Suchergebnisse - (( (statene OR skratene) python code analysis ) OR ( stat python code analysis ))*

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

Autoren: Li J Xu Y Ma S et al.

Quelle: Bioinformatics (Oxford, England) [Bioinformatics] 2025 Sep 01; Vol. 41 (9).

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE

MeSH-Schlagworte: Gene-Environment Interaction* , Deep Learning* , Lung Neoplasms*/genetics , Skin Neoplasms*/genetics , Melanoma*/genetics, Datasets as Topic ; Humans ; Age Factors ; Sex Factors ; Smoking

Domain-specific decision modelling and statistical analysis for combat system effectiveness simulation.

Autoren: Li, Xiaobo Lei, Yonglin Vangheluwe, Hans et al.

Quelle: Journal of Statistical Computation & Simulation; Jun2014, Vol. 84 Issue 6, p1261-1279, 19p

Schlagwörter: STATISTICS, SIMULATION methods & models, DECISION making, QUANTITATIVE research, MATHEMATICAL optimization, DOMAIN-specific programming languages, PYTHON programming language, BAYESIAN analysis, PARAMETER estimation

Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.

Autoren: Ehteram, Mohammad Ghanbari-Adivi, Elham

Quelle: Environmental Science & Pollution Research; Aug2023, Vol. 30 Issue 40, p92903-92921, 19p

Schlagwörter: WATER table, STANDARD deviations, GROUNDWATER recharge, WATER rights

Uncertainty quantification for severe-accident reactor modelling: Results and conclusions of the MUSA reactor applications work package

Autoren: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Weitere Verfasser: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Quelle: ISSN: 0306-4549 ; Annals of Nuclear Energy ; https://hal.science/hal-04777159 ; Annals of Nuclear Energy, 2025, 211, pp.110962. ⟨10.1016/j.anucene.2024.110962⟩.

Schlagwörter: Nuclear reactor, Severe accident, Modelling, Uncertainty quantification, MUSA, Source Term, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], [STAT]Statistics [stat]

Relation: info:eu-repo/grantAgreement//847441/EU/Euratom research and training programme 2014 – 2018/Euratom

Verfügbarkeit: https://hal.science/hal-04777159
https://hal.science/hal-04777159v1/document
https://hal.science/hal-04777159v1/file/Final_version_ANE20225.pdf
https://doi.org/10.1016/j.anucene.2024.110962

PM4NGS, a project management framework for next-generation sequencing data analysis.

Autoren: Vera Alvarez, Roberto Pongor, Lorinc Mariño-Ramírez, Leonardo et al.

Quelle: GigaScience; Jan2021, Vol. 10 Issue 1, p1-9, 9p

Schlagwörter: DATA analysis, NUCLEOTIDE sequencing, SEQUENCE analysis, PROJECT management, PIPELINE inspection, LAPTOP computers

A comparison of machine learning algorithms for survival with missing data ; Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Autoren: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Weitere Verfasser: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Quelle: 54es Journées de Statistique de la SFdS
JDS 2023 - 54e Journées de Statistique de la SFds
https://hal.science/hal-04384220
JDS 2023 - 54e Journées de Statistique de la SFds, SFdS, Jul 2023, Bruxelles (BEL), Belgique. ⟨10.48550/arXiv.2303.13590⟩

Schlagwörter: Machine Learning (cs.LG), Machine Learning (stat.ML), Machine learning, neural networks, survival data, simulated data, missing data, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.AP]Statistics [stat]/Applications [stat.AP]

Geographisches Schlagwort: Bruxelles (BEL), Belgium

Verfügbarkeit: https://hal.science/hal-04384220
https://hal.science/hal-04384220v1/document
https://hal.science/hal-04384220v1/file/preprint_arxiv.pdf
https://doi.org/10.48550/arXiv.2303.13590

Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care.

Autoren: Duarte, Martha Salamanca, Mayra Gonzalez, Juan M. et al.

Quelle: Clinical Nursing Research. Jun2024, Vol. 33 Issue 5, p355-369. 15p.

Schlagwörter: *MENTAL depression risk factors, *RISK assessment, *PREDICTION models, *SOCIAL determinants of health, *RECEIVER operating characteristic curves, *QUESTIONNAIRES, *PRIMARY health care, *RETROSPECTIVE studies, *QUANTITATIVE research, *DESCRIPTIVE statistics, *NURSING interventions, *SURVEYS, *HEALTH behavior, *MEDICAL records, *ACQUISITION of data, *SOCIODEMOGRAPHIC factors, *MACHINE learning, *DATA analysis software, *ACCURACY, *COMPARATIVE studies, *SOCIAL classes, *SENSITIVITY & specificity (Statistics), *SOCIAL isolation, *ALGORITHMS, *REGRESSION analysis, *MENTAL depression, *EVALUATION

Geografische Kategorien: FLORIDA

Extended sample size calculations for evaluation of prediction models using a threshold for classification.

