EVALUATING THE EFFECT OF DATASET SIZE ON PREDICTIVE MODEL USING SUPERVISED LEARNING TECHNIQUE

Data mining: concepts and techniques

Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543

Neural Networks And Learning Machines

Haykin, S. (2010) · Cited by 6,611

Mining of Massive Datasets

Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,307

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints

van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

Özel, T., & Karpat, Y. (2004) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2004.09.007 · Cited by 702

Predicting student performance: an application of data mining methods with an educational web-based system

Bidgoli, B. M., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2004) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2003.1263284 · Cited by 334

Estimating Dataset Size Requirements for Classifying DNA Microarray Data

Mukherjee, S., Tamayo, P., Rogers, S., Rifkin, R. M., Engle, A., Campbell, C., Golub, T. R., & Mesirov, J. P. (2003) · Journal of Computational Biology · doi:10.1089/106652703321825928 · Cited by 279

Data mining approach for predicting student performance

Osmanbegović, E., & Suljić, M. (2012) · Economic Review · doi:10.5281/zenodo.15695548 · Cited by 242

DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE

Suljić, M., & Osmanbegović, E. (2012) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 199

Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review

Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. L. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105287 · Cited by 331

Data-driven personal thermal comfort prediction: A literature review

Feng, Y., Liu, S., Wang, J., Yang, J., Jao, Y., & Wang, N. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112357 · Cited by 123

An artificial neural network−pharmacokinetic model and its interpretation using Shapley additive explanations

Ogami, C., Tsuji, Y., Seki, H., Kawano, H., To, H., Matsumoto, Y., & Hosono, H. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12643 · Cited by 86

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

Bhuiyan, M. A. E., Yang, F., Biswas, N. K., Rahat, S. H., & Neelam, T. J. (2020) · Forecasting · doi:10.3390/forecast2030014 · Cited by 84

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Dumakor-Dupey, N. K., Arya, S., & Jha, A. (2021) · Minerals · doi:10.3390/min11060601 · Cited by 64

Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades

Aust, J., & Pons, D. J. (2022) · Applied Sciences · doi:10.3390/app12042250 · Cited by 61

An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data

Vaughan, L., Zhang, M., Gu, H., Rose, J. B., Naughton, C. C., Medema, G. J., Allan, V., Roiko, A. H., Blackall, L. L., & Zamyadi, A. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.159748 · Cited by 54

The effect of rebalancing techniques on the classification performance in cyberbullying datasets

Khairy, M., Mahmoud, T. M., & El‐Hafeez, T. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09084-w · Cited by 53

Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions

Gogoi, P., & Valan, J. A. (2024) · International Urology and Nephrology · doi:10.1007/s11255-024-04281-5 · Cited by 53

Multi-unit Discrete Hopfield Neural Network for higher order supervised learning through logic mining: Optimal performance design and attribute selection

Rusdi, N. ’., Kasihmuddin, M. S. M., Romli, N. A., Manoharam, G., & Mansor, M. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101554 · Cited by 52

A Two-Phase Machine Learning Approach for Predictive Maintenance of Low Voltage Industrial Motors

Nikfar, M., Bitencourt, J., & Mykoniatis, K. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.210 · Cited by 49

How effective is machine learning in stock market predictions?

Ayyıldız, N., & İskenderoğlu, Ö. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24123 · Cited by 44

A comprehensive review on wood chip moisture content assessment and prediction

Rahman, A., Marufuzzaman, M., Street, J., Wooten, J. R., Gude, V. G., Buchanan, R., & Wang, H. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113843 · Cited by 45

Morphological traits of reef corals predict extinction risk but not conservation status

Raja, N. B., Lauchstedt, A., Pandolfi, J. M., Kim, S. W., Budd, A. F., & Kiessling, W. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13321 · Cited by 36

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy

Garcia‐Vozmediano, A., Maurella, C., Ceballos, L. A., Crescio, E., Meo, R., Martelli, W., Pitti, M., Lombardi, D., Meloni, D., Pasqualini, C., et al. (2024) · Veterinary Research · doi:10.1186/s13567-024-01323-9 · Cited by 35

Imputing environmental impact missing data of the industrial sector for Chinese cities: A machine learning approach

