Littérature scientifique sur le sujet « PREDICTION DATASET »
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Articles de revues sur le sujet "PREDICTION DATASET"
Burmakova, Anastasiya, et Diana Kalibatienė. « Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset : A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment ». Applied Sciences 12, no 16 (18 août 2022) : 8252. http://dx.doi.org/10.3390/app12168252.
Texte intégralAbdullahi, Dauda Sani, Dr Muhammad Sirajo Aliyu et Usman Musa Abdullahi. « Comparative analysis of resampling algorithms in the prediction of stroke diseases ». UMYU Scientifica 2, no 1 (30 mars 2023) : 88–94. http://dx.doi.org/10.56919/usci.2123.011.
Texte intégralGangil, Tarun, Krishna Sharan, B. Dinesh Rao, Krishnamoorthy Palanisamy, Biswaroop Chakrabarti et Rajagopal Kadavigere. « Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning ». PLOS ONE 17, no 12 (15 décembre 2022) : e0277168. http://dx.doi.org/10.1371/journal.pone.0277168.
Texte intégralRau, Cheng-Shyuan, Shao-Chun Wu, Jung-Fang Chuang, Chun-Ying Huang, Hang-Tsung Liu, Peng-Chen Chien et Ching-Hua Hsieh. « Machine Learning Models of Survival Prediction in Trauma Patients ». Journal of Clinical Medicine 8, no 6 (5 juin 2019) : 799. http://dx.doi.org/10.3390/jcm8060799.
Texte intégralSinaga, Benyamin Langgu, Sabrina Ahmad, Zuraida Abal Abas et Intan Ermahani A. Jalil. « A recommendation system of training data selection method for cross-project defect prediction ». Indonesian Journal of Electrical Engineering and Computer Science 27, no 2 (1 août 2022) : 990. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp990-1006.
Texte intégralMorgan, Maria, Carla Blank et Raed Seetan. « Plant disease prediction using classification algorithms ». IAES International Journal of Artificial Intelligence (IJ-AI) 10, no 1 (1 mars 2021) : 257. http://dx.doi.org/10.11591/ijai.v10.i1.pp257-264.
Texte intégralNunez, John-Jose, Teyden T. Nguyen, Yihan Zhou, Bo Cao, Raymond T. Ng, Jun Chen, Benicio N. Frey et al. « Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1 ». PLOS ONE 16, no 6 (28 juin 2021) : e0253023. http://dx.doi.org/10.1371/journal.pone.0253023.
Texte intégralAhamed, B. Shamreen, Meenakshi S. Arya et Auxilia Osvin V. Nancy. « Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation ». Advances in Human-Computer Interaction 2022 (19 septembre 2022) : 1–14. http://dx.doi.org/10.1155/2022/9220560.
Texte intégralPartin, Alexander, Thomas S. Brettin, Yitan Zhu, Jamie Overbeek, Oleksandr Narykov, Priyanka Vasanthakumari, Austin Clyde et al. « Abstract 5380 : Systematic evaluation and comparison of drug response prediction models : a case study of prediction generalization across cell lines datasets ». Cancer Research 83, no 7_Supplement (4 avril 2023) : 5380. http://dx.doi.org/10.1158/1538-7445.am2023-5380.
Texte intégralPreethi, B. Meena, R. Gowtham, S. Aishvarya, S. Karthick et D. G. Sabareesh. « Rainfall Prediction using Machine Learning and Deep Learning Algorithms ». International Journal of Recent Technology and Engineering (IJRTE) 10, no 4 (30 novembre 2021) : 251–54. http://dx.doi.org/10.35940/ijrte.d6611.1110421.
Texte intégralThèses sur le sujet "PREDICTION DATASET"
Klus, Petr 1985. « "The Clever machine"- a computational tool for dataset exploration and prediction ». Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/482051.
Texte intégralEl propósito de mis estudios doctorales era desarrollar un algoritmo para el análisis a gran escala de conjuntos de datos de proteínas. Esta tesis describe la metodología, el trabajo técnico desarrollado y los casos biológicos envueltos en la creación del algoritmo principal –el cleverMachine (CM) y sus extensiones multiCleverMachine (mCM) y cleverGO. El CM y mCM permiten la caracterización y clasificación de grupos de proteínas basados en características físico-químicas, junto con la abundancia de proteínas y la anotación de ontología de genes, para así elaborar una exploración de datos correcta. Mi método está compuesto por científicos tanto computacionales como experimentales con una interfaz amplia, fácil de usar para un monitoreo y clasificación de secuencia de proteínas de alto rendimiento.