Autoren: Whittle R Ensor J Archer L et al.

Quelle: BMC medical research methodology [BMC Med Res Methodol] 2025 Jul 01; Vol. 25 (1), pp. 170. Date of Electronic Publication: 2025 Jul 01.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE

MeSH-Schlagworte: Models, Statistical*, Sample Size ; Humans ; Predictive Value of Tests ; Risk Assessment/methods ; Sensitivity and Specificity ; Algorithms

Quantifying the nuclear localization of fluorescently tagged proteins.

Autoren: Hurbain J Ten Wolde PR Swain PS

Quelle: Bioinformatics advances [Bioinform Adv] 2025 May 12; Vol. 5 (1), pp. vbaf114. Date of Electronic Publication: 2025 May 12 (Print Publication: 2025).

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Oxford University Press Country of Publication: England NLM ID: 9918282081306676 Publication Model: eCollection Cited Medium: Internet ISSN: 2635-0041 (Electronic) Linking ISSN: 26350041 NLM ISO Abbreviation: Bioinform Adv Subsets: PubMed not MEDLINE

Effects of a post-acute care management program on subsequent acute care utilization for hospitalized patients discharged to skilled nursing facilities.

Autoren: Armstrong D Agovi AM Gehr AW et al.

Quelle: BMC health services research [BMC Health Serv Res] 2026 Jan 07. Date of Electronic Publication: 2026 Jan 07.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: BioMed Central Country of Publication: England NLM ID: 101088677 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1472-6963 (Electronic) Linking ISSN: 14726963 NLM ISO Abbreviation: BMC Health Serv Res Subsets: MEDLINE

A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.

Autoren: Pripp AH Łosińska K Korkosz M et al.

Quelle: Rheumatology international [Rheumatol Int] 2024 Jul; Vol. 44 (7), pp. 1265-1274. Date of Electronic Publication: 2024 Apr 24.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer International Country of Publication: Germany NLM ID: 8206885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1437-160X (Electronic) Linking ISSN: 01728172 NLM ISO Abbreviation: Rheumatol Int Subsets: MEDLINE

MeSH-Schlagworte: Rheumatic Diseases*/therapy, Humans ; Treatment Outcome ; Propensity Score ; Observational Studies as Topic/methods ; Regression Analysis ; Data Interpretation, Statistical

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Autoren: Kostouraki A Hajage D Rachet B et al.

Quelle: Statistics in medicine [Stat Med] 2024 Jun 15; Vol. 43 (13), pp. 2672-2694. Date of Electronic Publication: 2024 Apr 15.

Publikationsart: Journal Article; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE

MeSH-Schlagworte: Propensity Score*, Humans ; Observational Studies as Topic ; Computer Simulation ; Probability ; Randomized Controlled Trials as Topic ; Models, Statistical ; Lung Neoplasms

Comparative effectiveness of laparoscopic versus open colectomy in colon cancer patients: a study protocol for emulating a target trial using cancer registry data.

Autoren: Abera SF Robers G Kästner A et al.

Quelle: Journal of cancer research and clinical oncology [J Cancer Res Clin Oncol] 2025 Jan 11; Vol. 151 (1), pp. 34. Date of Electronic Publication: 2025 Jan 11.

Publikationsart: Clinical Trial Protocol; Journal Article

Info zur Zeitschrift: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 7902060 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-1335 (Electronic) Linking ISSN: 01715216 NLM ISO Abbreviation: J Cancer Res Clin Oncol Subsets: MEDLINE

MeSH-Schlagworte: Colectomy*/methods , Colonic Neoplasms*/surgery , Colonic Neoplasms*/pathology , Colonic Neoplasms*/mortality , Laparoscopy*/methods, Adult ; Female ; Humans ; Male ; Registries ; Survival Rate ; Clinical Trials, Phase III as Topic ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic

Rapid development of image analysis research tools: Bridging the gap between researcher and clinician with pyOsiriX.

Autoren: Blackledge MD Collins DJ Koh DM et al.

Quelle: Computers in biology and medicine [Comput Biol Med] 2016 Feb 01; Vol. 69, pp. 203-12. Date of Electronic Publication: 2015 Dec 18.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE

MeSH-Schlagworte: Image Processing, Computer-Assisted* , Programming Languages*, Absorptiometry, Photon/*methods , Tomography, X-Ray Computed/*methods, Humans

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

Autoren: Ince, Robin A.A. Giordano, Bruno L. Kayser, Christoph et al.

