Modified Random Forest Approach for Resource Allocation in 5G Network

De, Parnika; Singh, Shailendra

doi:10.14569/ijacsa.2016.071152

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Due to implementation complexities need for the Deep learning method is felt which is fulfilled by the convolutional Network was proposed in year 1978, it works on the fixed data set with fixed output. It is obvious that this method best suitable for the image processing [6].…”

Section: Deep Learningmentioning

confidence: 99%

Improved Heart Disease Prediction Using Deep Neural Network

Ashraf¹,

Rizv²,

Sharma³

2019

AJCST

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Heart disease is biggest challenge for medical professionals. Modern life style made it an epidemic; according to a survey conducted by WHO heart attack is leading cause of death all over the world especially in the western world. It is surveyed that 23% of the death in US is due to Heart related disease [1]. It has been observed assistance is needed for helping medical professionals in detecting the chance of heart attack in the human. In recent times a lot of work related to providing an automated support system for predicting chance of Heart attack in human has been done. After advancement of computer science, researchers felt that they can help in some of the key interdisciplinary areas like medical science. Machine learning techniques are compared on the single data set which does not reflect true potential of any algorithms. They also suffer from some of the key anomalies such as accuracy and manual data set pre-processing. In this paper, we propose Deep Neural Network methods for creating an automated system for heart attack prediction. It is tested on multiple dataset to find out true potential and providing certainty in the accuracy. Method also promises to remove all the mentioned anomalies from the system like lack of accuracy and automated approach in pre- processing of the data set. In result analysis, it has been observed that prediction is much more efficient and minimum accuracy achieved through this proposed method is 87.64% on any of the data set taken under consideration.

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Section: Deep Learningmentioning

confidence: 99%

Improved Heart Disease Prediction Using Deep Neural Network

Ashraf¹,

Rizv²,

Sharma³

2019

AJCST

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Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO

Purushothaman¹,

Nagarajan

2020

International Journal of Electronics

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Advancements in 5G Technology: Enhancing Connectivity and Performance in Communication Engineering

Patel,

Yarlagadda,

Dhameliya

et al. 2022

Eng. int. (Dhaka)

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The present research delves into how 5G technology developments can improve communication engineering performance and connectivity. The primary goals are to assess 5G's technical viability, performance potential, and real-world obstacles while offering recommendations for best practices in implementation and use. The study synthesizes current literature, research papers, and industry reports using a secondary data-based review technique to examine the technological underpinnings, performance improvements, real-world applications, and future directions of 5G. According to key findings, 5G dramatically improves data rates, lowers latency, and allows for ample device connectivity, opening up new possibilities for revolutionary applications in industries like industrial automation, smart cities, and healthcare. Strategic policy interventions are necessary to address cybersecurity risks, coverage restrictions, interoperability issues, and expensive infrastructure costs. To overcome these issues, provide fair access, and optimize the socioeconomic benefits of 5G, policymakers are encouraged to prioritize spectrum allocation, simplify regulatory frameworks, and provide incentives for investments. Future studies and developments in Edge Computing and AI-driven network optimization are emphasized as essential to improving 5G networks even more. The potential of 5G to alter communication engineering and accelerate inclusive digital transformation is shown by this thorough examination.

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Modified Random Forest Approach for Resource Allocation in 5G Network

Cited by 3 publications

References 40 publications

Improved Heart Disease Prediction Using Deep Neural Network

Improved Heart Disease Prediction Using Deep Neural Network

Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO

Advancements in 5G Technology: Enhancing Connectivity and Performance in Communication Engineering

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