Search Results - acm: c.: computer system organization/c.4: performance of system/c.4.4: performance attributes*

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

5A Nine Year Study of File System and Storage Benchmarking

Authors: Avishay Traeger Erez Zadok Nikolai Joukov et al.

Contributors: Avishay Traeger Erez Zadok Nikolai Joukov et al.

Source: http://staff.ustc.edu.cn/~jpq/paper/flash/2008-TOS-A nine year study of file system and storage benchmarking.pdf.

Subject Terms: Categories and Subject Descriptors, D.4.8 [Operating Systems, Performance—Measurements, C.4 [Computer Systems Organization, Performance of Systems—Measurement techniques, per- formance attributes General Terms, Design, Experimentation, Measurement, Performance Additional Key Words and Phrases, Benchmarks, file systems, storage systems ACM Reference Format

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.488.481

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.488.481
http://staff.ustc.edu.cn/~jpq/paper/flash/2008-TOS-A nine year study of file system and storage benchmarking.pdf

A Nine Year Study of File System and Storage Benchmarking

Authors: Avishay Traeger Erez Zadok Nikolai Joukov et al.

Contributors: Avishay Traeger Erez Zadok Nikolai Joukov et al.

Source: http://www.fsl.cs.sunysb.edu/docs/fsbench/fsbench.pdf.

Subject Terms: Categories and Subject Descriptors, D.4.8 [Operating Systems, Performance—Measurements, C.4 [Computer Systems Organization, Performance of Systems—Measurement techniques, performance attributes General Terms, Design, Experimentation, Measurement, Performance Additional Key Words and Phrases, Benchmarks, file systems, storage systems ACM Reference Format

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.8547; http://www.fsl.cs.sunysb.edu/docs/fsbench/fsbench.pdf

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.8547
http://www.fsl.cs.sunysb.edu/docs/fsbench/fsbench.pdf

A Clustering-Driven LDAP Framework.

Authors: KOUTSONIKOLA, VASSILIKI VAKALI, ATHENA

Source: ACM Transactions on the Web; Jul2011, Vol. 5 Issue 3, p12-12:34, 34p

Subject Terms: LDAP (Computer network protocol), ALGORITHMS, PERFORMANCE, CLIENT/SERVER computing, COMPUTER network protocols, APPLICATION software, INFORMATION technology, QUERY (Information retrieval system)

Efficient Attributes for Anonymous Credentials.

Authors: Camenisch, Jan Groß, Thomas

Source: ACM Transactions on Information & System Security (TISSEC); 2012, Vol. 15 Issue 1, p4-30, 30p

Subject Terms: LINEAR systems, BINARY number system, PERFORMANCE evaluation, COMPUTER security, ALGORITHMS, IDENTIFICATION cards, CELL phones

The Use of Normal Multiplication Tables for Information Storage and Retrieval.

Authors: Motzkin, Dalia Montgomery, C. A.

Source: Communications of the ACM; Mar1979, Vol. 22 Issue 3, p193-207, 15p, 4 Diagrams, 10 Charts

Subject Terms: INFORMATION storage & retrieval systems, CHARACTER sets (Data processing), MULTIPLICATION tables, ELECTRONIC file management, ALGORITHMS, COMPUTER storage devices

Time-triggered implementations of dynamic controllers

Authors: Truong Nghiem George J. Pappas Rajeev Alur et al.

Contributors: Truong Nghiem George J. Pappas Rajeev Alur et al.

Source: http://www.georgejpappas.org//papers/a58-nghiem.pdf.

Subject Terms: Categories and Subject Descriptors, C.3 [Computer Systems Organization, Special-Purpose and Application-Based Systems—Real-time and embedded systems, C.4[Computer Systems Organization, Performance of Systems—Modeling techniques, performance attributes, J.7 [Computer Applications, Computers in Other Systems—Real time, command and control General Terms, Design, Performance Additional Key Words and Phrases, Control, dynamic controller, time-triggered, implementation, performance, PID, PI, scheduling ACM Reference Format

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.170

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.170
http://www.georgejpappas.org//papers/a58-nghiem.pdf

Task Mapping and Priority Assignment for Soft Real-Time Applications under Deadline Miss Ratio Constraints

Authors: Sorin Manolache Petru Eles Zebo Peng et al.

Contributors: Sorin Manolache Petru Eles Zebo Peng et al.

Source: http://www.ida.liu.se/labs/eslab/publications/pap/db/sorma_asm_ecs08.pdf.

Subject Terms: Categories and Subject Descriptors, B.8.0 [Hardware, Performance and Reliability—General, C.4 [Computer Systems Organization, Performance of Systems—Performance attributes, D.4.7 [Software, Operating Systems—Organization and Design General Terms, Performance, Theory Additional Key Words and Phrases, Schedulability analysis, soft real-time systems, stochastic task execution times, mapping, priority assignment ACM Reference Format

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.5174; http://www.ida.liu.se/labs/eslab/publications/pap/db/sorma_asm_ecs08.pdf

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.5174
http://www.ida.liu.se/labs/eslab/publications/pap/db/sorma_asm_ecs08.pdf

Adaptivity and Self-Organization in Organic Computing Systems.

