Search Results - acm: c.: computer systems organization/c.1: processes architectural

Architectural support in industry: a reflection using C-POSH.

Authors: Bril, R. J. Krikhaar, R. L. Postma, A.

Source: Journal of Software Maintenance & Evolution: Research & Practice; Jan/Feb2005, Vol. 17 Issue 1, p3-25, 23p, 6 Diagrams, 1 Chart

Subject Terms: SOFTWARE architecture, COMPUTER software development, SOFTWARE maintenance, COMPUTER systems, COMPUTER science

An Approach for Modeling Architectural Design Rules in UML and its Application to Embedded Software.

Authors: Mattsson, Anders Fitzgerald, Brian Lundell, Björn et al.

Source: ACM Transactions on Software Engineering & Methodology; Mar2012, Vol. 21 Issue 2, p10-10.29, 29p

Subject Terms: SOFTWARE architecture, EMBEDDED computer systems, SOFTWARE engineering, COMPUTER simulation, COMPUTER software developers

IT governance, architectural competency, and the Vasa.

Authors: Brown, William C.

Source: Information Management & Computer Security; 2006, Vol. 14 Issue 2, p140-154, 15p, 2 Charts

Subject Terms: INFORMATION technology management, RISK management in business, COMPUTER science, RESPONSIBILITY, WARSHIPS, BUSINESS planning, CORPORATE governance, COMPUTER systems, MANAGEMENT

ARCHITECTURAL ABSTRACTIONS IN THE DESIGN OF DISTRIBUTED EMBEDDED SYSTEMS PLATFORMS.

Authors: Kolchurin, Maxim Platunov, Alexey Fedor, Asminkin et al.

Source: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM; 2024, Vol. 24, p19-26, 8p

Subject Terms: VIRTUAL machine systems, INDUSTRIALISM, COMPUTER architecture, TECHNICAL information, ARCHITECTURAL designs

Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications.

Authors: Leon, Vasileios Hanif, Muhammad Abdullah Armeniakos, Giorgos et al.

Source: ACM Computing Surveys; Jul2025, Vol. 57 Issue 7, p1-36, 36p

Subject Terms: ARTIFICIAL neural networks, DIGITAL signal processing, ARTIFICIAL intelligence, ARCHITECTURAL details, COMPUTER systems

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

Phase-Change Memory: An Architectural Perspective.

Authors: ZILBERBERG, OMER WEISS, SHLOMO TOLEDO, SIVAN

Source: ACM Computing Surveys; Jun2013, Vol. 45 Issue 3, p29:1-29:33, 33p, 10 Diagrams, 1 Chart, 1 Graph

Subject Terms: COMPUTER architecture, PHASE change memory, DYNAMIC random access memory, ARCHITECTURAL design, DATA protection, COMPUTER systems

ASPLOS-IV proceedings : fourth international conference on architectural support for programming languages and operating systems : Santa Clara, California, April 8-11, 1991 / spons. by ACM in coop. with the IEEE Computer Society.

Authors: International Conference on Architectural Support for Programming Languages and Operating Systems (4 ; 1991 ; Santa Clara). Association for Computing Machinery. Institute of Electrical and Electronics Engineers. Computer Society.

Contributors: International Conference on Architectural Support for Programming Languages and Operating Systems (4 ; 1991 ; Santa Clara). Association for Computing Machinery. Institute of Electrical and Electronics Engineers. Computer Society.

Source: Bibliogr. przy rozdz.
Index.

Subject Terms: Sieci komputerowe, Komputery -- konferencje

Access URL: http://katalog.nukat.edu.pl/lib/item?id=chamo:1759164&theme=nukat

HLD METHODOLOGY: THE ROLE OF ARCHITECTURAL ABSTRACTIONS IN EMBEDDED SYSTEMS DESIGN.

Authors: Platunov, Alexey Kluchev, Arkady Penskoi, Aleksandr

Source: Proceedings of the International Multidisciplinary Scientific GeoConference SGEM; 2014, Vol. 1, p209-218, 10p

Subject Terms: EMBEDDED computer systems, COMPUTER architecture, ADAPTIVE computing systems, COMPUTING platforms, REAL-time computing

Architectural User Interfaces: Themes, Trends and Directions in the Evolution of Architectural Design and Human Computer Interaction.

