Publications

@ARTICLE{10843698, author={Balapan, Azat and Yeralkhan, Rauan and Aryslanov, Alikhan and Kalimuldina, Gulnur and Yeshmukhametov, Azamat}, journal={IEEE Access}, title={A Novel Pattern Recognition Method for Self-Powered TENG Sensor Embedded to the Robotic Hand}, year={2025}, volume={13}, number={}, pages={14101-14112}, keywords={Robot sensing systems;Hands;Thumb;Shape;Sensors;Data collection;Tactile sensors;Data visualization;Convolutional neural networks;Carbon;Dataset collection;machine learning;robot hand design;signal processing;TENG sensor}, doi={10.1109/ACCESS.2025.3530465}}

@ARTICLE{Neafie2025152, author = {Neafie, Jessica and Albakassova, Maira and Bayramov, Emil}, title = {Assessing socio-economic and natural vulnerability to oil spills: a case study of Azerbaijan’s Caspian shoreline}, year = {2025}, journal = {International Journal of Water Resources Development}, volume = {41}, number = {1}, pages = {152 – 175}, doi = {10.1080/07900627.2024.2434595}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212235791&doi=10.1080%2f07900627.2024.2434595&partnerID=40&md5=c611e7c16efb441548a4fd839ff05a68}, affiliations = {Political Science and International Relations, Nazarbayev University, Astana, Kazakhstan; Department of Economics and Business, Central European University, Vienna, Austria; School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan; Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Astana, Kazakhstan}, correspondence_address = {J. Neafie; Political Science and International Relations, Nazarbayev University, Astana, Kazakhstan; email: Jessica.neafie@nu.edu.kz}, publisher = {Routledge}, issn = {07900627}, coden = {IJWDD}, language = {English}, abbrev_source_title = {Int. J. Water Resour. Dev.}, type = {Article}, publication_stage = {Final}, source = {Scopus}, note = {Cited by: 1; All Open Access, Hybrid Gold Open Access} }

@ARTICLE{10.3389/feart.2025.1489969, AUTHOR={Shah, Zahid and Bayramov, Emil and Neafie, Jessica and Seltmann, Reimar and Rahnama, Zohreh }, TITLE={Determination of geospatial criteria and prediction of potential areas for porphyry copper deposits in Kazakhstan}, JOURNAL={Frontiers in Earth Science}, VOLUME={Volume 13 - 2025}, YEAR={2025}, URL={https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1489969}, DOI={10.3389/feart.2025.1489969}, ISSN={2296-6463},}

@ARTICLE{Karabay2025, author = {Karabay, Aknur and Varol, Huseyin Atakan and Chan, Mei Yen}, title = {Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene}, year = {2025}, journal = {Scientific Reports}, volume = {15}, number = {1}, doi = {10.1038/s41598-025-95770-9}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003136951&doi=10.1038%2fs41598-025-95770-9&partnerID=40&md5=d72d8840dd6a35529b82f64cbbada1a8}, affiliations = {Institute of Smart Systems and Artificial Intelligence, Nazarbaeyv University, Astana, 010000, Kazakhstan; Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan}, correspondence_address = {M.Y. Chan; Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan; email: yen.chan@nu.edu.kz}, publisher = {Nature Research}, issn = {20452322}, pmid = {40269053}, language = {English}, abbrev_source_title = {Sci. Rep.}, type = {Article}, publication_stage = {Final}, source = {Scopus}, note = {Cited by: 0} }

@ARTICLE{Adilmetova2025729, author = {Adilmetova, Gulnoza and Nassyrov, Ruslan and Meyerbekova, Aizhan and Karabay, Aknur and Varol, Huseyin Atakan and Chan, Mei-Yen}, title = {Evaluating ChatGPT's Multilingual Performance in Clinical Nutrition Advice Using Synthetic Medical Text: Insights from Central Asia}, year = {2025}, journal = {Journal of Nutrition}, volume = {155}, number = {3}, pages = {729 – 735}, doi = {10.1016/j.tjnut.2024.12.018}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215563512&doi=10.1016%2fj.tjnut.2024.12.018&partnerID=40&md5=7b14244f36c7ba33eb47360dddd3a026}, affiliations = {Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Medicine, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan}, correspondence_address = {M.-Y. Chan; Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, Kazakhstan; email: yen.chan@nu.edu.kz}, publisher = {Elsevier B.V.}, issn = {00223166}, coden = {JONUA}, pmid = {39732434}, language = {English}, abbrev_source_title = {J. Nutr.}, type = {Article}, publication_stage = {Final}, source = {Scopus}, note = {Cited by: 1; All Open Access, Hybrid Gold Open Access} }

