The Aeroleaf project is an innovative renewable energy solution that combines wind and solar power generation in a compact and efficient system. It is designed in the shape of a tree, where the leaf-like structures function as mini wind turbines while also integrating solar panels. This hybrid approach allows the system to generate electricity from both wind and sunlight, increasing overall energy efficiency and reliability...........

[1]. Hosseinian, S.M., Mohseni, M., & Karimi, M.S. Advanced blade profiles for improved efficiency in Savonius wind turbines: the Aeroleaf case study. Scientific Reports, 16, 1022.
[2]. Rathore, M.K., Agrawal, M., Baredar. Fabrication and Performance Analysis of the Aero-Leaf Savonius Wind Turbine Tree. Energies, 16(7), 3015.
[3]. Yadav, S., Mishra. Aero Leaf Wind Turbine Tree for Electricity Generation. IJSDR, Vol. 7, Issue 4, pp. 173–179.
[4]. Karimi, Hosseinian, & Mohseni, M. Performance comparison of Aeroleaf and conventional Savonius turbines. Scientific Reports.
[5]. Manwell, J.F., McGowan, J.G., & Rogers, A.L. Wind Energy Explained: Theory, Design and Application. Wiley.

The massive development in the Internet of Things deployments requires efficient methods for selecting optimal wireless protocols in conflicting situations. In this paper, the authors propose a hybrid Multi-Criteria Decision-Making model augmented with sensitivity analysis for selecting optimal IoT protocols. Five wireless protocols: Wi-Fi HaLow, NB-IoT, LoRaWAN, Sigfox, and Zigbee are considered in terms of Power consumption, Data Rate, Latency, Coverage range, and Cost. Robustness of ranking of the wireless protocols was tested by One-at-a-Time (OAT) sensitivity analysis by changing the value of criterion weights by ±10%. Results proved the robustness and reliability of the framework since.........

[1]. Ahmad, M., Hu, J., Ahmad, M., & Khurshid, F. (2019). Optimal Cluster Leader Selection Using MCDM Methods in MWSN: A comparative study. IEEE International Conference on Intelligent Systems and Knowledge Engineering, 240-247. https://doi.org/10.1109/ISKE47853.2019.9170426
[2]. Cui, H., Dong, S., Hu, J., Chen, M., Hou, B., Zhang, J., Zhang, B., Xian, J., & Chen, F. (2023). A hybrid MCDM model with Monte Carlo simulation to improve decision-making stability and reliability. Information Sciences, 647, 119439. https://doi.org/10.1016/j.ins.2023.119439
[3]. IHS Technology, (2016). IoT Platforms: Enabling the Internet of Things https://cdn.ihs.com/www/pdf/enabling_IoT.pdf
[4]. Radulescu, C. Z., & Radulescu, M. (2024). A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems. Electronics. https://doi.org/10.3390/electronics13040789

This study presents the design and simulation of a 4,000 MTPD Urea plant in Aspen HYSYS. The plant was first designed through detailed material to get per day requirement of 3,256.887 Tons of CO2 and 2,266.80152 Tons for NH3 to get the 4,000TPD at 99% urea purity. Conclusive energy balance gave heat duties for the different units was done and the Urea Reactor design gave a volume of 396.991m3 with a length of 80.86m. A simulation was developed for the high-pressure urea synthesis section of the plant. The validity of the simulation was demonstrated by comparing against plant data from the Indorama plant.........

[1]. Agarwal A., Patel R., & Kumar S. (2019). "Modeling and simulation of urea synthesis reactor: A thermodynamic and kinetic approach." Chemical Engineering Research and Design.
[2]. Agarwal, M., Gupta, R. & Saxena, R. K. (2008). "Kinetic modelling of Urea Synthesis Reactor". Chemical Engineering Journal,136(2–3),302 308
[3]. Ahmed, K., Osman, A., & Hassan, M. (2021). Dynamic simulation and analysis of urea synthesis processes using Aspen HYSYS: Start-up, shutdown, and upset scenarios. Journal of Chemical Engineering and Process Technology, 12(3), 415–429.
[4]. Alexandre C. D., (2003), "Computer Aided Chemical Engineering," Elsevier, (13) 33-58
[5]. Alexey L. V. & Gennady F. V, (2016), "Thermodynamic Model of the Urea Synthesis Process", Journal of Chemical and Engineering Data.