Quelle: Human Brain Mapping; Mar2017, Vol. 38 Issue 3, p1541-1573, 33p

Desarrollo de clasificador multiclase de malware ofuscado mediante redes neuronales y modelos estadísticos ; Development of multi-class classifier of obfuscated malware using neural networks and statistical models

Autoren: Rocha Rodríguez, Juan David Pineda Ballén, Nicolás David Gaona Barrera, Andrés Eduardo

Weitere Verfasser: Rocha Rodríguez, Juan David Pineda Ballén, Nicolás David Gaona Barrera, Andrés Eduardo

Schlagwörter: Redes neuronales, Modelos estadísticos, Malware, Ofuscado, Ingeniería Electrónica -- Tesis y disertaciones académicas, Redes neuronales (Computación), modelos estadísticos aplicados a la seguridad informática, Malware -- Detección, Neural networks, Statistic models, Obfuscated

Dateibeschreibung: pdf; application/pdf; application/zip

Relation: ¿Qué es el malware? es-MX. dirección: https : / / www . mcafee . com / es - mx / antivirus/malware.html (visitado 09-05-2023).; Trojan Win32 Refroso, es, mar. de 2023. dirección: https://bit.ly/45YAlgb (visitado 15-08-2023).; Ransomware: qué es y cómo eliminarlo, es. dirección: https://bit.ly/3tXuSc5 (visitado 27-07-2023).; Modelo OSI: 7 capas y ciberataques habituales %7C Stackscale, es-ES, Section: Compu tación, abr. de 2023. dirección: https : / / www . stackscale . com / es / blog / modelo-osi/ (visitado 10-07-2023).; ¿Qué es ofuscación de código? %7C KeepCoding Bootcamps, es, Section: Blog, jun. de 2022. dirección: https : / / keepcoding . io / blog / que - es - ofuscacion - de - codigo/ (visitado 12-07-2023).; Cómo funciona un Antivirus. Tipos de Antivirus y su función. dirección: https: //ceminfor.es/como-funciona-antivirus/ (visitado 09-08-2023).; Malware %7C What is Malware & How to Stay Protected from Malware Attacks, en-US. dirección: https://www.paloaltonetworks.com/cyberpedia/what is-malware (visitado 09-05-2023); T. Alsmadi y N. Alqudah, «A Survey on malware detection techniques,» en 2021 International Conference on Information Technology (ICIT), jul. de 2021, págs. 371-376. doi:10.1109/ICIT52682.2021.9491765.; V. Kouliaridis y G. Kambourakis, «A Comprehensive Survey on Machine Lear ning Techniques for Android Malware Detection,» en, Information, vol. 12, n.o 5, pág. 185, mayo de 2021, issn: 2078-2489. doi:10.3390/info12050185. direc ción: https://www.mdpi.com/2078-2489/12/5/185; What is a computer virus? es-CO. dirección: https://co.norton.com/blog/ malware/what-is-a-computer-virus (visitado 15-08-2023).; ¿Qué es el malware y cómo protegerse de los ataques? es-es. dirección: https: //www.avast.com/es-es/c-malware (visitado 09-05-2023).; Análisis de memoria de malware %7C Conjuntos de datos %7C Instituto Canadiense de Ciberseguridad %7C UNB, es. dirección: https://www.unb.ca/cic/datasets/ malmem-2022.html (visitado 12-07-2023); Nuevo TROYANO REFROSO, uno mas que se propaga por USB. es, Publisher: Jose Carlos Mejias Arenas, nov. de 2009. dirección: http://www.zonavirus.com/noticias/2009/nuevo-troyano-refroso-uno mas-que-se-propaga-por-usb.asp (visitado 09-05-2023).; McAfee, ¿Qué hacen los troyanos y cómo eliminarlos? es-es, jul. de 2021. direc ción: https://www.mcafee.com/blogs/es-es/internet-security/que-son los-virus-troyanos-y-como-deshacernos-de-ellos/ (visitado 09-05-2023; IBM IBM Security, IBM Security X-Force Threat Intelligence Index 2022 Full Report. dirección: https://www.ibm.com/downloads/cas/ADLMYLAZ (visitado 10-06-2023).; O. Fedor, Must-Know Ransomware Statistics, 2023. dirección: https://www. antivirusguide.com/cybersecurity/ransomware-statistics/; 10 retos y desafíos de la Ciberseguridad para vencer al Cibercrimen, es. dirección: https://bit.ly/46Xmkkc (visitado 15-08-2023).; Comunicación, Innovación en ciberseguridad: estrategia y tendencias (II), es ES, nov. de 2019. dirección: https://www.gradiant.org/blog/innovacion ciberseguridad-2/ (visitado 15-08-2023).; V. Krastev, La ofuscación de Malware - la clave para un éxito de la infección, es, mar. de 2016. dirección: https://sensorstechforum.com/obfuscation in-malware-the-key-to-a-successful-infection/ (visitado 15-08-2023).; C. A. M. Londoño, N. J. R. Galvis y J. S. R. Cardona, VULNERAVILIDAD, TIPOS DE ATAQUES Y FORMAS DE MITIGARLOS EN LAS CAPAS DEL MODELO OSI EN LAS REDES DE DATOS DE LAS ORGANIZACIONES. di rección: https://repositorio.utp.edu.co/server/api/core/bitstreams/ 40f4e143-67ec-47db-873f-ab2149d97d93/content (visitado 10-07-2023); admin, ESET Endpoint la Innovación en Antivirus en 2023 - INNOVES.ES, es, mar. de 2020. dirección: https : / / innoves . es / eset - endpoint - la - innovacion-en-antivirus-para-empresas/ (visitado 15-08-2023).; Los 5 ataques de ransomware más devastadores de 2021 hasta ahora, es, ago. de 2023. dirección: https : / / www . splashtop . com / es / blog / the - 5 - most - devastating-ransomware-attacks-of-2021so-far (visitado 29-08-2023).; Models - Hugging Face, jun. de 2023. dirección: https : / / huggingface . co / models (visitado 29-08-2023).; Retos en la implementación de inteligencia artificial, es. dirección: https:// softimiza . co / blog / retos - y - desafios - de - la - implementacion - de - inteligencia-artificial (visitado 10-05-2023).; Ransomware: qué es y cómo eliminarlo, es. dirección: https://es.malwarebytes. com/ransomware/.; ¿Qué es un troyano? es. dirección: https://co.norton.com/blog/malware/ what-is-a-trojan.; Spyware: detección, prevención y eliminación, es-es. dirección: https://www. avast.com/es-es/c-spyware; 1.13. Feature selection, en. dirección: https://scikit-learn/stable/modules/ feature_selection.html.; A. Rocje, «Árboles de decisión y Series de tiempo,» Español, Tesis de Maes tría en Ingeniería Matemática, Universidad de la República, Montevideo, Uru guay, dic. de 2009. dirección: https://www.colibri.udelar.edu.uy/jspui/ bitstream/20.500.12008/24343/1/Roc09.pdf (visitado 12-09-2023).; L. Breiman, J. Friedman, R. A. Olshen y c. J. Stone, Classification and regression trees, 1.a ed. California: CRC. dirección: http://gen.lib.rus.ec/book/index. php?md5=26908b6eda02ca4faf25adbf57a12b26.; B. Bueno, Predicción semanal de precios de la energía eléctrica utilizando bosques aleatorios. dirección: https : / / oa . upm . es / 47648 / 1 / TFG _ BLANCA _ MARTIN _ BUENO.pdf (visitado 15-09-2023).; J. Boters, Bosques aleatorios supervisados y no supervisados. Aplicación al aná lisis de desigualdades económicas en el mundo. dirección: https : / / bit . ly / 49kWkka (visitado 15-09-2023).; ¿Qué es un bosque aleatorio? %7C IBM, es-mx. dirección: https://www.ibm.com/ mx-es/topics/random-forest (visitado 09-05-2023); T. Chen y C. Guestrin, «XGBoost: A Scalable Tree Boosting System,» 2016, Pu blisher: arXiv Version Number: 3. doi:10.48550/ARXIV.1603.02754. dirección: https://arxiv.org/abs/1603.02754 (visitado 20-09-2023).; M. Sanchez, Modelo predictivo de venta cruzada en productos de vida y salud: Random Forest vs XGBoost, jun. de 2022.; I. Fasel y B. Fortenberry, GBoost: A Generative Framework for Boosting with Applications to Real-Time Eye Coding. dirección: https://shorturl.at/fqP18 (visitado 20-09-2023).; XGBoost Documentation — xgboost 2.1.0-dev documentation. dirección: https: //xgboost.readthedocs.io/en/latest/ (visitado 20-09-2023); Graphical-scheme-of-XGBoost-model.png (850×680). dirección: https://bit. ly/3FI9H0r (visitado 20-09-2023).; J. A. Molina Ferreiro, «Clasificador de curvas de luz utilizando modelo XGBoost y técnicas de balance de datos,» es, 2022, Accepted: 2022-05-30T21:29:52Z Pu blisher: Universidad de Chile. dirección: https://repositorio.uchile.cl/ handle/2250/185788 (visitado 20-09-2023).; M. Mishra, Convolutional Neural Networks, Explained, en, sep. de 2020. dirección: https://towardsdatascience.com/convolutional-neural-networks explained-9cc5188c4939 (visitado 30-08-2023); Introduction to Deep Learning: What Are Convolutional Neural Networks? Video, en. dirección: https://la.mathworks.com/videos/introduction- to deep-learning-what-are-convolutional-neural-networks--1489512765771. html (visitado 30-08-2023).; Chapter 9. Convolucional Networks. dirección: https://bit.ly/40kYRad (visi tado 30-08-2023).; Max pooling in Convolutional neural network — MATLAB Number ONE. di rección: https://matlab1.com/max- pooling- in- convolutional- neural network/ (visitado 30-08-2023).; Correlación de Pearson (formulación), dic. de 2009. dirección: https://web. archive.org/web/20091215105427/http://www.psico.uniovi.es/Dpto_ Psicologia/metodos/tutor.6/fcope.html (visitado 29-08-2023).; Principal Component Analysis (Springer Series in Statistics), en. New York: Springer-Verlag, 2002, isbn: 978-0-387-95442-4. doi:10.1007/b98835. direc ción: http://link.springer.com/10.1007/b98835 (visitado 29-08-202; A. Grané, Análisis de componentes principales. dirección: https://bit.ly/ 3u68P2S (visitado 29-08-2023).; Información mutua – Rubén Sánchez, es, sep. de 2016. dirección: http : / / rubensm.com/informacion-mutua/ (visitado 30-08-2023).; Z. Zhao, R. Anand y M. Wang, Maximum Relevance and Minimum Redun dancy Feature Selection Methods for a Marketing Machine Learning Platform, arXiv:1908.05376 [cs, stat], ago. de 