A machine learning algorithm for classification of mental tasks

Manoharan, Hariprasath; Haleem, Sulaima Lebbe Abdul; Shitharth, S.; Kshirsagar, Pravin R.; Tirth, Vineet; Thangamani, M.; Chandan, Radha Raman

doi:10.1016/j.compeleceng.2022.107785

“…With the advances of ultrasound technology, pulmonary ultrasound is known as a visual "stethoscope" [4,5]. The characteristics of real-time, safe, repeatable, visualized, and radiation-free of pulmonary ultrasound are irreplaceable advantages of X-ray and CT, which have been widely used in patients with severe condition assessment and monitoring [6,7]. However, there are few studies on artificial airway care for severe patients, and the quality of artificial airway care for ARDS patients will directly affect the control of pulmonary infection and improvement of ventilation and blood flow.…”

Section: Introductionmentioning

confidence: 99%

Application of Visual Artificial Airway in Patients with ARDS Assisted by Pulmonary Ultrasound

Xia

¹

,

Ma

²

,

Hu

³

et al. 2022

BioMed Research International

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Objective. To explore the application of pulmonary ultrasound in visual nursing of artificial airway in patients with acute respiratory distress syndrome (ARDS). Methods. Seventy-eight ARDS patients with mechanical ventilation admitted from February 2021 to January 2022 were included and divided into the intervention group and the control group. The control group was given routine airway nursing, and the intervention group was given visual airway nursing management through lung ultrasound. The arterial blood gas analysis indexes, mechanical ventilation time, ICU treatment time, total hospitalization time, aspiration, and the incidence of ventilator-associated pneumonia (VAP) were compared between the two groups. Results. After treatment, PaO2, PaCO2, SPO2, and oxygenation indexes were significantly improved compared with those before treatment, and the indexes in the intervention group were better than those in the control group after treatment, and the differences were statistically significant ( P < 0.05 ). The mechanical ventilation time ( 5.39 ± 0.68 vs. 7.92 ± 0.59 days), ICU treatment time ( 8.05 ± 1.14 vs. 10.71 ± 1.16 days), and total hospitalization time ( 12.05 ± 2.20 vs. 15.68 ± 2.18 days) in the intervention group were significantly shorter than those in the control group ( P < 0.05 ). The incidences of aspiration (2.56% vs. 15.38%) and VAP (5.13% vs. 20.51%) in the intervention group was significantly lower than that in the control group ( P < 0.05 ). Conclusion. The application of visual artificial airway management assisted by lung ultrasound in ARDS patients can shorten the treatment time and hospitalization time of mechanical ventilation, reduce the incidence of aspiration and VAP, and improve the prognosis of patients.

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“…where, c = 1, 2 … 𝐹 𝑛 and t = 1, 2 …𝑚 𝑡 . In this model, each crow has the ability to evoke the location or position for hiding the food source, which accomplished before starting the next iteration as estimated as (7).…”

Section: Ulmcso Based Feature Selectionmentioning

confidence: 99%

“…A remote health monitoring framework for heart disease and diabetes prediction using … (Adari Ramesh) 847 generating the alert to the healthcare professionals [7]. This pipeline can be implemented with the modules of data imputation, optimization and classification.…”

Section: Introductionmentioning

confidence: 99%

A remote health monitoring framework for heart disease and diabetes prediction using advanced artificial intelligence model

Ramesh¹,

Subbaraya²,

Krishnegowda³

2023

IJEECS

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Remote health monitoring frameworks gained significant attention due to their real intervention and treatment standards. The proposed work intends to design an artificial intelligence (AI) based remote health monitoring framework for predicting heart disease and diabetes from the given medical datasets. In this framework, the smart devices are used to gather the health information of patients, and the obtained information is integrated together by using different nodes that includes the detecting node, visualization node, and prognostic node. Then, at that point, the health care dataset preprocessing is performed to standardize the characteristics by recognizing the missing qualities and taking out the unessential characteristics. Consequently, the unified levy modeled crow search optimization (ULMCSO) algorithm is employed to select the optimal features based on the global fitness function, which helps increase the accuracy and reduce the training time of the classifier. Finally, the probabilistic guided naïve distribution (PGND) based classification model is utilized for predicting the label as to whether normal or disease affected. During an evaluation, two different datasets, such as PIMA and Hungarian, are used to validate and compare the results of the proposed model by using various performance measures.

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“…Visits to various clinics are used to obtain information. With Doctor's consent, information is accessed and reviewed ( 21 , 22 ). While conducting a depression analysis, several fields of doctors' opinions on depression are examined.…”

Section: Literature Surveymentioning

confidence: 99%

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

Kshirsagar

¹

,

Manoharan²,

Shitharth

³

et al. 2022

Front. Public Health

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The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far. Even some advanced technologies do not support distinct classes of individuals as language writing skills vary greatly across numerous places, making the central operations cumbersome. As a result, the primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. The CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotion analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analyzed to test the usefulness of CNN for recognizing abnormalities, and the extracted features provide an enhanced value of around 74%higher than the conventional models.

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A machine learning algorithm for classification of mental tasks

Cited by 12 publications

References 15 publications

Application of Visual Artificial Airway in Patients with ARDS Assisted by Pulmonary Ultrasound

Application of Visual Artificial Airway in Patients with ARDS Assisted by Pulmonary Ultrasound

A remote health monitoring framework for heart disease and diabetes prediction using advanced artificial intelligence model

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

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