Measurement of oxygen content in water with purity through soft sensor model

Thiruneelakandan, A.; Kaur, Gaganpreet; Vadnala, Geetha; Bharathiraja, N.; Pradeepa, K; Retnadhas, Mervin

doi:10.1016/j.measen.2022.100589

Cited by 14 publications

(5 citation statements)

References 23 publications

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“…When applied to real-world issues that reflect the natural selection method, these methods often provide more specific solutions. The first stage of their suggested three-phase technique had been an attribute selection procedure that was carried out by maintaining an organized list of characteristics that have been maintained in diminishing rank ordering [34][35][36][37][38][39]. New characteristics were generated in the second stage of applying the method of selecting additional characteristics from each subtype of the characteristics of the original database [40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Talari,

Kaur

et al. 2024

PLoS ONE

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Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model’s first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system’s result is to enhance the classifier’s performance in spotting illness early.

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Section: Related Workmentioning

confidence: 99%

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Talari,

Kaur

et al. 2024

PLoS ONE

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“…Dealing with “dirty data”, characterized by missing values, inconsistent formatting, or noisy measurements, also remains a complex issue. Current ML models often assume the availability of clean and well-structured data, which is not always the case in real-world industrial settings [ 46 , 47 ]. However, several challenges persist, such as the integration of soft sensors into processes governed by first-principle models giving rise to concerns related to data security and privacy, given the heavy reliance of soft sensors on data [ 48 , 49 ].…”

Section: State Of the Artmentioning

confidence: 99%

Soft Sensing of LPG Processes Using Deep Learning

Sifakis,

Sarantinoudis,

Tsinarakis

et al. 2023

Sensors

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This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

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“…Within the YOLO framework, feature extraction is carried out using CNNs, obviating the need for a Region Proposal Network (RPN). Detection speed is enhanced as these features are directly introduced to a regression network, which subsequently provides both the object bounding box and class probability [21]. Both RNN and DCNN algorithms employ an end-to-end network approach, analyzing entire images through regression.…”

Section: Related Workmentioning

confidence: 99%

Efficient Differentiation of Biodegradable and Non-Biodegradable Municipal Waste Using a Novel MobileYOLO Algorithm

Suman,

Arulanantham

2023

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In the realm of waste management, the accurate identification of biodegradable and nonbiodegradable items remains a critical challenge. An advanced real-time object detection method, termed "MobileYOLO", was proposed, leveraging the strengths of the YOLO v4 framework. The MobileNetv2 network was integrated, and a section of the conventional computation was substituted with depth-wise separable convolutions utilizing the PAnet and head network. To enhance feature expressiveness capabilities during feature fusion, a refined lightweight channel attention mechanism, known as Efficient Channel Attention (ECA), was introduced. The Improved Single Stage Headless (ISSH) context module was incorporated into the micro-object identification branch to broaden the receptive field. Evaluations conducted on the KITTI dataset indicated an impressive accuracy of 95.7%. Remarkably, when compared to the standard YOLOv4, the MobileYOLO model exhibited a reduction in model parameters by 53.12M, a decrease in connectivity size by one-fifth, and an augmentation in detection speed by 85%.

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Measurement of oxygen content in water with purity through soft sensor model

Cited by 14 publications

References 23 publications

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Soft Sensing of LPG Processes Using Deep Learning

Efficient Differentiation of Biodegradable and Non-Biodegradable Municipal Waste Using a Novel MobileYOLO Algorithm

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