Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images

Authors: Kobara, Yawo M., Akpan, Ikpe Justice^{Aff2, IDs1027802501538y_cor1}, Nam, Alima Damipe, AlMukthar, Firas H., Peter, Mbuotidem

Source: Journal of Imaging Informatics in Medicine. :1-21

Data Science for Wind Energy

Authors: Ding, Yu

Subject Terms: Bayesian Additive Regression Trees, SVM Model, Power Curve Model, Wind Speed, GEV Distribution, PACF Plot, Wind Turbine, Binning Method, Local Wind Field, ARMA Model, Wind Field Analysis, Ahead Forecast, Wind Speed Forecast, Power Curve, Wind Farm, Data Science Methods, Test Turbine, Importance Sampling Density, Be, GMRF Model, Computer Simulators, CMC, Power Coefficient, Importance Sampling Method, Posterior Predictive Distribution, Data mining, Alternative and renewable energy sources and technology, Artificial intelligence, Probability and statistics

File Description: application/pdf

Access URL: https://library.oapen.org/handle/20.500.12657/101466

Data-Driven Sustainability: Analyzing Rural Road Infrastructure in Marechal Cândido Rondon, Paraná, Brazil

Authors: Cima, Elizabeth Giron^Aff4, da Rocha-Junior, Weimar Freire^Aff4, Rocha, Alberto Alves^Aff5, Uribe-Opazo, Miguel Angel^Aff6, Lotte-Júnior, Pedro Norberto^Aff4

Contributors: Toni, Bourama, Series Editor^Aff1, Awe, O. Olawale, editor^Aff2, A. Vance, Eric, editor^Aff3

Source: Practical Statistical Learning and Data Science Methods : Case Studies from LISA 2020 Global Network, USA. :615-626

Constraining the Algorithmic Landscape: A Constructive Approach to Witness Incompressibility and Uniform Verification Limits

Authors: Murphy, Andrew J

Subject Terms: Logic, Science, Entropy, Information Theory, Polynomial Time, Kolmogorov, Mathematical analysis, Witness Generation, PNP, Polynomial, NP, Data science, Mathematical model, Decision Theory, Data Science/methods, Information, Mathematical Proofs, FOS: Mathematics, Mathematical Computing, Probability, P vs NP, Mathematical method, Computational science, Data Science, Pure mathematics, Mathematical Concepts, Models, Theoretical, Applied mathematics, Probability Theory, NP-completeness, Mathematics/methods, Mathematical logic, Kolmogorov Complexity, Cryptography, Data Science/classification, Mathematics/classification, Information Technology, Mathematics, Algorithms, Formal Proofs

WiNoDa Knowledge Lab: Ein Datenkompetenzzentrum für Naturwissenschaftliche Sammlungen und Objektzentrierte Daten

Authors: Miklashevsky, Alex, Heinle, Karolin, Albrecht, Cora, Loginova, Katja, Szigli, Kinga, Petersen, Mareike

Subject Terms: WiNoDa, Museums, Data Science, Data Science/trends, Biobehavioral Sciences/statistics & numerical data, Biodiversity, Competency-Based Education, Data science, Data Science/methods, Data Science/standards, Professional Competence/standards, Data Science/statistics & numerical data, Science/statistics & numerical data, Data Science/education

Hybrid Data Science Approaches to Predict the Academic Performance of Students

Authors: Malik, Saleem^{Aff39, Aff39, Aff40}

Contributors: Angrisani, Leopoldo, Series Editor^Aff1, Arteaga, Marco, Series Editor^Aff2, Chakraborty, Samarjit, Series Editor^Aff3, Chen, Jiming, Series Editor^Aff4, Chen, Shanben, Series Editor^Aff5, Chen, Tan Kay, Series Editor^Aff6, Dillmann, Rüdiger, Series Editor^Aff7, Duan, Haibin, Series Editor^Aff8, Ferrari, Gianluigi, Series Editor^Aff9, Ferre, Manuel, Series Editor^Aff10, Jabbari, Faryar, Series Editor^Aff11, Jia, Limin, Series Editor^Aff12, Kacprzyk, Janusz, Series Editor^Aff13, Khamis, Alaa, Series Editor^Aff14, Kroeger, Torsten, Series Editor^Aff15, Li, Yong, Series Editor^Aff16, Liang, Qilian, Series Editor^Aff17, Martín, Ferran, Series Editor^Aff18, Ming, Tan Cher, Series Editor^Aff19, Minker, Wolfgang, Series Editor^Aff20, Misra, Pradeep, Series Editor^Aff21, Mukhopadhyay, Subhas, Series Editor^Aff22, Ning, Cun-Zheng, Series Editor^Aff23, Nishida, Toyoaki, Series Editor^Aff24, Oneto, Luca, Series Editor^Aff25, Panigrahi, Bijaya Ketan, Series Editor^Aff26, Pascucci, Federica, Series Editor^Aff27, Qin, Yong, Series Editor^Aff28, Seng, Gan Woon, Series Editor^Aff29, Speidel, Joachim, Series Editor^Aff30, Veiga, Germano, Series Editor^Aff31, Wu, Haitao, Series Editor^Aff32, Zamboni, Walter, Series Editor^Aff33, Zhang, Junjie James, Series Editor^Aff34, Tan, Kay Chen, Series Editor^Aff35, Shetty, N. R., editor^Aff36, Prasad, N. H., editor^Aff37, Nagaraj, H. C., editor^Aff38

