Search Results - acm: c.: computer system organizations/c.4: performance of system/c.4.3: modeling techniques~

1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras

Authors: Okunola, Abiodun

Analysis of an M/G/1 queue with repeated inhomogeneous vacations -- Application to IEEE 802.16e power saving

Authors: Alouf, Sara Altman, Eitan Azad, Amar Prakash et al.

Contributors: Alouf, Sara Altman, Eitan Azad, Amar Prakash et al.

Source: https://inria.hal.science/inria-00266552 ; [Research Report] RR-6488, INRIA. 2008, pp.31.

Subject Terms: M/G/1 queue with repeated inhomogeneous vacations, power save mode, system response time, constrained optimization, numerical analysis, ACM: C.: Computer Systems Organization/C.4: PERFORMANCE OF SYSTEMS/C.4.3: Modeling techniques, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.8: Queueing theory, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.11: Renewal theory, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.15: Stochastic processes, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.6: Optimization, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.5: Local and Wide-Area Networks, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]

Relation: https://inria.hal.science/hal-00641076v1; https://inria.hal.science/hal-00641082v1

Availability: https://inria.hal.science/inria-00266552
https://inria.hal.science/inria-00266552v3/document
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A Mean Field Approach for Optimization in Particles Systems and Applications

Authors: Gast, Nicolas Gaujal, Bruno Middleware efficiently scalable (MESCAL) et al.

Contributors: Gast, Nicolas Gaujal, Bruno Middleware efficiently scalable (MESCAL) et al.

Source: https://inria.hal.science/inria-00368011 ; [Research Report] RR-6877, INRIA. 2009, pp.23.

Subject Terms: Markov Decision Processes, Mean Field, Optimization, Particles System, Grid Broker, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.4: Markov processes, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.15: Stochastic processes, ACM: C.: Computer Systems Organization/C.4: PERFORMANCE OF SYSTEMS/C.4.3: Modeling techniques, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]

Relation: info:eu-repo/semantics/altIdentifier/arxiv/0903.2352; ARXIV: 0903.2352

Availability: https://inria.hal.science/inria-00368011
https://inria.hal.science/inria-00368011v3/document
https://inria.hal.science/inria-00368011v3/file/RR-6877.pdf

HinCTI: A Cyber Threat Intelligence Modeling and Identification System Based on Heterogeneous Information Network.

Authors: Gao, Yali Li, Xiaoyong Peng, Hao et al.

Source: IEEE Transactions on Knowledge & Data Engineering; Feb2022, Vol. 34 Issue 2, p708-722, 15p

Subject Terms: CYBER intelligence (Computer security), CYBERTERRORISM, INFORMATION networks, SYSTEM identification, SECURITIES analysts, CYBER physical systems

Recommendation system based on semantic scholar mining and topic modeling on conference publications.

Authors: Jelodar, Hamed Wang, Yongli Xiao, Gang et al.

Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Mar2021, Vol. 25 Issue 5, p3675-3696, 22p

Subject Terms: RECOMMENDER systems, ONLINE social networks, COMPUTER science conferences, WEB portals, GIBBS sampling

The PowerPC Performance Modeling Methodology.

Authors: Poursepanj, Ali

Source: Communications of the ACM; Jun94, Vol. 37 Issue 6, p47-55, 9p

Subject Terms: POWERPC microprocessors, ARCHITECTURE, MEMORY, COMPUTER storage devices, ELECTRONIC systems, MICROPROCESSORS

Theory of continuous polling systems applied to the Design of Message Ferry Routes in sensor networks

Authors: Voleti, Veeraruna Kavitha Altman, Eitan Models for the performance analysis and the control of networks (MAESTRO) et al.

Contributors: Voleti, Veeraruna Kavitha Altman, Eitan Models for the performance analysis and the control of networks (MAESTRO) et al.

Source: https://inria.hal.science/inria-00573799 ; 2011.

Subject Terms: Continuous Polling systems, Virtual Workload, Pseudo Conservation Laws, Ferry, Wireless LAN, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.8: Queueing theory, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.5: Local and Wide-Area Networks, ACM: C.: Computer Systems Organization/C.4: PERFORMANCE OF SYSTEMS/C.4.3: Modeling techniques, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]

Availability: https://inria.hal.science/inria-00573799
https://inria.hal.science/inria-00573799v2/document
https://inria.hal.science/inria-00573799v2/file/HALFinANORRev2.pdf

professional activities.

Source: Communications of the ACM; Sep81, Vol. 24 Issue 9, p610-614, 5p

Subject Terms: ASSOCIATIONS, institutions, etc., COMPUTERS, CONFERENCES & conventions, OFFICE practice automation, COMPUTER graphics, COMPUTER input-output equipment

Geographic Terms: UNITED States

A Survey of Machine Learning for Computer Architecture and Systems.

Authors: NAN WU YUAN XIE

Source: ACM Computing Surveys; Apr2023, Vol. 55 Issue 3, p1-39, 39p

Subject Terms: COMPUTER architecture, COMPUTER systems, MACHINE learning, SYSTEMS design, COMPILERS (Computer programs), SERVER farms (Computer network management), ARCHITECTURAL design

Process Modeling in Web Applications.

Authors: Brambilla, Marco Ceri, Stefano Fraternali, Piero et al.

