Tackling pandemics in smart cities using machine learning architecture

Ngabo, Desire; Wang, Dong; Ibeke, Ebuka; Iwendi, Celestine; Masabo, Emmanuel

doi:10.3934/mbe.2021418

Cited by 30 publications

(14 citation statements)

References 27 publications

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“…Moreover,Ngabo et al (2021) [21] also utilized the above three algorithms for classification prediction of epidemic diseases and compared them, and the results also showed that NB had the best application effect in data classification. The conclusion was consistent with the results of this experiment, and also provided reasonable support for the results.…”

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confidence: 99%

Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients

Xie

Feng

2022

BioMed Research International

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Dexmedetomidine (Dex) is an alpha-2 agonist used for sedation during various procedures. Dex activates 2-adrenoceptors, and causes the decrease of sympathetic tone, with attenuation of the neuroendocrine and hemodynamic responses to anesthesia and surgery; it reduces anesthetic and opioid requirements; and causes sedation and analgesia. Objective: it was to compare the perioperative effects of different doses of Dex at different timing in patients using Dex during the perioperative period adopting a medical data classification algorithm based on optimized semi-supervised collaborative training (Tri-training). Methods: 495 patients requiring surgical treatment in Xingtai People’s Hospital were randomly selected as the study subjects. The patients were divided into group A (used before induction), group B (used during induction), and group C (used after induction) according to different induction timing, with 165 cases in each group. Then, groups A, B, and C were divided into groups A1, B1, and C1 (0.4 μg/(kg·h) rate), groups A2, B2, and C2 (0.6 μg/(kg·h) rate), and groups A3, B3, and C3 (0.8 μg/(kg·h) rate) according to the dose used, with 55 cases in each group. Intraoperative anesthesia and postoperative adverse reactions were compared among the 9 groups. Results: the similarity between the Tri-training algorithm optimized by Naive Bayes (NB) classification algorithm and the actual classification (93.49%) was clearly higher than that by decision tree (DT) and K-nearest neighbor (kNN) classification algorithm (76.21%, 74.31%); heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) values decreased obviously after Dex used in groups B1, C1, B2, C2, B3, and C3 (P <0.05), but did not change significantly in groups A1, A2, and A3 (P >0.05); the proportion of patients with satisfactory Ramsay score in group A3 was distinctly superior than that in groups A1, A2, B1, B2, B3, C1, C2, and C3 (P <0.05); the incidence of adverse reactions in group A3 was significantly inferior than that in groups A1, A2, B1, B2, B3, C1, C2, and C3 (P <0.05). Conclusion: the optimization effect of NB classification algorithm was the best, and the injection of Dex at the injection rate of 0.8 μg/(kg·h) before induction of anesthesia could apparently improve the fluctuation of HR, SBP, and DBP during perioperative period, and effectively reduce the occurrence of adverse reactions in patients, with better sedative effect on patients.

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confidence: 99%

Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients

Xie

Feng

2022

BioMed Research International

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“…Predictive Analytics is a type of advanced analytics that offers several benefits, including sophisticated machine learning and deep learning as performed in [11][12][13][14]. It is a proactive strategy that allows forecasts, i.e., it aids in predicting what will most likely occur.…”

Section: Learning Analyticsmentioning

confidence: 99%

Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model

Krishnan

Nair

Saamuel³

et al. 2022

Sustainability

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In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

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“…This approach aims to validate treatment and handling, focusing on pandemic control and coordination. The prediction and modeling of the pandemic are discussed by Iwendi et al (13) and Ngabod et al (14), who propose a technique for classifying pandemic growth in smart cities. Under the process processing state, this dedicated networking model can be utilized, as discussed by Ahmed et al, under a dynamic user cluster grouping approach (15)(16)(17).…”

Section: Literature Surveymentioning

confidence: 99%

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Ahmed

Singh

Basha

et al. 2021

Front. Public Health

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COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

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Tackling pandemics in smart cities using machine learning architecture

Cited by 30 publications

References 27 publications

Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients

Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients

Smart Analysis of Learners Performance Using Learning Analytics for Improving Academic Progression: A Case Study Model

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

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