2019. dirección: http://arxiv.org/abs/ 1908.05376 (visitado 30-08-2023).; S. Kumar, Automate your Feature Selection Workflow in one line of Python co de, es, mar. de 2021. dirección: https://towardsdatascience.com/automate your - feature - selection - workflow - in - one - line - of - python - code - 3d4f23b7e2c4 (visitado 30-08-2023).; H. o. A. featurewiz AutoViML and, featurewiz, ago. de 2023. dirección: https: //github.com/AutoViML/featurewiz (visitado 30-08-2023).; S. L.S., Project Rand, ago. de 1951. dirección: https : / / www . rand . org / content/dam/rand/pubs/research_memoranda/2008/RM670.pdf (visitado 12-06-2024).; Universidad Nacional Abierta y a Distancia, J. C. Vesga Ferreira, G. Granados Acuña, Universidad Nacional Abierta y a Distancia, J. E. Sierra Carrillo y Uni versidad Pontificia Bolivariana, «The value of shapley as a strategy for Resour ce Optimization on Power Line Communication (PLC),» Ingeniería y Ciencia, págs. 189-211, 2015, issn: 17949165, 22564314. doi:10.17230/ingciencia. 11 . 22 . 9. dirección: http : / / publicaciones . eafit . edu . co / index . php / ingciencia/article/view/2556 (visitado 12-06-2024); La Información ¿Tiene Valor? es, Section: Ciberseguridad, jun. de 2023. direc ción: https://www.rberny.com/2023/06/23/la-informacion-tiene-valor/ (visitado 08-09-2023).; Qué es una firma de archivo de malware y cómo funciona, es. dirección: https:// ciberseguridad.com/amenzas/firma-archivo-malware/ (visitado 06-10-2023).; S. Khalid, T. Khalil y S. Nasreen, «A survey of feature selection and feature ex traction techniques in machine learning,» en 2014 Science and Information Con ference, London, UK: IEEE, ago. de 2014, págs. 372-378, isbn: 978-0-9893193- 1-7 978-0-9893193-3-1 978-0-9893193-2-4. doi: 10 . 1109 / SAI . 2014 . 6918213. dirección: https : / / ieeexplore . ieee . org / document / 6918213 (visitado 06-10-2023).; M. H. L. Louk y B. A. Tama, «Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit,» en, Algorithms, vol. 15, n.o 9, pág. 332, sep. de 2022, issn: 1999-4893. doi:10.3390/a15090332. dirección: https://www.mdpi. com/1999-4893/15/9/332 (visitado 03-10-2023).; D. Smith, S. Khorsandroo y K. Roy, «Supervised and Unsupervised Learning Techniques Utilizing Malware Datasets,» en 2023 IEEE 2nd International Confe rence on AI in Cybersecurity (ICAIC), Houston, TX, USA: IEEE, feb. de 2023, págs. 1-7, isbn: 9798350396966. doi:10.1109/ICAIC57335.2023.10044169. dirección: https : / / ieeexplore . ieee . org / document / 10044169/ (visitado 03-10-2023).; A. Nugraha y J. Zeniarja, «Malware Detection Using Decision Tree Algorithm Based on Memory Features Engineering,» Journal of Applied Intelligent System, vol. 7, n.o 3, págs. 206-210, dic. de 2022, issn: 2502-9401, 2503-0493. doi: 10. 33633/jais.v7i3.6735. dirección: http://publikasi.dinus.ac.id/index. php/jais/article/view/6735 (visitado 03-10-2023).; R. Sheatsley, B. Hoak, E. Pauley y P. McDaniel, «The Space of Adversarial Stra tegies,» 2022, Publisher: arXiv Version Number: 2. doi:10.48550/ARXIV.2209. 04521. dirección: https://arxiv.org/abs/2209.04521 (visitado 03-10-2023).; M. Al-Qudah, Z. Ashi, M. Alnabhan y Q. Abu Al-Haija, «Effective One-Class Classifier Model for Memory Dump Malware Detection,» en, Journal of Sensor and Actuator Networks, vol. 12, n.o 1, pág. 5, ene. de 2023, issn: 2224-2708. doi:10.3390/jsan12010005. dirección: https://www.mdpi.com/2224-2708/12/1/ 5 (visitado 03-10-2023).; T. Carrier, P. Victor, A. Tekeoglu y A. Lashkari, «Detecting Obfuscated Malware using Memory Feature Engineering:» en Proceedings of the 8th International Conference on Information Systems Security and Privacy, Online Streaming, — Select a Country —: SCITEPRESS - Science y Technology Publications, 2022, págs. 177-188, isbn: 978-989-758-553-1. doi:10.5220/0010908200003120. dirección: https://bit.ly/3siw3Cr (visitado 03-10-2023).; E. Gyamfi y A. D. Jurcut, «Novel Online Network Intrusion Detection System for Industrial IoT Based on OI-SVDD and AS-ELM,» IEEE Internet of Things Journal, vol. 10, n.o 5, págs. 3827-3839, mar. de 2023, issn: 2327-4662, 2372- 2541. doi:10.1109/JIOT.2022.3172393. dirección: https://ieeexplore. ieee.org/document/9768133/ (visitado 03-10-2023).; Z. Li, S. Chen, H. Dai, D. Xu, C.-K. Chu y B. Xiao, «Abnormal Traffic Detection: Traffic Feature Extraction and DAE-GAN With Efficient Data Augmentation,» IEEE Transactions on Reliability, vol. 72, n.o 2, págs. 498-510, jun. de 2023, issn: 0018-9529, 1558-1721. doi:10.1109/TR.2022.3204349. dirección: https: //ieeexplore.ieee.org/document/9893038/ (visitado 03-10-2023).; L. N. Tidjon y F. Khomh, «Reliable Malware Analysis and Detection using Topology Data Analysis,» 2022, Publisher: arXiv Version Number: 2. doi: 10. 