@ARTICLE{10976640, author={Keutayeva, Aigerim and Jesse Nwachukwu, China and Alaran, Muslim and Otarbay, Zhenis and Abibullaev, Berdakh}, journal={IEEE Access}, title={Neurotechnology in Gaming: A Systematic Review of Visual Evoked Potential-Based Brain-Computer Interfaces}, year={2025}, volume={13}, number={}, pages={74944-74966}, keywords={Visualization;Electroencephalography;Systematic literature review;Signal processing;Reliability;Video games;Brain-computer interfaces;Fatigue;Internet;Training;Brain-computer interfaces;visual evoked potentials;signal processing;neurotechnology;gaming accessibility;electroencephalography;virtual reality;augmented reality;machine learning;neurorehabilitation}, doi={10.1109/ACCESS.2025.3564328}}

@article{https://doi.org/10.1002/admt.202401282, author = {Issabek, Moldir and Oralkhan, Sabyrzhan and Anash, Adeliya and Nurbergenova, Nuriya and Balapan, Azat and Yeshmukhametov, Azamat and Rakhmanov, Yeltay and Kalimuldina, Gulnur}, title = {AI-Enhanced Gait Analysis Insole with Self-Powered Triboelectric Sensors for Flatfoot Condition Detection}, journal = {Advanced Materials Technologies}, volume = {10}, number = {6}, pages = {2401282}, keywords = {flatfoot detection, gait analysis, machine learning, smart sensing insole, triboelectric nanogenerator (TENG)}, doi = {https://doi.org/10.1002/admt.202401282}, url = {https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/admt.202401282}, eprint = {https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/admt.202401282}, abstract = {Abstract Flatfoot is an orthopedic malformation where the inner foot arch flattens during motion, potentially leading to chronic musculoskeletal issues. Monitoring foot conditions is essential, as existing screening methods often need more objectivity and cost-effectiveness. This research develops an insole-based screening device using self-powered triboelectric nanogenerators (TENGs) as tactile sensors and Arduino hardware setup. TENG converts mechanical energy into electrical energy, offering simple fabrication, cost-effectiveness, longevity, and high-output power benefits. The device records the electrical output generated by foot movements via Arduino and sends data via Bluetooth to the processing tool. Machine learning algorithms employing the Random Forest Classifier process the data to detect flatfoot conditions accurately after training sessions. For data collection, 100 participants are asked to march and walk for the same duration and distance, ensuring consistent output. Analysis reveals that the insoles' middle sensors produce higher electricity levels in individuals with flatfoot, showing a uniform pressure distribution across the foot and contact in the arch area. In contrast, individuals with normal feet exert more pressure on the toe and heel foot areas. The system achieves an overall accuracy of 82\%, demonstrating the insole's potential as a commercial flatfoot detection device.}, year = {2025} }

@ARTICLE{10751786, author={Chibar, Rustam and Kostyukova, Valeriya and Khajikhanov, Soibkhon and Kenzhebek, Daryn and Zhakatayev, Altay and Orazbayev, Bakhtiyar and Kappassov, Zhanat}, journal={IEEE Sensors Journal}, title={Honeycomb-Inspired Metamaterial for Tactile Sensors With Variable Stiffness}, year={2025}, volume={25}, number={1}, pages={577-586}, keywords={Sensors;Robot sensing systems;Collision avoidance;Robots;Metamaterials;Tactile sensors;Dynamics;Honeycomb structures;Force;Magnetic materials;Collision detection;compliance and impedance control;force and tactile sensing;honeycombs;nonlinear stiffness;physical interactions;potential energy storage;tactile sensors;variable stiffness}, doi={10.1109/JSEN.2024.3492498}}