Statistical Models for Predicting Chikungunya Incidences in India

Verma, S. S.; Sharma, Nonita

doi:10.1109/icsccc.2018.8703218

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…Teach-er evaluation processes concentrate on the core activity of teaching covering areas such as planning and preparation, the classroom environment and instruction of teaching. The primary purpose of such feedback is to help the faculty to identify the strengths and weaknesses of their teaching and evaluation methods [1,2,3].…”

Section: Discussionmentioning

confidence: 99%

“…As discussed earlier, several researchers have undertaken this challenge of devising a scientific approach to evaluate students' feedback to gain constructive insights. Among these researchers, the author in [3] have discussed the usage of data mining technique referred as Educational Data Mining (EDM). In their work, authors in [3] have explored the data mining techniques which can be used to improve the students; performance by enhancing the quality of teaching learning process.…”

Section: Related Workmentioning

confidence: 99%

“…Among these researchers, the author in [3] have discussed the usage of data mining technique referred as Educational Data Mining (EDM). In their work, authors in [3] have explored the data mining techniques which can be used to improve the students; performance by enhancing the quality of teaching learning process.…”

Section: Related Workmentioning

confidence: 99%

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Students' feedback- An effective tool towards enhancing the Teaching Learning Process

Mangla

Mehta

Pattnaik³

et al. 2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Evaluation is a key activity to improve the quality of service in every domain. However, it becomes quite challenging to measure the performance in some areas. Teaching is one such domain where it is bit intricating to evaluate the performance of teaching community. Here, in this work, authors have proposed effective usage of students’ feedback to enhance the quality of teaching learning process.OBJECTIVES: The objective of this paper is to scientific and well-defined approach for teacher’s performance evaluation. This can help the faculty to identify the strengths and weaknesses of their teaching and evaluation methods.METHODS: Data analysis and data visualization techniques have been used gain useful insights of the stduents’ feedback on various parameters. In order to carry out the simulation, authors have considered the teaching learning process in an engineering college.RESULTS: It is evident from the results obtained 12 that more than 50% agree that the feedback system is fare and beneficial for the quality improvement in teachers.CONCLUSION: Such analysis not only provides the useful insights regarding avenue for im-provement, but also helps the management for appraisals to outperforming teachers.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Students' feedback- An effective tool towards enhancing the Teaching Learning Process

Mangla

Mehta

Pattnaik³

et al. 2023

ICST Transactions on Scalable Information Systems

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“…Regarding the other two diseases transmitted by the Ae. aegypti, we found a small number of articles addressing Zika virus disease and chikungunya's numeric prediction models (28)(29)(30)(31)(32)(33).…”

Section: Arboviruses (Counts) Predictionmentioning

confidence: 99%

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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“…This time span may be equivalent to weekly, monthly, quarterly, annual, etc. [2]. A time series represents a series of t real value data is shown as Z 1 , .…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

Verma¹,

Sharma²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is conducted on the collected dataset of conjunctivitis disease, and further compared for different performance measures. The result shows that the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), ACF1 (Auto Correlation Function) of the proposed ensemble is decreased significantly. Considering the RMSE, for instance, error values are reduced by 39.23%, 9.13%, 20.42%, and 17.13% in comparison to Auto-ARIMA, NAR, ETS, and HW model respectively. This research concludes that the accuracy of the forecasting of diseases can be significantly increased by applying the proposed stack generalization ensemble model as it minimizes the prediction error and hence provides better prediction trends as compared to Auto-ARIMA, NAR, ETS, and HW model applied discretely.

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Statistical Models for Predicting Chikungunya Incidences in India

Cited by 15 publications

References 13 publications

Students' feedback- An effective tool towards enhancing the Teaching Learning Process

Students' feedback- An effective tool towards enhancing the Teaching Learning Process

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

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