https://journals.bohrpub.com/index.php/bijscit <p><strong>ISSN: 2583-2026 (Online)</strong></p> <p><strong>BOHR International Journal of Smart Computing and Information Technology (BIJSCIT)</strong> is an open access peer-reviewed journal that publishes articles which contribute new results in all the areas of Smart Computing and Information Technology. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in this area.</p> BOHR Publishers en-US BOHR International Journal of Smart Computing and Information Technology 2583-2026 <p>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.</p> https://journals.bohrpub.com/index.php/bijscit/article/view/787 <p>In this paper, four different solvers available in the optimization toolbox of MATLAB for nonlinear constraints and objective functions have been discussed. Among these solvers, numerical comparisons have been made using CPUTIME as the parameter. The MATLAB solvers described in this study have been applied to obtain the global optimum values of Rosenbrock’s banana functions in multi-dimensions. Additionally, graphical analysis is provided for a visual illustration of the convergence of the optimal solution of Rosenbrock’s function.</p> Anup Kumar Thander Dwaipayan Bhowmik Copyright (c) 2025 Anup Kumar Thander, Dwaipayan Bhowmik https://creativecommons.org/licenses/by/4.0 2025-02-12 2025-02-12 6 1 12 17 10.54646/bijscit.2025.47 https://journals.bohrpub.com/index.php/bijscit/article/view/798 <p>Road abnormalities such as cracks, unevenness, potholes, and manholes are increasing the number of road disasters in today’s world, particularly in nations like India. Accidents and irreplaceable loss result from uneven and damaged roadways, as well as unneeded openings. The introduction of more Big Data sources through citizen recording devices has created a new foundation for public infrastructure management and control, as well as policy design. Roads that are maintained on a regular basis are less likely to be involved in accidents. However, manually inspecting road damage is costly, time-intensive, and requires a large amount of manpower. Automatically detecting and reporting the presence of potholes, manholes, and other anomalies such as cracks to the appropriate departments can aid in the recovery of road conditions. Detailed real-time performance object detection frameworks (YOLOv5 and RCNN) for detections of potholes are presented. The main objective of this manuscript is to propose a framework that utilizes machine learning and deep learning models for detecting surface flaws.</p> P. V. Siva Kumar Copyright (c) 2025 P. V. Siva Kumar https://creativecommons.org/licenses/by/4.0 2025-01-25 2025-01-25 6 1 1 11 10.54646/bijscit.2025.46