Chen, X., Shuai, C., Zhao, B., Zhang, Y., & Li, K. (2023) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2023.107050 · Cited by 33

Prediction of Marathon Performance using Artificial Intelligence

Lerebourg, L., Saboul, D., Clémençon, M., & Coquart, J. B. (2022) · International Journal of Sports Medicine · doi:10.1055/a-1993-2371 · Cited by 29

Characterisation of data resources forin silicomodelling: benchmark datasets for ADME properties

Przybylak, K. R., Madden, J. C., Covey‐Crump, E. M., Gibson, L., Barber, C., Patel, M. L., & Cronin, M. (2017) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2017.1316449 · Cited by 28

Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies

Li, F., Rana, M. S., & Qurashi, M. A. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00672-4 · Cited by 27

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

Wahab, S., Suleiman, M. F., Shabbir, F., Mahmoudabadi, N. S., Waqas, S., Herl, N., & Ahmad, A. (2024) · Journal of Civil Engineering Frontiers · doi:10.38094/jocef40271 · Cited by 25

Advancing hydrogen storage: Explainable machine learning models for predicting hydrogen uptake in metal-organic frameworks

Alatefi, S., Agwu, O. E., Amar, M. N., & Alkouh, A. B. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107304 · Cited by 24

A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases

Attai, K. F., Amannejad, Y., Pour, M. V., Obot, O. U., & Uzoka, F. (2022) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed7120398 · Cited by 28

Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents

Alatefi, S., Agwu, O. E., Amar, M. N., & Djema, H. (2025) · Fuel · doi:10.1016/j.fuel.2025.135073 · Cited by 23

Applications of artificial intelligence algorithms in artificial lift systems: A critical review

Agwu, O. E., Alatefi, S., Azim, R. R. A., & Alkouh, A. B. (2024) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2024.102613 · Cited by 23

Data-driven prediction of building energy consumption using an adaptive multi-model fusion approach

Lin, P., Zhang, L., & Zuo, J. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109616 · Cited by 22

Feasibility of use limited data to establish a relationship between chemical composition and the enzymatic glucose yield using machine learning

Niu, Y., Tye, Y. Y., Hemashini, T., & Leh, C. P. (2025) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2025.107956 · Cited by 18

Prediction of Uniaxial Compressive Strength and Elastic Modulus of Migmatites by Microstructural Characteristics Using Artificial Neural Networks

Saedi, B., & Mohammadi, S. D. (2021) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-021-02575-z · Cited by 18

Data-Driven, Physics-Based, or Both: Fatigue Prediction of Structural Adhesive Joints by Artificial Intelligence

Fernandes, P. H. E., Silva, G. C., Pitz, D. B., Schnelle, M., Koschek, K., Nagel, C., & Beber, V. C. (2023) · Applied Mechanics · doi:10.3390/applmech4010019 · Cited by 18

Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

Khan, N., Nwafor-Okoli, C., Ekpin, V. I., Attai, K. F., Chukwudi, N., Sabi, H. M., Akwaowo, C., Osuji, J. C., Benavente, L., & Uzoka, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119638 · Cited by 19

Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging

Abbasi, N., Lacson, R. C., Kapoor, N., Licaros, A., Guenette, J. P., Burk, K. S., Hammer, M. M., Desai, S. P., Eappen, S., Saini, S., et al. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.29120 · Cited by 18

Machine learning algorithms for predicting the photoionization cross section of CdS/ZnSe core/shell spherical quantum dots surrounded by dielectric matrices

Cherni, A., Zeiri, N., Yahyaoui, N., BAŞER, P., Said, M., Saadaoui, S., & Murshed, M. N. (2025) · Results in Physics · doi:10.1016/j.rinp.2025.108186 · Cited by 17

SMART choice (knee) tool: a patient‐focused predictive model to predict improvement in health‐related quality of life after total knee arthroplasty

Zhou, Y., Dowsey, M. M., Spelman, T. D., Choong, P., & Schilling, C. G. (2023) · ANZ Journal of Surgery · doi:10.1111/ans.18250 · Cited by 17

DrSim: Similarity Learning for Transcriptional Phenotypic Drug Discovery

Wei, Z., Zhu, S., Chen, X., Zhu, C., Duan, B., & Liu, Q. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.09.006 · Cited by 14

