Recent trends in computational intelligence for educational big data analysis

Educational data mining and learning analytics: An updated survey

Romero, C., & Ventura, S. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1355 · Cited by 1,163 · see citing works

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Data mining in education

Romero, C., & Ventura, S. (2013) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1075 · Cited by 869 · see citing works

Analyzing undergraduate students' performance using educational data mining

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.05.007 · Cited by 591 · see citing works

Predicting academic performance of students from VLE big data using deep learning models

Waheed, H., Hassan, S., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106189 · Cited by 578 · see citing works

Genetic fuzzy systems: taxonomy, current research trends and prospects

Herrera, F. (2008) · Evolutionary Intelligence · doi:10.1007/s12065-007-0001-5 · Cited by 570 · see citing works

Big data for cyber physical systems in industry 4.0: a survey

Xu, L. D., & Duan, L. (2018) · Enterprise Information Systems · doi:10.1080/17517575.2018.1442934 · Cited by 530 · see citing works

A review of the-state-of-the-art in data-driven approaches for building energy prediction

Sun, Y., Haghighat, F., & Fung, B. C. M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110022 · Cited by 511 · see citing works

Review of optimization techniques applied for the integration of distributed generation from renewable energy sources

Abdmouleh, Z., Gastli, A., Ben‐Brahim, L., Haouari, M., & Al-Emadi, N. A. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.05.087 · Cited by 508 · see citing works

Mining Big Data in Education: Affordances and Challenges

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R. B., & Warschauer, M. (2020) · Review of Research in Education · doi:10.3102/0091732x20903304 · Cited by 454 · see citing works

A survey of open source tools for machine learning with big data in the Hadoop ecosystem

Landset, S., Khoshgoftaar, T. M., Richter, A. N., et al. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0032-1 · Cited by 438 · see citing works

Big data: From beginning to future

Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.07.009 · Cited by 410 · see citing works

International Journal of Information Management

Hills, P. J. (2006) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2006.01.003 · Cited by 406 · see citing works

Big data privacy: a technological perspective and review

Jain, P., Gyanchandani, M., & Khare, N. (2016) · Journal Of Big Data · doi:10.1186/s40537-016-0059-y · Cited by 405 · see citing works

Handbook of Learning Analytics

Lang, C. E., Siemens, G. E., Wise, A. E., & Gašević, D. (2017) · Society for Learning Analytics Research (SoLAR) eBooks · doi:10.18608/hla17 · Cited by 401 · see citing works

Particle swarm optimization of deep neural networks architectures for image classification

Fernandes, F. E., & Yen, G. G. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.05.010 · Cited by 395 · see citing works

Random Forests for Big Data

Genuer, R., Poggi, J., Tuleau-Malot, C., & Vialaneix, N. (2017) · Big Data Research · doi:10.1016/j.bdr.2017.07.003 · Cited by 385 · see citing works

Computing in the curriculum: Challenges and strategies from a teacher’s perspective

Sentance, S., & Csizmadia, A. P. (2016) · Education and Information Technologies · doi:10.1007/s10639-016-9482-0 · Cited by 362 · see citing works

Early dropout prediction using data mining: a case study with high school students

Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Fardoun, H. M., & Ventura, S. (2015) · Expert Systems · doi:10.1111/exsy.12135 · Cited by 324 · see citing works

Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention

Xing, W., & Du, D. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633118757015 · Cited by 324 · see citing works

Deep learning goes to school: toward a relational understanding of AI in education

Perrotta, C., & Selwyn, N. (2020) · Learning, Media and Technology · doi:10.1080/17439884.2020.1686017 · Cited by 299 · see citing works

A novel k NN algorithm with data-driven k parameter computation

Zhang, S., Cheng, D., Deng, Z., Zong, M., & Deng, X. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.09.036 · Cited by 279 · see citing works

The state of the art and taxonomy of big data analytics: view from new big data framework

Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09685-9 · Cited by 274 · see citing works