Source: Advances in Communication and Applications : Proceedings of ERCICA 2023, Volume 2. 1105:521-539

Visualizing data to explore the affective and human dimensions of education research

Authors: Selkrig, Mark, McDonough, Sharon, Keamy, Ron, Brandenburg, Robyn

Subject Terms: 39 Education, Arts-based methods, Data visualization, Data-science methods, Education research

Relation: International Journal of Research and Method in Education Vol. 48, no. 5 (2025), p. 494-507; http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/210450; vital:20907; https://doi.org/10.1080/1743727X.2025.2482912

Availability: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/210450
https://doi.org/10.1080/1743727X.2025.2482912

Quantum Cheese Or A Universal Substrate for Bounded Symbolic Logic via Spiral Compression And a Computational Model for the Turing Halting Problem

Authors: Daniels, Justin

Subject Terms: Computation, Turing, Halting Problem, Coding, Compression, Data Compression, Data Science, Data Science/statistics & numerical data, Data Science/education, Data Science/classification, Data Science/instrumentation, Data Science/methods, Number Theory, Recursive Geometry, Logic, Mathematical logic, Epsitemic

Relation: https://zenodo.org/records/15983072; oai:zenodo.org:15983072; https://doi.org/10.5281/zenodo.15983072

Availability: https://doi.org/10.5281/zenodo.15983072
https://zenodo.org/records/15983072

Analysis of regional features of the tibial plateau fractures in the Rostov region

Authors: George S. Golube, Stanislav G. Andrienko, Roman A. Khadi

Source: Travmatologiâ i Ortopediâ Rossii, Vol 29, Iss 2, Pp 74-87 (2023)

Subject Terms: post-traumatic knee osteoarthritis, Orthopedic surgery, 03 medical and health sciences, 0302 clinical medicine, treatment complications, prevalence, 8. Economic growth, tibial plateau fractures, data science methods, RD701-811, 3. Good health

Access URL: https://doaj.org/article/d9db32f84fde4ee0bf70b3490a88d0a0

Constraining the Algorithmic Landscape: A Constructive Approach to Witness Incompressibility and Uniform Verification Limits ; Contraindre le paysage algorithmique : une approche constructive de l'incompressibilité des témoins et des limites de la vérification uniforme ; Begrenzung des algorithmischen Raums: Ein konstruktiver Ansatz zur Zeugeninkompressibilität und zu einheitlichen Verifikationsgrenzen

Authors: Murphy, Andrew J, orcid:0009-0009-6283-

Subject Terms: Computational science, Mathematical logic, Mathematics, Mathematics/methods, Mathematical analysis, Pure mathematics, Applied mathematics, Mathematical method, Mathematical model, Models, Theoretical, Cryptography, Mathematical Computing, Mathematical Concepts, Science, Data Science, Information, Information Technology, Information Theory, Algorithms, Mathematics/classification, Entropy, Probability, Probability Theory, Logic, Data Science/classification, Data Science/methods, Decision Theory, Kolmogorov, Kolmogorov Complexity

Relation: https://zenodo.org/communities/mathematics/; https://zenodo.org/records/15538251; oai:zenodo.org:15538251; https://doi.org/10.5281/zenodo.15538251

Availability: https://doi.org/10.5281/zenodo.15538251
https://zenodo.org/records/15538251

Constraining the Algorithmic Landscape: A Constructive Approach to Witness Incompressibility and Uniform Verification Limits ; Une preuve constructive minimale que 𝑃 ≠ 𝑁 𝑃 P  =NP via l'incompressibilité des témoins ; Begrenzung der algorithmischen Landschaft: Ein konstruktiver Zugang zur Zeugen-Unkomprimierbarkeit und zu uniformen Verifikationsgrenzen