Influence of Sample Matrix on Determination of Histamine in Fish by Surface Enhanced Raman Spectroscopy Coupled with Chemometric Modelling

Vidaček, S., Valinger, D., Mikac, L., Ivanda, M., Kljusurić, J. G., & Janči, T. (2021) · Foods · doi:10.3390/foods10081767 · Cited by 14

Leaf-Counting in Monocot Plants Using Deep Regression Models

Xie, X., Ge, Y., Walia, H., Yang, J., & Yu, H. (2023) · Sensors · doi:10.3390/s23041890 · Cited by 18

Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity

Erman, A., Ferreira, J., Abu-Ashour, W., Guadagno, E., St‐Louis, E., Emil, S. G. S., Cheung, J. S., & Poenaru, D. (2025) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2024.162151 · Cited by 12

The role and choice of molecular descriptors for predicting retention times in HPLC: A comprehensive review

Bandini, E., Kajtazi, A., Szücs, R., & Lynen, F. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118207 · Cited by 12

Remaining useful life prediction with insufficient degradation data based on deep learning approach

Lyu, Y., Jiang, Y., Zhang, Q., & Chen, C. (2021) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2021.4.17 · Cited by 11

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Gazzarrini, E., Cersonsky, R. K., Bercx, M., Adorf, C. S., & Marzari, N. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01248-z · Cited by 11

Defining an Integrated and Computed Methodology Approach for Sentiment and Psychographic Analysis in Tourism Research

Izzo, F., & Picone, Q. (2022) · Journal of Tourism and Services · doi:10.29036/jots.v13i25.393 · Cited by 11

Sustainable Quality in Data Preparation

Pernici, B., Cappiello, C., Bono, C. A., Sancricca, C., Catarci, T., Angelini, M., Filosa, M., Palmonari, M., Paoli, F. D., Bergamaschi, S., et al. (2025) · Journal of Data and Information Quality · doi:10.1145/3769120 · Cited by 10

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Duchanoy, C. A., Calvo, H., & Moreno-Armendáriz, M. A. (2020) · Sensors · doi:10.3390/s20185332 · Cited by 9

Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity

Shankar, P., Johnen, A., & Liwicki, M. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190211 · Cited by 9

Exploiting temporal information in echocardiography for improved image segmentation

Hu, J., Smistad, E., Salte, I. M., Dalen, H., & Løvstakken, L. (2022) · IEEE International Ultrasonics Symposium · doi:10.1109/ius54386.2022.9958670 · Cited by 9

Deep Learning Approach to Generate a Synthetic Cognitive Psychology Behavioral Dataset

Choi, J., Nah, Y., Ko, I., & Han, S. (2021) · IEEE Access · doi:10.1109/access.2021.3120083 · Cited by 9

Data Driven Prognostics of Milling Tool Wear :A Machine Learning Approach

Vijay, S. T., Pillai, V. M., & Kuraichen, B. (2021) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe53109.2021.9751990 · Cited by 8

Dataset Transformation System for Sign Language Recognition Based on Image Classification Network

Choi, S.-G., Park, Y., & Sohn, C.-B. (2022) · Applied Sciences · doi:10.3390/app121910075 · Cited by 8

Improving IRI-2016 global total electron content maps using ELM neural network

Dehvari, M., Karimi, S., Farzaneh, S., & Sharifi, M. A. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.07.022 · Cited by 8

Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers

Bortey, L., Edwards, D. J., Roberts, C., & Rillie, I. (2024) · Buildings · doi:10.3390/buildings14113509 · Cited by 8

A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction

Zhu, S., Zhang, M., Wang, C., Guo, J., Chen, X., & Xie, M. (2024) · Journal of Hydrologic Engineering · doi:10.1061/jhyeff.heeng-6091 · Cited by 8

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Ströbel, R., Mau, M., Puchta, A., & Fleischer, J. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020049 · Cited by 11

Improving Machine Leaming Accuracy using Data Augmentation in Recruitment Recommendation Process

Nurhopipah, A., Ceasar, Y., & Priadana, A. (2021) · East Indonesia Conference on Computer and Information Technology (EIConCIT) · doi:10.1109/eiconcit50028.2021.9431908 · Cited by 7

