iosr-JCE  Volume-28 ~ Issue-5 ~ Sep. - Oct. 2026

Abstract: Physics-Informed neural networks (PINNs) are one of the most active frontiers in scientific machine learning, used to solve the forward and inverse problems governed by partial differential equations. It incorporates governing physical laws and equations into neural network training i.e., loss function. This review discusses twenty-four primary sources, beginning with the biological inspiration and architecture of artificial neural networks, proceeds....

Key Word: Physics-informed neural networks; PINNs; adaptive sampling; collocation points; spectral bias; neural tangent kernel; scientific machine learning; heat transfer.

[1] Maind, S. B., & Wankar, P. (2014). Research paper on basic of artificial neural network. International Journal on Recent and Innovation Trends in Computing and Communication, 2(1), 96-100.
[2] Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378, 686-707.DOI: 10.1016/j.jcp.2018.10.045. https://doi.org/10.1016/j.jcp.2018.10.045.
[3] Cuomo, S., Di Cola, V. S., Giampaolo, F., Rozza, G., Raissi, M., & Piccialli, F. (2022). Scientific machine learning through physics–informed neural networks: Where we are and what’s next. Journal of scientific computing, 92(3), 88.DOI: 10.1007/s10915-022-01939-z. arXiv:2201.05624. https://doi.org/10.1007/s10915-022-01939-z.
[4] Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021). DeepXDE: A deep learning library for solving differential equations. SIAM review, 63(1), 208-228. DOI: 10.1137/19M1274067. arXiv:1907.04502. https://doi.org/10.1137/19M1274067.

Abstract: Background: Performance measurement is indispensable in today’s world. Huge amount of time and efforts could be put into planning, policies development and process implementation, however, if we are not able to objectively decipher whether or not we are making progress, as expected, then it will be difficult to ascertain the status of the system. How do we measure progress in information security?
This paper clearly outlines the importance of information security metrics in industry. A number of security metrics, associated....

Key Word: Metrics, Information Security, Indicators, Security Performance

[1]. Rana, K., & Abbas, A. (2016). Security metrics and the risks: An overview. International Journal of Computer Trends and Technology, 41(2), 106–112.
[2]. Gartner. (n.d.). Security metrics. https://www.gartner.com/en/information-technology/glossary/security-metrics.
[3]. Ioan, A. (2020). Mobile security metrics. International Journal of Innovative Science and Research Technology, 5(4), 1225–1229.
[4]. Gori, G., Rinieri, L., Melis, A., Al Sadi, A., Callegati, F., & Prandini, M. (2024). A systematic analysis of security metrics for industrial cyber-physical systems. Electronics, 13(7), Article 1208.
[5]. Sherif, E., Yevseyeva, I., Fernandes, V. B., & Cook, A. (2024). The smart approach to selecting good cyber security metrics. Journal of Internet Services and Information Security, 14(4), 312–330..

Abstract: The largest and most obscure section of food waste worldwide is food that people throw away in their homes. The UNEP Food Waste Index Report 2024 reported that in 2022, a total of 1.05 billion tonnes of food was wasted. This means one in five of food pieces that could be eaten by the people were not in use. Out of that total 631 million....

Key Word:Artificial Intelligence, Food Waste Prediction, Smart Composting, Edge. Computer Vision, Compost Sensors, Compost Maturity, Federated Learning, IoT Sensors, Computing.
Circular Economy.

[1]. United Nations Environment Programme and WRAP. The 2024 Food Waste Index Report: Think Eat Save. Nairobi: UNEP, March 2024.
[2]. Bejarano-Meza, M. E., Ramos Soncco, J. A., Vilca Quispe, M. L, and Chanove Manrique, A. M. Performance testing of a smart composting system based on the Internet of Things with photovoltaic solar energy to produce natural substrate out of domestic waste. Environmental Technology and Innovation 43 (2026): 105030.
[3]. Alsalem, K. Enhanced food waste classification with LSTM and ensemble classifiers. Discover Sustainability 6 (2025): 443.
[4]. Fendor, Z., van der Velden, B. H. M., Wang, X., Carnoli, A. J., Mutlu, O., and Hürriyetoğlu, A. Federation in food research. Journal of Agriculture and Food Research 23 (2025): 102238.
[5]. Lee, E., Shurson, G., Oh, S-H., and Jang, J.-C. The management of food waste recycling a sustainable future: A case study of South Korea. Sustainability 16, no. 2 (2024): 854..

Abstract: Background: The usefulness of climate science information is substantially diminished when it reaches end users in languages or formats outside their familiarity. This paper presents Sahel Climate Voice, an Android mobile application combining domain-specific lexical resources, neural machine translation, text-to-speech synthesis, cloud-based inference, and mobile deployment to serve speakers of Bambara, Hausa, and Mooré. Materials and Methods: The lexical foundation of the system is BUMANI_CIC (Climate Information Corpus), whose initial release consolidated....

Key Word: climate information services; low-resource NLP; Bambara; Hausa; Mooré; neural machine translation; text-to-speech.

[1]. Abela, K., Tanti, M., & Borg, C. (2026). Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), 78-86. https://doi.org/10.18653/v1/2026.loresmt-1.6
[2]. Al Farouq, M. H., Wassie, A. K., & Moslem, Y. (2025). Bemba speech translation: Exploring a low-resource African language. In Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025). https://doi.org/10.18653/v1/2025.iwslt-1.37
[3]. Alabi, J. O., Hedderich, M. A., Adelani, D. I., & Klakow, D. (2025). Charting the landscape of African NLP: Mapping progress and shaping the road ahead. arXiv. https://doi.org/10.48550/arxiv.2505.21315
[4]. Alfonsi, R. M., Naidoo, M., & Gasparatos, A. (2024). Adoption and desirable characteristics of information and communication technologies for urban small-scale food producers in South Africa. Frontiers in Sustainable Food Systems, 8, 1332978. https://doi.org/10.3389/fsufs.2024.1332978
[5]. Appiah, C. E., Quarmine, W., Osei-Amponsah, C., Okem, A. E., & Sarpong, D. B. (2025). Improving smallholder farmers' access to and utilization of climate information services in sub-Saharan Africa through social networks: A systematic review. Climate Services, 37, 100528. https://doi.org/10.1016/j.cliser.2024.100528.