Comparison of CPU Damage Prediction Accuracy Between Certainty Factor and Forward Chaining Techniques
Article Sidebar
Read Counter : 343
Download : 265
Main Article Content
Abstract
The CPU plays a vital role in determining the performance of a computer system in contemporary computing. If the CPU sustains damage, it may result in significant interruption to the computer's functioning. This study presents a computational technique that aims to enhance the accuracy of CPU damage predictions. The system utilises fundamental knowledge of damage diagnosis and is validated via evaluating 11 early damage symptoms that are often seen. The Certainty Factor and Forward Chaining approaches ascertain CPU damage by quantifying the degree of truth in the expert's opinion conclusions via a comparison of the symptoms of harm. The second algorithm assesses the confidence level in a development by considering the weight assigned to the system by two parties: the user and the expert. The suggested algorithm yields the mean accuracy of the certainty factor approach in diagnosing computer damage utilising the constructed system. The diagnostic system has a precision rate of 84.9%, indicating that 9 out of 10 diagnoses made by the system align with those made by an expert. Next, the outcomes of the forward chaining algorithm test. All questions about symptoms were answered affirmatively, except for one test which had a negative response. A total of 39 diagnoses were obtained, with an average value of 82.9%. The study findings indicate that the suggested confidence factor method is more suited for use in embedded systems or web-based applications, however it is constrained by low processing.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Aisa, S. (2021). System Weaning Food Product Using Forward Chaining Method. In 3rd International Conference on Cybernetics and Intelligent Systems, ICORIS 2021. https://doi.org/10.1109/ICORIS52787.2021.9649643
Awad, M. A. (2021). An Efficient Modified Genetic Algorithm for Integrated Process Planning-Job Scheduling. In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021 (pp. 319–323). https://doi.org/10.1109/MIUCC52538.2021.9447610
Boyacı, A. Ç. (2022). Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets. Environmental Science and Pollution Research, 29(2), 1985–1997. https://doi.org/10.1007/s11356-021-15703-7
Büyüközkan, G. (2021). A combined hesitant fuzzy MCDM approach for supply chain analytics tool evaluation. Applied Soft Computing, 112. https://doi.org/10.1016/j.asoc.2021.107812
Çalık, A. (2021). A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Computing, 25(3), 2253–2265. https://doi.org/10.1007/s00500-020-05294-9
Chinnam, N. B. (2022). Universally Accessible Structural Data on Macromolecular Conformation, Assembly, and Dynamics by Small Angle X-Ray Scattering for DNA Repair Insights. In Methods in Molecular Biology (Vol. 2444, pp. 43–68). https://doi.org/10.1007/978-1-0716-2063-2_4
Fajriani, F. (2023). A comparison between forward chaining-certainty factor and forward chaining-Dempster Shafer methods for ear, nose, and throat (ENT) expert system. In AIP Conference Proceedings (Vol. 2609). https://doi.org/10.1063/5.0124207
Fitri, Z. E. (2023). Combination of forward chaining and certainty factor methods for the early detection of Acute Respiratory Infections (ARI). Engineering and Applied Science Research, 50(4), 316–323. https://doi.org/10.14456/easr.2023.34
Garcia, M. B. (2021). Virtual Dietitian: A Nutrition Knowledge-Based System Using Forward Chaining Algorithm. In 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 (pp. 309–314). https://doi.org/10.1109/3ICT53449.2021.9581887
Ghadikolaei, S. S. (2023). A CFD modeling of heat transfer between CGNP Eco-friendly nanofluid and the novel nature-based designs heat sink: Hybrid passive techniques for CPU cooling. Thermal Science and Engineering Progress, 37. https://doi.org/10.1016/j.tsep.2022.101604
Ghasemi, S. E. (2021). Design optimization and experimental investigation of CPU heat sink cooled by alumina-water nanofluid. Journal of Materials Research and Technology, 15, 2276–2286. https://doi.org/10.1016/j.jmrt.2021.09.021
Gou, J. (2021). Knowledge Distillation: A Survey. International Journal of Computer Vision, 129(6), 1789–1819. https://doi.org/10.1007/s11263-021-01453-z
Goyal, S. (2021). Sustainable production and consumption: analysing barriers and solutions for maintaining green tomorrow by using fuzzy-AHP–fuzzy-TOPSIS hybrid framework. Environment, Development and Sustainability, 23(11), 16934–16980. https://doi.org/10.1007/s10668-021-01357-5
Hafizal, M. T. (2022). Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method. In Procedia Computer Science (Vol. 216, pp. 136–143). https://doi.org/10.1016/j.procs.2022.12.120
