@ARTICLE{chango2022review,
	author={Chango, Wilson and Lara, Juan A and Cerezo, Rebeca and Romero, Cristobal},
	title={A review on data fusion in multimodal learning analytics and educational data mining},
	journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume={12},
	year={2022},
	pages={e1458}
}
@ARTICLE{nsengiyumva2022critical,
	author={Nsengiyumva, Walter and Zhong, Shuncong and Luo, Manting and Zhang, Qiukun and Lin, Jiewen},
	title={Critical insights into the state-of-the-art NDE data fusion techniques for the inspection of structural systems},
	journal={Structural Control and Health Monitoring},
	volume={29},
	year={2022},
	pages={e2857}
}
@ARTICLE{SMOLINSKA201951,
	author = {Agnieszka Smolinska and Jasper Engel and Ewa Szymanska and Lutgarde Buydens and Lionel Blanchet},	
	title = {Chapter 3 - General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences},
	booktitle = {Data Fusion Methodology and Applications},
	volume = {31},
	year = {2019},
	pages = {51-79}	
}
@ARTICLE{gao2023lkg,
	author={Gao, Song and Chen, Yingjie and Shi, Fei and Peng and others},
	title={LKG-Net: lightweight keratoconus grading network based on corneal topography},
	journal={Biomedical Optics Express},
	volume={14},
	year={2023},
	pages={799--814}
}
@ARTICLE{al2023deep,
	author={Al-Timemy, Ali H and Alzubaidi, Laith and Mosa and others},	
	title={A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning},
	journal={Diagnostics},
	volume={13},
	year={2023},
	pages={1689}
}
@ARTICLE{feng2021kernet,
	author={Feng, Ruiwei and Xu, Zhe and Zheng, Xiangshang and Hu, Heping and Jin and others},	
	title={KerNet: a novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the Pentacam HR system},
	journal={IEEE Journal of Biomedical and Health Informatics},
	volume={25},
	year={2021},
	pages={3898--3910}
}
@article{daud2020keratoconus,
	author={Daud, Marizuana M and Zaki, W Mimi Diyana W and Hussain, Aini and Mutalib, Haliza Abdul},
	title={Keratoconus detection using the fusion features of anterior and lateral segment photographed images},
	journal={IEEE Access},
	volume={8},
	year={2020},
	pages={142282--142294}
}
@article{hashemi2020image,
	author={Hashemi, Sara and Veisi, Hadi and Jafarzadehpur, Ebrahim and Rahmani, Rouhollah and Heshmati, Zainabolhoda},
	title={An image processing approach for rigid gas-permeable lens base-curve identification},
	journal={Signal, Image and Video Processing},
	volume={14},
	year={2020},
	pages={971--979}	
}
@article{thorat2022detailed,
	author={Thorat, Chhanam and Bhat, Aishwarya and Sawant, Padmaja and Bartakke, Isha and Shirsath, Swati},
	title={A detailed review on text extraction using optical character recognition},
	journal={ICT Analysis and Applications},
	year={2022},
	pages={719--728}
}








@article{BANASZCZYK2022109261,
	title = {On a certain class of positive definite functions and measures on locally compact Abelian groups and inner-product spaces},
	journal = {Journal of Functional Analysis},
	volume = {282},
	number = {1},
	pages = {109261},
	year = {2022},
	issn = {0022-1236},
	doi = {https://doi.org/10.1016/j.jfa.2021.109261},
	url = {https://www.sciencedirect.com/science/article/pii/S0022123621003438},
	author = {Wojciech Banaszczyk},
	keywords = {Positive definite functions, Locally compact Abelian groups, Discrete Gaussian measures, Gaussian measures on lattices},
	abstract = {Let L be a lattice in Rn and φ the L-periodic Gaussian function on Rn given by φ(x)=∑y∈Le−‖x−y‖2. The paper was motivated by the following observation: φ(x+y)φ(x−y) is a positive definite function of two variables x,y. We say that a positive definite function φ on an Abelian group G is cross positive definite (c.p.d.) if, for each z∈G, the function φ(x+y+z)φ(x−y) of two variables x,y is positive definite. We say that a finite Radon measure on a locally compact Abelian group is c.p.d. if its Fourier transform is a c.p.d. function on the dual group. We investigate properties of c.p.d. functions and measures and give some integral characterizations. It is proved that the classes of c.p.d. functions and measures are closed with respect to certain natural operations. In particular, products, convolutions and Fourier transforms of c.p.d. functions and measures are c.p.d., whenever defined.}
}

