iosr-jmca   Volume-13 ~ Issue-5 ~ Sep.-Oct. 2026

ABSTRACT: Lassa fever remains a significant public health challenge in Nigeria and other West African countries, with recurrent outbreaks requiring timely surveillance, early detection, and rapid intervention. Conventional surveillance approaches are often dependent on passive reporting and laboratory confirmation, which may limit the ability..

Keywords: Agentic AI, Explainable Artificial Intelligence, Lassa Fever, Machine Learning, Infectious Disease Prediction, Decision Support, Public Health, Ebonyi State, Nigeria.

[1]. Acta Tropica. (2026). Improving Lassa fever risk mapping using self-organizing maps and spatial determinants. Acta Tropica, 277, 108040. https://doi.org/10.1016/j.actatropica.2026.108040
[2]. Carriero, A., de Hond, A., Cappers, B., Paulovich, F., Abeln, S., Moons, K. G. M., & van Smeden, M. (2025). Explainable AI in healthcare: To explain, to predict, or to describe? [Preprint].
[3]. Donoso-Guzmán, I., Kacafírková, K. S., Szymanski, M., Jacobs, A., Parra, D., & Verbert, K. (2025). A systematic review of user-centred evaluation of explainable AI in healthcare. [Preprint].[4].
Frasca, M., La Torre, D., Pravettoni, G., & Cutica, I. (2024). Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review. Discover Artificial Intelligence, 4, 15. https://doi.org/10.1007/s44163-024-00114-7
[5]. Freyer, N., Groß, D., & Lipprandt, M. (2024). The ethical requirement of explainability for AI-DSS in healthcare: A systematic review of reasons. BMC Medical Ethics, 25, 104. https://doi.org/10.1186/s12910-024-01103-2

ABSTRACT: Email communication represents an important source of digital evidence because email messages may contain message content, headers, timestamps, sender and recipient information, attachments, and other metadata. Deletion of a message from a user interface does not necessarily mean that all corresponding artifacts have been removed from locally stored or exported mailbox data. This study presents a comparative forensic examination of deleted email artifacts from Google Takeout and Microsoft Outlook using SysTools Viewer. Sample email communications were prepared and selected messages were deleted. Gmail data were acquired through Google Takeout....

Keywords: Email forensics; deleted email recovery; Google Takeout; Microsoft Outlook; MBOX; OST; SysTools Viewer; digital evidence; metadata

[1]. Al-Makkawi, T., Alsarhan, A., Al-Na'amneh, Q., Aljaidi, M., Almaiah, M., AlJamal, M., Alqura’n, R., & Aljawarneh, M. (2025). Open-source forensics tools for recovery of deleted data in unconventional ways. In Cryptography, biometrics, and anonymity in cybersecurity management. IGI Global.
[2]. Banday, M. T. (2011). Techniques and tools for forensic investigation of e-mail. International Journal of Network Security & Law, 3(6), 227–241.
[3]. Devendran, V. K., Shahriar, H., & Clincy, V. (2015). A comparative study of email forensic tools. Journal of Information Security, 6(2), 111–117.
[4]. Fairbanks, J. (2024). POST: Email archival, processing and flagging stack for incident responders.
[5]. Fagalde, K. (2006). Forensic processing of Outlook email: Recovering deleted email files and messages. Guidance Software User Conference Proceedings.

ABSTRACT: The increasing computational capability of smartphones has intensified thermal constraints in high-performance mobile applications. This study assesses whether the DroidRan Android hardware-behavior dataset enables sample-domain thermal prediction and compact dynamic modeling. Examination of all 36 workbooks and 180 worksheets identified 299,298 observations across 23 recorded variables, including battery voltage and temperature, three CPU thermal sensors, GPU temperature and frequency, eight CPU-core frequencies, memory..

Keywords: Modeling of dynamic systems, mobile computing, smartphone thermal management, artificial intelligence, multilayer perceptron, neural networks, thermal throttling, energy efficiency

[1] X. Zhang, H. Vadodaria, N. Li, K. D. Kang, and Y. Liu, “A smartphone thermal temperature analysis for virtual and augmented reality,” in Proc. 2020 IEEE Int. Conf. Artificial Intelligence and Virtual Reality (AIVR), 2020, pp. 301–306, doi: 10.1109/AIVR50618.2020.00061.
[2] J. R. B. Bantock, B. M. Al-Hashimi, and G. V. Merrett, “Mitigating interactive performance degradation from mobile device thermal throttling,” IEEE Embedded Systems Letters, vol. 13, no. 1, pp. 5–8, 2021, doi: 10.1109/LES.2020.2991327.
[3] G. P. Srinivasa, D. Werner, M. Hempstead, and G. Challen, “Thermal-aware overclocking for smartphones,” in Proc. 2021 IEEE Int. Symp. Performance Analysis of Systems and Software (ISPASS), 2021, pp. 220–222, doi: 10.1109/ISPASS51385.2021.00039.
[4] S. Chetoui and S. Reda, “CasCon: Cascaded thermal and electrical current throttling for mobile devices,” IEEE Embedded Systems Letters, vol. 14, no. 1, pp. 3–6, 2022, doi: 10.1109/LES.2021.3079707.
[5] H. Liu, J. Yu, and R. Wang, “Model predictive control of portable electronic devices under skin temperature constraints,” Energy, vol. 260, Art. no. 125185, 2022, doi: 10.1016/j.energy.2022.125185.