Open Access
Research Article
A comprehensive review of explainable artificial intelligence (XAI) in various domains : Applications and challenges
*Richa DattaCorresponding authorer.richacse@gmail.comDepartment of Computer Science and EngineeringMaharishi Markendeshwar Institute of Computer Technology &, Business Management (Hotel Management), Maharishi Markandeshwar (Deemed To Be University)Maharishi Markendeshwar Engineering CollegeAmbala, Haryana, 133203, India0009-0003-0504-0837View full profile → , Vaishali Mehta Wadhwadrvaishaliwashwa@gmail.comDepartment of Computer Science and EngineeringMaharishi Markandeshwar (Deemed To Be University)Maharishi Markendeshwar Engineering CollegeAmbala, Haryana, 133203, India0000-0003-2017-0528View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 10 Dec 2024
- Published Online:
- 30 Sep 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2015
- Pages:
- 1861–1869
Abstract
To make artificial intelligence, scientists look at how the brain works and how it learns. These investigations have led to the creation of software and smart systems. Artificial Intelligence (AI) is a way to solve everyday problems by leveraging technology and systems that act like people. We need to build trust and employ AI systems in important sectors since we rely on smart robots more and more in our daily lives. The short form of “Explainable Artificial Intelligence” is “XAI.” AI that can explain its decisions or predictions to people. XAI wants to make AI systems more open, accountable, and trustworthy, especially when they are utilized in important fields like security, healthcare, or finance.
Keywords
Subject Classifications
68T0168T0768T05
References
[1] T. K. Mohammed, D. N. Vasundhara, S. H. Mehanoor, E. Sreedevi, P. R. Kumar, C. Manihass, and S. F. Baba, “A novel fusion approach for advancement in crime prediction and forecasting using hybridization of ARIMA and recurrent neural networks,” Journal of Information Systems Engineering and Management, vol. 10, no. 38, pp. 404–420 (2025).
[2] G. L. Saini, P. D. Singh, K. D. Singh, and P. Narooka, “Enhancing data privacy through cryptographic innovations in discrete mathematical systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1461–1472 (2025).
[3] P. R. Kumar, R. K. Jha, and P. A. Kumar, “BrainHyperintensities: Automatic segmentation of white matter hyperintensities in clinical brain MRI images using improved deep neural network,” The Journal of Supercomputing, vol. 80, pp. 15545–15581 (2024).
[4] B. Goutam, M. F. Hashmi, Z. W. Geem, and N. D. Bokde, “A comprehensive review of deep learning strategies in retinal disease diagnosis using fundus images,” IEEE Access, vol. 10, pp. 57 796–57 823 (2022).
[5] P. R. Kumar, R. K. Jha, and A. Katti, “Brain tissues segmentation in neurosurgery: A systematic analysis for quantitative tractography approaches,” Acta Neurologica Belgica, vol. 124, pp. 1–15 (2023).
[6] R. K. Moje, B. Tiple, S. M. Patil, T. Jadhav, A. P. Munshi, and A. Revekar, “A framework for multi-task learning optimization in deep neural networks: Balancing task priorities for improved performance,” Journal of Information and Optimization Sciences, vol. 46, no. 4-B, pp. 1141–1151 (2025).
[7] M. Bellucci, N. Delestre, N. Malandain, and C. Zanni-Merk, “Towards a terminology for a fully contextualized xai,” Procedia Computer Science, vol. 192, pp. 241–250 (2021).
[8] J. Parikh, A. Bhargava, S. M. Antony, Y. Puri, U. Maniar, D. Singhal, M. Singh, and M. Sharma, “An edge AI based real-time spatial monitoring system,” Journal of Information and Optimization Sciences, vol. 46, no. 1, pp. 33–42 (2025).
[9] B. Shilpa, P. R. Kumar, and R. K. Jha, “LoRa DL: A deep learning model for enhancing the data transmission over LoRa using autoencoder,” The Journal of Supercomputing, vol. 79, pp. 17079–17097 (2023).
[10] M. N. Nikhate, K. L. Bondar, and S. B. Kiwne, “Fixed point theorems for self-mappings in generalized metric spaces,” Journal of Dynamical Systems and Geometric Theories, vol. 21, no. 2, pp. 121–126 (2023).
[11] P. R. Kumar and T. Ananthan, “Machine vision using LabVIEW for label inspection,” Journal of Innovation in Computer Science and Engineering, vol. 9, no. 1, pp. 58–62 (2019).
[12] Z. S. Alsham, E. Bahçekapılı, and A. Ayaz, “Trends in IoT applications in smart campuses: A topic modeling approach,” COLLNET Journal of Scientometrics and Information Management, vol. 19, no. 1, pp. 21–40 (2025).
[13] B. Shilpa, P. R. Kumar, and R. K. Jha, “Spreading factor optimization for interference mitigation in dense indoor LoRa networks,” in Proc. IEEE IAS Glob. Conf. Emerg. Technol. (GlobConET), pp. 1–5 (2023).
[14] H. Siriwardana, K. N. Singh, P. R. Kumar, and C. Misra, “Automated-road crossing system using real-time object tracking,” in Proc. 10th Int. Conf. Rel., Infocom Technol. Optim. (ICRITO), pp. 1–5 (2022).
Views: 163Downloads: 83Citations: 2