Educational data mining applications and tasks: A survey of the last 10 years

Bakhshinategh, B., Zai͏̈ane, O. R., ElAtia, S., & Ipperciel, D. (2017) · Education and Information Technologies · doi:10.1007/s10639-017-9616-z · Cited by 272 · see citing works

Big data analytics: Computational intelligence techniques and application areas

Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.03.024 · Cited by 271 · see citing works

Recent advances in neuro-fuzzy system: A survey

Shihabudheen, K. V., & Pillai, G. N. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.04.014 · Cited by 262 · see citing works

Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA

Hussain, S., Dahan, N. A., Ba-Alwi, F. M., & Ribata, N. (2018) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v9.i2.pp447-459 · Cited by 250 · see citing works

Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect

Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117362 · Cited by 250 · see citing works

Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings

Zhou, G., Moayedi, H., Bahiraei, M., et al. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120082 · Cited by 247 · see citing works

Text mining of industry 4.0 job advertisements

Bach, M. P., Bertoncel, T., Meško, M., & Krstić, Ž. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.07.014 · Cited by 244 · see citing works

Fog computing: from architecture to edge computing and big data processing

Singh, S. P., Nayyar, A., Kumar, R., & Sharma, A. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2701-2 · Cited by 240 · see citing works

Bitcoin price forecasting with neuro-fuzzy techniques

Atsalakis, G. S., Atsalaki, I. G., Pasiouras, F., et al. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.01.040 · Cited by 237 · see citing works

A Systematic Review of Deep Learning Approaches to Educational Data Mining

Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019) · Complexity · doi:10.1155/2019/1306039 · Cited by 230 · see citing works

Using Machine Learning Algorithms to Predict People’s Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

Akour, I. A., Alshurideh, M. T., Kurdi, B. H. A., Ali, A. A., & Salloum, S. A. (2021) · JMIR Medical Education · doi:10.2196/24032 · Cited by 219 · see citing works

Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques

Mueen, A., Zafar, B. A., & Manzoor, U. (2016) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2016.11.05 · Cited by 217 · see citing works

Enhancing prediction of student success: Automated machine learning approach

Zeineddine, H., Braendle, U., & Farah, A. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106903 · Cited by 214 · see citing works

An extension of the technology acceptance model in the big data analytics system implementation environment

Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.004 · Cited by 213 · see citing works

Predicting student dropout: A machine learning approach

Kemper, L., Vorhoff, G., & Wigger, B. U. (2020) · European Journal of Higher Education · doi:10.1080/21568235.2020.1718520 · Cited by 213 · see citing works

Applying Artificial Intelligence in Physical Education and Future Perspectives

Lee, H. S., & Lee, J. (2021) · Sustainability · doi:10.3390/su13010351 · Cited by 213 · see citing works

Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

Ciolacu, M. I., Tehrani, A. F., Binder, L., & Svasta, P. M. (2018) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme.2018.8599203 · Cited by 203 · see citing works

Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions

Ikegwu, A. C., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo, O. R. (2022) · Cluster Computing · doi:10.1007/s10586-022-03568-5 · Cited by 190 · see citing works

Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS

Villegas-Ch, W. E., Cañizares, M. R., & Palacios-Pacheco, X. (2020) · Applied Sciences · doi:10.3390/app10155371 · Cited by 180 · see citing works

Enabling Smart Data: Noise filtering in Big Data classification

García‐Gil, D., Luengo, J., García, S., & Herrera, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.002 · Cited by 176 · see citing works

A practical introduction to Random Forest for genetic association studies in ecology and evolution

Brieuc, M. S. O., Waters, C. D., Drinan, D. P., & Naish, K. A. (2018) · Molecular Ecology Resources · doi:10.1111/1755-0998.12773 · Cited by 172 · see citing works

Predicting Students Performance in Educational Data Mining

Guo, B., Zhang, R., Guang, X., Shi, C., & Yang, L. (2015) · International Symposium on Educational Technology · doi:10.1109/iset.2015.33 · Cited by 169 · see citing works