48550/ARXIV.2211.01535. dirección: https://arxiv.org/abs/2211.01535 (visitado 03-10-2023); H. Hallqvist y J. Luhr, Fast Classification of Obfuscated Malware with an Arti ficial Neural Network. dirección: https://www.diva-portal.org/smash/get/ diva2:1703918/FULLTEXT01.pdf (visitado 03-10-2023).; R. A. Khamis y A. Matrawy, «Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs,» 2020, Publisher: arXiv Version Number: 1. doi: 10 . 48550 / ARXIV . 2007 . 04472. dirección: https : //arxiv.org/abs/2007.04472 (visitado 03-10-2023).; Y. Gao, H. Miao, J. Chen, B. Song, X. Hu y W. Wang, «Explosive Cyber Security Threats During COVID-19 Pandemic and a Novel Tree-Based Broad Learning System to Overcome,» IEEE Transactions on Intelligent Transportation Sys tems, págs. 1-10, 2022, issn: 1524-9050, 1558-0016. doi:10.1109/TITS.2022. 3160182. dirección: https://ieeexplore.ieee.org/document/9743968/ (vi sitado 03-10-2023).; M. A. Talukder, K. F. Hasan, M. M. Islam et al., «A dependable hybrid machine learning model for network intrusion detection,» en, Journal of Information Se curity and Applications, vol. 72, pág. 103 405, feb. de 2023, issn: 22142126. doi:10.1016/j.jisa.2022.103405. dirección: https://linkinghub.elsevier. com/retrieve/pii/S2214212622002496 (visitado 03-10-2023); M. Dener, G. Ok y A. Orman, «Malware Detection Using Memory Analysis Data in Big Data Environment,» en, Applied Sciences, vol. 12, n.o 17, pág. 8604, ago. de 2022, issn: 2076-3417. doi:10.3390/app12178604. dirección: https: //www.mdpi.com/2076-3417/12/17/8604 (visitado 03-10-2023).; L. Zhiqiang, G. Mohi-Ud-Din, L. Bing, L. Jianchao, Z. Ye y L. Zhijun, «Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset,» en 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada: IEEE, ago. de 2019, págs. 299-303, isbn: 978-1-72812-440-7. doi: 10 . 1109 / SEGE . 2019 . 8859773. dirección: https : / / ieeexplore . ieee . org / document / 8859773/ (visitado 03-10-2023).; A. Mezina y R. Burget, «Obfuscated malware detection using dilated convolutio nal network,» en 2022 14th International Congress on Ultra Modern Telecommu nications and Control Systems and Workshops (ICUMT), Valencia, Spain: IEEE, oct. de 2022, págs. 110-115, isbn: 9798350398663. doi:10.1109/ICUMT57764. 2022.9943443. dirección: https://ieeexplore.ieee.org/document/9943443/ (visitado 03-10-2023).; A. Samy, H. Yu y H. Zhang, «Fog-Based Attack Detection Framework for Inter net of Things Using Deep Learning,» IEEE Access, vol. 8, págs. 74 571-74 585, 2020, issn: 2169-3536. doi:10.1109/ACCESS.2020.2988854. dirección: https: //ieeexplore.ieee.org/document/9072151/ (visitado 03-10-2023).; P. R. Kanna y P. Santhi, «Hybrid Intrusion Detection using MapReduce ba sed Black Widow Optimized Convolutional Long Short-Term Memory Neural Networks,» Expert Systems with Applications, vol. 194, pág. 116 545, mayo de 2022, issn: 0957-4174. doi:10.1016/j.eswa.2022.116545. dirección: https: //bit.ly/3SmS9yr (visitado 06-09-2023).; M. R. Ghazi y N. S. Raghava, «Machine Learning Based Obfuscated Malwa re Detection in the Cloud Environment with Nature-Inspired Feature Selec tion,» en 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Aligarh, India: IEEE, nov. de 2022, págs. 1-5, isbn: 978-1-66547-647-8. doi:10.1109/IMPACT55510.2022. 10029271. dirección: https://ieeexplore.ieee.org/document/10029271/ (visitado 03-10-2023).; L. Zhiqiang, G. Mohi-Ud-Din, L. Bing, L. Jianchao, Z. Ye y L. Zhijun, «Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset,» en 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), 2019, págs. 299-303. doi:10.1109/SEGE. 2019.8859773; N. Moustafa y J. Slay, «UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),» en 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Aus tralia: IEEE, nov. de 2015, págs. 1-6, isbn: 978-1-4673-7007-3 978-1-4673-7008-0. doi:10.1109/MilCIS.2015.7348942. dirección: http://ieeexplore.ieee. org/document/7348942/ (visitado 04-10-2023).; Datopian, Kddcup99, en. dirección: https://datahub.io/machine-learning/ kddcup99 (visitado 04-10-2023).; NSL-KDD %7C Datasets %7C Research %7C Canadian Institute for Cybersecurity %7C UNB, en. dirección: https : / / www . unb . ca / cic / datasets / nsl . html (visitado 04-10-2023).; T. Carrier, P. Victor y A. Tekeoglu, Detecting Obfuscated Malware using Memory Feature Engineering. dirección: https://pdfs.semanticscholar.org/b2e2/ 0dc7a34753311472a5f2314fbf866d7eddd0.pdf (visitado 08-03-2024).; kddcup99 %7C TensorFlow Datasets, es-419-x-mtfrom-en. dirección: https://www. tensorflow.org/datasets/catalog/kddcup99?hl=es-419 (visitado 08-03-2024).; B.