@incollection{LAWRENCE202242,
	title = {Measuring Respiratory Function},
	editor = {Sam M Janes},
	booktitle = {Encyclopedia of Respiratory Medicine (Second Edition)},
	publisher = {Academic Press},
	edition = {Second Edition},
	address = {Oxford},
	pages = {42-58},
	year = {2022},
	isbn = {978-0-08-102724-0},
	doi = {https://doi.org/10.1016/B978-0-08-102723-3.00243-2},
	url = {https://www.sciencedirect.com/science/article/pii/B9780081027233002432},
	author = {Philip Lawrence and Antonia McBride and Laurie McCartney and Rebecca Thursfield},
	keywords = {Exercise testing, Gas transfer, Infant lung function, Lung function, Nitric oxide, Spirometry, Whole body plethysmography},
	abstract = {Objective measures of respiratory function provide key information in pediatric medicine. This article will explore the most commonly used techniques in pediatric respiratory physiology and how the results can direct clinical decision making. It covers some key principles that apply when measuring respiratory function in children and young people, before focusing on each technique.}
}

@article{uwes,
	author =       "Uwes Anis Chaeruman, Basuki Wibawa, and Zulfiati Syahrial",
	title =        "{Determining the Appropriate Blend of Blended Learning: A Formative Research in the Context of Spada-Indonesia.}",
	journal =      "American Journal of Educational Research",
	volume =       "6",
	number =       "3",
	pages =        "88--195",
	year =         "1905",
	DOI =          "http://dx.doi.org/10.12691/education-6-3-5.",
	keywords =     "mbkm"
}

@article{HAMURA202233,
	title = {Bayesian predictive density estimation for a Chi-squared model using information from a normal observation with unknown mean and variance},
	journal = {Journal of Statistical Planning and Inference},
	volume = {217},
	pages = {33-51},
	year = {2022},
	issn = {0378-3758},
	doi = {https://doi.org/10.1016/j.jspi.2021.07.004},
	url = {https://www.sciencedirect.com/science/article/pii/S0378375821000719},
	author = {Yasuyuki Hamura and Tatsuya Kubokawa},
	keywords = {Bayesian predictive density estimation, Chi-squared distribution, Dominance, Kullback–Leibler divergence, Normal distribution, Unknown mean and variance},
	abstract = {In this paper, we consider the problem of estimating the density function of a Chi-squared variable on the basis of observations of another Chi-squared variable and a normal variable under the Kullback–Leibler divergence. We assume that these variables have a common unknown scale parameter and that the mean of the normal variable is also unknown. We compare the risk functions of two Bayesian predictive densities: one with respect to a hierarchical shrinkage prior and the other based on a noninformative prior. The hierarchical Bayesian predictive density depends on the normal variable while the Bayesian predictive density based on the noninformative prior does not. Sufficient conditions for the former to dominate the latter are obtained. These predictive densities are compared by simulation.}
}


@article{PEKER2021115540,
	title = {Application of Chi-square discretization algorithms to ensemble classification methods},
	journal = {Expert Systems with Applications},
	volume = {185},
	pages = {115540},
	year = {2021},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2021.115540},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417421009477},
	author = {Nuran Peker and Cemalettin Kubat},
	keywords = {Machine learning, Data mining, Discretization, Ensemble methods, Classification, Chi-square statistics},
	abstract = {Classification is one of the important tasks in data mining and machine learning. Classification performance depends on many factors as well as data characteristics. Some algorithms are known to work better with discrete data. In contrast, most real-world data contain continuous variables. For algorithms working with discrete data, these continuous variables must be converted to discrete ones. In this process called discretization, continuous variables are converted to their corresponding discrete variables. In this paper, four Chi-square based supervised discretization algorithms ChiMerge(ChiM), Chi2, Extended Chi2(ExtChi2) and Modified Chi2(ModChi2) were used. In the literature, the performance of these algorithms is often tested with decision trees and Naïve Bayes classifiers. In this study, differently, four sets of data discretized by these algorithms were classified with ensemble methods. Classification accuracies for these data sets were obtained through using a stratified 10-fold cross-validation method. The classification performance of the original and discrete data sets of the methods is presented comparatively. According to the results, the performance of the discrete data is more successful than the original data.}
}