Clayberg, Lauren (Lauren W. ). « Web element role prediction from visual information using a novel dataset ». Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/132734.
Texte intégralCataloged from the official PDF of thesis.
Includes bibliographical references (pages 89-90).
Machine learning has enhanced many existing tech industries, including end-to-end test automation for web applications. One of the many goals that mabl and other companies have in this new tech initiative is to automatically gain insight into how web applications work. The task of web element role prediction is vital for the advancement of this newly emerging product category. I applied supervised visual machine learning techniques to the task. In addition, I created a novel dataset and present detailed attribute distribution and bias information. The dataset is used to provide updated baselines for performance using current day web applications, and a novel metric is provided to better quantify the performance of these models. The top performing model achieves an F1-score of 0.45 on ten web element classes. Additional findings include color distributions for different web element roles, and how some color spaces are more intuitive to humans than others.
by Lauren Clayberg.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Oppon, Ekow CruickShank. « Synergistic use of promoter prediction algorithms : a choice of small training dataset ? » Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.
Texte intégralPromoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo
wet&rsquo
laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.
Vandehei, Bailey R. « Leveraging Defects Life-Cycle for Labeling Defective Classes ». DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2111.
Texte intégralSousa, Massáine Bandeira e. « Improving accuracy of genomic prediction in maize single-crosses through different kernels and reducing the marker dataset ». Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-07032018-163203/.
Texte intégralNo melhoramento de plantas, a predição genômica (PG) é uma eficiente ferramenta para aumentar a eficiência seletiva de genótipos, principalmente, considerando múltiplos ambientes. Esta técnica tem como vantagem incrementar o ganho genético para características complexas e reduzir os custos. Entretanto, ainda são necessárias estratégias que aumentem a acurácia e reduzam o viés dos valores genéticos genotípicos. Nesse contexto, os objetivos foram: i) comparar duas estratégias para obtenção de subconjuntos de marcadores baseado em seus efeitos em relação ao seu impacto na acurácia da seleção genômica; ii) comparar a acurácia seletiva de quatro modelos de PG incluindo o efeito de interação genótipo × ambiente (G×A) e dois kernels (GBLUP e Gaussiano). Para isso, foram usados dados de um painel de diversidade de arroz (RICE) e dois conjuntos de dados de milho (HEL e USP). Estes foram avaliados para produtividade de grãos e altura de plantas. Em geral, houve incremento da acurácia de predição e na eficiência da seleção genômica usando subconjuntos de marcadores. Estes poderiam ser utilizados para construção de arrays e, consequentemente, reduzir os custos com genotipagem. Além disso, utilizando o kernel Gaussiano e incluindo o efeito de interação G×A há aumento na acurácia dos modelos de predição genômica.
Johansson, David. « Price Prediction of Vinyl Records Using Machine Learning Algorithms ». Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96464.
Texte intégralBaveye, Yoann. « Automatic prediction of emotions induced by movies ». Thesis, Ecully, Ecole centrale de Lyon, 2015. http://www.theses.fr/2015ECDL0035/document.