Source: ACM Transactions on Autonomous & Adaptive Systems; 2010, Vol. 5 Issue 3, p10-10.32, 32p

Subject Terms: ADAPTIVE computing systems, SELF-organizing systems, AUTONOMIC computing, COMPUTER systems, ENGINEERING design, ROBUST control, SYSTEMS theory

The XtreemOS Resource Selection Service.

Authors: STRATAN, CORINA SACHA, JAN NAPPER, JEFF et al.

Source: ACM Transactions on Autonomous & Adaptive Systems; 2012, Vol. 7 Issue 4, p37:1-37:32, 32p, 2 Charts, 18 Graphs

Subject Terms: ALGORITHMS, MATHEMATICAL optimization, MEMBERSHIP, PERFORMANCE evaluation, COMPUTER software, ADAPTIVE control systems

STRAM: Measuring the Trustworthiness of Computer-Based Systems.

Authors: JIN-HEE CHO SHOUHUAI XU HURLEY, PATRICK M. et al.

Source: ACM Computing Surveys; Nov2019, Vol. 51 Issue 6, p1-47, 47p, 11 Diagrams, 3 Charts

Subject Terms: KEY performance indicators (Management), MOTOR ability, RISK assessment

Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS.

Authors: VAL, ELENA DEL REBOLLO, MIGUEL VASIRANI, MATEO et al.

Source: ACM Transactions on Autonomous & Adaptive Systems; Oct2014, Vol. 9 Issue 3, p12-12:24, 24p

Subject Terms: SERVICE-oriented architecture (Computer science), UTILITY theory, SELF-organizing maps, MULTIAGENT systems, DEMAND chain planning

Combinatorial optimization in system configuration design.

Authors: Levin, M.

Source: Automation & Remote Control; Mar2009, Vol. 70 Issue 3, p519-561, 43p, 26 Diagrams, 11 Charts

Subject Terms: COMBINATORIAL optimization, PRODUCT attributes, ELECTRONIC systems, COMPUTER software industry, TELECOMMUNICATION systems, PROGRAM transformation, COMBINATORIAL group theory, COMPUTER system conversion

ADAM: External Dependency-Driven Architecture Discovery and Analysis of Quality Attributes.

Authors: GANESAN, DHARMALINGAM LINDVALL, MIKAEL

Source: ACM Transactions on Software Engineering & Methodology; Mar2014, Vol. 23 Issue 2, p17:1-17:51, 51p

Subject Terms: COMPUTER software testing, COMPUTER software developers, SOFTWARE architecture, REVERSE engineering, COMPUTER operating systems

Visual Menu Techniques.

Authors: BAILLY, GILLES LECOLINET, ERIC NIGAY, LAURENCE

Source: ACM Computing Surveys; Feb2017, Vol. 49 Issue 4, p60:1-60:41, 41p, 22 Diagrams, 6 Charts

Subject Terms: MENUS (Computers), HUMAN-computer interaction, USER-centered system design, SIMULATION methods & models, COMPUTER performance

A flexible multi-dimensional QoS performance measure framework for distributed heterogeneous systems.

Authors: Jong-Kook Kim Debra Hensgen Taylor Kidd et al.

Source: Cluster Computing; Jul2006, Vol. 9 Issue 3, p281-296, 16p

Subject Terms: SPAN (Electronic computer system), DATA transmission systems, HIGH performance computing, PARALLEL processing

A Self-Organization Mechanism Based on Cross-Entropy Method for P2P-Like Applications.

Source: ACM Transactions on Autonomous & Adaptive Systems; 2010, Vol. 5 Issue 4, p15:1-15:31, 31p

Subject Terms: SELF-organizing systems, PEER-to-peer architecture (Computer networks), CROSS-entropy method, ALGORITHMS, ARTIFICIAL intelligence, COMPUTER systems, INTERNET, COMPUTER network protocols

Technical and Social Components of Peer-To-Peer Computing.

Authors: Lethin, Richard

Source: Communications of the ACM; Feb2003, Vol. 46 Issue 2, p30-32, 3p

Subject Terms: PEER-to-peer architecture (Computer networks), COMPUTER network architectures, INFORMATION resources management, TELECOMMUNICATION & society, COMPUTER networks, DECENTRALIZATION in management

TARGET SIZE, POSITION, AND MOVEMENT DIRECTION EFFECTS ON THE DRAG TASK PERFORMANCE OF A GAZE CONTROL DEVICE.

Authors: Minjee Kim Daehwan Jin Ilsun Rhiu et al.

Source: International Journal of Industrial Engineering; 2018, Vol. 25 Issue 5, p634-646, 13p

Subject Terms: COMPUTER input-output equipment, USER-centered system design, EYE movements, HUMAN-computer interaction, INDEPENDENT variables

Impact of e-leadership and team dynamics on virtual team performance in a public organization.

Authors: Elyousfi, Fatima Anand, Amitabh Dalmasso, Audrey

Source: International Journal of Public Sector Management; 2021, Vol. 34 Issue 5, p508-528, 21p

Subject Terms: VIRTUAL work teams, LEADERSHIP, PUBLIC-private sector cooperation, COMPUTER software, SOCIAL support