Authors: Murphy, Andrew J, orcid:0009-0009-6283-

Subject Terms: Computational science, Mathematical logic, Mathematics, Mathematics/methods, Mathematical analysis, Pure mathematics, Applied mathematics, Mathematical method, Mathematical model, Models, Theoretical, Cryptography, Mathematical Computing, Mathematical Concepts, Science, Data Science, Information, Information Technology, Information Theory, Algorithms, Mathematics/classification, Entropy, Probability, Probability Theory, Logic, Data Science/classification, Data Science/methods, Decision Theory, Kolmogorov, Kolmogorov Complexity

Relation: https://zenodo.org/communities/mathematics/; https://zenodo.org/records/15536270; oai:zenodo.org:15536270; https://doi.org/10.5281/zenodo.15536270

Availability: https://doi.org/10.5281/zenodo.15536270
https://zenodo.org/records/15536270

High Dimensional Rendering and Evaluation Metric Through Tissot's Indicatrix

Authors: Li, Mark

Subject Terms: Geometry, High Dimension, Data Science/methods, Linear algebra

Relation: https://zenodo.org/records/15454501; oai:zenodo.org:15454501; https://doi.org/10.5281/zenodo.15454501

Availability: https://doi.org/10.5281/zenodo.15454501
https://zenodo.org/records/15454501

Teaching of General Psychology: Problem Solving

Authors: Gibson, David^Aff5, Ifenthaler, Dirk^{Aff5, Aff6}, Greiff, Samuel^Aff7, Zumbach, Joerg, Section editor^Aff8, Bernstein, Douglas A., Section editor^Aff9, Narciss, Susanne, Section editor^Aff10, Marsico, Giuseppina, Section editor^{Aff11, Aff12}

Contributors: Zumbach, Joerg, editor^Aff1, Bernstein, Douglas A., editor^Aff2, Narciss, Susanne, editor^Aff3, Marsico, Giuseppina, editor^Aff4

Source: International Handbook of Psychology Learning and Teaching. :131-149

A Comparative Analysis to Measure Scholastic Success of Students Using Data Science Methods

Authors: Malik, Saleem^Aff40, Jothimani, K.^Aff40, Ujwal, U. J.^Aff40

Contributors: Angrisani, Leopoldo, Series Editor^Aff1, Arteaga, Marco, Series Editor^Aff2, Panigrahi, Bijaya Ketan, Series Editor^Aff3, Chakraborty, Samarjit, Series Editor^Aff4, Chen, Jiming, Series Editor^Aff5, Chen, Shanben, Series Editor^Aff6, Chen, Tan Kay, Series Editor^Aff7, Dillmann, Rüdiger, Series Editor^Aff8, Duan, Haibin, Series Editor^Aff9, Ferrari, Gianluigi, Series Editor^Aff10, Ferre, Manuel, Series Editor^Aff11, Hirche, Sandra, Series Editor^Aff12, Jabbari, Faryar, Series Editor^Aff13, Jia, Limin, Series Editor^Aff14, Kacprzyk, Janusz, Series Editor^Aff15, Khamis, Alaa, Series Editor^Aff16, Kroeger, Torsten, Series Editor^Aff17, Li, Yong, Series Editor^Aff18, Liang, Qilian, Series Editor^Aff19, Martín, Ferran, Series Editor^Aff20, Ming, Tan Cher, Series Editor^Aff21, Minker, Wolfgang, Series Editor^Aff22, Misra, Pradeep, Series Editor^Aff23, Möller, Sebastian, Series Editor^Aff24, Mukhopadhyay, Subhas, Series Editor^Aff25, Ning, Cun-Zheng, Series Editor^Aff26, Nishida, Toyoaki, Series Editor^Aff27, Oneto, Luca, Series Editor^Aff28, Pascucci, Federica, Series Editor^Aff29, Qin, Yong, Series Editor^Aff30, Seng, Gan Woon, Series Editor^Aff31, Speidel, Joachim, Series Editor^Aff32, Veiga, Germano, Series Editor^Aff33, Wu, Haitao, Series Editor^Aff34, Zamboni, Walter, Series Editor^Aff35, Zhang, Junjie James, Series Editor^Aff36, Shetty, N. R., editor^Aff37, Patnaik, L. M., editor^Aff38, Prasad, N. H., editor^Aff39

Source: Emerging Research in Computing, Information, Communication and Applications : Proceedings of ERCICA 2022. 928:27-41

Data Science Diabetes Prediction

Authors: Ahamed Musthafa Careem, orcid:0009-0002-4944-

Subject Terms: Data Science, Data Science/statistics & numerical data, Data Science/methods, Diabetes, Health/trends