Authors: Dade-Robertson, Martyn

Source: International Journal of Architectural Computing; Mar2013, Vol. 11 Issue 1, p1-20, 20p

Subject Terms: ARCHITECTURAL design, SPACE (Architecture), GRAPHICAL user interfaces, UBIQUITOUS computing, INTERACTION design (Human-computer interaction), INFORMATION society

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

Architectural design for resilience.

Authors: Liu, Dong Deters, Ralph Zhang, W.J.

Source: Enterprise Information Systems; May2010, Vol. 4 Issue 2, p137-152, 16p, 1 Color Photograph, 1 Black and White Photograph, 2 Diagrams

Subject Terms: ARCHITECTURAL design, SOFTWARE architecture, RELIABILITY in engineering, FAULT tolerance (Engineering), SERVICE-oriented architecture (Computer science)

Hardware Architectural Support for Control Systems and Sensor Processing.

Authors: VYAS, SUDHANSHU GUPTE, ADWAIT GILL, CHRISTOPHER D. et al.

Source: ACM Transactions on Embedded Computing Systems; Sep2013, Vol. 13 Issue 2, p1-25, 25p

Subject Terms: COMPUTER input-output equipment, COMPUTER architecture, AUTOMATIC control systems, SENSOR networks, DIGITAL signal processing, ALGORITHMS, PID controllers

An Architectural Framework for the Design, Analysis and Implementation of Interactive Systems.

Authors: François, Alexandre R.J.

Source: Computer Journal; Jul2011, Vol. 54 Issue 7, p1188-1204, 17p

Subject Terms: INTERACTIVE computer systems, SOFTWARE architecture, COMPUTER architecture, ELECTRONIC data processing, COMPUTER software reusability, COMPUTER software development, COMPUTER software testing, COMPUTER vision

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches.

Authors: STARZYŃSKA-GRZEŚ, MAŁGORZATA B. ROUSSEL, ROBIN JACOBY, SAM et al.

Source: ACM Computing Surveys; 2023 Suppl13s, Vol. 55, p1-25, 25p

Subject Terms: BUILT environment, COMPUTER architecture, EVIDENCE gaps, COMPUTER science, ARCHITECTURAL design, COMPUTER vision

Boosting Inter-process Communication with Architectural Support.

Authors: YUBIN XIA DONG DU ZHICHAO HUA et al.

Source: ACM Transactions on Computer Systems; Jul2022, Vol. 39 Issue 1-4, p1-35, 35p

Subject Terms: MESSAGE passing (Computer science), BOOSTING algorithms, FUCHSIA

Architecting Internet of Things Systems with Blockchain: A Catalog of Tactics.

Authors: YÁNEZ, WENDY BAHSOON, RAMI ZHANG, YUQUN et al.

Source: ACM Transactions on Software Engineering & Methodology; May2021, Vol. 30 Issue 3, p1-46, 46p

Subject Terms: INTERNET of things, PEER-to-peer architecture (Computer networks), BLOCKCHAINS, ARCHITECTURAL designs, SOFTWARE architecture, ARCHITECTURAL design, DATA recorders & recording

Simultaneous Scheduling and Assignment of Processes as a Method of Optimization of Architectural Solutions of Computer Systems.

Authors: Toporkov, V.

Source: Automation & Remote Control; Dec2001, Vol. 62 Issue 12, p2027-2036, 10p

Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management.

Authors: CHAO LI YUSHU XUE JING WANG et al.

Source: ACM Computing Surveys; Mar2019, Vol. 51 Issue 2, p1-34, 34p

Subject Terms: COMPUTER systems, ARCHITECTURAL design, EDGE computing, ELECTRONIC data processing, SERVER farms (Computer network management)

Mead Data Central's Product and Development Organization.

Source: Communications of the ACM; Sep94, Vol. 37 Issue 9, p80-81, 2p

Subject Terms: COMPUTER software development, SYSTEMS design, COMPUTER operating systems, COMPUTER input-output equipment, INFORMATION science

Company/Entity: MEAD Data Central Inc.