@article{MAHIEU2021104256,
	title = {A multiple-response chi-square framework for the analysis of Free-Comment and Check-All-That-Apply data},
	journal = {Food Quality and Preference},
	volume = {93},
	pages = {104256},
	year = {2021},
	issn = {0950-3293},
	doi = {https://doi.org/10.1016/j.foodqual.2021.104256},
	url = {https://www.sciencedirect.com/science/article/pii/S0950329321001397},
	author = {Benjamin Mahieu and Pascal Schlich and Michel Visalli and Hervé Cardot},
	keywords = {Chi-square statistic, Multiple-response Correspondence Analysis (MR-CA), Multiple-response dimensionality test of dependence, Multiple-response hypergeometric test, Analysis of multiple-response data},
	abstract = {Free-Comment (FC) and Check-All-That-Apply (CATA) provide a contingency table containing citation counts of descriptors by products. The analyses performed on this table are most often related to the chi-square statistic. However, such practices are not well suited because they consider experimental units as being the citations (one descriptor for one product by one subject) while the evaluations (vector of citations for one product by one subject) should be considered instead. This results in incorrect expected frequencies under the null hypothesis of independence between products and descriptors and thus in an incorrect chi-square statistic. Thus, analyses related to this incorrect chi-square statistic, which include Correspondence Analysis, can lead to wrong interpretations. This paper presents a modified chi-square square framework dedicated to the analysis of multiple-response data in which experimental units are the evaluations and which is, therefore, better suited to FC and CATA data. This new framework includes a multiple-response dimensionality test of dependence, a multiple-response Correspondence Analysis, and a multiple-response hypergeometric test to investigate which descriptors are significantly associated with which product. The benefits of the multiple-response chi-square framework over the usual chi-square framework are exhibited on real CATA data. An R package called “MultiResponseR” is available upon request to the authors and on GitHub to perform the multiple-response chi-square analyses.}
}



@book{mbkm2,
	title="{A Global Cities Education Network Report. A Global Cities Education Network Report.}",
	author={Saavedra, A. and Opfer, V.},
	isbn={},
	series={},
	year={2012},
	publisher={New York, Asia Society.},
	keywords = {mbkm}
}

@article{abadi2022digital,
	title={Digital Image Processing for Height Measurement Application Based on Python OpenCV and Regression Analysis},
	author={Abadi, Aji Bijaksana and Tahcfulloh, Syahfrizal},
	journal={JOIV: International Journal on Informatics Visualization},
	volume={6},
	number={4},
	pages={763--770},
	year={2022}
}

@inproceedings{cho2022image,
	title={Image Repair and Restoration Using Deep Learning},
	author={Cho, Rosa Kim and Sood, Kanika and Channapragada, Chinmayi Sree Chitra},
	booktitle={2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)},
	pages={1--8},
	year={2022},
	organization={IEEE}
}

@article{zhang2021review,
	title={A review of imaging modalities for detecting early keratoconus},
	author={Zhang, Xuemin and Munir, Saleha Z and Sami Karim, Syed A and Munir, Wuqaas M},
	journal={Eye},
	volume={35},
	number={1},
	pages={173--187},
	year={2021},
	publisher={Nature Publishing Group UK London}
}

@article{gao2020survey,
	title={A survey on deep learning for multimodal data fusion},
	author={Gao, Jing and Li, Peng and Chen, Zhikui and Zhang, Jianing},
	journal={Neural Computation},
	volume={32},
	number={5},
	pages={829--864},
	year={2020},
	publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@article{srinivas2018merging,
	title={Merging datasets through deep learning},
	author={Srinivas, Kavitha and Gale, Abraham and Dolby, Julian},
	journal={arXiv preprint arXiv:1809.01604},
	year={2018}
}

@article{tey2024potential,
	title={Potential applications of artificial intelligence in image analysis in cornea diseases: a review},
	author={Tey, Kai Yuan and Cheong, Ezekiel Ze Ken and Ang, Marcus},
	journal={Eye and Vision},
	volume={11},
	number={1},
	pages={10},
	year={2024},
	publisher={Springer}
}

@article{upadhyaya2020pentacam,
	title={Pentacam--A corneal tomography system},
	author={Upadhyaya, Abhishek and Khan, Sarah and Sahay, Pranita and Goel, Siddhi and Kumawat, Devesh},
	journal={Delhi Journal of Ophthalmology},
	volume={31},
	number={1},
	pages={90--95},
	year={2020},
	publisher={LWW}
}