A comprehensive review: Machine learning and its application in integrated power system

Kumbhar, Aanand; Dhawale, Pravin G.; Kumbhar, Shobha; Patil, Uday; Magdum, Pravin

doi:10.1016/j.egyr.2021.08.133

Cited by 69 publications

(15 citation statements)

References 14 publications

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“…Neurosurgical critically ill patients are mostly associated with stress hyperglycemia and negative nitrogen balance, and the body is in a hypermetabolic and hypercatabolic state after injury; early enteral nutrition support can improve the nutritional status of patients and enhance their immunity [ 1 ] but is prone to complications such as feeding intolerance and aspiration pneumonia [ 2 ]. To achieve better care outcomes, evidence-based care is derived from evidence-based medical theory by integrating the best available evidence-based medical evidence, the patient's actual situation, and the caregiver's skills and clinical experience [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

Jiang

Wang

Zhang

2022

BioMed Research International

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To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is primarily based on an integrated learning algorithm. Additionally, we have compared the prediction performance of each model. The patients were divided into control and combined treatment groups according to the random number table method, and 175 patients in each group were treated with a parenteral method and early enteral nutrition support, respectively. A reentry ICU prediction model based on the integrated learning method random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) was developed, and the prediction performance of integrated learning and logistic regression was compared. The average sensitivity, positive predictive value, negative predictive value, false-positive rate, false-negative rate, area under the receiver operating characteristic curve (AUROC), and Brier score after fivefold cross-validation were used to evaluate model effects, and the best performance model based on the top 10 predictor variables in order of importance was given. Among all models, GBDT ( AUROC = 0.858 ) was better than random forest ( AUROC = 0.827 ) and slightly better than AdaBoost ( AUROC = 0.851 ). The GBDT algorithm gave a higher ranking of importance for variables such as mean arterial pressure, systolic blood pressure, diastolic blood pressure, heart rate, urine volume, and blood creatinine and relatively poorer cardiovascular and renal function in neurosurgical intensive care patients.

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Section: Introductionmentioning

confidence: 99%

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

Jiang

Wang

Zhang

2022

BioMed Research International

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“…ML algorithms tune the model's internal parameters so that it fits a training dataset (learning phase), and when the training is over, the model can be used to generate accurate predictions on fresh input data (prediction phase) [68,69]. While there is a wealth of ML models, each with its rather complicated details and preferred application domains, their integration entails a general and standardized workflow which deserves a brief description (Figure 4).…”

Section: Standard Workflow Of Machine Learningmentioning

confidence: 99%

Bio-Driven Sustainable Extraction and AI-Optimized Recovery of Functional Compounds from Plant Waste: A Comprehensive Review

Alloun,

Calvio

2024

Fermentation

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The agrifood industry produces copious amounts of waste, which represent an execrable wastage of natural resources and result in economic losses over the entire value chain. This review compares conventional and biological methods for the recovery of functional compounds from plant wastes to rescues part of the intrinsic valuable elements contained therein. Biological methods involve bioprocesses based on hydrolytic enzymes and engineered bacterial strains, to facilitate the release of valuable compounds. Then, this review explores the innovative and transformative role of artificial intelligence and machine learning techniques for real-time monitoring, optimizing, and digitizing the extraction procedures. By combining the potential of biological extraction and AI integration, this review provides insights on how these approaches can revolutionize the agrifood sector, increasing the efficiency and environmental sustainability of the plant waste valorization process.

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“…Finally, the Bayesian models generally deal with the modeling of uncertainties in the data and model parameters. When a covariance or kernel function is employed to capture the similarity between two points with nonlinear regression models, it refers to the Gaussian process (Donti & Kolter, 2021; Kumbhar et al, 2021; Wang et al, 2021). The general classification approach of machine learning is illustrated in Figure 5.…”

Section: Overview Of Orc Plant and Data‐driven Modeling Approachmentioning

confidence: 99%

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

Oyekale

Oreko

2023

WIREs Energy & Environment

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The organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle. However, the traditional approach commonly adopted for its optimal design involves sequential consideration of working fluid selection, plant configuration, and component types, before the optimization of state parameters. This way, the design process fails to achieve an optimal design in most cases, since the process relies heavily on empirical judgments. To improve the design process, researchers have been exploring lately the suitability of machine learning techniques. It is however not clear yet if data‐driven designs of ORC plants are practically viable and accurate. To bridge this gap, this article reviews literature studies in the field. Overviews were first presented on the ORC technology and machine learning modeling approaches. Next, studies that applied machine‐learning methods for the design and performance prediction of ORC plants were discussed. Furthermore, studies that focused on ORC machine learning optimizations were discussed. The artificial neural network (ANN) approach was observed as the technique most frequently applied for ORC design and optimization. Additionally, researchers agree in general that machine‐learning methods can achieve accurate results, with significant reductions of computational time and cost. However, there is the risk of using inadequate data size in the machine learning design approach, or insufficient data set training time, all of which can affect accuracy. It is hoped that this effort would spur the practical implementation of machine learning techniques in the future design and optimization of ORC plants, toward the achievement of more sustainable energy technology. This article is categorized under: Sustainable Energy > Energy Efficiency Sustainable Energy > Other Renewables

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A comprehensive review: Machine learning and its application in integrated power system

Cited by 69 publications

References 14 publications

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

Bio-Driven Sustainable Extraction and AI-Optimized Recovery of Functional Compounds from Plant Waste: A Comprehensive Review

Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives

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