Texte intégralNever before have movies been as easily accessible to viewers, who can enjoy anywhere the almost unlimited potential of movies for inducing emotions. Thus, knowing in advance the emotions that a movie is likely to elicit to its viewers could help to improve the accuracy of content delivery, video indexing or even summarization. However, transferring this expertise to computers is a complex task due in part to the subjective nature of emotions. The present thesis work is dedicated to the automatic prediction of emotions induced by movies based on the intrinsic properties of the audiovisual signal. To computationally deal with this problem, a video dataset annotated along the emotions induced to viewers is needed. However, existing datasets are not public due to copyright issues or are of a very limited size and content diversity. To answer to this specific need, this thesis addresses the development of the LIRIS-ACCEDE dataset. The advantages of this dataset are threefold: (1) it is based on movies under Creative Commons licenses and thus can be shared without infringing copyright, (2) it is composed of 9,800 good quality video excerpts with a large content diversity extracted from 160 feature films and short films, and (3) the 9,800 excerpts have been ranked through a pair-wise video comparison protocol along the induced valence and arousal axes using crowdsourcing. The high inter-annotator agreement reflects that annotations are fully consistent, despite the large diversity of raters’ cultural backgrounds. Three other experiments are also introduced in this thesis. First, affective ratings were collected for a subset of the LIRIS-ACCEDE dataset in order to cross-validate the crowdsourced annotations. The affective ratings made also possible the learning of Gaussian Processes for Regression, modeling the noisiness from measurements, to map the whole ranked LIRIS-ACCEDE dataset into the 2D valence-arousal affective space. Second, continuous ratings for 30 movies were collected in order develop temporally relevant computational models. Finally, a last experiment was performed in order to collect continuous physiological measurements for the 30 movies used in the second experiment. The correlation between both modalities strengthens the validity of the results of the experiments. Armed with a dataset, this thesis presents a computational model to infer the emotions induced by movies. The framework builds on the recent advances in deep learning and takes into account the relationship between consecutive scenes. It is composed of two fine-tuned Convolutional Neural Networks. One is dedicated to the visual modality and uses as input crops of key frames extracted from video segments, while the second one is dedicated to the audio modality through the use of audio spectrograms. The activations of the last fully connected layer of both networks are conv catenated to feed a Long Short-Term Memory Recurrent Neural Network to learn the dependencies between the consecutive video segments. The performance obtained by the model is compared to the performance of a baseline similar to previous work and shows very promising results but reflects the complexity of such tasks. Indeed, the automatic prediction of emotions induced by movies is still a very challenging task which is far from being solved
Lamichhane, Niraj. « Prediction of Travel Time and Development of Flood Inundation Maps for Flood Warning System Including Ice Jam Scenario. A Case Study of the Grand River, Ohio ». Youngstown State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1463789508.
Texte intégralRai, Manisha. « Topographic Effects in Strong Ground Motion ». Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/56593.
Texte intégralPh. D.
Cooper, Heather. « Comparison of Classification Algorithms and Undersampling Methods on Employee Churn Prediction : A Case Study of a Tech Company ». DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2260.
Texte intégralLivres sur le sujet "PREDICTION DATASET"
Arseneault, René. Using Linear Modelling and Predictive Analytics Make Future Decisions Based on Large Employee HR Datasets. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom : SAGE Publications Inc., 2023. http://dx.doi.org/10.4135/9781529629491.
Texte intégralSentā, Kaiyō Kagaku Gijutsu. Sentanteki yojigen taiki kaiyō rikuiki ketsugō dēta dōka shisutemu no kaihatsu to kōseido kikō hendō yosoku ni hitsuyō na shokichika saikaiseki tōgō dētasetto no kōchiku : Heisei 14-nendo kenkyū seika hōkokusho = Research development of advanced four-dimensional data assimilation system using a climate model toward construction of high-quality reanalysis datasets for climate prediction. [Tokyo] : Monbu Kagakushō̄ Kenkyū Kaihatsukyoku, 2003.
Trouver le texte intégralKaiyō Kenkyū Kaihatsu Kikō (Japan). Sentanteki yojigen taiki kaiyō rikuiki ketsugō dēta dōka shisutemu no kaihatsu to kōseido kikō hendō yosoku ni hitsuyō na shokichika saikaiseki tōgō dētasetto no kōchiku : Heisei 17-nendo kenkyū seika hōkokusho = Research development of advanced four-dimensional data assimilation system using a climate model toward construction of high-quality reanalysis datasets for climate prediction. [Tokyo] : Monbu Kagakushō̄ Kenkyū Kaihatsukyoku, 2006.
Trouver le texte intégralKaiyō Kenkyū Kaihatsu Kikō (Japan), Hokkaidō Daigaku et Japan. Monbu Kagakushō. Kenkyū Kaihatsukyoku., dir. Sentanteki yojigen taiki kaiyō rikuiki ketsugō dēta dōka shisutemu no kaihatsu to kōseido kikō hendō yosoku ni hitsuyō na shokichika saikaiseki tōgō dētasetto no kōchiku : Heisei 18-nendo kenkyū seika hōkokusho = Research development of advanced four-dimensional data assimilation system using a climate model toward construction of high-quality reanalysis datasets for climate prediction. [Tokyo] : Monbu Kagakushō̄ Kenkyū Kaihatsukyoku, 2007.