Relation: https://zenodo.org/records/14543681; oai:zenodo.org:14543681; https://doi.org/10.5281/zenodo.14543681

Availability: https://doi.org/10.5281/zenodo.14543681
https://zenodo.org/records/14543681

Making teacher educators’ experiences visible: seeing inside the hopeful and ambiguous dimensions of practice

Authors: McDonough, Sharon, Keamy, Ron, Brandenburg, Robyn, Selkrig, Mark

Subject Terms: 3901 Curriculum and pedagogy, 3903 Education systems, 3904 Specialist studies in education, Arts-based methods, Data science methods, Practice architectures, Teacher educator work

Relation: Asia-Pacific Journal of Teacher Education Vol. 52, no. 4 (2024), p. 477-491; http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/202756; vital:19739; https://doi.org/10.1080/1359866X.2024.2381255

Availability: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/202756
https://doi.org/10.1080/1359866X.2024.2381255

ARIEL: Advanced Reinforcement Incentives & Emotions based Learning

Authors: Jason Langhorne, Mesiah Bishop

Subject Terms: Machine Learning/ethics, Artificial intelligence, Robotics/instrumentation, Artificial Intelligence/legislation & jurisprudence, Artificial Intelligence/statistics & numerical data, Swarm robotics, Quantum Dots/classification, Data Science/economics, Artificial Intelligence/standards, Robotics/economics, Probiotics/history, Unsupervised Machine Learning/classification, Data science, Machine Learning, Robotics/trends, Deep Learning/supply & distribution, Supervised Machine Learning/economics, Data Science/methods, Robotics/organization & administration, Deep Learning/economics, Machine Learning/standards, Robotics/methods, Artificial Intelligence/ethics, Supervised Machine Learning/ethics, Machine Learning/supply & distribution, Robotics/legislation & jurisprudence, Materials Science/statistics & numerical data, Robotics, Deep Learning/standards, Cognitive Science/statistics & numerical data, Robotics/history, Supervised Machine Learning/classification, Machine Learning/trends, Unsupervised Machine Learning/ethics, Robotics/classification, Machine Learning/history, Artificial Intelligence/classification, Data Science/classification, Supervised Machine Learning, Robotics/standards, Data Science/instrumentation, Deep Learning/statistics & numerical data, Artificial Intelligence/economics, Supervised Machine Learning/trends, Unsupervised Machine Learning/history, Data Science/trends, Soft robotics, Data Science/legislation & jurisprudence, Deep Learning/legislation & jurisprudence, Data Science/organization & administration, Artificial Intelligence/history, Military Science/statistics & numerical data, Deep Learning, Artificial Intelligence, Supervised Machine Learning/standards, Robotics/ethics, Machine learning, Machine Learning/classification, Environmental Science/statistics & numerical data, Deep Learning/history, Robotics/education, Quantum Theory/history, Artificial Intelligence/trends, Science/statistics & numerical data, Deep Learning/classification, Machine Learning/legislation & jurisprudence, Data Science/education, Robotics/statistics & numerical data, Probiotics, Data Science, Data Science/history, Quantum computers, Robotic surgery, Deep learning, Artificial Intelligence/supply & distribution, Machine Learning/economics, Deep Learning/ethics, Information Science/statistics & numerical data, Data Science/ethics, Data Science/standards, Quantum Theory, Data Science/statistics & numerical data, Neural Networks, Computer, Unsupervised Machine Learning, Deep Learning/trends

Data Science for Wind Energy

Authors: Ding, Yu

Subject Terms: Bayesian Additive Regression Trees, SVM Model, data mining, Power Curve Model, data analytics, Wind Speed, renewable energy, GEV Distribution, wind turbines, PACF Plot, machine learning, Wind Turbine, bayesian methods, Binning Method, data science methods, Local Wind Field, wind energy applications, ARMA Model, turbine reliability assessment, Wind Field Analysis, near-ground wind field analysis, Ahead Forecast, Wind Speed Forecast, Power Curve, Wind Farm, Test Turbine, Importance Sampling Density, Be, GMRF Model, Artificial intelligence, Databases, Environmental science, engineering and technology, Alternative and renewable energy sources and technology, Probability and statistics

File Description: image/jpeg

Access URL: https://directory.doabooks.org/handle/20.500.12854/133841
https://library.oapen.org/handle/20.500.12657/87420
https://library.oapen.org/bitstream/20.500.12657/87420/1/9780429956515.pdf

Variability in the analysis of a single neuroimaging dataset by many teams.