Deep Model for Dropout Prediction in MOOCs

Wang, W., Yu, H., & Miao, C. (2017) · International Conference on Crowd Science and Engineering (ICCSE) · doi:10.1145/3126973.3126990 · Cited by 156 · see citing works

Big data analytics and computational intelligence for cyber–physical systems: recent trends and state of the art applications

Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.021 · Cited by 155 · see citing works

Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs

Moreno-Marcos, P. M., Muñoz‐Merino, P. J., Maldonado‐Mahauad, J., Pérez‐Sanagustín, M., Alario‐Hoyos, C., & Kloos, C. D. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103728 · Cited by 143 · see citing works

Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

Bernard, J., Chang, T., Popescu, E., & Sabine Graf, A. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.021 · Cited by 141 · see citing works

Students performance prediction using KNN and Naïve Bayesian

Amra, I. A. A., & Maghari, A. Y. A. (2017) · doi:10.1109/icitech.2017.8079967 · Cited by 136 · see citing works

Big Data Analytics for Large-scale Wireless Networks

Dai, H., Wong, R. C.-W., Wang, H. H., Zheng, Z., & Vasilakos, A. V. (2019) · ACM Computing Surveys · doi:10.1145/3337065 · Cited by 131 · see citing works

Big Data Analytics in Industrial IoT Using a Concentric Computing Model

Rehman, M. H. U., Ahmed, E., Yaqoob, I., Hashem, M., Imran, M. A., & Ahmad, S. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700632 · Cited by 130 · see citing works

Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs.

Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2019.1636086 · Cited by 124 · see citing works

Predicting student academic performance using multi-model heterogeneous ensemble approach

Adejo, O. W., & Connolly, T. M. (2017) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-09-2017-0113 · Cited by 113 · see citing works

Hidden community detection in social networks

He, K., Li, Y., Soundarajan, S., & Hopcroft, J. E. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.019 · Cited by 112 · see citing works

Implementing AutoML in Educational Data Mining for Prediction Tasks

Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S. B., & Ragos, O. (2019) · Applied Sciences · doi:10.3390/app10010090 · Cited by 108 · see citing works

Facebook in context(s): Measuring emotional responses across time and space

Bayer, J., Ellison, N. B., Schoenebeck, S. Y., Brady, E. L., & Falk, E. B. (2016) · New Media & Society · doi:10.1177/1461444816681522 · Cited by 107 · see citing works

A Personalized Learning System for Parallel Intelligent Education

Tang, Y. G., Liang, J., Hare, R., & Wang, F. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.2965198 · Cited by 100 · see citing works

Review study on fuzzy cognitive maps and their applications during the last decade

Papageorgiou, E. I. (2011) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2011.6007670 · Cited by 96 · see citing works

Convolution Neural Network based Transfer Learning for Classification of Flowers

Wu, Y., Qin, X., Pan, Y., et al. (2018) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2018.8600536 · Cited by 95 · see citing works

Big data analytics: six techniques

Shu, H. (2016) · Geo-spatial Information Science · doi:10.1080/10095020.2016.1182307 · Cited by 93 · see citing works

Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis

Lorenzo-Trueba, J., Henter, G. E., Takaki, S., et al. (2018) · Speech Communication · doi:10.1016/j.specom.2018.03.002 · Cited by 91 · see citing works

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Chen, J., Feng, J. H., Sun, X., Wu, N., Yang, Z., & Chen, S. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/8404653 · Cited by 90 · see citing works

MOOC student dropout prediction model based on learning behavior features and parameter optimization

Jin, C. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1802300 · Cited by 90 · see citing works

A proposed social network analysis platform for big data analytics

Chang, V. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.11.002 · Cited by 86 · see citing works

Learning analytics using deep learning techniques for efficiently managing educational institutes

Veluri, R. K., Patra, I., Naved, M. I., et al. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.11.416 · Cited by 86 · see citing works

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Putnik, G. D., Costa, E., Alves, C., et al. (2015) · International Journal of Technology and Design Education · doi:10.1007/s10798-015-9318-z · Cited by 83 · see citing works

Strength of ensemble learning in multiclass classification of rockburst intensity

Zhang, J., Wang, Y., Sun, Y., & Li, G. (2020) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3111 · Cited by 83 · see citing works

Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm

Chi, Y., & Liu, J. (2015) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2015.2426314 · Cited by 78 · see citing works

Application of Computational Intelligence to Improve Education in Smart Cities

Gomede, E., Gaffo, F. H., Briganó, G. U., de Barros, R. M., & Mendes, L. D. S. (2018) · Sensors · doi:10.3390/s18010267 · Cited by 78 · see citing works

Can online discussions facilitate deep learning for students in General Education?