Trouver le texte intégralDelsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Sous la direction de Frédéric Ferraty et Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.
Texte intégralKumar, Ashish. Learning Predictive Analytics with Python : Gain Practical Insights into Predictive Modelling by Implementing Predictive Analytics Algorithms on Public Datasets with Python. Packt Publishing, Limited, 2016.
Trouver le texte intégralLearning Predictive Analytics with Python : Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python. Packt Publishing, 2016.
Trouver le texte intégralPeng, Handie. Economic Theories and Empirics on the Sex Market. Sous la direction de Scott Cunningham et Manisha Shah. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199915248.013.2.
Texte intégralJohnston, Benjamin, et Ishita Mathur. Applied Supervised Learning with Python : Use Scikit-Learn to Build Predictive Models from Real-world Datasets and Prepare Yourself for the Future of Machine Learning. Packt Publishing, Limited, 2019.
Trouver le texte intégralSchadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Sous la direction de Dennis S. Charney, Eric J. Nestler, Pamela Sklar et Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.
Texte intégralChapitres de livres sur le sujet "PREDICTION DATASET"
Kumar, Sandeep, et Santosh Singh Rathore. « Software Fault Dataset ». Dans Software Fault Prediction, 31–38. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8715-8_4.
Texte intégralSpenrath, Yorick, Marwan Hassani et Boudewijn F. van Dongen. « Online Prediction of Aggregated Retailer Consumer Behaviour ». Dans Lecture Notes in Business Information Processing, 211–23. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.
Texte intégralSyah, Rahmad, Marischa Elveny et Mahyuddin K. M. Nasution. « Clustering Large DataSet’ to Prediction Business Metrics ». Dans Software Engineering Perspectives in Intelligent Systems, 1117–27. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63322-6_95.
Texte intégralAljaaf, Ahmed J., Dhiya Al-Jumeily, Abir J. Hussain, Paul Fergus, Mohammed Al-Jumaily et Hani Hamdan. « Partially Synthesised Dataset to Improve Prediction Accuracy ». Dans Intelligent Computing Theories and Application, 855–66. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_84.
Texte intégralChiou, Andrew, et Xinghuo Yu. « Thematic Fuzzy Prediction of Weed Dispersal Using Spatial Dataset ». Dans Computational Intelligence for Modelling and Prediction, 147–62. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/10966518_11.
Texte intégralBhattacharya, Hindol, Arnab Bhattacharya, Samiran Chattopadhyay et Matangini Chattopadhyay. « LDA Topic Modeling Based Dataset Dependency Matrix Prediction ». Dans Communications in Computer and Information Science, 54–69. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8581-0_5.
Texte intégralZhang, Wei, Xiaofei Xing, Saqib Ali et Guojun Wang. « Internet Performance Prediction Framework Based on PingER Dataset ». Dans Algorithms and Architectures for Parallel Processing, 118–31. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05057-3_9.
Texte intégralLin, Ronghua, Yong Tang, Chengzhe Yuan, Chaobo He et Weisheng Li. « SCHOLAT Link Prediction : A Link Prediction Dataset Fusing Topology and Attribute Information ». Dans Computer Supported Cooperative Work and Social Computing, 340–51. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4549-6_26.
Texte intégralTsatsoulis, P. Daphne, Paige Kordas, Michael Marshall, David Forsyth et Agata Rozga. « The Static Multimodal Dyadic Behavior Dataset for Engagement Prediction ». Dans Lecture Notes in Computer Science, 386–99. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49409-8_31.
Texte intégralTan, Chuheng, et Ximing Zhong. « A Rapid Wind Velocity Prediction Method in Built Environment Based on CycleGAN Model ». Dans Computational Design and Robotic Fabrication, 253–62. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_22.
Texte intégralActes de conférences sur le sujet "PREDICTION DATASET"
Maggio, Simona, Victor Bouvier et Leo Dreyfus-Schmidt. « Performance Prediction Under Dataset Shift ». Dans 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956676.
Texte intégralChen, Yifan, et Fanzeng Xia. « Restaurants’ Rating Prediction Using Yelp Dataset ». Dans 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2020. http://dx.doi.org/10.1109/aeeca49918.2020.9213704.
