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Expert System Implementation of the Certainty Factor Method for Smartphone Damage Diagnosis

Syahrul Mubarak Abdullah, Hariani Ma'tang Pakka, Andi Syarifuddin, Ahmed Saeed Alghamdi

Abstract


Android smartphone is currently one of the most extensively utilized operating systems. Nevertheless, Android devices are susceptible to issues such as Ic Emmc, Ic Power, software malfunctions, Blank Screen, Hang, complete device malfunction, and boot loop. Prompt intervention is crucial when a smartphone experiences a problem to prevent more harm and safeguard the user. The Certainty Factor (CF) accounts for the inherent uncertainty in an expert's analysis. Expressions such as "uncertain," "highly probable," "likely," "very likely," "almost certain," and "certain" are frequently employed in this context. This study employed a manual questionnaire to assess the efficacy of the expert system in identifying malfunctions in Android devices. All five technicians and all five user respondents expressed significant agreement about the reliability of the expert system in the questionnaire, and the black box test yielded a perfect 100% success rate. Through accuracy testing, using 10 samples of expert analysis data and 10 samples of system data, it was determined that the expert system achieved an 80% accuracy rate in generating diagnostic conclusions based on the tested data.

Keywords


Android, Malfunction, Expert system, Reliability, Accuracy

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References


C. Liu, L. Zhou, W. Wang, and X. Zhao, “Concrete Surface Damage Volume Measurement Based on Three-Dimensional Reconstruction by Smartphones,” IEEE Sens. J., vol. 21, no. 10, pp. 11349–11360, 2021, doi: 10.1109/JSEN.2021.3067739.

H. Regenbrecht, A. Knott, J. Ferreira, and N. Pantidi, “To See and be Seen - Perceived Ethics and Acceptability of Pervasive Augmented Reality,” IEEE Access, vol. 12, no. March, pp. 32618–32636, 2024, doi: 10.1109/ACCESS.2024.3366228.

A. Gangwal and M. Conti, “Cryptomining Cannot Change Its Spots: Detecting Covert Cryptomining Using Magnetic Side-Channel,” IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 1630–1639, 2020, doi: 10.1109/TIFS.2019.2945171.

A. Nikam, P. Ranade, and T. Goswami, “Diagnosis of Leprosy through AI-based Mobile Application,” 2022 OPJU Int. Technol. Conf. Emerg. Technol. Sustain. Dev. OTCON 2022, pp. 1–6, 2023, doi: 10.1109/OTCON56053.2023.10114031.

W. Hashim, M. Alkhaled, A. Al-Naji, and I. Al-Rayahi, “A Review on Image Processing Based Neonatal Jaundice Detection Techniques,” 7th Int. Conf. Contemp. Inf. Technol. Math. ICCITM 2021, pp. 213–218, 2021, doi: 10.1109/ICCITM53167.2021.9677654.

A. A. A. Hafez, “Multi-level cascaded DC/DC converters for PV applications,” Alexandria Eng. J., vol. 54, no. 4, pp. 1135–1146, 2015, doi: 10.1016/j.aej.2015.09.004.

K. Kaur, D. Bhalla, and J. Singh, “Fault Diagnosis for Oil Immersed Transformer Using Certainty Factor,” IEEE Trans. Dielectr. Electr. Insul., vol. 31, no. 1, pp. 485–494, 2024, doi: 10.1109/TDEI.2023.3307513.

B. Ando, S. Baglio, C. O. Lombardo, and V. Marletta, “A multisensor data-fusion approach for ADL and fall classification,” IEEE Trans. Instrum. Meas., vol. 65, no. 9, pp. 1960–1967, 2016, doi: 10.1109/TIM.2016.2552678.

Y. Madhwal, “Implementation of Tokenised Supply Chain Using Blockchain Technology,” Proc. - 21st IEEE Int. Symp. a World Wireless, Mob. Multimed. Networks, WoWMoM 2020, pp. 66–67, 2020, doi: 10.1109/WoWMoM49955.2020.00026.

H. Tamakawa and H. Yamamoto, “SfM/MVS-based Three-Dimensional Structural Diagnosis System for Damaged Houses,” Dig. Tech. Pap. - IEEE Int. Conf. Consum. Electron., vol. 2022-Janua, pp. 1–6, 2022, doi: 10.1109/ICCE53296.2022.9730248.

K. Abdur-rouf and K. Shaaban, “Development of prediction models of transportation noise for roundabouts and signalized intersections,” Transp. Res. Part D, vol. 103, no. June 2021, p. 103174, 2022, doi: 10.1016/j.trd.2022.103174.

D. Jeong, “Road Damage Detection Using YOLO with Smartphone Images,” Proc. - 2020 IEEE Int. Conf. Big Data, Big Data 2020, pp. 5559–5562, 2020, doi: 10.1109/BigData50022.2020.9377847.

A. Gadupudi et al., “An Adaptive Deep Learning Model for Crop Yield Prediction,” 2024 2nd Int. Conf. Comput. Commun. Control, pp. 1–5, 2024, doi: 10.1109/IC457434.2024.10486733.

N. Kumar, D. Acharya, and D. Lohani, “An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion,” IEEE Internet Things J., vol. 8, no. 2, pp. 869–880, 2021, doi: 10.1109/JIOT.2020.3008896.

H. Shang et al., “Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China,” Remote Sens., vol. 15, no. 20, 2023, doi: 10.3390/rs15204952.

A. N. Tusher, S. Islam, M. T. Islam, S. R. Sammy, M. S. Rahman, and M. S. Sadik, “User Perspective Bangla Sentiment Analysis for Online Gaming Addiction using Machine Learning,” 6th Int. Conf. I-SMAC (IoT Soc. Mobile, Anal. Cloud), I-SMAC 2022 - Proc., pp. 538–543, 2022, doi: 10.1109/I-SMAC55078.2022.9987343.

N. Aslam et al., “Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features,” Comput. Mater. Contin., vol. 76, no. 3, pp. 3167–3188, 2023, doi: 10.32604/cmc.2023.039721.




DOI: https://doi.org/10.33387/ijeeic.v1i2.7810

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