-C. C. Center (BCCC), ahlashkari/VolMemLyzer, original-date: 2020-11-13T13:09:40Z, ene. de 2024. dirección: https://github.com/ahlashkari/VolMemLyzer (visi tado 09-03-2024).; S. SATPATHY, SMOTE for Imbalanced Classification with Python, en, oct. de 2020. dirección: https://www.analyticsvidhya.com/blog/2020/10/overcoming class-imbalance-using-smote-techniques/ (visitado 08-03-2024).; J. Brownlee, How to Calculate Correlation Between Variables in Python, en-US, abr. de 2018. dirección: https : / / machinelearningmastery . com / how - to - use-correlation-to-understand-the-relationship-between-variables/ (visitado 12-03-2024).; A. Gupta, Feature Selection Techniques in Machine Learning (Updated 2024), en, oct. de 2020. dirección: https : / / www . analyticsvidhya . com / blog / 2020/10/feature-selection-techniques-in-machine-learning/ (visitado 12-03-2024).; B. Auffarth, M. López y J. Cerquides, Comparison of Redundancy and Relevance Measures for Feature Selection in Tissue Classification of CT images. dirección: https://www.csc.kth.se/~auffarth/publications/redrel.pdf (visitado 12-03-2024).; M. Natarajan, Understanding Cumulative Explained Variance in PCA with Python, en, mar. de 2024. dirección: https : / / medium . com / @megha . natarajan / understanding- cumulative- explained- variance- in- pca- with- python 653e3592a77c (visitado 31-03-2024).; Vertex AI, es. dirección: https : / / cloud . google . com / vertex - ai (visitado 31-03-2024).; X. Xu, Z. Chen, D. Xu et al., Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition, arXiv:2204.03737 [cs, eess], abr. de 2022. dirección: http://arxiv.org/abs/2204.03737 (visitado 03-04-2024).; Dirección: https://www.deeplearningbook.org/contents/regularization. html (visitado 03-04-2024).; P. Srivastava, Essentials of Deep Learning : Introduction to Long Short Term Memory, en, dic. de 2017. dirección: https : / / www . analyticsvidhya . com / blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/ (visitado 30-04-2024).; Miguel, Regularización L1 vs L2 y ¿Cuándo usar cuál? es, nov. de 2020. direc ción: https://manualestutor.com/aprendizaje-automatico/regularizacion l1-vs-l2-y-cuando-usar-cual/ (visitado 26-04-2024).; J. Durán, Técnicas de Regularización Básicas para Redes Neuronales, en, oct. de 2019. dirección: https : / / medium . com / metadatos / t % C3 % A9cnicas - de - regularizaci%C3%B3n-b%C3%A1sicas-para-redes-neuronales-b48f396924d4 (visitado 26-04-2024); sklearn.ensemble.RandomForestClassifier, en. dirección: https://scikit-learn. org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier. html (visitado 06-02-2024); N. Zhu, C. Zhu, L. Zhou, Y. Zhu y X. Zhang, «Optimization of the Random Forest Hyperparameters for Power Industrial Control Systems Intrusion Detec tion Using an Improved Grid Search Algorithm,» en, Applied Sciences, vol. 12, n.o 20, pág. 10 456, ene. de 2022, Number: 20 Publisher: Multidisciplinary Digital Publishing Institute, issn: 2076-3417. doi:10.3390/app122010456. dirección: https://www.mdpi.com/2076-3417/12/20/10456 (visitado 17-04-2024).; S. G. C. G y B. -, «Grid Search Tuning of Hyperparameters in Random Fo rest Classifier for Customer Feedback Sentiment Prediction,» en, International Journal of Advanced Computer Science and Applications, vol. 11, n.o 9, 2020, issn: 21565570, 2158107X. doi:10.14569/IJACSA.2020.0110920. dirección: http://thesai.org/Publications/ViewPaper?Volume=11&Issue=9&Code= IJACSA&SerialNo=20 (visitado 17-04-2024).; sklearn.ensemble.AdaBoostClassifier, en. dirección: https : / / scikit - learn . org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier. html (visitado 06-02-2024).; R. Gao y Z. Liu, «An Improved AdaBoost Algorithm for Hyperparameter Opti mization,» Journal of Physics: Conference Series, vol. 1631, n.o 1, pág. 012 048, sep. de 2020, issn: 1742-6588, 1742-6596. doi:10.1088/1742-6596/1631/1/ 012048. dirección: https://iopscience.iop.org/article/10.1088/1742- 6596/1631/1/012048 (visitado 19-04-2024).; X. Xiong, X. Guo, P. Zeng, R. Zou y X. Wang, «A Short-Term Wind Power Forecast Method via XGBoost Hyper-Parameters Optimization,» Frontiers in Energy Research, vol. 10, pág. 905 155, mayo de 2022, issn: 2296-598X. doi:10.3389/fenrg.2022.905155. dirección: https://www.frontiersin.org/ articles/10.3389/fenrg.2022.905155/full (visitado 19-04-2024); https://hdl.handle.net/11349/93705

