Energy Efficient VM Consolidation Technique in Cloud Computing Using Cat Swarm Optimization

Mangalampalli, Sudheer; Sree, Pokkuluri Kiran; Raju, Pothuraju; Raju, P. J. R. Shalem; N, S. S. S. N. Usha Devi; Mangalampalli, Vamsi Krishna

doi:10.1007/978-981-19-2347-0_36

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Machine learning (ML) techniques have emerged as valuable tools in developing effective and efficient clinical decision support systems. ML can leverage large datasets and complex patterns to provide personalized recommendations, improve diagnostic accuracy, and optimize treatment strategies [8].…”

Section: Machine Learning In Clinical Decision Supportmentioning

confidence: 99%

"Machine Learning for Quality in Health Care: A Comprehensive Review"

Kiran Sree

2023

BJSTR

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Machine learning (ML) has emerged as a powerful tool in the healthcare sector, revolutionizing various aspects of patient care, including diagnostics, treatment, monitoring, and overall quality improvement. This comprehensive review aims to explore the applications of ML techniques in improving quality of care in healthcare settings. We discuss the challenges and opportunities associated with the implementation of ML in healthcare, present an overview of the different ML algorithms and methodologies employed, and provide a critical analysis of the impact of ML on healthcare quality. The paper also addresses ethical and privacy concerns associated with ML in healthcare and highlights future research directions in this domain [1].

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Section: Machine Learning In Clinical Decision Supportmentioning

confidence: 99%

"Machine Learning for Quality in Health Care: A Comprehensive Review"

Kiran Sree

2023

BJSTR

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Review on Healthcare Quality Using Machine Learning Methods

Sree,

Usha Devi

2023

AI and IoT-Based Technologies for Precision Medicine

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Machine learning methods have emerged as powerful tools for enhancing healthcare quality by leveraging large datasets and identifying patterns that can inform decision-making and improve patient outcomes. This review explores the application of machine learning in healthcare quality, focusing on areas such as disease diagnosis and prognosis, personalized treatment planning, predictive analytics, fraud detection, and remote care. Machine learning algorithms have demonstrated promising results in disease detection, enabling early and accurate diagnoses. By analyzing patient data, including medical images, electronic health records, and genetic information, these algorithms can provide insights into disease progression and prognosis. Moreover, medical history, and treatment response data, leading to improved treatment effectiveness and reduced adverse events. Predictive analytics techniques facilitate the identification of patients at risk of adverse events, enabling proactive interventions to prevent complications and improve patient safety.

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Deep Learning for Identification of Behavioral Changes

Pokkuluri,

N.,

Khang

2024

Advances in Finance, Accounting, and Economics

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This study explores the application of long short-term memory (LSTM) networks for the identification of behavioral changes. LSTM networks, a type of recurrent neural network (RNN), excel at modeling sequential data and capturing long-range dependencies, making them well-suited for analyzing temporal patterns in human behavior. The research investigates how LSTM networks can effectively learn from sequential behavioral data, such as activity logs, physiological signals, or speech patterns, to detect deviations from normal behavioral patterns. By leveraging LSTM's ability to retain information over extended time intervals, the study aims to develop models capable of recognizing subtle shifts in behavior that may indicate changes in mental health, emotional states, or lifestyle habits. Additionally, the research explores techniques to enhance the interpretability of LSTM-based behavioral change detection models, addressing challenges related to model transparency and explainability.

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Energy Efficient VM Consolidation Technique in Cloud Computing Using Cat Swarm Optimization

Cited by 3 publications

References 11 publications

"Machine Learning for Quality in Health Care: A Comprehensive Review"

"Machine Learning for Quality in Health Care: A Comprehensive Review"

Review on Healthcare Quality Using Machine Learning Methods

Deep Learning for Identification of Behavioral Changes

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