Urban flooding has become a persistent environmental challenge in many Nigerian cities due to poor drainage infrastructure, rapid urbanization, indiscriminate waste disposal, and increasing rainfall intensity associated with climate variability. This study presents a Geographic Information System (GIS) and Remote Sensing-based assessment of drainage channels and flood pathways within the extension area of Imo State University (IMSU), Owerri, Nigeria. The objectives of the study were to map the drainage network, identify flood-prone zones and flood pathways, evaluate the effectiveness of existing drainage systems, and develop a geospatial database for flood management and sustainable urban planning..........

[1]. Barde, M. M., Tukur, A. L., &Mubi, M. A. (2019).Detection and mapping of flood prone areas of Jimeta, Adamawa State, Nigeria using GIS techniques.Journal of Environmental Sciences, 12(4), 45–58.
[2]. Eteh, D. R., &Okpobiri, O. (2021). Floodplain mapping and risk assessment of the Orashi River using remote sensing and GIS in the Niger Delta region, Nigeria. Journal of Geology and Environmental Management, 8(2), 66–80.
[3]. Ejikeme, J. O., &Idowu, S. B. (2024).Mapping and analysis of urban drainage system in Asaba Metropolis using remote sensing and geographic information system.International Journal of Geospatial Research, 11(1), 21–39.
[4]. Folorunsho, R., &Awosika, L. (2001).Flood mitigation and drainage system management in Nigeria.Environmental Management Journal, 6(2), 13–21.
[5]. Okorafor, O. O., Chibuike, M. I., Nwakuba, N. R., &Nwaobi, F. A. (2021).Mapping of flood prone areas in Imo State, South Eastern Nigeria using GIS techniques.Nigerian Journal of Environmental Sciences, 14(3), 102–118.

The rapid advancement of deep learning techniques has increased the demand for explainable models, particularly in critical domains such as medical image analysis where decision transparency is essential. This survey provides a comprehensive review of explainable artificial intelligence (XAI) approaches applied to deep learning-based medical image analysis. A structured XAI framework is proposed to categorize these methods based on specific explainability criteria. Existing research on XAI techniques in medical imaging is examined and organized according to the proposed framework as well as different anatomical regions. Finally, the survey highlights future research directions and emerging opportunities for XAI in medical image analysis.

[1] Arun, N., et al. (2021). Assessing the validity of saliency maps for explainable AI in medical imaging. Medical Image Analysis.
[2] Adadi, A., Berrada, M., 2018. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access 6, 52138–52160. doi:10.1109/ACCESS. 2018.2870052.
[3] D. T. Huff, A. J. Weisman, R. Jeraj, 2021. Interpretation and visualization techniques for deep learning models in medical imaging. Physics in Medicine & Biology, 66, 04TR01.
[4] Jia, X., et al. (2020). Explainable AI in medical imaging: Current status and future directions. Frontiers in Artificial Intelligence.
[5] Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B., 2019. Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. USA 116, 22071–22080. doi:10.1073/pnas.1900654116

The rapid rise of cybercrime, fraud, and network-related offenses has stressed manual forensic investigation to its limits. Investigators must now sift through massive amounts of digital data—disk images, packet captures, chat logs, and surveillance footage—under tight judicial deadlines. This task greatly exceeds what human analysts can do without computer support. To address this issue, this paper introduces AI-FAF (Artificial Intelligence Forensic Analytics Framework), a seven-stage pipeline that integrates deep learning, traditional machine learning, and graph reasoning into a unified workflow...........