Texte intégralVerma, Chaman, Veronika Stoffova, Zoltan Illes et Ahmad S. tarawneh. « RESIDENCE STATE AND COUNTRY PREDICTION OF STUDENT TOWARDS ICT FOR THE REAL-TIME ». Dans eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-120.
Texte intégralShaukat, Zain Shaukat, Rashid Naseem et Muhammad Zubair. « A Dataset for Software Requirements Risk Prediction ». Dans 2018 IEEE International Conference on Computational Science and Engineering (CSE). IEEE, 2018. http://dx.doi.org/10.1109/cse.2018.00022.
Texte intégralSherk, Thomas, Minh-Triet Tran et Tam V. Nguyen. « SharkTank Deal Prediction : Dataset and Computational Model ». Dans 2019 11th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2019. http://dx.doi.org/10.1109/kse.2019.8919477.
Texte intégralAlsaraireh, Jameel, et Mary Agoyi. « New Dataset for Software Defect Prediction Model ». Dans 2022 10th International Conference on Smart Grid (icSmartGrid). IEEE, 2022. http://dx.doi.org/10.1109/icsmartgrid55722.2022.9848620.
Texte intégralMunoz-Gonzalez, Angel, et Ryota Horie. « EEG Signal Power Prediction Using DEAP Dataset ». Dans 2022 7th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2022. http://dx.doi.org/10.1109/iciibms55689.2022.9971594.
Texte intégralSalman, Nuha Ahmed, et Saad Talib Hasson. « A Prediction Approach for Small Healthcare Dataset ». Dans 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2023. http://dx.doi.org/10.23919/splitech58164.2023.10193552.
Texte intégralSohn, Samuel S., Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic et Mubbasir Kapadia. « Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/185.
Texte intégralWu, Shuhui, Yongliang Shen, Zeqi Tan et Weiming Lu. « Propose-and-Refine : A Two-Stage Set Prediction Network for Nested Named Entity Recognition ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/613.
Texte intégralRapports d'organisations sur le sujet "PREDICTION DATASET"
Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard et Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), juin 2022. http://dx.doi.org/10.21079/11681/44483.
Texte intégralLetcher, Theodore, Sandra LeGrand et Christopher Polashenski. The Blowing Snow Hazard Assessment and Risk Prediction model : a Python based downscaling and risk prediction for snow surface erodibility and probability of blowing snow. Engineer Research and Development Center (U.S.), mars 2022. http://dx.doi.org/10.21079/11681/43582.
Texte intégralPaparazzoa, Ersilia, Vincenzo Lagani, Silvana Geracitano, Luigi Citrigno, Mirella Aurora Aceto, Antoinio Malvaso, Francesco Bruno, Giuseppe Passarino et Alberto Montesanto. An ELOVL2 based epigenetic clock for forensic age prediction : a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, décembre 2022. http://dx.doi.org/10.37766/inplasy2022.12.0006.
Texte intégralZhu, Xian-Kui, Brian Leis et Tom McGaughy. PR-185-173600-R01 Reference Stress for Metal-loss Assessment of Pipelines. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), août 2018. http://dx.doi.org/10.55274/r0011516.
Texte intégralKoduru, Smitha, et Jason Skow. PR-244-153719-R01 Quantification of ILI Sizing Uncertainties and Improving Correction Factors. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), août 2018. http://dx.doi.org/10.55274/r0011518.
Texte intégralHart, Carl R., D. Keith Wilson, Chris L. Pettit et Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, juillet 2021. http://dx.doi.org/10.21079/11681/41182.
Texte intégralPuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante et Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, décembre 2020. http://dx.doi.org/10.22617/wps200434-2.
Texte intégralAlviarez, Vanessa, Michele Fioretti, Ken Kikkawa et Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, août 2021. http://dx.doi.org/10.18235/0003493.
Texte intégralIdakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang et Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), juillet 2021. http://dx.doi.org/10.21079/11681/41302.
Texte intégralKoutsourelakis, P. Unsupervised Group Discovery and LInk Prediction in Relational Datasets : a nonparametric Bayesian approach. Office of Scientific and Technical Information (OSTI), mai 2007. http://dx.doi.org/10.2172/908093.
Texte intégral