Source: ACM Transactions on Software Engineering & Methodology; Oct2006, Vol. 15 Issue 4, p360-409, 50p

Subject Terms: WORLD Wide Web, ORGANIZATION, COMPUTER software, SOFTWARE engineering, MULTIMEDIA systems

ACM REFERENCE GUIDES.

Source: Communications of the ACM; Jan1971, Vol. 14 Issue 1, p62-63, 2p

Subject Terms: SPECIAL interest groups (Associations), DATA transmission systems, ELECTRONIC data processing, MEDICAL research, COMPUTER science, PROFESSIONAL associations

Company/Entity: ASSOCIATION for Computing Machinery

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

Authors: Rossetto, Anubis Graciela de Moraes Noetzold, Darlan Silva, Luis Augusto et al.

Source: Sensors (14248220); Jul2024, Vol. 24 Issue 13, p4212, 25p

Subject Terms: COMPUTER architecture, SPYWARE (Computer software), PREDICTION models, DATA security failures, COMPUTER monitors, AUTOMATIC speech recognition

International Symposium on Computer Performance Modeling, Measurement and Evaluation : March 29-31 1976, Cambridge. Massachusetts / ed. by P. P. S. Chen and Mark Franklin ; sponsored by ACM-SIGMETRICS, IPIP-Working Group 7.3.
Proceedings of the International Symposium on Computer Performance Modeling, Measurement and Evaluation

Authors: International Symposium on Computer Performance Modeling, Measurement and Evaluation (1976 ; Cambridge, Massachusetts). Chen, Peter P. S. (1947- ). Franklin, Mark A. (1940- ). et al.

Contributors: International Symposium on Computer Performance Modeling, Measurement and Evaluation (1976 ; Cambridge, Massachusetts). Chen, Peter P. S. (1947- ). Franklin, Mark A. (1940- ). et al.

Source: Bibliogr. przy ref.

Subject Terms: Komputery -- ocena -- konferencje

Access URL: http://katalog.nukat.edu.pl/lib/item?id=chamo:4001581&theme=nukat

1977 Candidates for ACM Council.

Source: Communications of the ACM; Apr1977, Vol. 20 Issue 4, p290-293, 4p

Subject Terms: COMPUTER industry, MEMBERSHIP, EDUCATIONAL attainment, AWARDS, SOCIETIES

People: COTTERMAN, William W., WHITE, Michael P., ATCHISON, William F.

ACM REFERENCE GUIDES.

Source: Communications of the ACM; Mar1970, Vol. 13 Issue 3, p199-200, 2p

Subject Terms: ASSOCIATIONS, institutions, etc., COMPUTER industry, SPECIAL interest groups (Associations), COMMITTEES, ARTIFICIAL intelligence, DATA transmission systems

Company/Entity: ASSOCIATION for Computing Machinery

Floor the Ceil & Ceil the Floor: Revisiting AIMD Evaluation

Authors: Rao, Ashwin Legout, Arnaud Cessac, Bruno et al.

Contributors: Rao, Ashwin Legout, Arnaud Cessac, Bruno et al.

Source: https://inria.hal.science/hal-00733890 ; [Research Report] 2012.

Subject Terms: AIMD, Sensitivity, Fairness, Efficiency, Integer Arithmetic, ACM: C.: Computer Systems Organization/C.4: PERFORMANCE OF SYSTEMS/C.4.3: Modeling techniques, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation

Availability: https://inria.hal.science/hal-00733890
https://inria.hal.science/hal-00733890v1/document
https://inria.hal.science/hal-00733890v1/file/revisit-aimd-floor-ceil.pdf

Impact of mobility on call block, call drops and optimal cell size in small cell networks

Authors: Ramanath, Sreenath Voleti, Veeraruna Kavitha Altman, Eitan et al.

Contributors: Ramanath, Sreenath Voleti, Veeraruna Kavitha Altman, Eitan et al.

Source: https://inria.hal.science/inria-00573966 ; 2011.

Subject Terms: ACM: C.: Computer Systems Organization/C.4: PERFORMANCE OF SYSTEMS/C.4.3: Modeling techniques, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]

Availability: https://inria.hal.science/inria-00573966
https://inria.hal.science/inria-00573966v1/document
https://inria.hal.science/inria-00573966v1/file/2DSmallCellMobility.pdf

Reliability and Performance of Mirrored Disk Organizations.

Authors: Alexander Thomasian Jun Xu

Source: Computer Journal; 2008, Vol. 51 Issue 6, p615-615, 1p

Subject Terms: ASYMPTOTIC expansions, DATA transmission systems, DIGITAL communications research, DATA protection research, COMPUTER storage devices, ELECTRONIC file management

An evaluation of novice end-user computing performance: Data modeling, query writing, and comprehension.

Authors: Chan, H. C. Teo, H. H. Zeng, X. H.

Source: Journal of the American Society for Information Science & Technology; Jun2005, Vol. 56 Issue 8, p843-853, 11p

Subject Terms: ELECTRONIC data processing, DATABASES, COMPUTER science, INFORMATION science, DATABASE management, ELECTRONIC information resources

A Checkpoint of Research on Parallel I/O for High-Performance Computing.

Authors: BOITO, FRANCIELI ZANON INACI, EDUARDO C. BEZ, JEAN LUCA et al.

Source: ACM Computing Surveys; Mar2019, Vol. 51 Issue 2, p1-35, 35p

Subject Terms: COMPUTER systems, COMPUTER performance, EXTRACTION techniques, SCIENTIFIC community, CONFERENCES & conventions