Keywords : Neural Network


Determining the Influencing Factors of COVID 19 on Mental Health Using Neural Network

Menonjyoti Kalita; Golam Imran Hussain

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 6S, Pages 126-129
DOI: 10.47392/irjash.2021.177

 
In 2020 there is outbreak of the pandemic COVID 19. Due to the pandemic most of the countries enforced full lockdown. During lockdown the mental health of the common people of all ages are affected. The study aims to investigate the factors which influence the mental health. Mental health is influenced by the various factors. In this research the artificial neural network is used to predict the factors influencing the mental health due to COVID 19 pandemic. Experimental result showed that artificial neural network not only has better accuracy up to 81.5% but also faster training process. Based on these results, the determinant factors of mental health among people are: frustration, fear, sudden change in daily life style, anxiety to perceive changes as a challenge.

Identification of CT Lung Tumor Using Fuzzy Clustering Algorithm

Jalal deen K; Karthigai Priya G; Magesh B; Kubendran R

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICEST 1S, Pages 30-33
DOI: 10.47392/irjash.2021.016

The main principle for the system-based study of lung cancers in CT images is cancer cell recognition and segmentation. Anyhow, in low-contrast pictures, it is a complex job as the low-level images are too small to detect. We are proposing a new technique in this project for the automated detection of lung cancers. Alternatively, by probability density function estimation, we enhance the intensity contrast of CT images. We use the expectation maximization / maximization of the posterior marginal to find cancerous areas. Finally, to decrease noise and classify focal cancers, we use shape limitation. The resolution of more than 95 percent of this fuzzy-based segmentation method is achieved and 9 percent accuracy is also given.

Intelligence slicing: A synthesized framework to integrate artificial intelligence into 5G networks

Chandrakala V; Surya Kumar M S R

International Research Journal on Advanced Science Hub, 2020, Volume 2, Issue 8, Pages 57-61
DOI: 10.47392/irjash.2020.94

For best-performing networks from 5G and above, it must support a wide range of needs. It is understood that more transmission, resource assistance and communication systems will be required. Achieving these tasks can be challenging as network infrastructure becomes more complex and massive. A good solution is to incorporate more robust AI technology that has been tested to provide answers ranging from channel prediction to autonomous network management, as well as network security. Today, however, the latest technology to integrate AI into wireless networks is limited to using a unique AI algorithm to solve a specific problem. A comprehensive framework that can fully utilize the power of AI in solving various network problems remains an open problem. Therefore, this paper introduces the idea of the spy pieces on which the AI unit is installed and delivers on one condition. Intelligence units are used to flexibly control the intelligence of AI algorithms with two comprehension strategies to perform different intellectual tasks: 1) Neural network-based channel predictions and 2) Industrial network-based security acquisition, to illustrate this framework.