Verfügbarkeit: https://hdl.handle.net/11349/93705

Primary data for BJC Carbonic anhydrase IX is a pH-stat that sets an acidic tumour extracellular pH in vivo

Autoren: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John et al.

Schlagwörter: Carbonic anhydrase IX, Extracellular pH, Intracellular pH, ISUCA, Magnetic Resonance Spectroscopy, MRS, pH-stat, Tumour pH

Dateibeschreibung: jMRUI http://www.jmrui.eu/ python www.python.org Excel; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet; application/octet-stream

Relation: https://www.repository.cam.ac.uk/handle/1810/361916; https://doi.org/10.17863/CAM.26029

Verfügbarkeit: https://www.repository.cam.ac.uk/handle/1810/361916
https://doi.org/10.17863/CAM.26029

Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints.

Autoren: Vallarano, Nicolò Bruno, Matteo Marchese, Emiliano et al.

Quelle: Scientific Reports; 7/27/2021, Vol. 11 Issue 1, p1-33, 33p

Schlagwörter: RANDOM graphs, STATISTICAL physics, BITCOIN, INTERNET

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Autoren: Coroller T Sahiner B Amatya A et al.

Quelle: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2024 Apr; Vol. 115 (4), pp. 745-757. Date of Electronic Publication: 2024 Jan 31.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Wiley Country of Publication: United States NLM ID: 0372741 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-6535 (Electronic) Linking ISSN: 00099236 NLM ISO Abbreviation: Clin Pharmacol Ther Subsets: MEDLINE

MeSH-Schlagworte: Artificial Intelligence* , Multiomics*, Humans ; Machine Learning ; Genomics

Sulfur dioxide in the mid-infrared transmission spectrum of WASP-39b.

Autoren: Powell D Feinstein AD Lee EKH et al.

Quelle: Nature [Nature] 2024 Feb; Vol. 626 (8001), pp. 979-983. Date of Electronic Publication: 2024 Jan 17.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: PubMed not MEDLINE; MEDLINE