The Future of AI-enabled servers in the cloud- A Survey

Pasumarty, Rohitha; N, Raja Praveen K; Mahesh, T R

doi:10.1109/i-smac52330.2021.9640925

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…As an important data mining technique similar to classification, clustering involves grouping a set of data objects when the groupings are unknown. In clustering, grouping produces classes or clusters of objects, where [6] objects within a cluster share many traits but differ greatly from those in other clusters. Dissimilarities are taken into consideration when describing objects based on their attribute values.…”

Section: Related Workmentioning

confidence: 99%

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Maddala,

Sreedevi

2023

RAiSE-2023

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Section: Related Workmentioning

confidence: 99%

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Maddala,

Sreedevi

2023

RAiSE-2023

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“…They have provided an overview of various neural network methods. Convolutional neural networks, according to Hakan Wieslander, Gustav Forsli, and Ewert Bengtsson [7], have been shown to be reliable for image classification tasks. Using two datasets encompassing oral cells and cervical cells, the performance of two distinct network designs, ResNet and VGG, was assessed.…”

Section: Literature Surveymentioning

confidence: 99%

Early Detection of Cancer using Machine Learning (ML) Techniques

Mahesh

Kumar²

2023

International Journal of Information Technology, Research and A

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. A combination of expert AI and highlight determination calculations produces improved results in the early detection and forecast of oral cancer. The main goal and commitment of this audit study is to summarize the use of AI technologies for accurate early prediction of oral malignant development.

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“…Artificial Neural Network (ANN) machine learning algorithm mainly works based on weight processing parameter. It mainly used as topological-based algorithm in a multi-layered and back-propagation weighted technique for the implementation of crop yield prediction based on quality of soil [6,7]. But as this ANN algorithm is applicable to number of input attributes are very less, so there is no automatic algorithm to predict the suitable crop for sample data space.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Crop Yield Based-on Soil Moisture using Machine Learning Algorithms

Mahesh

2023

International Journal of Information Technology, Research and A

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Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Based on various parameters, farming issues and farming system, there is lot of change in production rate and market prices. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like pH, NPK (Nitrogen, Phosphorous and potassium) values using various sensors. Farmers can predict or come to a decision the type of soil moisture values, farmers can decide the type of crop to be planted. In this paper, we proposed decision tree supervised machine learning algorithm to improve our results for the prediction of crop yield based on soil moisture parameters to achieve economic growth for achievement of better results.

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The Future of AI-enabled servers in the cloud- A Survey

Cited by 15 publications

References 19 publications

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Early Detection of Cancer using Machine Learning (ML) Techniques

Prediction of Crop Yield Based-on Soil Moisture using Machine Learning Algorithms

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