A comprehensive review on wood chip moisture content assessment and prediction

Rahman, A., Marufuzzaman, M., Street, J., Wooten, J. R., Gude, V. G., Buchanan, R., & Wang, H. (2024) · doi:10.21079/11681/48220 · Cited by 7

Neural network-based surrogate model in postprocessing of topology optimized structures

Persia, J. T., Sung, M. K., Lee, S., & Burns, D. E. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11039-2 · Cited by 6

Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices

Park, K., Lee, S. E., Lee, C., Hwang, H. D., Yoon, D. H., Choi, E. C., & Lee, E. M. (2024) · Annals of Medicine · doi:10.1080/07853890.2024.2405077 · Cited by 6

A machine learning model of virtual water networks over time

Oreggioni, F., García, S., Gomez, M., & Mejía, A. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103819 · Cited by 6

The Effect of Rebalancing Techniques on the Classification Performance in Cyberbullying DatasetsPreprint

Khairy, M., Mahmoud, T. M., & El‐Hafeez, T. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1730456/v1 · Cited by 6

Self-organizing map (SOM) for species distribution modelling of birds species at Kenyir landscape

Asmara, S. M., David, G., Abdullah, M. T., Din, W. I. S. W., Phon, D. N. E., & Abidin, A. F. Z. (2019) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v9i6.pp5235-5243 · Cited by 5

Comparing Performance of Machine Learning Algorithms in a Flood Prediction Model with Real Data Sets

Universiti Sains Malaysia, P., & Hassan, F. H. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/2381.42019 · Cited by 5

Investigating the Impact of Synthetic Data Distribution on the Performance of Regression Models to Overcome Small Dataset Problems

Sutojo, T., Syukur, A., Rustad, S., Shidik, G. F., Santoso, H. A., Purwanto, P., & Muljono, M. (2020) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic50169.2020.9234265 · Cited by 5

Student Academic Performance Prediction on Problem Based Learning Using Support Vector Machine and K-Nearest Neighbor

Assegaf, B. (2017) · Journal of Telematics and Informatics · doi:10.12928/jti.v5i1.22-28 · Cited by 5

Identification of a Specific Biomarker of Acinetobacter baumannii Global Clone 1 by Machine Learning and PCR Related to Metabolic Fitness of ESKAPE Pathogens

Álvarez, V. E., Quiroga, M. P., & Centrón, D. (2023) · mSystems · doi:10.1128/msystems.00734-22 · Cited by 6

A New Pair of Watchful Eyes for Students in Online Courses

Raza, S., Sharma, B. N., & Chaudhary, K. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2021.620080 · Cited by 4

GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification

Krishnan, D., Jerin Mahibha, C., & Thenmozhi, D. (2022) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2022.semeval-1.114 · Cited by 4

Deep Learning Bio–Signal Analysis from a Wearable Device

Skubisz, M., & Jeleń, Ł. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84340-3_28 · Cited by 3

Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study

Hussain, S., Munir, U., & Chaudhry, M. S. (2022) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/2021030517 · Cited by 3

Connected model to optimize performance

Lerebourg, L., & Coquart, J. B. (2023) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2022.1054783 · Cited by 3

Cross-Domain Sentiment Analysis in Portuguese using BERT

Britto, L. F. S., Pessoa, L. G. M., & Agostinho, S. C. C. (2022) · doi:10.5753/eniac.2022.227217 · Cited by 4

Artificial Neural Network (ANN)-Based Determination of Fractional Contributions from Mixed Fluorophores using Fluorescence Lifetime Measurements

Netaev, A., Schierbaum, N., & Seidl, K. (2023) · Journal of Fluorescence · doi:10.1007/s10895-023-03261-9 · Cited by 3

The Use of Data Augmentation as a Technique for Improving Fake News Detection in the Romanian Language

Țucudean, G., & Bucos, M. (2022) · International Symposium on Electronics and Telecommunications (ISETC) · doi:10.1109/isetc56213.2022.10010213 · Cited by 2

Diabetes disease prediction system using HNB classifier based on discretization method

Al-Hameli, B. A., Alsewari, A. A., Basurra, S. S., Bhogal, J., & Ali, M. A. H. (2023) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2021-0037 · Cited by 2

Learning Purified Feature Representations from Task-irrelevant Labels

Li, Y., Wang, C., Li, Y., Zheng, H.-T., & Shen, Y. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892229 · Cited by 2