Hirata, H. (2021). Reducing the Repairing Penalty on Misspeculation in Thread-Level Speculation. In ACM International Conference Proceeding Series (pp. 39–45). https://doi.org/10.1145/3468081.3471120
Issa, U. (2022). Hybrid AHP-Fuzzy TOPSIS Approach for Selecting Deep Excavation Support System. Buildings, 12(3). https://doi.org/10.3390/buildings12030295
Khan, S. (2022). Transformers in Vision: A Survey. ACM Computing Surveys, 54(10). https://doi.org/10.1145/3505244
Khan, S. A. (2021). A knowledge-based experts’ system for evaluation of digital supply chain readiness. Knowledge-Based Systems, 228. https://doi.org/10.1016/j.knosys.2021.107262
Khatari, M. (2021). Multidimensional Benchmarking Framework for AQMs of Network Congestion Control Based on AHP and Group-TOPSIS. International Journal of Information Technology and Decision Making, 20(5), 1409–1446. https://doi.org/10.1142/S0219622021500127
Li, H. (2021). A failure analysis of floating offshore wind turbines using AHP-FMEA methodology. Ocean Engineering, 234. https://doi.org/10.1016/j.oceaneng.2021.109261
Messing, A. (2021). Forward Chaining Hierarchical Partial-Order Planning. In Springer Proceedings in Advanced Robotics (Vol. 17, pp. 364–380). https://doi.org/10.1007/978-3-030-66723-8_22
Mittal, S. (2022). A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations. IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5095–5115. https://doi.org/10.1109/TNNLS.2021.3071762
Naryanto, R. F. (2022). Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 27(3), 1601–1609. https://doi.org/10.11591/ijeecs.v27.i3.pp1601-1609
Pagano, A. (2021). A Decision Support System Based on AHP for Ranking Strategies to Manage Emergencies on Drinking Water Supply Systems. Water Resources Management, 35(2), 613–628. https://doi.org/10.1007/s11269-020-02741-y
Plancher, B. (2021). Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA. IEEE Robotics and Automation Letters, 6(2), 2335–2342. https://doi.org/10.1109/LRA.2021.3057845
Putri, T. E. (2021). Expert system for digital single lens reflex (DSLR) camera recommendation using forward chaining and certainty factor. In AIP Conference Proceedings (Vol. 2329). https://doi.org/10.1063/5.0042292
Putro, M. D. (2022). A Fast CPU Real-Time Facial Expression Detector Using Sequential Attention Network for Human-Robot Interaction. IEEE Transactions on Industrial Informatics, 18(11), 7665–7674. https://doi.org/10.1109/TII.2022.3145862
Rogulj, K. (2021). Knowledge-based fuzzy expert system to the condition assessment of historic road bridges. Applied Sciences (Switzerland), 11(3), 1–43. https://doi.org/10.3390/app11031021
Roh, Y. (2021). A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective. In IEEE Transactions on Knowledge and Data Engineering (Vol. 33, Issue 4, pp. 1328–1347). https://doi.org/10.1109/TKDE.2019.2946162
Roy, P. K. (2023). A credit scoring model for SMEs using AHP and TOPSIS. International Journal of Finance and Economics, 28(1), 372–391. https://doi.org/10.1002/ijfe.2425
Sathyan, R. (2023). An integrated Fuzzy MCDM approach for modelling and prioritising the enablers of responsiveness in automotive supply chain using Fuzzy DEMATEL, Fuzzy AHP and Fuzzy TOPSIS. Soft Computing, 27(1), 257–277. https://doi.org/10.1007/s00500-022-07591-x
Satria, A. (2022). Application of the Certainty Factor and Forward Chaining Methods to a Cat Disease Expert System. In 2022 3rd International Conference on Artificial Intelligence and Data Sciences: Championing Innovations in Artificial Intelligence and Data Sciences for Sustainable Future, AiDAS 2022 - Proceedings (pp. 83–88). https://doi.org/10.1109/AiDAS56890.2022.9918803
Shahbazian, N. (2022). Identification of geometric and mechanical factors predictive of bird-beak configuration in thoracic endovascular aortic repair using computational models of stent graft deployment. JVS-Vascular Science, 3, 259–273. https://doi.org/10.1016/j.jvssci.2022.05.056
Unal, Y. (2022). Sustainable supplier selection by using spherical fuzzy AHP. Journal of Intelligent and Fuzzy Systems, 42(1), 593–603. https://doi.org/10.3233/JIFS-2191214
Younes, A. (2022). Spatial suitability analysis for site selection of refugee camps using hybrid GIS and fuzzy AHP approach: The case of Kenya. International Journal of Disaster Risk Reduction, 77. https://doi.org/10.1016/j.ijdrr.2022.103062
Zhao, Y. (2022). Preoperative systemic inflammatory response index predicts long-term outcomes in type B aortic dissection after endovascular repair. Frontiers in Immunology, 13. https://doi.org/10.3389/fimmu.2022.992463
Zhu, G. N. (2022). A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty. Advanced Engineering Informatics, 51. https://doi.org/10.1016/j.aei.2021.101454