Authors: Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J.A., Adcock, R.A., Avesani, P., Baczkowski, B.M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., Benoit, R.G., Berkers, RMWJ, Bhanji, J.P., Biswal, B.B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn, K.L., Bowring, A., Braem, S., Brooks, H.R., Brudner, E.G., Calderon, C.B., Camilleri, J.A., Castrellon, J.J., Cecchetti, L., Cieslik, E.C., Cole, Z.J., Collignon, O., Cox, R.W., Cunningham, W.A., Czoschke, S., Dadi, K., Davis, C.P., Luca, A., Delgado, M.R., Demetriou, L., Dennison, J.B., Di, X., Dickie, E.W., Dobryakova, E., Donnat, C.L., Dukart, J., Duncan, N.W., Durnez, J., Eed, A., Eickhoff, S.B., Erhart, A., Fontanesi, L., Fricke, G.M., Fu, S., Galván, A., Gau, R., Genon, S., Glatard, T., Glerean, E., Goeman, J.J., Golowin, SAE, González-García, C., Gorgolewski, K.J., Grady, C.L., Green, M.A., Guassi Moreira, J.F., Guest, O., Hakimi, S., Hamilton, J.P., Hancock, R., Handjaras, G., Harry, B.B., Hawco, C., Herholz, P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C.P., Huettel, S.A., Hughes, M.E., Iacovella, V., Iordan, A.D., Isager, P.M., Isik, A.I., Jahn, A., Johnson, M.R., Johnstone, T., Joseph, MJE, Juliano, A.C., Kable, J.W., Kassinopoulos, M., Koba, C., Kong, X.Z., Koscik, T.R., Kucukboyaci, N.E., Kuhl, B.A., Kupek, S., Laird, A.R., Lamm, C., Langner, R., Lauharatanahirun, N., Lee, H., Lee, S., Leemans, A., Leo, A., Lesage, E., Li, F., Li, MYC, Lim, P.C., Lintz, E.N., Liphardt, S.W., Losecaat Vermeer, A.B., Love, B.C., Mack, M.L., Malpica, N., Marins, T., Maumet, C., McDonald, K., McGuire, J.T., Melero, H., Méndez Leal, A.S., Meyer, B., Meyer, K.N., Mihai, G., Mitsis, G.D., Moll, J., Nielson, D.M., Nilsonne, G., Notter, M.P., Olivetti, E., Onicas, A.I., Papale, P., Patil, K.R., Peelle, J.E., Pérez, A., Pischedda, D., Poline, J.B., Prystauka, Y., Ray, S., Reuter-Lorenz, P.A., Reynolds, R.C., Ricciardi, E., Rieck, J.R., Rodriguez-Thompson, A.M., Romyn, A., Salo, T., Samanez-Larkin, G.R., Sanz-Morales, E., Schlichting, M.L., Schultz, D.H., Shen, Q., Sheridan, M.A., Silvers, J.A., Skagerlund, K., Smith, A., Smith, D.V., Sokol-Hessner, P., Steinkamp, S.R., Tashjian, S.M., Thirion, B., Thorp, J.N., Tinghög, G., Tisdall, L., Tompson, S.H., Toro-Serey, C., Torre Tresols, J.J., Tozzi, L., Truong, V., Turella, L., van 't Veer, A.E., Verguts, T., Vettel, J.M., Vijayarajah, S., Vo, K., Wall, M.B., Weeda, W.D., Weis, S., White, D.J., Wisniewski, D., Xifra-Porxas, A., Yearling, E.A., Yoon, S., Yuan, R., Yuen, KSL, Zhang, L., Zhang, X., Zosky, J.E., Nichols, T.E., Poldrack, R.A., Schonberg, T.

Subject Terms: Female, Humans, Male, Brain/diagnostic imaging, Brain/physiology, Data Analysis, Data Science/methods, Data Science/standards, Datasets as Topic/statistics & numerical data, Functional Neuroimaging, Logistic Models, Magnetic Resonance Imaging, Meta-Analysis as Topic, Models, Neurological, Reproducibility of Results, Research Personnel/organization & administration, Research Personnel/standards, Software

Relation: Nature; https://iris.unil.ch/handle/iris/219460; serval:BIB_7E653B5D475C; 000535225300001

Availability: https://iris.unil.ch/handle/iris/219460
https://doi.org/10.1038/s41586-020-2314-9

Information Retrieval and Recommender Systems

Authors: Bellogín, Alejandro^Aff4, Said, Alan^Aff5

Contributors: Kacprzyk, Janusz, Series Editor^Aff1, Said, Alan, editor^Aff2, Torra, Vicenç, editor^Aff3

Source: Data Science in Practice. 46:79-96