Aderibigbe, S. A. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06414 · Cited by 76 · see citing works

Deep Learning Recommendations of E-Education Based on Clustering and Sequence

Safarov, F., Kutlimuratov, A., Abdusalomov, A. B., et al. (2023) · Electronics · doi:10.3390/electronics12040809 · Cited by 76 · see citing works

A systematic review on machine learning models for online learning and examination systems

Kaddoura, S., Popescu, D. E., & Hemanth, J. D. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.986 · Cited by 75 · see citing works

A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data

Rathore, P., Kumar, D., Bezdek, J. C., Rajasegarar, S., & Palaniswami, M. S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2842191 · Cited by 72 · see citing works

Evaluation of Physical Education Teaching Quality in Colleges Based on the Hybrid Technology of Data Mining and Hidden Markov Model

Zeng, Y. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i01.12533 · Cited by 71 · see citing works

Artificial Intelligence in Education

Flogie, A., & Aberšek, B. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.96498 · Cited by 66 · see citing works

Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

López-Belmonte, J., Robles, A. S., Moreno-Guerrero, A.-J., & González, M. E. P. (2020) · Symmetry · doi:10.3390/sym12040495 · Cited by 65 · see citing works

A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC

Monllaó Olivé, D., Huynh, D. Q., Reynolds, M., Dougiamas, M., & Wiese, D. (2020) · Journal of Computing in Higher Education · doi:10.1007/s12528-019-09230-1 · Cited by 64 · see citing works

Using Machine Learning to Score Multi-Dimensional Assessments of Chemistry and Physics

Maestrales, S., Zhaı, X., Touitou, I., Baker, Q., Schneider, B., & Krajcik, J. S. (2021) · Journal of Science Education and Technology · doi:10.1007/s10956-020-09895-9 · Cited by 63 · see citing works

Advantages and limitations of genetic algorithms for clustering records

Beg, A. H., & Islam, M. Z. (2016) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2016.7604009 · Cited by 60 · see citing works

Taming the IoT data deluge: An innovative information-centric service model for fog computing applications

Tortonesi, M., Govoni, M., Morelli, A., Riberto, G., Stefanelli, C., & Suri, N. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.06.009 · Cited by 59 · see citing works

A conceptual framework for teaching computational thinking in personalized OERs

Moon, J., Do, J., Lee, D., & Choi, G. W. (2020) · Smart Learning Environments · doi:10.1186/s40561-019-0108-z · Cited by 57 · see citing works

Model detecting learning styles with artificial neural network

Hasibuan, M. S., Nugroho, L. E., & Santosa, P. I. (2019) · Journal of Technology and Science Education · doi:10.3926/jotse.540 · Cited by 51 · see citing works

Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models

Geigle, C., & Zhai, C. (2017) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3051457.3053986 · Cited by 48 · see citing works

Predicting Student Performance in an Educational Game Using a Hidden Markov Model

Tadayon, M., & Pottie, G. J. (2020) · IEEE Transactions on Education · doi:10.1109/te.2020.2984900 · Cited by 47 · see citing works

A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools

Chen, S., & Ding, Y. (2023) · Social Sciences · doi:10.3390/socsci12030118 · Cited by 46 · see citing works

An Efficient Routing Protocol in Mobile Ad-hoc Networks by using Artificial Immune System

Sarkohaki, F., Fotohi, R., & Ashrafian, V. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080473 · Cited by 45 · see citing works