Machine Learning-based BGP Traffic Prediction

Farasat, T., Rathore, M. A., Khan, A., Kim, J., & Posegga, J. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00262 · Cited by 1

Exploring the Influence of Data Characteristics on Machine Learning Outcomes

Sancricca, C., Castiglione, P., & Cappiello, C. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-95397-2_14 · Cited by 1

Evaluation of Machine Learning Models to Improve Daily Precipitation Estimations From Orbital Remote Sensing and Reanalysis Data

Freitas, E. D. S., Coelho, V., Bertrand, G., Lemos, F. C., & Almeida, C. D. N. (2025) · International Journal of Climatology · doi:10.1002/joc.70036 · Cited by 1

Development of Physics-Informed Neural Networks (PINNs) for the natural ventilation of a greenhouse – Part 1: Real-time prediction of time-dependent airflow

Choi, Y.-B., & Lee, I. B. (2026) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2026.104440 · Cited by 1

Synergy-driven catalysis in biochar-calcite-Ag nanocomposites for rapid 4-nitrophenol reduction

Stuvart, A., Arun, T. R., Raman, N., Karthikeyan, M., & Mahakur, V. K. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.102445 · Cited by 1

Best Practices in Dropout Prediction

Alcolea, J. J., Ortigosa, Á., Carro, R. M., & Blanco, O. J. (2020) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-5074-8.ch015 · Cited by 2

Impact of Radio Map Size on Indoor Localization Accuracy

Sediela, M. S., Gadebe, M. L., & Kogeda, O. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10522-7_36 · Cited by 1

A Push-Pull Train for Safety of Autonomous Vehicle Software

Wadekar, S. A. (2023) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets56262.2023.10041324 · Cited by 1

A Detailed Analysis of Public Industrial Control System Datasets

Mubarak, S., Islam, M. R., Balla, A., & Habaebi, M. H. (2023) · International Journal of Security and Networks · doi:10.1504/ijsn.2023.10058681 · Cited by 3

Prediction of Unconfined Compressive Strength in Stabilized Clay Soil Using Artificial Neural Networks

Indriani, L., Riyadi, S., & Zaki, A. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/202414406002 · Cited by 1

CPI Index Prediction Based on SARIMA-LSTM-CatBoost Model

Liang, J., & Yanru, Z. (2024) · Asia-Pacific Artificial Intelligence and Big Data Forum (AIBDF) · doi:10.1145/3718491.3718665 · Cited by 1

A Comparative Analysis of Public Cyber Security Datasets

Дмитриевич, Е. С., & Zhuravlev, A. P. (2020) · 2018Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO) · doi:10.1109/synchroinfo49631.2020.9166001 · Cited by 1

Model Klasifikasi Berita Palsu Menggunakan Bidirectional LSTM dan Word2vec sebagai Vektorisasi

Pakpahan, J. A., Panjaitan, Y. C., Amalia, J., & Pakpahan, M. B. (2022) · JATISI (Jurnal Teknik Informatika dan Sistem Informasi) · doi:10.35957/jatisi.v9i4.1332 · Cited by 1

Using Regression To Reduce L2 Teachers' Scoring Workload: Predicting Essay Quality From Several Rhetorical Moves

(2019) · i-manager’s Journal on English Language Teaching · doi:10.26634/jelt.9.3.15653

Towards All-around Knowledge Transferring: Learning From Task-irrelevant LabelsPreprint

Li, Y., Liu, R., Zhang, Z., Ding, N., Shen, Y., Tao, L., & Zheng, H.-T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08470

An apta-aggregation based machine learning assay for rapid quantification of lysozyme through texture parameters

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การพัฒนาโมเดลการวิเคราะห์อารมณ์ครูในชั้นเรียนผ่านการรู้จำคำพูดโดยใช้การเรียนรู้เชิงลึก

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Development of a Framework for Generative AI-Based Safety Management via Prompt Engineering

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A Dual-Reweighting Defense Strategy Against Data Poisoning Attacks in Medical Image Classification Models

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Impact of Training Data Size on Model Accuracy: Mediating Role of Algorithm Complexity

Zaheer, D. (2026) · J-STAR: Journal of Social & Technological Advanced Research

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