[1] H. Farid, "Image forgery detection," IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16–25, Mar. 2009.
[2] B. Bayar and M. C. Stamm, "A deep learning approach to universal image manipulation detection using a new convolutional layer," in Proc. ACM Workshop on Information Hiding and Multimedia Security, 2016, pp. 5–10.
[3] P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, "Learning rich features for image manipulation detection," in Proc. IEEE/CVF CVPR, 2018, pp. 1053–1061.
[4] C. Brun, A. Desnos, and J.-F. Marion, "Anomaly-based intrusion detection through LSTM sequence modelling on system call traces," Computers & Security, vol. 99, pp. 102–117, Dec. 2020.
[5] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," in Proc. 4th Int. Conf. ICISSP, 2018, pp. 108–116

The Retail-Ease POS (Point of Sale) System is an intelligent and adaptive retail management solution designed to streamline sales operations, inventory control, and customer transactions. The system introduces an integrated framework for modern retail automation, dynamically managing billing, stock levels, and data reporting through a secure and scalable architecture. It incorporates automated inventory tracking, real-time billing, and data analytics modules to enhance operational efficiency and decision-making accuracy.........

1. A Retail Point-of-Sale (POS) system that integrates automated billing, real-time inventory management, and sales analytics to streamline retail operations, improve transaction accuracy, and enhance customer experience.
2. The system as claimed in Claim 1, wherein the automated billing module processes product information, calculates total amounts, applies discounts or taxes, and generates digital receipts for efficient and error-free checkout operations.
3. The system as claimed in Claim 1, further comprising an inventory management module configured to monitor stock levels in real time, trigger low-stock alerts, and automate reordering from suppliers.

Co3O4 thick films are deposited on glass substrate by screen printing technique. All characterization was carried out for unannealed, annealed at 250℃-400℃ . The XRD analysis indicates prepared films are polycrystalline nature with cubic structure having preferential orientation through (311) plane. Crystallite size is found to be 18.516nm. The lattice parameter found to be 8.036-8.138 A0. approaches to standard value. SEM analysis films show agglomeration of nanoparticles, occurrence of spherical-shaped grain aggregations. Spherical grain size increases 47.66 to 77.33nm with annealing temperature.........

[1] A. El Bachiri, L. Soussi, O. Karzazi, A. Louardi, A. Rmili, H. Erguig & B. El Idriss Electrochromic and photoluminescence properties of cobalt oxide thin films prepared by spray pyrolysis https://www.researchgate.net/ publication /00387010.2018.1556221 (2018) 1-7
[2] Manish Shinde, Nilam Qureshi, Sunit Rane, Uttam Mulik, Dinesh Amalnerkar Synthesis of Cobalt Oxide Nanostructures by Microwave Assisted Solvothermal Technique Using Binary Solvent System Physical Chemistry Communications, Volume 2 Issue 1 (2015) 1-9
[3] C. Ravi Dhasa, R. Venkatesha, K. Jothivenkatachalamb, nn,1 , A. Nithyab , B. Suji Benjaminc , A. Moses Ezhil Rajc , K. Jeyadheepand , C. SanjeevirajVisible light driven photocatalytic degradation of Rhodamine B and Direct Red using cobalt oxide nanoparticles : https://www.researchgate.net/publication/275227227 Ceramics International (2015) 1-13
[4] M. Manickam1 · V. Ponnuswamy1 · C. Sankar1 · R. Mariappan2 · R. Suresh1 Silicon Influence of Substrate Temperature on the Properties of Cobalt Oxide Thin Films Prepared by Nebulizer Spray Pyrolysis (NSP) Technique DOI 10.1007/s12633-015-9316-5 © Springer Science+Business Media Dordrecht (2015)1-11
[5] Mariappan R, Ponnuswamy V, Suresh P, Suresh R, Ragavendar M, Sankar C (2013) Mater Sci Semicond Process 16:825–832