease & utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer’s disease. CNN, DCGANs, and SRGANs are used in this paper to present a Deep Learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data.

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The air quality index is an index to decide the situation of the air quality. The air quality index is a measure of how air pollutants impact a persons' fitness within a time period. It is a standardized degree this is used to suggest the pollutant (so2, no2, pm 2.5, pm 10, etc.) levels. We designed a model that could estimate the air quality index based totally on ancient records of a few preceding years. The performance of this model is progressed through making use of numerous Estimation-Problem logics. Our model could be able to correctly predict the air quality index of a complete county or any nation or any bounded area supplied with the ancient records of pollutant concentration. In our model by implementing a support-vector machine, we achieved better performance than other models and for that our model gets an accuracy of 96%. With the help of support-vector machine, our model estimates the air quality to predict the air quality index of a given location primarily based totally on its ancient records of the pollution of a few preceding years. Our purpose is to increase a non-linear updatable version for real-time air quality index forecasting, to doubtlessly update the models presently being used.

In this digital era, Natural language Processing is not just a computational process rather it is a way to communicate with machines as humanlike. It has been used in several fields from smart artificial assistants to health or emotion analyzers. Imagine a digital era without Natural language processing is something which we cannot even think of. In Natural language Processing, firstly it reads the information given and after that begins making sense of the information. After the data has been properly processed, the real steps are taken by the machine throwing some responses or completing the work. In this paper, I review the journey of natural language processing from the late 1940s to the present. This paper also contains several salient and most important works in this timeline which leads us to where we currently stand in this field. The review separates four eras in the history of Natural language Processing, each marked by a focus on machine translation, artificial intelligence impact, the adoption of a logico-grammatical style, and an attack on huge linguistic data. This paper helps to understand the historical aspects of Natural language processing and also inspires others to work and research in this domain.