The machine learning horizon in cardiac hybrid imaging

Juárez‐Orozco, L. E., Martinez‐Manzanera, O., Nesterov, S. V., Kajander, S. A., & Knuuti, J. M. (2018) · European Journal of Hybrid Imaging · doi:10.1186/s41824-018-0033-3 · Cited by 45 · see citing works

Improved modeling of intelligent tutoring systems using ant colony optimization

Rastegarmoghadam, M., & Ziarati, K. (2017) · Education and Information Technologies · doi:10.1007/s10639-016-9472-2 · Cited by 43 · see citing works

Mining Smart Learning Analytics Data Using Ensemble Classifiers

Kausar, S., Oyelere, S. S., Salal, Y. K., Hussain, S., Çifçi, M. A., Hilĉenko, S., Iqbal, M. S., Wenhao, Z., & Xu, H. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i12.13455 · Cited by 42 · see citing works

Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Poczęta, K., Papageorgiou, E. I., & Gerogiannis, V. C. (2020) · Mathematics · doi:10.3390/math8112059 · Cited by 42 · see citing works

Challenges and techniques in Big data security and privacy: A review

Bao, R., Chen, Z., & Obaidat, M. S. (2018) · Security and Privacy · doi:10.1002/spy2.13 · Cited by 40 · see citing works

Hidden Markov Model based Speech Synthesis: A Review

Kayte, S. N., Mundada, M. R., & Gujrathi, J. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015906965 · Cited by 39 · see citing works

Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

Machado, M. D. O. C., Bravo, N. F. S., Martins, A. F., Bernardino, H. S., Barrére, E., & de Souza, J. F. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09864-z · Cited by 38 · see citing works

Sequential approximation optimization assisted particle swarm optimization for expensive problems

Cai, X., Gao, L., & Li, F. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105659 · Cited by 37 · see citing works

Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach

Naaj, M. A., Mehdi, R. A. K., Mohamed, E. A., & Nachouki, M. (2023) · Education Sciences · doi:10.3390/educsci13030313 · Cited by 37 · see citing works

Costs of Managerial Attention and Activity as a Source of Sticky Prices: Structural Estimates from an Online Market

Ellison, S. F., Snyder, C. M., & Zhang, H. (2018) · National Bureau of Economic Research · doi:10.3386/w24680 · Cited by 36 · see citing works

Exploring the High Potential Factors that Affects Students’ Academic Performance

Kaviyarasi, R., & Balasubramanian, T. (2018) · International Journal of Education and Management Engineering · doi:10.5815/ijeme.2018.06.02 · Cited by 36 · see citing works

A Hybrid Machine Learning Model for Grade Prediction in Online Engineering Education

Kanetaki, Z., Stergiou, C. I., Bekas, G., Troussas, C., & Sgouropoulou, C. (2022) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v12i3.23873 · Cited by 36 · see citing works

Machine Learning with Big DataPreprint

Tyagi, A. K., & Rekha, G. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3356269 · Cited by 35 · see citing works

Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data

Vidhya, K., & Shanmugalakshmi, R. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03132-w · Cited by 33 · see citing works

Design and Implementation of Early Warning System Based on Educational Big Data

Wang, Z., Zhu, C., Ying, Z., et al. (2018) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2018.8599357 · Cited by 32 · see citing works

Students’ Success Prediction based on Bayes Algorithms

Khalaf, A., Majeed, A., Akeel, W., et al. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017915506 · Cited by 30 · see citing works

Personalizing Online Educational Tools

Lee, M. J., & Ferwerda, B. (2017) · doi:10.1145/3039677.3039680 · Cited by 29 · see citing works

Deep learning fusion conceptual frameworks for complex human activity recognition using mobile and wearable sensors

Nweke, H. F., Al-Garadi, M. A., Mujtaba, G., Alo, U. R., & Waqas, A. (2018) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet.2018.8346364 · Cited by 27 · see citing works

Big Data Real Time Ingestion and Machine Learning

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Design of an Intelligent Educational Evaluation System Using Deep Learning

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